Noise level simulation method as well as computer readable medium and system for it.

专利摘要:
The present invention relates to a method for calculating a noise level generated by a sound source, in particular an aircraft, which moves along a defined trajectory (116) relative to the at least one reception point (R), for at least one reception point (R). the method comprising the steps of: providing a sound propagation model for an airspace extending between the at least one receiving point and the at least one expected trajectory (115) along which the sound source is likely to move relative to the receiving point; Calculating the noise level by inputting the defined trajectory (116) into the sound propagation model and calculating sound propagation between the sound source and the at least one receiving point (R) for each of a number of different sound source locations (114) located along the defined trajectory. Furthermore, the present invention relates to a computer readable medium containing computer readable instructions for enabling a computer system to perform a method of the present invention. Finally, the present invention relates to a computer system designed to perform a method according to the present invention.
公开号:CH713630A1
申请号:CH00411/17
申请日:2017-03-28
公开日:2018-09-28
发明作者:Hegner Christian；Jäger Peter；Köpfli Micha；Wunderli Jean-Marc；Zellmann Christoph
申请人:Bundesamt Fuer Zivilluftfahrt；
IPC主号:

专利说明:

Description: The present invention relates to a method for calculating a noise level, which is generated by a sound source, in particular an aircraft, which moves along a defined trajectory in relation to the at least one reception point, for at least one reception point. The present invention further relates to a computer-readable medium that contains computer-readable instructions. In conclusion, the present invention relates to a computer system.
BACKGROUND OF THE INVENTION Under the current circumstances in densely populated areas such as Europe, traffic control and planning are subject to numerous factors that affect it. One of these influencing factors, which is of great importance and is dealt with with increasing sensitivity, is traffic-related noise levels, as perceived by the public in residential and commercial areas. In air traffic in particular, noise levels generated by aircraft as sound sources, which are perceived in areas near the airport, determine the public acceptance of existing or planned takeoff or landing routes, i.e. Aircraft lanes during takeoffs and landings.
The control and planning of such railways is also complicated, in particular for traffic, by the fact that the number of passengers per year is constantly increasing. In 2016, approximately 3.5 billion passengers used an aircraft worldwide. Estimates indicate that within 20 years, i.e. by 2036, the number of passengers in aviation will increase to around 7 million passengers per year. This will most likely increase the number of take-offs and landings as the number of passengers increases more or less proportionally.
From a technical point of view, the runways of aircraft during takeoffs and landings are generally statically dependent on certain individual conditions of a particular airport and its surroundings, for example the orientation and length of runways, or geological conditions such as mountains, hills etc. from a technical point of view, a dynamic dependency of the railways on certain temporal conditions such as weather conditions and traffic load. In addition to these technical conditions, the railways must comply with legal requirements that are determined by geographic parameters such as international borders and other restrictions, such as military areas, and time parameters such as night flight bans.
The latter, in particular, are mainly imposed by permissible noise levels in the vicinity of airports. The discussion about trajectories, in particular takeoff and landing routes, is already very emotional between airport operators and airlines on the one hand and the public on the other, with politicians as a third party somewhere in between. Since the course of aircraft and thus their runways are more or less predetermined by predefined takeoff and landing routes and routes cross borders between nation states and states, the respective discussions take place on an international level.
Due to the high level of emotion, such discussions and thus also the planning and control of the railways are tedious and time-consuming. The parties involved in the discussion are often unable to find solutions or compromises due to the lack of objective data on noise levels in affected residential and commercial areas.
Instead, the discussions are based on mere assumptions and subjective interpretations of possible noise levels with respect to reception points.
An objectification of the discussions is desirable in order to streamline the decision-making process and to make it more transparent, in order to be able to make fair decisions in which no party feels overlooked, and thus to increase planning security and save costs and effort.
One way to provide a basis for objective decisions is to calculate aircraft noise levels at certain receiving points. State-of-the-art aircraft noise calculations are already used worldwide as essential tools for land use planning and control. They enable the assessment and optimization of noise reduction methods for a quieter environment. Both topics are important elements of the "Balanced Approach to Aircraft Noise" of the International Civil Aviation Organization ICAO. The first element is already well established through conventional immission models, which are developed to calculate the sound level at the receiver , For this purpose, internationally harmonized procedures such as the publications ICAO Doc. 9911 or ECAC Doc. 29 are used, but national guidelines such as AZB in Germany and national programs such as FLULA2 in Switzerland also apply. With the latter element, more differentiated models that describe the sound source and propagation separately are necessary in order to calculate individual flights precisely. ANOPP, SIMUL and PANAM are currently available programs that meet this requirement. They all use similar semi-empirical emission models in their framework to describe the main sources of an aircraft.
However, immission models for compliance with legal requirements and differentiated semi-empirical models for scientific purposes have limitations when evaluating noise reduction methods. For example, the influence of the speed of the aircraft or its configuration on noise emissions is not taken into account in the noise forecast using emission models and is therefore missing in the calculation of the respective emissions
CH 713 630 A1 represents a significant restriction. In addition, the acoustic description is simplified to generalized spectral classes (Doc. 9911 / Doc. 29) or attached to a standard atmosphere (FLULA2), which increases the model uncertainty in both cases. These inadequacies play no role in emission models that forecast the spectra and directivity of each source in great detail. However, these require a very detailed input of geometry data and physical flight parameters (e.g. primary jet speed or air flow mass) for accurate forecasts. Another disadvantage of noise calculation programs according to the prior art is the restricted accessibility of these programs to other users and a very restricted database.
SUMMARY It is a concern of the present invention to address at least some of these disadvantages and shortcomings of the prior art noise calculation programs described above.
According to the present invention, this object is at least partially achieved by the provision of a method according to main claim 1, a computer-readable medium according to claim 41 and a computer system according to claim 42.
According to the present invention, the objectives listed above are achieved in particular in that a method for calculating for at least one reception point of a noise level from a sound source, in particular an aircraft, which moves with respect to the at least one reception point along a defined trajectory , is generated that includes the following steps:
- Providing a sound propagation model for an air space which extends between the at least one reception point and at least one expected trajectory along which the sound source is likely to move in relation to the reception point;
- Calculating the noise level by entering the defined trajectory in the sound propagation model and calculating a sound propagation between the sound source and the at least one reception point for each of a number of different sound source locations that are arranged along the defined trajectory.
In the case of a computer-readable medium, the above-mentioned objectives are at least partially achieved in that the computer-readable medium contains computer-readable instructions that enable a computer to carry out a method according to the present invention.
In a computer system, the objectives set out above are at least partially achieved by the computer system being designed to carry out a method according to the present invention.
This solution offers the advantage over the prior art that in a first step for the expected trajectory general parameters of an immission model for the calculation of a noise level at the at least one reception point with desired details of an emission model can be calculated and spectra and directivity of the Sound source can be predicted at any point on the predefined trajectory with great depth of detail. Furthermore, a simulation of the sound propagation between the number of different sound source locations along the defined trajectory and the at least one reception point can use a detailed acoustic description of the atmosphere and geological conditions in a level of detail that requires a lot of computing power that a spontaneous adjustment of model parameters due to relatively long calculation times of z. B. not possible for several hours or days.
If the sound propagation model for the air space extending between the defined trajectory and the at least one reception point is present, in a second step an actual sound propagation between a defined trajectory and the at least one reception point can be calculated based on said sound propagation model. The defined trajectory can deviate at least slightly from the expected trajectory. However, the sound propagation model for airspace is applied to the defined trajectory, which can be easily changed and modified in order to benefit from the desired level of detail in the calculations of sound emissions and emissions. This enables a fast simulation of noise levels at at least one reception point, which can be changed and recalculated almost immediately or at most within minutes and hours.
In other words, in a first step, the framework for sound propagation is calculated in a sufficiently detailed manner based on general parameters that apply to the expected trajectory curve. In a second step, additional model parameters such as fine adjustments to the path curve of a vehicle and the vehicle type are defined, and the actual sound propagation between the vehicle running along the defined path curve and the at least one reception point is simulated within a very short time frame based on the defined framework. Different scenarios for airspaces can be calculated for expected orbits in range in preparation for simulating a noise level generated by different types of sound sources and expected orbits.
Thus, on the one hand, any planning and control devices can quickly simulate noise levels for certain reception points under different conditions, in particular using different defined paths, in order to route a vehicle, e.g. B. an aircraft to optimize in terms of noise immission. on the other hand
CH 713 630 A1, respective simulations can be made available to the public quickly and can be changed according to public fears regarding exposure to noise. In particular, this supports the factualization of discussions between operators, airlines, politicians and the public regarding aircraft routes and thus facilitates the planning and control of air traffic.
The solution according to the invention can be combined and improved by the following further embodiments, which are each advantageous in themselves.
[0021] In some embodiments, the sound propagation is calculated based on at least one sound emission value of the sound source. The sound emission values can include total sound level, frequency spectrum and directivity characteristics of a sound source. Sound emission values from certain sound sources can be modeled based on backpropaired overflight measurements, which are separated into noise from the airframe and engines using a novel separation process that is used during model development. This means that complex measurements with microphone arrangements are not necessary, and the model can be applied to any type of aircraft powered by a turbofan.
In some embodiments, the at least one sound emission value is calculated depending on a sound radiation pattern associated with a type of sound source for each of a number of different sound source locations located along the defined trajectory. If the sound source runs along the defined trajectory, its sound radiation pattern can change. Such changes may occur due to changes in operating modes and arrangements within the sound source, as well as changes in the orientation and location of the sound source within the airspace. With regard to aircraft, operating modes, parameters and arrangements can change along the defined trajectory, as well as the fact that throttle and flap settings and the status of the landing gear are changed, which changes the respective noise emissions. Furthermore, the jet direction of the engines and the alignment of slats as sound sources are changed by different orientations and locations of the aircraft along the defined trajectory.
[0023] In some embodiments, the sound radiation pattern is based on a sound power level associated with the type of sound source. Different types of sound sources can be assigned to specific sound power levels. The sound power levels can change along the defined trajectory.
[0024] In some embodiments, the sound power level is estimated based on progression parameters of the sound source, which are derived from the defined trajectory. Such course parameters can e.g. B. rise angle, sinking speeds, top point of the approach, speeds and the like of an aircraft, but are not limited to these. These history parameters enable the estimation of operating modes, parameters and arrangements such as weight, throttle, flap and chassis settings.
[0025] In some embodiments, the sound radiation pattern is based on a directional characteristic associated with the type of sound source. Certain types of sound sources can have certain directional characteristics. For example, the nozzles or fans of an aircraft can direct the sound in a certain way. The different ways in which they direct sound and in which the direction of the sound has been changed in accordance with operating and course parameters can be taken into account when calculating the sound radiation pattern.
[0026] In preferred embodiments, the sound propagation model comprises a direct sound propagation scenario and a complex propagation scenario;
- In the case of the direct sound propagation scenario, direct sound propagation between the sound source and the at least one reception point is assumed; and
- In the complex propagation scenario, a complex sound propagation between the sound source and the at least one reception point is assumed.
The direct sound propagation scenario can be used for the calculation of the direct sound propagation either as part of a complex sound propagation at a certain point in time and location of the sound source based on the reception point or in addition to a complex sound propagation. With complex sound propagation, all types of reflections and other effects on airborne sound can be taken into account. Thus, the complex sound propagation is generally far more demanding in terms of computing power than direct sound propagation. When deciding between scenarios, if complex sound propagation can be neglected due to the fact that direct sound propagation provides sufficient accuracy, omitting the calculation of complex sound propagation can minimize the computational effort support and thus save time and energy when simulating noise levels in accordance with a method according to the present invention.
[0028] In some embodiments, a line of sight angle between the sound source and a horizon is determined for each of the number of different sound source locations;
- wherein the direct sound propagation model is applied to line of sight angles that exceed a de minimis threshold; and
- Assuming that at line of sight angles that exceed the de minimis threshold, complex sound propagation becomes negligible.
CH 713 630 A1 The use of the line of sight angle to differentiate the scenarios for the application of the direct sound propagation model and the complex sound propagation model represents a simple condition for a change between the models according to the different sound source locations along the defined trajectory.
[0030] In some embodiments, a homogeneous atmosphere within the airspace is assumed for the direct sound propagation scenario. Using a homogeneous atmosphere for direct sound propagation simulation helps minimize the complexity of the calculations included. The complex sound propagation simulation is in turn preferably based on the assumption of an inhomogeneous, i.e. heterogeneous atmosphere within the airspace. Alternatively or additionally, if desired, the direct sound propagation scenario can also use calculations based on the assumption of a heterogeneous atmosphere within the airspace.
[0031] In some embodiments, the direct sound propagation scenario includes at least one of a geometric deviation model, a dissipation model, a barrier effect model, an anti-fouling model, and a ground effect model for airspace. The effects of geometric deviation and a dissipation of sound within the airspace can thus be considered. Barrier effects, i.e. The effects of any kind of obstacles such as buildings, geological formations or the like that border on the airspace can be considered. Growth damping modeling is particularly helpful when calculations are to be provided for different primary surfaces and / or seasons, in which the surrounding vegetation is assumed to be leafy or not. Modeling floor effects supports the simulation of damping and reflection of sound according to individual characteristics of the floor adjacent to the airspace.
In some embodiments, the ground effect model comprises at least one of a spherical wave propagation determination, a rough terrain model, a surface texture variation and a coherency loss model for modeling a coherence loss of different sound paths between the sound source and the reception point. The spherical wave propagation determination contains the assumption that sound is emitted by the sound source and, based on the sound source, passes through the air space as spherical wave h. Modeling an uneven terrain adjacent to the airspace allows simulation of reflection and attenuation of the sound on the ground. This can include surface texture variations, e.g. B. Roughness and condition of the soil can change along the air space and over time. When running between the sound source and the at least one reception point, the sound propagation can be divided into different paths by certain obstacles, which leads to the sound waves running along these paths being separated so that they lose their cohesion. Then the sound propagation must be calculated separately for each of the paths.
[0033] In some embodiments, the complex spreading scenario includes at least one of a weather correction model, an obstacle reflection model, and a forest diffusion model for airspace. In the weather influence correction model, temporal changes in the atmosphere of the air space are considered in particular. In the obstacle reflection model, all reflections and obstacles between the sound source and the at least one reception point are considered. The forest diffusion model simulates diffusion effects in connection with sound waves hitting forest structures.
[0034] In some embodiments, the weather exposure correction model adapts a calculation of sound dissipation within the airspace to local temperature and humidity values of the airspace. The propagation of sound within the airspace depends on the temperature and humidity of the air. By looking at local temperatures and humidity values, sound propagation within the airspace can be precisely simulated for different weather conditions such as wind, precipitation, fog, as well as different times of day and seasons in which sound propagation is changed according to the respective weather conditions.
[0035] In some embodiments, the weather exposure correction model adapts a calculation of barrier effects within the airspace to vertical gradients of at least one of wind speed values and temperature values of the airspace. Wind and temperature within the airspace determine the reflection and diffraction of sound at obstacles adjacent to the airspace. Taking temperature and / or wind speed values into account when calculating barrier effects supports the precise simulation of noise levels at the at least one reception point in different weather conditions.
In some embodiments, the barrier effects include a sound shadow zone effect for at least one sound shadow area within the air space, in which sound beams emitted directly and / or indirectly from the sound source meet the at least one reception point based on updraft conditions that were identified based on the vertical gradients of the at least one wind speed value , do not reach directly, a. In other words, under certain weather conditions, the at least one reception point can be shielded from the sound waves by an obstacle in such a way that the sound waves pass above and / or next to the at least one reception point, which is located in the sound shadow region of the obstacle.
[0037] In some embodiments, a residual sound exposure of the at least one reception point that is within the at least one sound shadow region is based on at least one of a diffraction effect model and a scattering effect model that are based on a sound beam that is along the at least one sound 5
CH 713 630 A1 shadow area passes, applied, calculated. Thus, even if the at least one reception point located in a sound shadow area is not directly influenced by sound passing an obstacle, the effects of fractions and scattering by the obstacle on the sound can result in residual sound waves reaching the at least one reception point and at least partially contribute to the noise level simulated for the at least one reception point.
[0038] In a preferred embodiment, a method according to the present invention further comprises the following steps
- division of the airspace into adjoining subspaces;
Calculating a subspace model for each of the subspaces to determine a finite sound propagation within each of the subspaces;
- Composing the sound propagation model from the subspace models, wherein at least one border area between adjoining subspaces represents at least one of a virtual sound source and a virtual reception point for a virtual sound transmission value, which represents a virtual sound power level transmitted between at least two adjoining subspaces.
Arithmetic operations for simulating sound propagation within the airspace can thus be distributed to the subspaces in such a way that the total sound propagation is calculated iteratively. The boundary areas between the subspaces become virtual sound sources and virtual reception points via which sound waves are transmitted from one subspace to the other. The border areas can e.g. B. corners of the subspaces that share the same or adjacent coordinates as adjacent subspaces, whereby the border regions of the subspaces form a three-dimensional grid. The subspaces can be viewed as cells within which attenuation factors for sound propagation are calculated according to the properties of each cell. The airspace is thus represented as a cell model.
Attenuations can be calculated between each edge, vertex or corner point of the three-dimensional grid of the cell model and each reception point. Based on these iteratively calculated attenuations, an overall attenuation of the sound between the sound source and the reception point can be calculated. The overall attenuation can then be used to calculate a noise level for the at least one reception point that is generated by a sound source that moves along a defined trajectory with respect to the at least one reception point. This calculation based on the overall weakening can be carried out immediately, i.e. Iteration calculations between corner points of the subspaces are not necessary if the overall weakening is present.
During the calculation of immissions, i.e. Noise levels at the at least one reception point, which are caused by an aircraft as a function of the position of the aircraft at a certain point along a trajectory curve, can be attenuated with respect to the at least one reception point or a plurality of reception points from the attenuations of the eight surrounding corner points of the three-dimensional Grid can be interpolated. For the calculation of immissions, reception points can also be arranged along a grid of reception points arranged on the floor. A sound source grating and a grating of reception points can be independent of one another.
[0042] In some embodiments, a transmission of virtual sound power values is calculated for a number of possible combinations of virtual sound sources and virtual reception points. In particular after sound deflection, diffraction, scattering or the like, the sound waves can pass through the air space along different paths. These different paths lead to a number of possible combinations of sound propagation between different rooms. Taking all possible combinations into account, the sound propagation within the airspace can be precisely simulated.
In some embodiments, a virtual attenuation is calculated for each or each of the virtual sound sources and / or virtual reception points, the virtual attenuation reflecting an attenuation of the virtual sound power value when transmitted between the virtual sound sources and / or virtual reception points. In other words, a virtual attenuation of the sound is calculated according to properties within the subspace or between subspaces within each subspace and / or for each transmission of sound between subspaces. Such properties can in turn include any type of weather or other values discussed herein for simulating airborne sound propagation, taking into account boundaries and obstacles adjacent to the airspace.
[0044] In some embodiments, virtual attenuations are stored in a subspace database. By storing the virtual attenuations within a subspace database, the virtual attenuations can be retrieved quickly and quickly from the subspace database. This supports the rapid calculation of sound propagation within the airspace by using the virtual attenuations stored in the subspace database.
As already stated above, virtual attenuations can be used to calculate an overall attenuation. Receiving points can be grouped into sub-territories into which a landscape adjacent to an air space around the orbits can be divided. The sub-territories can have a tiled pattern on the floor
Form CH 713 630 A1. For each sub-territory, total attenuations derived from the virtual attenuations of the subspaces and / or the total attenuations can be stored in the subspace database and / or a supplementary database, which enables efficient parallelization of computing steps in a method according to the present invention. For reception points within a subterritory, attenuations related to all corner points of the three-dimensional virtual sound source grating can be stored within the same database.
In some embodiments, the virtual attenuations are read from the subspace database while the noise level is being calculated. This allows the noise level simulation to be carried out immediately, i.e. more or less in real time based on the virtual attenuations stored in the subspace database. The subspace database can be updated according to a change in the environmental conditions influencing the airspace.
[0047] In some embodiments, the propagation model includes an intermediate weakening interpolated between the defined trajectory and the at least one border area. It can be assumed that the actually defined trajectory crosses the subspaces. Thus, the defined trajectory for most of the locations of a vehicle does not coincide with the border areas. An intermediate attenuation is calculated for remaining sections of the sound paths between the actual location of the vehicle along a defined trajectory and respective border areas in order to provide a precise simulation of the sound propagation.
In some embodiments, the air space is divided along a homogeneous horizontal grid with fixed distances between the subspaces in a longitudinal direction and a transverse direction of the air space. The division of the airspace along a homogeneous horizontal grid facilitates the definition of the subspaces, which are thus evenly distributed and can have the same sizes along the horizontal grid.
In some embodiments, the airspace is divided along a heterogeneous vertical grid with the heights of the subspaces to be taken along along a height direction of the airspace. In other words, the height of the subspaces increases with the increasing distance of the subspaces from the floor. This helps minimize the total number of subspaces that need to be created to represent the airspace. The calculation effort is reduced by minimizing the total number of sub-rooms. Such a minimization of the number of sub-rooms takes into account that the air space and the atmosphere can be regarded as increasingly homogeneous as the height rises above the ground. Furthermore, if the height rises above the ground, fewer obstacles have to be taken into account when modeling the airspace. As the distance from the floor increases, the subspaces can become larger and still provide sufficient increments for simulating sound propagation within the airspace.
[0050] In some embodiments, the subspaces have a cuboid shape. The cuboid shape results from the fact that the horizontal grid and the vertical grid consist of straight lines which are arranged at right angles to one another. In other words, the horizontal and vertical grids are arranged in such a way that they form a Cartesian coordinate system, which facilitates calculations.
In some embodiments, the boundary regions are formed by eight corners of each of the cuboid subspaces. The border areas can thus be defined without any problems. Sound propagation within the airspace divided into subspaces is simulated by considering the corners of the subspaces as virtual sound sources and virtual sound reception points. Virtual attenuations can be calculated between the virtual sound sources and the virtual sound reception points. As already explained above, however, a grid of virtual sound sources can differ from a grid of reception points. In addition, front points of surrounding buildings and / or other obstacles or any points located in a desired manner, for example measuring or recording points, can also be used as reception points.
In some embodiments, the sound propagation between the sound source and the at least one reception point is calculated both in the time domain and in the frequency domain. Time domain effects and frequency domain effects of sound propagation, in particular a sound attenuation during the course to the subspaces, can thus be simulated.
[0053] In some embodiments, a frequency spectrum of sound propagation in the frequency domain is divided into frequency bands, and sound propagation is calculated for each of the frequency bands. By dividing the frequency spectrum to be considered for sound propagation in the frequency range into different frequency bands, different sound wave characteristics can be considered as a function of the wavelength. Correspondingly, certain parameters and / or constants of the propagation model can change depending on the wavelength and thus the frequency band. Furthermore, computing operations necessary for simulating sound propagation can be facilitated and minimized to the extent that, for. B. algorithms are used for lower frequency bands than for medium and higher frequency bands. For example, the frequency bands can be divided into a spectrum of one-third intervals. This enables a third octave analysis and calculation in the frequency domain.
In some embodiments, sound propagation is calculated using unweighted sound pressure levels and the noise level is displayed using A-weighted sound pressure levels. The calculation of sound propagation with unweighted sound pressure levels enables a simulation of sound propagation based on purely physical properties,
CH 713 630 A1 whereas the display of sound pressure levels and / or noise levels with A-weighted sound pressure levels supports the visualization of the perception of the respective noise level by people. The actual perception of the noise level by people at a specific reception point can thus be precisely simulated.
In preferred embodiments, the noise level is weighted with a population value that represents an estimated population of the at least one reception point with a number of people at a predefined point in time at which the sound source moves along the defined trajectory. If the at least one reception point is in a residential area, it can be assumed that the residential area has a certain population value corresponding to the number of people present at the at least one reception point at a specific time of the day and / or year. For example, residential areas are densely populated, i.e. the actual population numbers more or less correspond to a theoretical number of people who are officially registered in the residential area, especially at night and / or on weekends, whereas residential areas tend to be less during working or business hours, e.g. from 9:00 a.m. to 5:00 p.m. are populated. In contrast, commercial areas are usually densely populated during working or business hours, so it can be assumed that commercial areas are only sparsely populated at night and / or at weekends. Thus, evaluating the noise level with a population value that represents the estimated population of the at least one reception point at a predefined time helps simulate the actual impact of the noise level on the population.
In preferred embodiments, a calculation of the sound propagation is distributed between a client device that provides a number of client FLOPs per cycle and a computing system that provides a number of cluster FLOPs per cycle, the number of clients -FLOPs per cycle is less than the number of cluster FLOPs per cycle. The client device can be a standalone computer in an office or at a service provider that provides engineering services including the simulation of noise levels discussed herein. The computer network can be any network of interconnected computers or data centers that provides computing power measured in FLOPS that exceeds the computing power of the client device.
It is particularly advantageous if the calculation of the sound propagation model for the air space extending between the at least one reception point and the at least one estimated path along which the sound source is likely to move with respect to the reception point is carried out by the computer network, the sufficient Provides computing power while being less accessible and flexible than the client device in terms of operation and intervention in the calculations. The client device is then used to calculate the noise level by entering the defined trajectory and the sound propagation model provided by the computer network. The computing power of the client device should be changed for calculating the total spread between the sound source and the at least one reception point for each of a number of different sound source locations, which along the defined trajectory curve, according to the respective requirements as desired for simulating different sound sources and defined trajectories may be arranged are sufficient.
In some embodiments, at least one total sound attenuation record that represents attenuation of sound along at least one sound path created between the sound source and the receiver is made up of a number of partial attenuation value records generated by the computing network and various Show sound attenuation characteristics of the airspace for a given scenario. The receiver can be the at least one reception point. The partial attenuation value data records can include the partial space data records which provide virtual attenuations for the individual partial spaces of the air space. In addition, the partial attenuation value records can reflect different sound attenuation characteristics for respective scenarios and the fact that different weather and / or population scenarios are considered. By preprocessing the scenarios in the computer network, they can be made easily accessible to the client device in order to quickly simulate noise levels at the at least one reception point by using the at least one overall sound attenuation data record.
In some embodiments, the total attenuation value record is calculated by the computing network and transmitted to the client device or calculated by the client device. In other words, the total attenuation value data set can be calculated completely by the computer network in order to then be transmitted to the client device, i.e. by downloading the total attenuation value record from the computer network by the client device or uploading the total attenuation value data record to the client device by the computer network. Furthermore, each total attenuation value record stored in a client device can be updated in a similar manner. On the other hand, at least some steps can be taken in the calculation and update of the total attenuation value record on the client device. If, for example, a transmission band between the client device and the computer network has certain restrictions that do not make it possible to regularly transmit the total attenuation value data record from the computer network to the client device, certain calculation and update processes can instead be carried out locally on the client device be, e.g. B. by only differential recalculation or update of the overall attenuation value data set.
[0060] In some embodiments, a sound profile is provided that includes at least one average noise level at the at least one reception point; and wherein the at least one mean noise level is associated with one of a bundle of sound source tracks. The sound profile can be estimated and / or defined with a plurality
CH 713 630 A1 can be calculated. For each estimated and / or defined path curve, an average noise level can be calculated for the at least one reception point. Thus, when performing the actual simulation of the noise level, the sound profile can be used to quickly calculate noise levels based on the at least one mean noise level associated with at least one of the plurality of estimated and / or defined paths. This supports the minimization of computing effort and facilitates and thus accelerates simulation operations according to the present invention.
In other words, a calculation of sound propagation as well as a calculation of sound profiles, the average immissions from an aircraft of a certain type that is along a certain trajectory, i.e. Route, running, caused, reproducing, based on previously calculated attenuations, advantageously carried out on a computer network or central computer. Simulation of single flights and calculations of sound profile overlays based on traffic volume for the calculation of a traffic scenario can be carried out on a client device for easy accessibility and changeability.
In some embodiments, the sound profile is calculated by the computer network and transmitted to the client device. The computer network thus has two sound profiles available for rapid change, and a subsequent calculation of different scenarios of sound propagation leads to respective noise levels at the at least one reception point on the basis of the respective profile. For this purpose, the sound profile can be transmitted from the computer network to the client device as described above with reference to the overall attenuation value data record.
In some embodiments, an overlay of at least two sound profiles is calculated by the client device. In other words, the client device can overlay several sound profiles in order to interpolate simulations or to quickly provide different but overlapping simulations of the sound propagation and thus noise levels at the at least one reception point. This supports the rapid provision and rapid adaptation of noise level simulations according to the present invention.
In preferred embodiments, a method according to the present invention further comprises a data preparation step in which a number of source points corresponding to the different sound source locations along the defined path curve are defined. By defining the number of source points, sound propagation simulations can be calculated for each of the number of source points. In view of the high computing effort involved, such calculations can e.g. be carried out by the computer network in order to make it easily accessible to the client device.
In some embodiments, the sound propagation model is calculated based on at least the number of source points, a number of reception points and a geodata set that represents a geological environment of the air space. As a result, the sound propagation model initially looks at the source points, the receiving points and the geodesics in a pre-calculated manner. Based on such calculations made in advance, all further data such as sound source parameters, trajectories and / or weather data can be entered into the sound propagation model in order to refine the model. Since the initial consideration of source points, reception points and geodesics can require considerably more computing power than refining the model through the sound source parameters, paths and / or weather data, such a calculation carried out in advance supports the minimization of computational effort and time when refining the model in accordance with the desired source parameters, tracks and / or weather data.
[0066] In a preferred embodiment, the at least one sound source is an aircraft. Such an aircraft can be an aircraft, for example a jet aircraft for civil or military use, a helicopter or the like. Various different types of such aircraft can be modeled in accordance with the present invention. For the sound source modeling, acoustic overflight measurements can be carried out in order to measure sound emissions based on records of real air traffic in the vicinity of airports or the like.
BRIEF DESCRIPTION OF THE FIGURES In order to describe the manner in which advantages and functions of the disclosure can be obtained, a more detailed description of the principles briefly described above is provided below with reference to the embodiments thereof, which are illustrated in the attached drawings. These drawings depict only exemplary embodiments of the present disclosure and are therefore not to be regarded as limiting the scope thereof.
The following applies to the figures:
1 shows a schematic representation of a beam tracking algorithm to derive the sound path from the source S to the receiver R in a method according to an embodiment of the present invention;
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2 shows a schematic illustration of a measuring arrangement in accordance with a method according to an embodiment of the present invention;
Fig. 3 shows a schematic representation of a flight path axis system with polar angles θ and φ in the longitudinal and transverse directions;
Fig. 4 shows a schematic representation of an angle coverage by any measurement setup for a short start (left) and two flights with different take-off points (right), for two microphones (I: φ ~ 40 °; II: φ = 0 °);
5 shows schematic diagrams which show the directional uncertainty in the case of radar data at a distance of 1 km (left) and 5 km (right) before touchdown;
FIG. 6 shows a schematic illustration of a setup for measurements at a greater distance at an airport in accordance with a method according to an embodiment of the present invention:
7 is a flowchart illustrating steps of processing data from measurements to an emission record at the source in accordance with an embodiment of the present invention;
8 shows diagrams illustrating the results of engine test runs on exemplary A330-300 aircraft;
9 shows two diagrams that illustrate an exemplary influence of N1 on L _em ;
FIG. 10 shows two diagrams that illustrate an exemplary influence of Ma on the noise emission level of the aircraft type A320;
11 shows two diagrams which show an exemplary distribution of measured flap lever positions of the aircraft type A320 as a function of the Ma number;
FIG. 12 shows two diagrams which show an exemplary influence of the landing gear on the noise emission of the aircraft type A320 when landing at idle;
13 is a diagram illustrating steps of a process for model development and data separation according to an embodiment of the present invention;
14 shows three diagrams illustrating a data separation example for the A320 at 100 Hz;
FIG. 15 is a diagram illustrating an exemplary average energy over frequency correction factor for A320 aircraft;
16 shows two diagrams illustrating an exemplary coefficient of determination over frequency;
FIG. 17 shows two diagrams which illustrate exemplary spectral directional characteristics of the aircraft type A320 when departing with a high-performance setting;
18 shows two diagrams which show exemplary spectra for final approach and take-off with high-performance setting of the aircraft type A320;
19 shows two diagrams which show exemplary spectra for final approach and take-off with high-performance setting of the aircraft type E170;
FIG. 20 shows two diagrams which show exemplary spectra of a landing approach of an A320 aircraft with the flaps fully raised and the landing gear extended at θ = 130 °;
Figure 21 shows two diagrams illustrating exemplary spectral directivity characteristics for a low power setting takeoff;
22 shows two diagrams which illustrate an exemplary directivity in the longitudinal direction of the total L _w for three different take-off power settings of the aircraft type A320;
FIG. 23 shows two diagrams which illustrate an exemplary directivity in the transverse direction of the total L _w for three different take-off power settings of the aircraft type A320;
FIG. 24 shows six diagrams which exemplify two landing approaches in the idle setting and with the undercarriage extended and high-lift elements on a receiver about 15 km before the landing threshold;
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25 shows two diagrams which exemplarily show spectra of a landing approach of an A320 aircraft at idle about 15 km before the landing threshold;
26 shows two diagrams which exemplarily show spectra of a landing approach of an A330 aircraft at idle about 15 km before the landing threshold;
FIG. 27 shows two diagrams that represent an exemplary radiation balance for an exemplary day in combination with the wind speed and different conditions during the day representing weather categories;
28 shows three graphs illustrating exemplary temperature, wind, and humidity profiles for an exemplary day at 10:00 a.m.
29 shows three graphs illustrating exemplary temperature, wind, and humidity profiles for an exemplary day at 9:00 a.m.
30 shows two graphs illustrating exemplary temperature, wind, and humidity profiles for an exemplary day between 9:00 AM and 12:00 PM;
31 shows a schematic illustration of a calculation scenario based on an flight path;
32 is a diagram illustrating an exemplary variation in air absorption from a homogeneous atmosphere shifting to current conditions to a uniform atmosphere with averaged conditions;
FIG. 33 is a diagram illustrating an exemplary variation in air absorption from a homogeneous atmosphere shifting to current conditions to a uniform atmosphere with averaged conditions;
Fig. 34 shows two diagrams which exemplify differences in air absorption between a homogeneous atmosphere under current conditions and COSMO-2 profiles;
FIG. 35 shows two diagrams which exemplify differences in air absorption between idealized profiles and COSMO-2 profiles;
36 shows a diagram illustrating an exemplary influence of the dissipation variance on the A-weighted spectrum at a receiver;
37 shows a schematic perspective view of a subspace used in a model for calculating attenuations in accordance with a method according to the present invention;
38 shows a schematic perspective view of an air space divided into subspaces in accordance with a method according to the present invention; and
39 is a schematic diagram illustrating a system for performing a method according to an embodiment of the present invention.
DETAILED DESCRIPTION OF THE FIGURES One embodiment of the present invention is a method and a corresponding computer-implemented time step program for aircraft noise calculation, in which sound source and propagation calculation are strictly separated from one another. In contrast to the simulation model FLULA2, which was developed at the Federal Material Testing and Research Institute (EMPA) for the acoustic investigation of complex scenarios such as annual air traffic at an earlier point in time, the program according to one embodiment of the present invention focuses on single flight events in order to use of either generic data, e.g. from a flight simulator (FFS) or cockpit data from real flights to carry out examinations and optimize noise reduction procedures.
The aircraft as a sound source is described by physical laws and to scale empirical data on flight parameters such as power setting or speed and aircraft configuration (slats, flaps, landing gear). In order to obtain a sufficient data basis for various types of aircraft and engines, extensive measurements of real air traffic were carried out at Zurich Airport.
A sophisticated sound propagation model is adapted to the special circumstances of aircraft noise calculation. The sound emission and propagation models are combined in a geographic information system. This interface enables you to prepare projects, carry out calculation tasks and obtain useful tools for analyzing and presenting results. In addition to single flight events, the algorithms and the program structure also enable the calculation of complex scenarios such as annual air traffic.
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Although the emission model causes more effort in the preparation of the input data, this does not have a relevant impact on the calculation time. In contrast, the efficiency of the dispersion model plays a crucial role.
Acoustic Emission Model A semi-empirical acoustic emission model according to an embodiment of the present invention is based on a combination of data measured in real air traffic with generalized physical laws that establish the relationship between flight configuration and acoustic emission, including information about frequency spectrum and directivity. Thus, results and experiences from the MODAL research program of the German Aerospace Center are also considered. In the immediate vicinity of the airport, SD sound directional characteristics are created, which reflect the stationary flight conditions of the initial climb and final approach. At greater distances from the airport, it is not possible to cover a wide range of polar angles. Sound directivity effects may not be created. However, measurements are still of interest to determine the noise emission for different flight conditions. Mobile measuring stations are used at numerous different locations at distances of up to 25 km from the airport. If the directivity cannot be determined at distant locations, spectral sound level differences are used as a substitute solution to take changes in the flight configuration into account.
Cockpit data is used to determine the flight configuration of the measured aircraft events. Such cockpit data are available, for example, for the Swiss air fleet, namely the aircraft types of the Airbus A320 series, the A330-300, the A340-300 and the Avrò RJ100. The cockpit data covers all the necessary information in high time resolution, for example flight path, spatial orientation, real air speed, rotational speeds of the engines and the position of slats, flaps and landing gear. In the case of aircraft of other airlines for which no cockpit data are available, the ground speed and the bank angle can be derived from the flight path. In addition, the speed of the low-pressure turbine (N1) is estimated by spectral evaluation of the acoustic data as an indicator of the power setting. This method works exactly in the immediate vicinity of the airport. At a greater distance with heavily weakened signals, the evaluation of N1 poses a greater challenge.
Sound propagation model The calculation of sound propagation in a method and computer-implemented program according to an embodiment of the present invention is based on a model called sonX, which has a propagation core that is optimized from an acoustic point of view as well as in terms of performance.
The sonX propagation model refers to point sources. Direct sound is calculated based on vertical sections of the terrain from the source to the receiver, including buildings and other barriers. The calculation is carried out in two steps. In a first step, a calculation is made assuming a homogeneous atmosphere. Geometric deviation, dissipation according to ISO 9613-1, barrier and soil effects as well as vegetation damping according to ISO 9613-2 are taken into account. Barrier effects can be calculated, for example, as implemented in ISO 9613-2. An analytical solution for spherical waves is used to calculate soil effects, which has been expanded for uneven terrain and varying surface conditions. In a second step, the weather influences on sound propagation, in particular the influence of local temperature and humidity on dissipation and the consequences of vertical wind and temperature gradients on screen effects, are determined. The latter is carried out using a beam tracking algorithm, which derives the sound path from the source to the receiver along possible barrier edges from the effective speed of sound for any profile.
Figure 1 shows a beam tracking algorithm to derive the sound path from source S to receiver R. As an additional effect, the formation of sound shadow zones can be derived. Reflections on buildings and walls are taken into account. The model distinguishes between coherent and mirrored reflections and scattering. Diffuse reflections from forest edges and cliffs are reproduced by two separate models. The sound propagation modeling is done in third octave bands. A frequency range from 20 Hz to 5 kHz is used for aircraft noise.
Simulation Tool According to the time step concept, a single flight is represented by source positions that follow the flight path in predetermined time steps, usually one second. At each position, the angle-dependent noise emission is calculated with the current power setting, aircraft configuration and orientation. At any recipient location, the amounts from each source position are summarized in chronological order. Acoustic values such as equivalent continuous sound pressure level or maximum sound pressure level can be derived from the resulting level-time protocols.
CH 713 630 A1 The resulting simulation tool is used for the detailed analysis of single flights as well as the calculation of complex scenarios with processing of several tens of thousands of flights in order to create noise maps of large areas with several ten times the square meters. In the latter case, the calculation time is a critical point that can be reduced by two measures.
First, a differentiation of the propagation situation is initiated. If sound propagates near the floor, a direct model as described above with reference to Fig. 1 is used. At higher altitudes, however, the situation is much simpler, since there are no shielding effects and the time-consuming processing of terrain profiles can be omitted. Furthermore, the influence of temperature and humidity on the dissipation is the only relevant weather influence. In addition, the direct sound dominates and reflections can be neglected. For this, a simplified approach is practiced that only takes into account geometric deviation, dissipation and a standardized soil effect. The line of sight angle with respect to the horizon line is used as a criterion for distinguishing complex and simplified situations.
Second, the detailed or complex sound propagation calculations are made before the actual aircraft noise calculations, and the resulting attenuations are stored in a demand database. For this purpose, an air space is divided into basic shapes of rectangular shape, which each form a cell-like subspace. A sound propagation calculation is made for each of the eight corners by the cells that the aircraft actually flies through. During the simulation of single flights, the relevant attenuations are derived from the values of the cell corners according to the database by interpolating at the actual source position.
In order to avoid inconsistencies in the transition from the simple to the differentiated or complex model, dissipations per meter are calculated in advance with sonX for given weather conditions and stored in a look-up table for different layers of the atmosphere. During the single flight simulation, attenuations can either be accessed from the database or, in the simple case, can be calculated directly using the latter dissipation values.
Measurement concept Measurements are carried out at a public airport, for example Zurich Airport, with real air traffic, which enables the most important types of commercial aircraft operating at comparable airports to be recorded. Due to different lengths of runways, operating concepts and destinations at the airport, a wide range of flight parameters can be collected for the development of the semi-empirical sound source model. Independent measurements are carried out both at a short and a large distance from the airport.
2 shows a measurement arrangement on the runways 16, 28 and 34 of the airport. In the immediate vicinity of the airport, data for 3D directivity patterns are collected by placing six to eight microphones next to the runways. The angles covered depend on the microphone position and the lift-off point, which can differ by up to 500 m for the same aircraft type and up to 1500 m for different aircraft types, mainly due to different takeoff weights and thrust capacities. For this purpose, the microphones are not only located at the end of the runways, but also along the runways in order to detect early take-off. During the day, most of the data can be recorded on runway 28 (departure to the west) and an additional runway (approach from the north, not shown).
Due to German air traffic restrictions in the early morning (6:00 a.m. to 7:00 a.m.), some types of aircraft fly exclusively from the south (runway 34). Heavy aircraft types such as the A330 or A380 normally also take off south from runway 16. Since these types are acoustically relevant, measurements are also carried out on runways 16/34. The locations of the microphones can be optimized to cover a wide range of polar angles and thus accumulate enough data for the SD directional characteristics.
At greater distances, approximately twelve microphones are installed at locations up to 25 km from the airport, which cover different flight configurations. Departures are measured along the regular flight route west of runway 28 and east of runway 16. Landing approaches are only measured on runway 34, since there is sufficient data available for all types of aircraft.
Flight paths in the immediate vicinity of the airport (time and position) are determined by an optical tracking system. Further away from the airport, the requirements for the accuracy of flight path location are lower, and radar data should provide sufficient accuracy. These systems are particularly necessary for the aircraft of airlines where no cockpit data is available. A mobile multilateration system is used to validate tracking data derived from different sources.
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Forecasting the Angular Variety for 3D Directional Effects In order to be able to create reliable 3D directional characteristics, a wide range of polar angles must be covered. The microphone positions were optimized accordingly using a Matlab program that predicts the coverage of the polar angles. Since the departure positions can differ greatly, flight paths which are derived from radar data of different aircraft types and which are provided with an early and a late departure point per type are used as input data for the forecasting tool. A relative vector r _gimic for a microphone was determined for each discrete source position (s) of the selected flight path, as described in aircraft-carried normal world coordinates (index g) as defined in DIN9300-1 according to equation (1) below. Since all sample data are given in Swiss country coordinates CH1903, the x _g axis is oriented towards the geographical north.
'mie' ^x patii Ü0 y _m ie-y _{ì> a} ih ^ (1)
(2) With equation (2) above, the vector r _g , _{mic is transformed} into the caries-like flight path axis system (index k, see FIG. 3) in order to determine the polar angles from each source position to the microphone. Since information on the angle of inclination is generally not available, the influence on the aircraft orientation is neglected, but will be the subject of future investigations. The transformation matrix is converted into the flight path.
FIG. 3 shows a schematic illustration of a flight path axis system with polar angles θ and φ in the longitudinal and transverse directions. The polar angles θ and φ for each discrete point of the flight path are determined by the Lambert cosine law with the vector r _{k mic} (GI. 2) or vector r _kimiCiyz and the unit vectors X _k and Z _k as shown in Fig. 3 and according to the ones below Equations (3) and (4) are determined.

= arccos
(3)
( / Λ p = arccos Lk, mic, y: ’ik = arccos kjnic (y ₍ „2) ^ίλ5
(4).
In the calculations, the portion of the flyover from shortly after take-off to a height of 1,500 feet above the runway is considered. This flight segment represents an almost stationary flight with constant speed and constant power settings. The aerodynamic noise of the landing gear that is retracted in this phase can be neglected due to the dominance of the engines. A height of 30 feet or a slope angle of 10 ° are suggested as the criteria for starting the evaluation, while throttling at 1,500 feet is the criterion for ending the measurement.
4 shows, by way of example, results of the forecasting tool, in particular the resulting polar angles for any flight paths, which are represented graphically as flight path axes with normalized vectors on one side of the hemisphere, which represents the underside of the aircraft. (Only one side is shown graphically, since the sound directivity is modeled symmetrically along the longitudinal axis, X _k ). On a flight path with short take-off, almost all microphones show good coverage of 9 due to their location in front of the take-off point. The data are limited to 9 angles between 15 ° and 170 °. However, these angles are negligible in relation to their sound contribution. In addition, even if the angular coverage of a single flyover is not sufficient, the variation of the flight paths of a statistically sufficient number of events will fill this gap, as shown in Fig. 4 (right). Good data coverage can be predicted within the marked surfaces of two exemplary microphone positions.
CH 713 630 A1 For the optimization of the microphone positions shown in FIG. 2, all eight microphones of any arrangement are calculated. The positions are optimized for different flight paths of different aircraft types on each of the runways covered by the measurements. In addition, an example of a final arrangement as shown in Fig. 2 is also dependent on various other conditions such as security requirements at the airport, inaccessibility of some areas and acoustically disadvantageous locations with reflections or strong background noise.
Directional Uncertainty Reliable 3D directional characteristics require precise determination of the aircraft position, which affects the accuracy of the polar angles. The standard inaccuracies u _e and υ _φ (confidence interval 68%) can be estimated by the error propagation law based on the uncertainties of the horizontal and vertical aircraft position by applying equations (5) and (6) below to equations (3) and (4) above ,
-r c0
1¾

(5)
(6) where x _{k is} the longitudinal axis, y _{k is} the transverse axis and Z _{k is} the vertical axis of the flight path axis system (FIG. 3). The quantities of u _e and υ _φ are quantified for radar data with a lateral tolerance of 230 m and a vertical tolerance of 46 m. This estimate gives an upper limit for u _e and u ₉ since other positioning systems such as cockpit data are more accurate. Assuming a rectangular distribution within the tolerance limits, the transformation of tolerances t into standard uncertainties u _t can be calculated according to equation (7) below as follows
(7) Fig. 5 shows schematic diagrams showing the directional uncertainty for radar data at a distance of 1 km (left) and 5 km (right) before touchdown. The microphone position is laterally shifted from 0 to 100, 200 and 300 m. Above: glide path; Middle: uncertainty u _e ; Below: uncertainty υφ. (Note: different scales of standard uncertainties for 1 km.)
Interim conclusion for data acquisition The model described above for a method and a computer-implemented program according to an embodiment of the present invention takes flight configuration parameters into account and thus fulfills the requirements for acoustic optimization of flight methods. The sound emission model in function of the flight configuration and the underlying sound source database are decisive for the development of such a model. Extensive measurements must be carried out in the immediate vicinity and at a great distance from the respective airport, e.g. Zurich. A particular focus is on the development of reliable 3D formwork characteristics. The latter require optimized microphone positioning for a wide coverage of polar angles. The forecast of the polar angle coverage of a specific measurement arrangement is of great help in optimizing the measurement setup. Furthermore, the determination of the polar angle must itself be reliable. Their uncertainties are therefore estimated as an example for a typical landing approach. The results show that radar data for measurement positions at a great distance from the airport (uncertainties of 0.6 °) are sufficiently accurate, while other systems are required in the immediate vicinity of the airport.
Using a hybrid case-dependent propagation model in combination with an attenuation database, it is possible to provide a simulation tool with high flexibility and accuracy without increasing the computational effort. A significant advantage of the mitigation database is that it can be reused for numerous calculations once it has been created for a particular airport. Further steps are the evaluation of the sound source data in combination with the tracking data, the development of the sound emission model and the integration into the simulation tool in accordance with a method and a computer-implemented program according to an embodiment of the present invention.
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Sound source model for aircraft as a function of flight conditions Based on the measurement structure and data sources described above, a sound source model for vehicles, in particular aircraft or aircraft, takes into account respective flight conditions of the aircraft in accordance with a method and a computer-implemented program according to an embodiment of the present invention.
Input Data - Measurements and Data Sources As stated above, acoustic flyover measurements of real air traffic around a public airport, e.g. Zurich. In order to cover a wide range of typical flight conditions, microphones are placed both in the immediate vicinity of the airport and at distances of up to 20 km.
6 shows the measurement set-up further away from the airport, including landing approaches from the south and two departure routes, which cover wide-body and standard fuselage aircraft. Microphone locations 1-10 (dots) and typical flight paths for departures from runway 16 (solid lines) and runway 28 (dashed line) as well as approaches to runway 34 (dash-dotted lines) are shown in FIG. 6. The ten independent locations are equipped with omnidirectional free-field microphones, which are arranged at a height of 10 m above the ground. The microphone locations for departures are selected such that they cover flight conditions after throttling, during acceleration and during continuous climbing in cruise configuration. During the approach, the microphone locations were distributed along the glide path while the aircraft was decelerating for the final approach.
Measurements in the immediate vicinity of the airport, within a distance of 2.5 km from takeoff or touchdown, provide data for both the final approach and the initial climb in a wide range of radiation angles. Eight omnidirectional free-field microphones were installed at a height of 4 m above the ground in each of the three measured runway directions in the vicinity.
[0102] An optical tracking system and multilateration (MLAT) provide position data in the vicinity of the airport with great accuracy. In the further distance, where the accuracy of radar data is sufficient, the latter were used. Black box data with routes based on GPS are provided by international airlines (e.g. SWISS). The flight recorder data also provide air speed, engine parameters, aircraft orientation, configuration of the airframe, environmental conditions etc. For aircraft types without available flight recorder data, the rotational speed of the engine N1 is extracted from short-term spectra of the acoustic measurements. In the present example measurements, flight recorder data from a total of six aircraft types with 161 to 673 flights and thirteen combinations of aircraft and engine types, so-called reference types, which are based on the N1 determination with 27 to 334 flights, are available (see Table 1 below).
Input Data - Data Processing Figure 7 shows the data processing used to back propagate the measurements and create the input data set for the acoustic emission model. The sound wave files are analyzed with a fixed time interval of 50 ms and filtered on third octave bands with 24 medium frequencies from 25 Hz to 5 kHz in order to obtain the sound pressure level L _p (f). This frequency range is chosen to cover the characteristics of aircraft noise, which includes high acoustic energy in the low frequency range from the nozzle. Frequencies above 5 kHz are quickly attenuated due to the usually relatively long distances between the aircraft and the receiver and can therefore only be measured very close to the source.
In order to prevent back propagation of background noise, only the part of the level-time protocol (of every third octave band) that is 6 dB above and below the minimum level before and after the event is selected for the analysis. In addition, events with unwanted noise are discarded. The sonX sound propagation model is modified and used to calculate the corresponding attenuation and speed of sound for each source-receiver combination. Source positions and flight parameters are synchronized to the acoustic data and corrected for the delay of the sound as it passes through the atmosphere.
Geometric deviation, atmospheric absorption, soil effect, vegetation damping and the influence of vertical gradients of wind, temperature and relative humidity are taken into account. Each flight uses individual weather profiles from the well-known numerical weather forecast model COSMO-2 to reproduce the actual atmospheric conditions as precisely as possible. The coefficient of atmospheric absorption is calculated for certain plane surfaces with a maximum step size of 100 m.
Effects due to the movement of the source are corrected by applying frequency shift and level gain. In equation (8) below, the Doppler factor DF is defined as a function of the relative Mach number Ma from the source towards the receiver, where 9 is the radiation angle between the flight path and the vector from source to receiver.
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DF = 1 - Ma cos (ff) (8) Flight effects (FE) that can be categorized for sound sources that move by plane consist of the kinematic effect that the movement of the source related to the receiver (Doppler) corresponds, as well as the dynamic effect, which corresponds to the movement of the source in relation to the propagation medium. Combining both effects leads to level gain AL _FE , which is defined in equation (9) below.
(9) Equation (10) below summarizes the back propagation process for obtaining the sound emission level L _em for each third octave band. According to the present invention, the sound emission level L _{em is to be} regarded as equivalent to a sound power level L _w , the directivity D being already included. This explanation is necessary because only the lower hemisphere of the aircraft can be measured from the ground.
L _em {f) = L _w (f) + D (f) - & L _fe == + Σ Ä (f) - ΔΜρε 0Q) The back propagation from the receiver to the source is based on for all receivers (microphone locations) Short-term sound pressure levels L _p , which are corrected for the attenuation ΣΑ and the flight effect AL _FE , made. In accordance with the definition of the sound power level, the geometric deviation in ΣΑ includes the deformation constant Ιος ₁₀ (4π). The frequency shift is defined by the frequency ratio of source and receiver, which corresponds to the inverse Doppler factor. For third octave bands, the frequency shift was implemented assuming energy evenly distributed over each band. The frequency shift is applied after back propagation to the source position according to equation (10) above.
Input Data - Resulting Data Set As a basis for the model development, a data set is prepared for all events of each individual combination of aircraft type and engine type. The level of detail for the classification is limited to general aircraft types, so that no distinction is made with optional equipment such as winglets or double ring combustion chambers.
For example, the A320 family from the aircraft manufacturer Airbus is divided into the types A319, A320 and A321, which primarily differ in length and maximum take-off weight. Different engine options, either CFM56 or V2500, are available for each type within the group, which implies six possible data sets when all combinations are measured.
Each data set consists of 24 subsets for the evaluated third octave bands with middle frequencies from 25 Hz to 5 kHz. A subset includes the corresponding emission levels L _em in dB from equation (10) above, the calculated radiation angles θ, φ in degrees, flight parameters such as the rotational speed of the engines N1 in%, the Mach number Ma and the atmospheric parameters pressure p in Pa, temperature T in ° C, density p in kg / m ³ and speed of sound c in m / s. If flight recorder data is available, the setting and sliding angles as well as the configuration settings are also available. The Mach number Ma could be related to the actual air speed (based on the moving air) of the aircraft in black box data, but is related to the flight path speed (based on the ground) for all aircraft for consistency reasons.
Additionally, for traceability during model development, the event identification number and microphone are appended to each data point (line). An event with a flight period of 60 s adds 1,200 data points per microphone to the data set. For all flights and measurement locations, this can amount to one to two million data points per subset.
In order to be able to compare the model forecasts with the measured data in section V, the data set is filtered according to the corresponding flight conditions and directional angle in each frequency band. For example, flight parameters for a typical flight phase and a relevant directional angle are selected in order to predict the sound emission level. Then the same parameters are used with a certain interval around each parameter to generate a subset from the complete data set (φ = 60 ° ± 5 °, N1 = 93% ± 2% etc.). The arithmetic mean Lem is then calculated and compared with the arithmetic mean of the predicted values Lem (see below). The comparisons
CH 713 630 A1 with measured data are not independent because the model is matched to the same data set, but they enable an assessment of whether the model approach is suitable.
Model development The model is created by means of multiple linear regression. This procedure enables the effects of various influencing parameters and their interactions to be identified in great detail. It can be applied to the sound emission level because the logarithmic levels are normally distributed. In a first step, outliers are removed from the data set (see the section below). Then the parameters for the models are selected (see below). A new process for separating the data set into airframe and engine noise is then carried out (see below). This separation of the two main source mechanisms is advantageous because it enables each mechanism to be described more precisely. For example, both models can include different parameters or take different relationships of the same parameter into account.
Data processing, data analysis and adaptation of the source models can e.g. using the standard software Matlab 2014b for mathematical calculations. The models are e.g. using the Matlab Statistical Toolbox using the “fitlm” command, which uses a small square compensation (OLS) and enables an individual evaluation of the data points.
Outliers Before estimating the model coefficients, outliers are removed by an adaptive outlier detection which uses the robust Mahalanobis distance (RD) for automatic detection of outliers. One advantage of this method is the adjusted threshold, which is adapted to the sample size.
If the data set originates from a multivariate normal distribution, no outliers would be recognized compared to a fixed threshold.
Data Exploration The available parameters of the presented data set are analyzed using exploratory analysis and knowledge from the literature. The findings are used to select the most important parameters for the statistical model presented below. In addition, parameters are discarded if they correlate with other parameters to ensure reliable model coefficients. Finally, the relationship of the parameters to L _{em is} disclosed in order to satisfy the linearity of the statistical model.
For many engines, N1 is the control parameter of the power setting. In contrast to the thrust or jet speed of the engine, this is a directly measurable parameter. It correlates with the jet velocity, which can be considered the main physical cause of nozzle noise. At frequencies below 1 kHz, where jet mixed noise dominates, N1 can be used as a replacement for the jet velocity. In addition, the leaf passing frequency (BPF) and fan broadband noise with a center frequency of 2.5 times BPF [20] are directly related to N1. For most engine types, the BPF and thus the broadband noise are above 1 kHz, so that N1 is an appropriate predictive value for the entire engine spectra.
Figure 8 shows graphs illustrating the results of engine test runs performed on the exemplary A330-300 (TRENT772B) aircraft for two exemplary third octave bands, with L _p measured in a 170 m radius in four different directions has been. The test runs were carried out by SWISS, measurements were carried out by EMPA and Zurich Airport. Permission to use the data has been kindly granted. An exemplary engine test run of an A330-300 with TRENT772B is evaluated to establish the functional relationship between sound pressure level and N1 for each third octave band, since no such relationship is known. The stationary aircraft excludes all airframe noise sources and flight effects from the measurements. Short-term linear sound pressure levels (L _p ) are measured at four microphone locations within a radius of 170 m around the aircraft. The engine test is performed twice up and down by ramping up six different engine loads from idle to the highest possible engine pressure ratio on the ground. Thus, two to four mean L _{p are} available for each time interval with constant N1 (FIG. 3). A regression model was tuned in each direction, 0 ° corresponds to the nose of the aircraft, N1 is tuned as a second degree polynomial.
According to the diagrams a) and b) in FIG. 8, which show two exemplary frequency bands, the functional relationship changes with the frequency and the direction. At 31.5 Hz, the fit curve is slightly parabolic at the front and almost linear towards the rear of the aircraft. In addition, the likelihood increases to the rear. At some frequencies, for example at 2 kHz, the parabola actually opens downwards for about 15 °. For frequencies in between, which are not shown here, the relationships are very similar with predominantly linear or slightly quadratic behavior (open upwards). Thus, a flexible second-degree polynomial approach enables the relationship between L _p or Lem and N1 to be represented.
FIG. 9 shows two diagrams that illustrate an exemplary influence of N1 on L _{em of} the exemplary aircraft E170, equipped with the turbo engine CF34-8E, for 2 kHz. The data points are filtered for 0.2 <Ma <0.24,
CH 713 630 A1 but airframe noise can affect the levels at low n1. Generally speaking, the turbofan engines of today's civil aircraft are very similar and the mechanisms of noise generation are the same. It can therefore be assumed that the quadratic approach is also valid for other turbojet engines. This assumption can be tested exploratively using the data set backpropagated in FIG. 9 for the Embraer E170 engine (CF34-8E) at 2 kHz. Although airframe noise is included in L _em for a low N1, the same trends can be seen. In diagram a) in FIG. 9, the sound emission toward the front is a parabolic opened downwards, while the opposite applies to the radiation angle in diagram b) of FIG. 9. This fits well with the results of the test run.
The Mach number Ma = U / c was chosen to take into account the speed-dependent sound sources. It shows the mean flow velocity U at the source and the local velocity of the sound c in a single, dimensionless quantity. Furthermore, the Mach number is an aerodynamic index that is interpreted as a compressible flow condition and thus ensures comparable flow phenomena.
[0125] The dependence of the sound emission on Ma can be provided by an aeroacoustic analogy. The generation of sound by the fluctuating fluid is described by the classic wave equation, which is expanded by three basic source terms: monopole, dipole and quadrupole. The theoretical free-field solutions, which are obtained, for example, using equation (11) below, show that the sound power W is proportional to the air density p, a characteristic size of the source D, the mean flow velocity U and the Mach number Ma. The exponent x depends on the source mechanism (monopoly x = 1, dipole x = 3, quadrupole x = 5).
fix pD ² l i '' λ l (11) In order to derive the relationship of the sound power level L _w , the logarithm to the base 10 according to equation (12) below is used. The units of the parameters are normalized with p ₀ , D _o , U _o .
101og ₁₀ (Mir j (12) The dependencies of equation (12) are transferred to L _em , which is proportional to L _w as defined in equation (10). In a statistical model, the implementation of U and Ma is problematic Because these parameters are highly correlated (multicollinearity), multicollinearity can have a major impact on the regression coefficient estimates, even if the model equation is still useful at its known intervals, the individual impact of the parameters can be poorly estimated and would lead to incorrect extrapolations To prevent multicollinearity, only log-io (Ma) is taken into account instead of logio (U), so the regression coefficient reflects the power x of Ma at which the total airframe noise scales influenced primarily by the effects of the reduced relative jet speed To enable the model for the start run, a simplified correlation of L <*> U by [22] can be used. Thus L _em °° Ma is assumed for engine noise.
FIG. 10 shows two diagrams which represent an exemplary influence of Ma on the noise emission level of the aircraft type A320 at a) take-off and b) landing approach at 250 Hz. The dashed line is a linear regression in
a) and a logarithmic regression in b). The exemplary dependence of L _em on Ma at 250 Hz for typical flight conditions at high power take-off is shown in diagram a) and the landing approach with idling is shown in a diagram
b) shown in Fig. 10. Engine noise is expected to dominate in diagram a), and linear regression seems appropriate to extrapolate the decrease in Ma to zero. In contrast, it can be expected that the airframe noise dominates during the approach in diagram b). However, a regression using the base 10 log of Ma is appropriate as a linear regression, but a linear approach would not reflect the physical properties of airframe noise and thus overestimate levels for lower and higher Ma.
[0129] An effect of air density is included in the airframe model to take into account the generalized aeroacoustic theory in Equation (12) above. The air density correlates strongly with air pressure p and temperature T, which are excluded from the regression model in order to prevent a multi-collinearity of the predicted values. The multicollinearity can be checked using the variance inflation (VIF), which can be determined for each possible parameter. No strong multicollinearity remains after discarding p and T. Nevertheless, it can be decided to also reject the speed of sound c, since it is already included above Ma. Indeed, the Ma and p VIFs continue to decrease after discarding c. It is therefore possible to select only p as a variable for the statistical model. From equation (10) above, the logarithmic transformation is applied to p. The reshaping may be necessary to ensure linear behavior with respect to the coefficients of the airframe model. The Mach number and the air density can be converted logarithmically, ie the linear coefficients stand for the exponents of the variables within the logarithm. The air density can be normalized by the density at mean sea level as defined by the International Standard Atmosphere ISA (p ₀ = 1.225 kg / m ³ )
CH 713 630 A1 in order to obtain a dimensionless size of the variable. At zero airspeed or density, the transformations tend towards minus infinity, which is physically appropriate. In practice, Ma can be set to 10 ^-3 to get a real value, and p should never be extrapolated as much.
IMa = log _so (Ma) tp - lOgjQ [j
MM (14) The sound emission of an aircraft has a directional effect, which can best be described using spherical coordinates. In particular, the longitudinal radiation, which is represented by the polar angle 8, changes greatly with the aircraft type, the frequency band and the power setting, as shown in FIG. 10. The transverse radiation, represented by the azimuth angle φ, is also taken into account depending on the aircraft type, frequency band and power setting. The directivity in the transverse direction can lead to level differences of up to 3 dB above φ. In addition, the directivity in the transverse direction can have considerable discrepancies with the generalized corrections, which only differentiate between engines mounted on wings and on the fuselage.
A second order Fourier series can be selected to represent the directivity in the longitudinal direction. A higher order was also tested during model development, but resulted in problematic favors at the borders where less data is available. The directivity at high frequencies was particularly critical for conditions at a great distance from the receivers. The directionality in the transverse direction is modeled using a semi-four row (second-order) to simplify the number of terms and also to prevent problematic favors in areas with little data coverage.
FIG. 11 shows two diagrams which represent an exemplary distribution of measured flap lever positions of the aircraft type A320 as a function of the Ma number, in contrast to the landing approach, the flap lever position 1 relates to a different deflection angle of the flaps during departure. The configuration of the aircraft is modeled by three categorical variables: position of the landing gear (retracted: 0, extended: 1), position of the flap lever (0 to 4, fixed combinations of slats and flap derivative) and application of the brake flaps (deactivated: 0, activated: 1). This information is taken from the flight recorder data. Of course, due to the measurement of actual air traffic, the data is not balanced, and it may not be possible to obtain data for all configuration combinations. Furthermore, the valve settings correlate strongly with different intervals of the Mach numbers due to processes and structural restrictions, as shown in FIG. 11. In addition, the flap lever position 1 is indicated as 1 + F for the take-off, which corresponds to a different deflection angle of the flaps than during the landing approach (10 ° instead of 0 °). Despite all the difficulties, the influence of the aircraft configuration is of interest and is therefore taken into account with the help of a specific model structure, which is described in more detail below.
FIG. 12 shows two diagrams which illustrate an example of the influence of the landing gear on the noise emission of the A320 when landing at idle for 250 Hz in (a) and 2 kHz in (b). Measured data and the regression lines show a clear effect on the noise emission when the undercarriage is extended. The data points shown in FIG. 12 for representing the effect of the landing gear come from measurements for landing approaches with the landing gear retracted and extended. Each data set is matched with a simple logarithmic regression to show the influence of the chassis. Diagram a) of FIG. 12 shows a slightly larger favor of regression with the landing gear extended. At low Ma the emission levels are similar, but at high Ma of 0.3 the level difference is 2.6 dB. At 2 kHz in diagram b) of FIG. 12, the impact of the undercarriage on the dimensionality of the sound generation is significantly higher. At Ma = 0.3 the difference is already 6 dB.
Additional parameters can be considered or discarded during model development due to insignificance or for practical reasons. In particular, the angle of attack and the angle of attack, which are available from the flight recorder data, can be excluded because no correlations with the emission level are found. In addition, discarding both angles is also appropriate since they are usually not available for forecasting.
Data Separation Method Fig. 13 is a diagram illustrating steps of a model development and data separation process according to an embodiment of the present invention. The process is repeated for every third octave band. Following the exemplary process shown in Fig. 13 to show the model development and the separation of total emission levels from a frequency band in the data set into airframe and engine noise, in step 1 the data set is separated into two parts: part one contains all Data where the engines were idling, ie landing approaches only, and part two contains all other data from landing approaches and take-offs with engines under load.
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An appropriate limit for the division is determined by data plots from L _em over N1, as in FIG. 14, where there is no correlation below 40% for N1. It can therefore be assumed that airframe noise dominates the overall L _em for this subset.
In step 2, an airframe baseline model and an engine baseline model are matched to their respective datasets to reveal the main effects of each source. The baseline models include only minor changes from the models described in more detail below. With the help of the predicted initial sound emission levels for airframe noise _s ) and engine noise, a source ratio can be calculated for each data point in the original data set (step 3). The ratio q '(see equation (15) below) is defined as the predicted engine noise emissions divided by the sum of the predicted engine and airframe emissions. A ratio of 0 means that only airframe noise contributes to the overall emission, and 1 corresponds to an exclusive engine noise emission. It should be noted that all forecasts from these models are marked with a caret to distinguish them from input data; the superscript i indicates that the original models are used.
(15) However, this method implies that the original models must be extrapolated. For example, the basic airframe model is based on landing approaches with a Mach number that is less than 0.35. In contrast, starts are measured up to a Mach number of 0.45. As a result, the model must be extrapolated to predict (¢ ,,, .. / ,,,) for each data point in the full data set. However, the extrapolation of the Mach number is based on physical knowledge (see above), so that a plausible first estimate is possible.
Based on the ratio qi, two separate data sets, each including all measurements for landing approach and departure, are generated for each third octave band (step 4). One represents the sound emission of the engines (¢ ,,,. ,,, _s ) (see equation (16) below), and the other represents the sound emission level of the airframe (¢, .. / ,,.) (See equation below (17)).
-r lOlogto (¢ (/)) (15) tJJi L.JJ) + 101og _w (1 - ¢ (/)) (17) Fig. 14 shows three diagrams showing a data separation example for the A320 at 100 Hz represent. The backpropagated data in the upper area is divided in order to estimate the noise emissions associated with airframe noise (bottom left) and engine noise (bottom right). 14, the two data sets of the original L _em are compared with one another. The airframe levels (bottom left) dominate for N1 <40%, which corresponds to the implication of the assumption in step 1. In the example, they are about 20 dB below the total levels for departures. In contrast, engine levels dominate during takeoffs (bottom right) and lose influence as N1 falls. Overall, the original data set can be reconstructed by energetically adding the individual levels of both data sets.
In step 5, the end models for airframe and engine noise as defined herein are matched to the separate data sets presented in FIG. 13. Steps 3 through 5 are repeated once to improve the estimation of the relationship between airframe and engine noise, since the regular models, unlike the original models, were matched to the entire data set. Finally, the energy sums of the airframe model and the engine model add up to the predicted Ι_θ _π . The complete process of data separation and model adaptation is carried out 24 times for each third octave band.
Source Models Below are presented source models according to an exemplary embodiment of the present invention based on findings as described above. The models are built based on knowledge of the main parameters and their interactions, e.g. the Mach dependency of the chassis. From a statistical perspective, this approach is similar to forward selection with a scope that only includes the relevant parameters for both sources. Each model with a new parameter or a new interaction is compared with the coefficient of determination R ² , the mean square deviation oe and the Akaike information criterion (AIC). All criteria should be compared across all frequencies to find a global optimum. The agreement with the model assumptions was visually confirmed by means of residual plots.
CH 713 630 A1 As an example, the source models are based on the A320 data set, which provides flight recorder data and a large number of flights. They can then be tested and further improved on the basis of the five other exemplary aircraft types with flight recorder data to confirm that the models can be used for different aircraft and engine types. The resulting models are presented as further developed models (see below). If no flight recorder data is available, the configuration is unknown to the aircraft, e.g. thirteen reduced models can be created (see below).
Further Developed Models The sound emission level of the airframe can be modeled by the sum of the source terms and the radiation angle terms as summarized in equation (18) below. The dependency on the frequency f indicates that all coefficients of the source and radiation angle terms are matched to all third-octave bands, even if this is not expressly noted for reasons of readability.
Quellentermi
GLtf p. f ... ï 0)
Radiation Angle Term (18) Equation (19) below gives the source date! of the airframe model again. L _a0 is the intercept captured, and a _a1 to e _a3 are the frequency-dependent coefficients of all model parameters. The main parameters are the logarithmic transformations IMa and Ip, which reflect the behavior of the aeroacoustic sound generation in accordance with known semi-empirical models. In addition, each configuration change of chassis, flaps or brake flaps (SB) is modeled in discrete steps, since these parameters are categorical. In the case of flaps and brake flaps, interactions with the undercarriage are also considered in order to take account of changes in the absolute impact if the noise emission level is increased by the extended undercarriage. In addition, the undercarriage and brake flaps interact with IMa to take account of the speed-dependent sound generation. Interaction with IMa is neglected for the valves, since each valve position is only used in a certain small range of Mach numbers (see FIG. 11), so a Ma interaction can only be determined with great uncertainties. Nevertheless, the greatly different combinations of flap positions and Mach number range for landing approach and departure in FIG. 11 must be taken into account in the model in one way or another. Another categorical parameter, the Proc flight procedure (departure: 1, landing approach: 0) can be introduced for this purpose, which also takes into account the different deflection angles. The parameter also takes into account the observation on the A320 family that frequency bands with hollow tones during landing approaches can show a strong level increase with increasing Mach numbers, while this is not the case with departures. It can be assumed that the local flow field differs from the departure due to different angles of attack and flap positions during the approach.
-Lü. «Fm = LaO + i - IMa + Proc · (a, λ +« _a 3IMA] + 'ί, ι · lp + Ge, ars) c ,, i + c «2 Proc + c _a - s IMa) + Flapsj + d „2, · Gears + ί7 _α 3 · Proc) + SB (<: _B i + <' _a 2 Gears e ";> IMa) The directivity of the airframe model (see equation (20) below) is expressed as axially symmetrical radiation along the longitudinal axis of the aircraft, since the directivity in the transverse direction is mainly associated with reflections of the engine noise at the airframe (see below). The polar angle θ is taken into account with a second-order Fourier series in order to model the directivity in the longitudinal direction. The coefficients of the airframe directivity are k _a to n _a . Interactions are not included, ie the shape of the emission directivity is the same for all flight conditions. This simplification is justified because the data set has already been corrected for the flight effect (see Equation 10 above).
A £ e. _o y _m = k _a cosΘ · + · I ,. · Cos20 m _a sin0 -f- n “sin20 (20) The noise emission level of the engine noise L _be m.eng (f) is determined by the sum of the source terms and a more detailed approach to the radiation angle terms as in equation (21) below modeled together.
(19)
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L ™ ₉ (f) = î ₀ . _t , _<9 (A / « ₅ jVl, AT ' ² ) + ÄLe.ens (d, AT, A ^r l ² ) + ΔΪ ^ (φΗνΐ) s -------------- ---- _v . · .----------------------------------- ------------- ---------------------- / (21)
Source terms radiation angle terms '' Source terms for engine noise (see equation (22) below) include the intercept Leo collected and three parameters with their coefficients a _e i, b _e2 . The main source term for engine noise is N1. The square approach for N1 reflects the nozzle and fan noise as outlined below. In addition, the parameter Ma takes into account the change in the source strength of the jet mixture due to aircraft speed.
Lq _{> î îq} - T 'Ala + b ..] - 4' 4-. · 'TVf (22) The exemplary engine test run shows that the relationship of L _em to N1 is strongly dependent on the polar angle θ depends. The Fourier terms of the directivity Δία *,. Thus interact with N1 and with N1 ² (see equation (23) below). The corresponding model coefficients are k _e j to n _e , j with the index j for each interaction. The directivity in the transverse direction (see equation (23) below), which reproduces the installation effect, is included as a second order half-Fourier series, ie exclusively with sinus terms of φ. Analogous to the directionality in the longitudinal direction, each term has an interaction with N1 with the coefficients o _ej , p _e j. Inclusion of the interaction with N1 ² can also be tested, but no significant improvement can be expected from the present example.
AL, ., · = (À ' _e , / cosÖ -4- l _f ._ j · cos20 4- m ,. _tJ sin 0 4- n _c j · sin2 (9) - (14- 1V1 4- Nl ² ) (23)
ΔΑ _ς ,., "_Ί; = (o _f! j · sinip + -sin2ip) · (1 +; V1) (24) With the approach of the equations (23) and (24) listed above, the form of the 3D directivity is made possible with to change the engine setting N1. Since each third octave band is tuned separately, the spectral content of the overall directivity also varies with the power of the engines.
The initial airframe model used for the first calculation of the q ¹ ratio for data separation as set forth above is identical to equation (19) above, but the Proc parameter is omitted because all data points of the initial model are approaching landings belong. In addition, the directivity in equation (19) above is not changed. The engine output model as in equation (22) above is reduced by the parameter N1 ² . This prevents the noise emission level for extrapolation for N1 from rising below 40%. For this purpose, the directivity in the longitudinal direction is reduced by N1 ² in equation (23) above.
Reduced model If no flight recorder data is available, all models must be reduced by the unknown parameters. If, for example, the configuration parameters for airframe noise are missing, they have to be removed from the model approach (see equations (25) and 26 below). The effects of the configuration are still implicit in the data record. In contrast to the further developed model, the Mach dependence on L _em now takes into account the average configuration of all measured flights. The parameter Proc is therefore retained in the model for the reasons explained above. The radiation angle terms remain unchanged (see equation (20) above).
== Llj.a fm. (J Μ (I .Iß. PfOC)
source Termini
......._------ ✓
Radiation angle terms (25) - a _n i IMa Proc ~ ra _a '3' IMa) -rb _ti [Ip (26) [0152] In the case of engine noise, the parameter originating from the flight recorder data is N1. This parameter is crucial because it has the greatest impact on L _em . In the case of missing flight recorder data, N1 can be determined based on spectral analyzes. The engine model of the reduced model is therefore the same as that of the further developed model (see equations (21) to (24) above).
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Weighting During measurements, the polar angle θ changes only slowly on a distant aircraft, whereas it changes quickly when the aircraft flies over the microphones. Consequently, only a few data points of the evenly spaced acoustic samples are available in the most relevant range of θ and vice versa. For this, the data must be weighted in order to reduce an influence of the inhomogeneous distribution of data points over θ, which is inversely proportional to the time derivative θ '= dö / dt. To do this, the model is provided with an algorithm of weighted least squares (WLS) analogous to the linear sine regression of the usual least squares method.
Since each flight-receiver combination has a different geometry, θ 'is standardized by the maximum value per event and receiver, noted in equation (27) below as w ₀ , i. Standardization prevents a higher weighting of measured levels when an aircraft is closer to a receiver than when it is further away and θ 'is generally lower. The weights are then normalized by their mean w ₀ to ensure that the sum of all weights w ¹ used for the WLS algorithm corresponds to the number n of observations included in the analysis (see equation (28)).
«• ü.i = max {F) _{i; li {cnKr <; eriv (: r} ><> = wo.i / w _o = y 'Wi = n: (28)
Energy Correction As a result of the least square estimate, the model predicts the arithmetic mean of the sound emission level Lem in dB. A correction is required to predict the average energy Le, which is the arithmetic mean of the noise emission in watts. Since L _{em is} normally distributed, which is a prerequisite for linear regression, the energy correction can be determined analytically to 0.115 σ ² . The variance σ ² can be represented by the error variance estimate 3i (/), applied to each model according to equations (29) and (30) below.
~ Lem, af m (f) + 0.115 (29)
Lem, eng J) 7 ,,,,,. Eng (J) 0.115 * (30) FIG. 15 shows a diagram illustrating an exemplary correction factor for an average energy over frequency for the aircraft type A320. It can be seen from FIG. 15 that the error variance for microphones at a further distance is significantly higher than for microphones in the close range, which is primarily caused by a higher variance due to turbulence and uncertainties in the back propagation. In general, microphones for departures further away are further from the source, which supports the interpretation that the variance is dependent on the distance. The energy correction should only include the variance of the sound emission level and the uncertainty of the measurement. For this purpose, the correction is applied to the error variance of the data in the close range (total error of approach and departure).
[0157] The energy sum i ©; of the coordinated final models for airframe noise (see equation (29) above) and engine noise (see equation (30) above) gives the predicted total emission spectra of the modeled aircraft according to equation (31) below.
Lp.m.total {.f) “-7 ~ ίη, α / ίη (/) '11 ,,,,, ^ (/) (31)
Reference Types In the present example, a total of 19 acoustic reference types are created on the basis of the input data presented above. Table 1 below gives an overview of the reference types with details of the corresponding aircraft and engine types, the origin of the data and the number of flights on which the models are based. For example, the entry A320_CFM56-5B, the data of which is frequently used in the description below for illustrative purposes, is based on flight recorder data from the A320-200 equipped with CFM56-5B with a total of 673 flights. Entry E170 without black box data is based on a total of 89 flights and is used to prove the feasibility of the reduced model.
CH 713 630 A1 Table 1: Reference types with grouped aircraft and engine types and their database (number of flights). If the input data is based on the flight recorder, the further developed model (Adv.) Is created, for other types with N1 determination the reduced model (Red.) Is created.
[0160]
reference type Sub-types and engine input model Dep. Certified nr. A31'T CFM56-5B Airbus A319-100. GFM56-5B FDR Adv. 120 41 161 A320_ CFM56-5B Airbus A320-200, CFM56-5B FDR .Adv. 424 249 673 A321_ CFM56-5B Airbus A321-200. CFM56-5B FDR Adv. 300 126 426 A32X, CFM56 OA Airbus A320 Family, CFM56-5A NI Red. 57 15 72 A32X- _V2500 Airbus A320-I'ainily _; V2500 NI Red. 198 33 231 A333_ TRENT " Airbus A330-300, TRENT " FDR Adv. 249 136 385 A3 CFM56-5C Airbus A340-300, CFM56-5C FDR Adv. 160 120 286 A38S_ GP7270 Airbus A38Ü-800, GP7270 NI Red. 26 2 28 A38S_ Trents Airbus A3SO-800, TRENTO NI Red. 38 20 58 IÌ737 CFM56-3 Boeing B737 Classic (-300 to-500). CFMÖ6-3 NI Red. 84 39 123 13737 CFM56-7B Boeing 13737 NG (-600 to -900). CFM56-7B NI Red. 297 37 334 B763 PW4060 Boeing 767-300, PW4060 NI Red. 9 34 43 B76X_ _CF6-80C2 Boeing 767-Family (-200 to -400), CF-80C2 NI Red. 19 57 76 CRJ9 _CF34-8Cä Bombardier Regional Jet CRJ-900, CF34-80C2 NI Red. 71 22 93 E170__ CF34-8E Embraer ER.J 170, GF34-8E NI Red. 62 27 89 E190_ CF34-10E Embraer ER.J 190, CF34-10E NI Red. 243 49 292 F100_ TAY650-15 Fokker 100. TAY650-15 nl Red. 234 61 295 FA7X _PW307 Dassault Falcon "X. PW307 NI Red. 17 10 27 R.J1II _LF507 BAE SYSTEMS AVRÒ RJ-100, LF507 FDR. Adv. 324 202 526
If the number of measured flights is small, some aircraft types of the same aircraft family can be grouped. In general, only types with an identical engine are grouped, since the engines are the main source of noise, which can lead to considerable deviations in noise emissions. For example, all sub-types of the B737 are grouped with the classic engine option CFM56-3, while all types of the new generation that are equipped with the modern CFM56-7B are grouped separately. Grouping an aircraft family is appropriate and improves the model because a wider range of parameters are covered due to different takeoff weights and processes. In particular, different aircraft types use different N1s for takeoff, since a different thrust is required for different takeoff weights.
Performance of the models Figure 16 shows two diagrams illustrating an exemplary coefficient of determination over frequency. Airframe and engine models are compared to their separate data sets, while the overall model is compared to the original data set. 11 shows the coefficient of determination R ² over frequency for all third octave bands. Ruotai reproduces the performance of the model from equation (24) to reproduce the original data set of the back-propagated data Ι_θπ. Overall, the goodness of fit of the overall models shows a good informative value of the regression compensation with values between 0.7 and 0.8 for the A320 (a), but also for the E170 (b). R ² afm and R ² closely describe the goodness of fit of the source models to the separate data sets. The engine model shows R ² _narrow values for most frequency bands, which are slightly above 0.8. In contrast, that indicates
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_{Airframe model of} the A320 R ² _afm values between 0.2 and 0.6 with a much higher fluctuation between the different frequency bands. With the E170, R ² afm mainly varies between 0.4 and 0.7.
[0163] A specific aspect of R ² is related to the frequency range in which the sound sources radiate. For example, R ² _{close to} A320 in diagram a) of FIG. 16 is between 50 and 400 Hz when the nozzle noise dominates. Similarly, R ^{2 is} narrow at 2 to 3 kHz, the bands that contain the A320's leaf pass frequency (BPF) on departure. Airplane row sound sources can be identified in the same way. In accordance with measurements on a full-size A320 flight in the low-speed wind tunnel DNW-LLF, the slats (included with the parameter flaps) emit a considerable sound between 100 and 300 Hz. A distinctive hollow tone in the wing at 500 and 630 Hz can also be seen. And finally, an additional noise of the slat side edge between 1 and 1.6 kHz is noticeable.
In these frequency ranges, R ² afm has local maximum values. In contrast, there are no explicit sound sources for 50 to 100 Hz and above 1.6 kHz, and therefore R ² afm is low. No corresponding measurements in the wind tunnel are known for E170 as for the A320, but the measurements indicate a tone at 100 Hz, which also increases R ² afm. In general, the airframe of the sound sources wear E170 at all frequencies, since R ² is _af m high.
Model Comparison Exemplary results of spectra and directional characteristics are shown for different flight conditions and compared with measurements.
FIG. 17 shows two diagrams which illustrate exemplary spectral directional characteristics of the aircraft type A320 when taking off with high-performance setting. In FIG. 17, spectral directional characteristics are predicted and compared with averaged measurement data with similar flight parameter settings as set out above. A departure with a high performance setting of N1 = 93% is shown. The directivity in the longitudinal direction in diagram a) of FIG. 17 is presented to an observer on the side at φ = 60 °. All frequency bands show a good agreement between forecast and measured data. Frequencies in the low range such as 125 and 250 Hz show a typical jet pattern, which is clearly pronounced towards the rear. At high frequencies such as 2 kHz, the characteristic shows local maximum values in the front and rear area by the fan. Thus, the Fourier series and the interactions with N1 and N1 ² enable the model to accurately reproduce the directivity in the longitudinal direction.
In the same way, the directivity in the transverse direction, which is shown in diagram b) in FIG. 17 from the rear at θ = 130 °, agrees well with the measurements, overall the radiation in the transverse direction is less important and less over the frequency bands variable. The half-Fourier series with interaction with N1 thus represents a valid approach. Directional effects in the longitudinal and transverse directions are also in good agreement with the overall L _em , which supports the selected model approach for the directional effect.
18 shows two diagrams which represent exemplary spectra for final approach (N1 = 55%, Ma - 0.23) and take-off with high performance setting (N1 = 93%, Ma = 0.24) for the aircraft A320. 17 shows spectra for typical flight conditions at takeoff and final approach for θ = 90 ° in diagram a) of FIG. 18 and θ = 130 ° in diagram b) of FIG. 18. As expected, the predicted dominant sound source at start is the engine. The airframe model is not shown in this case in order to avoid an overlay with the spectra of the final approach. In addition to the strong low-frequency broadband noise of the nozzle in the rear area, the blade passing frequency at 2.5 kHz is also well available. In contrast, the airframe model is relevant for the final approach. At 90 °, the center frequencies are still dominated by engine noise, presumably turbomachinery noise. At 130 ° (rear) airframe noise dominates the center frequencies. Interestingly, while the nozzle noise is very low for N1 = 55%, the fan's broadband noise is around 2.5 kHz in the range of approximately 2.5 times the sheet passing frequency (2.5 * 1.65 kHz = 4.1 kHz) the engine spectra in diagrams a) and b) of FIG. 18 can still be seen.
FIG. 19 shows two diagrams which represent exemplary spectra for final approach (N1 = 50%, Ma = 0.2) and start at high performance setting (N1 - 86%, Ma = 0.24) for the aircraft E170. The corresponding spectra of the aircraft E170 in FIG. 19 show very similar results to those for the A320 in FIG. 18. The overall spectra are in good agreement with the measurements, and departures are dominated by engine noise. With typical final approach settings, engine and airframe noise, in contrast to the source-dependent contributions such as the A320, contribute evenly across all frequencies. The leaf passing frequency for takeoffs at 3 kHz is not observed in either diagram a) and b) of FIG. 19, which is in agreement with the measurements. For the final approach, the leaf passing frequency in diagram a) of FIG. 19 at 1.7 kHz can be found in both the forecast and the measurements.
The fact that each frequency band is itself tuned enables the model to form different spectral shapes. For example, the quadratic dependence on N1 enables the engine spectra to be switched from high nozzle noise to low power settings, as is shown in particular in the rear area of the aircraft in FIGS. 18 and 19. In addition, tapes with tones that occur from a uniform broadband spectrum are also taken into account.
CH 713 630 A1 As a result, according to the present invention, a new sound emission model is presented in order to overcome the limitations in the prediction of aircraft noise models according to the prior art with a moderate number of necessary input parameters. As a considerable advantage of the present invention, compared to known models such as Doc. 9911 or FLULA2 airframe noise, which is represented by the influence of the Mach number and configuration of the aircraft, are modeled separately from the engine noise. At the same time, only a minimum of process parameters for engine noise are required, which do not require detailed knowledge of engine performance such as mass flow or jet speed, as is the case in semi-empirical models according to the state of the art such as ANOPP or PANAM.
According to the present invention, jet and fan noise of the engines with N1 are considered the main parameter that can be acoustically determined to develop the respective models. A novelty according to the present invention makes it possible to separate the overall sound emission levels in airframe and engine noise. This means that no complex microphone arrangement measurements are required to separate the two sound sources. The separation enables airframe and engine noise to be predicted independently of one another, which is an advantage when assessing noise reduction methods. On this basis, new combinations of modeled airframes and engines for which input data are missing or which so far have not flown in such a combination can be created. A further refinement towards the detailed modeling of individual sound sources continues to be a challenge, especially in the scope of covering a wide range of aircraft types with different dimensions.
The parameters chosen for the exemplary embodiments described herein make it possible to adequately reproduce the directivity and spectra for typical flight conditions. In particular, the A320 aircraft with black box data and a high number of flights and the E170 aircraft without black box data and with only 89 flights show similarly good results. This is the case for all types listed in Table 1 above. Thus, the further developed models as well as the reduced models are comparable and enable the creation of models for different input data and aircraft types.
Prerequisites for creating the model according to the present invention for other aircraft types are (i) measurements at various locations near the airport and at a greater distance, (ii) back propagation to the source and (iii) spectral analyzes to determine N1 or processing of black box data. A limitation of the separation of airframe and engine noise is the assumption that airframe noise dominates in idle engines. In addition, the validity of the separation cannot be proven because no data is available.
Since the source model is based on the sound emission level, it can be combined with any sound propagation model to calculate the sound immission at the receiver. However, the dispersion model should take into account all effects that were used in back propagation (e.g. soil effect). A simulation program can be used to acoustically optimize new processes such as the CDA landing approach, which could expand current studies on rail optimization with noise as a cost function. The sound source model thus enables different noise masses such as the effectively perceived noise level (EPNL), the event level and the maximum level to be calculated with free choice of spectral weighting.
[0176] A sound source model according to the present invention for turbofan-driven aircraft closes the gap between conventional models and top models according to the prior art. It provides two separate models for airframe and engine noise emissions. Both were created based on knowledge of explanatory data analysis, physical knowledge and statistical models. Source models for a wide range of relevant aircraft and engine types were created.
In the best case, the flight parameters that link the sound emission level with the current flight conditions are flight recorder data or alternatively radar data with an additional analysis for determining N1 via the fan's leaf passing frequency. If available, it is recommended to use the advanced model for single flight studies. Nevertheless, the reduced model can also be used in a similar way, although the effect of the flight configuration cannot be exactly represented. The reduced model would improve the accuracy of current noise maps, at least for airport scenarios and annual calculations.
The exemplary embodiments presented demonstrate the ability of a model in accordance with the present invention to perform studies on noise reduction methods. It is possible to provide data from optimizations or flight simulators to calculate and compare noise mass and the affected population. The methodology presented can also be used to develop sound emission models for helicopters, propeller-driven aircraft or military jets without separating the sources.
Three-dimensional spectral directional characteristic as a function of flight conditions As described above, a semi-empirical sound source model according to the present invention is created by means of multiple linear regression. This requires an adequate empirical data set to match the model coefficients to a typical range of flight conditions. In particular, measurements are too different
CH 713 630 A1 flight conditions and a wide range of angle covers are required to create a model according to the present invention.
Data processing The sound pressure levels (SPL) at the measurement sites can be back-propagated to the source to obtain source power distribution levels SPL using the sophisticated sonX propagation model and taking into account the actual atmospheric conditions. Then the Doppler effect due to the movement of the source must be corrected by applying a frequency shift and an intensity gain. The resulting acoustic data, directional angle and L _w , must be synchronized and supplemented with flight recorder data, which provide a variety of parameters such as the course of the path, rotational speed N1 of the turbine and configuration changes of the undercarriage and flaps.
As already stated above, a complete model can be formulated for each third octave band from 25 Hz to 5 kHz, noted as parameter f in the description below. The directivity is modeled three-dimensionally using spherical coordinates consisting of the polar angle θ and the azimuth angle φ. Both angles are defined in relation to the flight path axis system as shown in FIG. 1 in order to simplify the requirements for input data for a forecast, since the actual orientation of the aircraft is generally not known. The azimuth φ is additionally corrected by the aircraft's bank angle, which can be calculated from the trajectory.
[0182] The sound source model can be divided into two sub-models, one for engine noise and another one for airframe noise, each of which takes into account different source mechanisms as explained above. An engine model according to the present invention, as described below in equation (32), includes both directional angles in order to take into account the longitudinal directionality and the lateral installation effect of the engines. The most important parameter is N1, which reflects the power of the engine and is therefore directly related to the jet speed and the rotation of the fan and turbines. With the help of engine test runs, as described in detail above, the conclusion can be drawn that L _{w is} also dependent on N1 ² . Using interactions between N1 and θ and φ, the directivity can change with the power setting. In addition, the Mach number Ma of the aircraft, which influences the flow and in particular the source strength of the nozzle due to the surrounding flow, is included in the model.
t _w , engins (/) = φ, NI, NI ² , Ma), iorj = 25 Hz ... 5 kHz (32) [0183] Airframe noise can primarily be modeled with a two-dimensional directionality, since the measurement setup for landings , in which all aircraft follow approximately the same trajectory, do not allow reliable detection of lateral effects. The most important constant explanatory variables are the Mach number Ma and the density p of the surrounding medium, both of which are converted to the base 10 by the logarithm. The transformations are required to linearize the variables and correspond to semi-empirical models for airframe noise. In addition, the model includes various categorical variables in order to reproduce changes in the configuration, ie chassis position, flap position and SB brake flaps. The full model also includes interactions, such as chassis and Mach number, to account for the difference in air speed dependency with and without the chassis extended. Finally, the “procedura” (proc) factor is included because it has been shown that some effects, such as changing the level of the flaps, are different in takeoff and landing situations. A different angle of attack and thus lift coefficients could be the cause, which directly affects noise generation.
iw, airframetf) = ^ (θ, ίο ^ Μα), log ₁₀ (p), Gears, Flaps, SB, Proc), for / ^ 25 Hz ... 5 kHz (33) [0184] Both models for airframe and engine noise are then summarized energetically in order to obtain the total source power distribution level depending on the parameters under consideration. Since the model parameters can be obtained from an adaptation by means of a mean-least squares algorithm and thus represent the arithmetic mean of L _w , an energy correction is introduced in order to correct the mean source power as described in detail above. The Doppler frequency shift and intensity gain are then used to take the flight effect into account.
Ìw (-f ~) ⁼ ^ w, engins (/) © iw.airfrttmeVj 25 Hz ... 5 krIz (34) The complete model (see equations (32) to (34) above) is used for six examples Airplane and engine type combinations of the Swiss International Air Lines for which flight recorder data were available were created
CH 713 630 A1 (see Table 2 below). A large number of flights are measured in order to create the available models on a statistically relevant basis, but a smaller number of flights would also be acceptable.
Table 2: Overview of acoustic reference types with flight data recorder and number of flights, which were integrated into a model according to the present invention.
reference type description starts landings total A319_CFM56-5B Airbus A319-100 wifh CFM56-5B 120 41 161 A320_CFM56-5B Airbus A320-200 with CFM56-5B 424 249 673 A321_CFM56-5B Airbus A321-200 with CFM56-5B 300 126 426 A333_TREN'T7 Airbus 330-300 with TRENT 700 249 136 385 A343_CFM56-5C Airbus A340-300 with CFM56-5C 166 120 286 RJ1H LF507 BAE SYSTEMS AVRÒ RJ-100 with LF507 324 202 526
As already explained above, model approaches according to the present invention make it possible to reduce the depth of detail if parameters are missing from the input data, which is the case if no black box data is available. As a result, the number of parameters in the airframe model drops.
tw.airrrame.redif) = (ß, logioOa), log ₁₀ (p), Proc), for / = 25 Hz ... 5 kHz ( ₃₅ ) The engine model remains in its form since N1 is the most important Is a parameter that should not be removed. If no flight recorder data is available, it can be determined by detecting the sheet passing frequency with the help of extended signal analysis. In the event of insufficient coverage of the transverse directional straightening angle, the model can simply be reduced to two-dimensionality by taking the azimuth angle φ out of the engine model.
Lv.snfline, 20 (/) = Fiß.Nl, NI ² , Ma), forf = 25 Hz ... 5 kHz / 3g)
Comparisons Three exemplary cases are presented to demonstrate the capabilities of the source model for three-dimensional directional characteristics according to the present invention and to compare it with exemplary measurements:
a) spectra of airframe and engine noise on final approach,
b) directivity at start, and
c) Simulation of landing approaches with the landing gear extended.
Comparisons (a) and (b) can be made for the reference type A320_CFM56-5B (A320) on the basis of unweighted L _w at the source in order to show the influence of flight conditions on spectra and directivity. In (c) both the simulated sound pressure level and L _AS of A320 and A333_TRENT7 (A330) are compared to show the strong effects of the landing gear.
Spectral Comparison A significant property of a model for three-dimensional directional characteristics according to the present invention is the spectral resolution in third octave bands. Furthermore, the source model and thus the spectra are separated into airframe and engine noise. Due to the dominance of engine noise during takeoffs, spectra can be predicted for the final approach of the A320 in order to demonstrate the interaction of engine and airframe noise using two different flight conditions: idle power (N1 = 30%) at Ma = 0.26 compared to N1 = 50 % at a final approach speed of Ma = 0.21. All flight parameters used for the forecast with the complete model are listed in Table 3 below. At the same time, the empirical data set, which is also used to adapt the model, can be filtered with a certain tolerance of the parameters according to the same flight conditions. The arithmetic mean L _w can be determined for each data class in order to check how exactly the regression model can reproduce the input data.
Table 3: Parameter settings for the forecast of final approaches and tolerances of the measurement data.
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model parameters Average tolerance s 130 ± 5 φ Η 0 i 90 / VI [%] 30. 50 i 5 Ma [-] 0.26, 0.21 ± 0.02 p [kg / nB] 1.12, 1.15Flaps Ì Goars / SB 4/1! 0
Directionality comparison To compare the directionality with measured data, typical parameter settings can be selected for a start, since the directionality is most pronounced at high engine power. Analogous to the description in the section below, the regression data set can be filtered within tolerance ranges of the predicted mean values (see Table 4 below) in order to check the reproducibility of a model according to the present invention.
First, the directivity in the longitudinal direction of the complete model (3D, see equation (32) above) is shown for four exemplary frequencies. Second, the integrated L «is shown across all frequencies for three power settings: a high power setting at 93%, a medium power setting at 90% and a low power setting close to power reduction (87%). The influence of the reduced model (2D, see equation (36) above) is also shown.
Table 4: Parameter settings for the forecast of departures and tolerances of the measurement data.
model parameters means tolerance s 90, 130 ± 5 φ [° 1 0. 50 45 / VI [%] 87, 90, 93 i 1 Ma [-] 00:24 ± 0.02. p [kg / m ³ ] 1.15 ± 0.02 Flaps / Gears / SB 2/0/0
Simulation of Actual Landing Approach Situations Finally, an exemplary landing approach situation for a receiver below the glide path is 15 km from landing threshold 34 of the exemplary airport, i.e. Zurich Airport, shown. In this situation, the azimuth angle φ is below 5 ° and the aircraft flies at a height of approximately 600 m above the receiver. Similar situations can be selected for aircraft type A320 and aircraft type A330 in order to show the influence of the extended landing gear during the overflight.
The level-time protocol (Las) of the complete 3D model according to the present invention (aww via equation (33)) and the reduced 3D model (without configuration, see equation (35) above) are with the measurement compared. In addition, the simulated flights are displayed as unrated spectrograms with the change in sound pressure level in third-octave bands over time, just as a sophisticated level meter would measure them.
Results - Spectral Comparison FIG. 20 shows two diagrams which show exemplary spectra of a landing approach of an A320 aircraft with the flaps fully turned up and the landing gear extended at θ = 130 °. Phase 1 (left) with idle power and Ma = 0.26 compared to phase 2 (right) with N1 = 55% and Ma = 0.21. Each overall spectrum is divided into predicted contributions from airframe (dash-dotted, blue) and engines (dashed, magenta). 20 shows two spectra of the aircraft type A320 in the final approach with the landing gear extended with the overall level and their respective contributions from noise from the airframe (dash-dotted, blue) and engine (dashed, magenta). In the first phase (left) the engines are idling and the Mach number is 0.26. In this case, the model indicates that the entire spectrum is dominated by airframe noise. Indeed, due to model development based on measurements of the entire aircraft, it is inevitable that the airframe model still includes the engine idling. In the second phase (right), the aircraft has reached landing speed and the engines are operating at 55% to maintain the glide path. The engine model Ma = 0.21 thus predicts a spectrum that dominates slightly below 160 Hz due to the increased jet broadband noise and above 2 kHz due to the increased fan broadband noise. The airframe spectrum still dominates the center frequencies, whereby the level is reduced by 4 dB compared to phase 1 due to the lower Mach number.
[0199] The empirical mean values show a good agreement with both phases. Deviations in frequencies below 100 Hz and above 3 kHz can be caused by a higher uncertainty of the measurements due to
CH 713 630 A1
Back propagation with high air absorption can be explained. Especially for frequencies between 125 Hz and 3 kHz, the interval that is decisive for A-weighted levels at the receiver, the deviations are below 1.1 dB.
Results - Directional Direction Comparison Fig. 21 shows two diagrams illustrating exemplary spectral directional characteristics for a departure with a low power setting (N1 = 87%). The directivity in the longitudinal direction against polar angle θ (left) and for φ = 130 ° and the corresponding directivity in the transverse direction against azimuth angle φ (right) are shown. For a typical take-off, the total L _w primarily results from the engine noise. As expected, the 125 Hz band is clearly pronounced in the rear region and has a peak at 140 °, which corresponds to the typical behavior of nozzle noise as shown in FIG. 21. At higher frequency bands, the maximum level shifts to 90 ° and changes its shape. In particular for 2 kHz, the shape changes completely and reaches the peak at 40 °. It is believed that it is the fan noise that dominates the front area. The complete model reproduces the measured data exactly with deviations of mainly below 2 dB. These deviations are within the standard deviation of the measurement, which varies between 1.6 dB and 3 dB depending on the frequency and 9.
In Fig. 21, the directivity in the transverse direction for θ = 130 ° is shown on the right side. The directionality in the transverse direction is more pronounced at frequencies above 125 Hz, since only frequencies with short wavelengths are reflected on the wing surfaces. Maximum levels for higher frequency bands that exceed the level at 0 ° by about 2 dB occur at azimuth angles of about 30 ° to 60 °. The model accurately predicts the measured values with deviations for φ <60 ° below 2 dB, while L _w for φ> 60 ° is overestimated. However, these angles are less important for noise calculations because they only contribute in far-away side areas.
22 shows two diagrams which represent an exemplary directivity in the longitudinal direction of the total L _w for three different take-off power settings of the aircraft type A320: below the aircraft at φ = 0 ° (left) and laterally at φ = 50 ° (right). 22 shows integrated, unweighted L _w for φ = 0 ° (left) and φ = 50 ° (right). For relevant polar angles from 60 ° to 120 °, an SD model according to the present invention predicts the measured data well with deviations below 1 dB. For polar angles outside this range, the deviations are for both models, especially for φ = 0 °. however larger. The 2D model shows slightly larger deviations on the left side, which are considerably larger for φ = 50 ° on the right side.
FIG. 23 shows two diagrams which represent an exemplary directivity in the transverse direction of the total L _w for three different take-off power settings of the aircraft type A320: perpendicular at θ = 90 ° (left) and backwards θ = 130 ° (right ). Results similar to those described above with reference to FIG. 22 can be seen for the transverse directional effect shown in FIG. 23 for θ = 90 ° (left). It turns out that the levels predicted with a 2D model would systematically overestimate the levels directly below the trajectory and underestimate the levels for lateral positions with φ between 20 ° and 60 °. In contrast, for θ = 130 ° in Fig. 23 (right) the deviations of the 2D and 3D models can be neglected at least for φ <60 °. Each 3% step of N1 leads to an increase in the total L _w by approximately 1 dB for θ = 90 ° and approximately 2 dB for θ = 130 °, which is reproduced well by both models.
Simulation of Actual Landing Situations Fig. 24 shows six diagrams which exemplify two approaches for landing with the vehicle idling and with the landing gear extended (dash-dotted vertical lines) and high-lift elements on a receiver about 15 km before the landing threshold. Flight parameters of the flight recorder data and the forecast and measured L _AS are shown for the aircraft types A320 (left) and A330 (right). Two landing approaches and their flight parameters are thus shown in FIG. 24. The A320 on the left approaches idling power, flap position 2 and extends the landing gear with a brief application of the brake flaps at the marked point. The same procedure applies to the A330 (right), but without using the brake flaps. The level-time logs of the measurement and the 3D model essentially match very well. However, this does not apply to the reduced model (3Dred), in which the configuration of the aircraft is not modeled. After the landing gear has been extended, the level of the A320 is only slightly lower, but for the A330 the level is considerably lower from 30 s to 50 s and does not correspond well with the measurement.
FIG. 25 shows two diagrams which exemplify exemplary spectra of a landing approach of an A320 aircraft at idle about 15 km before the landing threshold. Predicted (left) versus measured (right) spectrogram in third octave bands from 25 Hz to 5 kHz on one receiver. The model presented provides many details for a single flyover, comparable to a real measurement. FIG. 7 shows the simulated (complete model, left) and measured (right) spectrograms of the same event as in FIG. 24 left. The sound pressure level over time shows a very good agreement, with low frequencies in the rear area (A) being overestimated. The air absorption of high frequency bands (B) and the ground effect (C) are clearly visible and correlate with the measurements. Extending the undercarriage (D) shows a slight increase in the level for frequencies below 100 Hz in both the simulation and the measurement. A specific cavity tone of the A320, which occurs at 800 Hz and drops to 400 Hz due to the Doppler shift, is reproduced well, although it blurs across two frequency bands for the first 15 s (E).
CH 713 630 A1 [0206] FIG. 26 shows two diagrams which represent exemplary spectra of a landing approach of an aircraft of the type A330 at idle about 15 km before the landing threshold. Predicted (left) versus measured (right) spectrogram in third octave bands from 25 Hz to 5 kHz on one receiver. In the same manner as in FIG. 26, FIG. 26 shows the landing approach of the A330 as shown in FIG. 24 on the right. Again, there is good agreement between simulation (left) and measurement (right) with a slight overestimation of (A). The spreading effects (B, C) correlate with the measurement. The impact of the extended landing gear on (D) is striking across the entire frequency range and shows how important it is to include configuration parameters in the airframe model.
As a result, all comparisons of the flight conditions, namely departure, landing approach and final approach, agree very well with the measured data. In addition, it has been found that reducing the model to 2D directivity or neglecting the configuration may affect accuracy, but is still viable options if this data is not available to create a model in accordance with the present invention.
Influence of atmospheric stratification on the sound propagation of single flights. Sound propagation through the atmosphere is influenced by local conditions of temperature, humidity and wind speed. In the case of aircraft noise, the prevailing weather effect on sound propagation is dissipation. Second-order effects include level fluctuations caused by turbulence and, in rare cases with sound paths near the ground, the formation of sound shadow zones and influences on barrier effects as a result of temperature and wind gradients with a gradient.
One element of a method according to the present invention is the separation between the source model and the propagation model. The source model is based on a semi-empirical approach and takes airframe and engine noise from different flight conditions into account. The sonX sound propagation model is adapted to the specific problems of aircraft noise calculation. In addition to the detailed spread, calculations for the forecast of individual flights are also used in order to convert the data measured at the receiver to the source. The exact reproduction of the atmosphere is particularly important here. A differentiated distribution model also requires a greater level of detail and higher quality of the input data. Appropriate data of the vertical atmospheric profile can be obtained from different sources:
- From weather balloons or from measurements made by the aircraft itself.
- From simulation results of numerical weather forecast models like COSMO for many European countries.
- Based on measurements from ground stations in combination with idealized profiles.
In accordance with the present invention, weather data from the three different sources are presented and compared to measure their impact on the resulting sound attenuation for different source-receiver geometries. A method according to the present invention is described to derive idealized profiles from the weather data from ground stations. Based on these results, the benefit of detailed modeling of the atmosphere compared to the assumption of a homogeneous atmosphere is discussed.
Propagation Model All spread calculations according to the present invention can be done by the sonX model. The calculation from a point source to a receiver is carried out in two steps. First, the direct sound propagation for a uniform atmosphere with averaged conditions is calculated according to equation (37) below. This takes into account the geometric deviation (A _div ) and atmospheric dissipation (A _atm , _f ) depending on the frequency f according to ISO 9613-1. The model also takes into account barrier and soil effects (Agr / bar, f) and vegetation damping (A _fo i, _f ), which are negligible when considered according to the present invention.
'9, ⁼ - ÎX ⁺ fi ⁺ fiblj (37) Then additional weather effects such as atmospheric dissipation (A _atm , Meteo) due to local temperature and humidity conditions are calculated by equation (38) below. The attenuations are reported as level differences compared to the basic attenuations of equation (37). In addition, changes in screen effects and the emergence of sound shadow zones due to temperature and wind gradients (Dmet) are calculated using a beam tracking algorithm. These effects are not relevant for the current analysis.
~ (. ^ alm.Meteo ~~ ^ amt, Basie) ~ - ^ fol.BasiO ~ (38) [0213] Information about the vertical profile of the atmosphere can be provided as individual profiles (ie from prediction models or flight recording data) or as idealized profiles ( see section 2.3). A classification scheme has been introduced for the latter (see Table 5 below). The classification is based on the three main classes
CH 713 630 A1 unstable (U), neutral (N) and stable (S) reduced. Depending on the wind speed at a height of 10 m and the current radiation balance, a corresponding class can be determined for each specific atmospheric condition.
Tabaila 5: Classification scheme for different weather conditions.
Wind speedArea average Unstable<-18 W / no radiation balanceNeutral QStable> 180 W / nT 0-1 O.ö UO NO SO 1-2 1, 3 / ul NI S1 2.2 2.5 U2 N2 S2 3-5 4.0 U3 N3 S3 > 5 6.0 U4 N4 S4
Radiation balance and development of idealized profiles The measurement of the radiation balance is possible, but not available in every case, since several sensors are necessary. In Switzerland, for example, only the short-wave incoming radiation that is measured with a pyranometer is usually available. The radiation balance is thus determined in accordance with VDI standard 3789 part 2. With respect to a horizontal area, the radiation balance is the sum of the short-wave radiation and terrestrial long-wave radiation, see equation (39). Solar shortwave radiation is the difference between global radiation (G) and its reflection (R), which is dependent on the shortwave albedo of the earth's surface. The emitted heat radiation (E) from the earth can be simplified for natural floor surfaces as a blackbody that emits energy with the fourth power of the surface temperature. Atmospheric gases and clouds reflect back to Earth (A).
Q = (G - R) + (A - E) (39) Fig. 27 shows two diagrams that represent an exemplary radiation balance for an exemplary day in September 2013 at Zurich Airport (top). In combination with the wind speed, the weather categories reflect the different conditions during the time of day (below). The radiation balance can be calculated for each day of acoustic measurements to collect source data (Fig. 27, top). Weather data at station height (KLO station at Zurich Airport, 426 m above sea level) were used in ten-minute resolution as input for the radiation balance. The cloud N in eighths is a visually recorded parameter with a resolution of one hour, so it was interpolated with a piecewise cubic Hermitian interpolation polynomial to ensure a smooth curve for the counter radiation. The classification scheme from Table 5 above was implemented as in FIG. 27 (below) by means of the radiation balance and the wind speed.
For each class in Table 5, predefined Lin-Log profiles for temperature, wind and humidity were calculated, which represent idealized profiles for the weather category and different soil types. For use under specific conditions, the profiles have been moved in the direction of the current temperature and moisture on the floor. The weather data used herein had reference heights of 2 meters for temperature and humidity and 6 meters for wind speed. The profiles show considerable fluctuations near the ground. At higher altitudes (more than 100 m), however, they exhibit a uniform behavior with constant wind speed and direction and an adiabatic temperature gradient of 9.8 K / km for unsaturated air and 6.5 K / km for saturated air. Absolute humidity is assumed to be unchanged, so relative humidity increases with altitude up to a maximum of 100%.
Profile Data Fig. 28 shows two graphs illustrating exemplary temperature, wind, and humidity profiles for an exemplary day in September 2013 at 9:00 a.m. at Zurich Airport. The example shows idealized profiles (blue solid line) in high divergence from profiles from the numerical model COSMO-2 (magenta-colored dashed line) and black box data (red dash-dotted line). The dashed black line stands for a homogeneous atmosphere with values measured at station height. Exemplary studies underlying the present invention include flight data recorders (flight data recorders) from 223 departures at Zurich Airport provided by Swiss International Airlines. From this data, air temperature and wind speed and direction were prepared to produce vertical profiles as in FIG. 28. In a method according to the present invention, as explained above, the flight data recorder is further processed in combination with the acoustic measurements in order to develop the sound emission model as a function of the flight configuration.
All of the data considered in this paper is from flights measured within four weeks between August 21 and September 13, 2013. Most of the A320 family's departures took place at a time of day between
CH 713 630 A1
9 a.m. and 4 p.m. Thus, although variations in atmospheric conditions are limited, the temperature still varies between 12 ° C and 28 ° C and the humidity varies between 20% and 80%. In addition, ten flights of A340-300 aircraft and three other types were measured at around 11:00 p.m. when the temperature was still between about 17 ° C and 20 ° C and the humidity was about 65%.
As an alternative, profile data from the numerical weather forecast model COSMO-2 was used as input data for the dispersion calculation. COSMO is the "Consortium for Small-Scale Modeling" of national weather services in Germany, Greece, Italy, Poland, Romania, Russia and Switzerland. MeteoSwiss, which provided the data, uses the local scaling model COSMO-2 with a grid spacing of 2.2 km, which also includes the Alpine arch (17). Indeed, the numerical model integrates atmospheric observation data from radio probes, aircraft, wind profiler and surface level data. The hourly profiles provided include temperature, humidity, and wind speed and direction for surface levels 24 to 60, which correspond to surface heights of approximately 10 m to 4,900 m.
28, the four profile types are compared with one another by way of example. For a comparison with a homogeneous atmosphere based on current conditions, weather data at Zurich Airport at a reference height of 2 m above the height were used. 2 shows an unstable situation at 10:00 a.m., for which all data sources show a similar pattern over the entire height range up to 800 m.
29 shows three diagrams that represent exemplary temperature, wind and humidity profiles for an exemplary day in September 2013 at 10:00 a.m. at Zurich Airport. The example shows idealized profiles (blue solid line) in good agreement with profiles from the numerical model COSMO-2 (magenta dashed line) and black box data (red dash-dotted line). The dashed black line stands for a homogeneous atmosphere with values measured at station height. In contrast to FIG. 28, FIG. 29 depicts a situation in which the different profiles differ considerably from one another. With the COSMO-2 and black box profiles, the temperature decreases with increasing altitude, which indicates an unstable stratification. In contrast, the idealized profile already assumes a neutral state. In the same way, the moisture profiles are very different.
30 shows two diagrams that represent exemplary selected temperature and humidity profiles for an exemplary day in September 2013 between 9:00 a.m. and 12:00 p.m. at Zurich Airport. The numerical model COSMO-2 (magenta dashed line) indicates that stratification continues from a stable boundary layer at night to a typically unstable layer on a sunny day until noon. The classification scheme already assumes an unstable stratification (solid blue line). The idealized profile was not always able to correctly reproduce the time transition from a stable boundary layer at night to a typically unstable layer on a sunny day. In Fig. 4, the vertical profiles of the idealized profiles and COSMO-2 data are compared for three different times of the day between 9.30 a.m. and 12 noon. On this sunny day (see Fig. 27), the sun rises at 7.30 a.m. and the radiation balance leads to an unstable stratification two hours later when the classification scheme is used, in contrast, the data from COSMO-2 indicate this indicates that the change in stratification up to 500 m continues until noon and that the temperature and humidity profiles only converge slowly.
In summary, the wind and temperature profiles of the flight data recorder and COSMO-2 are fundamentally very consistent. The extrapolation of temperature from soil conditions to higher altitudes, as is done by the idealized profiles, seems to be valid in most cases. However, wind and moisture profiles differ significantly compared to the differentiated profiles from the COSMO-2 model.
Propagation Calculation Fig. 31 shows a schematic representation of a calculation scenario based on a flight path. Although the flight path of the 223 departures used is available, the same generic source points of a virtual flight path can be used for all flights to avoid differences in propagation due to different flight path geometries. The scenario depicted in FIG. 31 shows three different source positions (S), one at 500 m above receiver R1 and the other at 45 ° and 30 ° angles with respect to the direction of flight. A second receiver R ² was placed 500 m to the side. The receivers are placed 4 m above a level grassland.
The results are presented below as attenuations AMeteo (see equation (38) above) of the spreading calculation to show the differences between a homogeneous atmosphere based on current local conditions (LC) and averaged conditions for Switzerland. Furthermore, the results of the COSMO-2 data are compared with those of the homogeneous atmosphere based on current conditions (see equation (40) below). In addition, the results of the idealized standard profiles (IP) are compared with those of the COSMO-2 profiles (see equation (41) below). The differences in attenuation in each of the third octave bands up to 5 kHz are detailed below. In particular, the influence on the A-weighted sound exposure level L _{AE is} discussed.
CH 713 630 A1 [0226] Since the flight recorder data do not provide any information on moisture data, this information was taken for the sound propagation calculation COSMO-2. However, it was found that the results are very similar to the calculations of the COSMO-2 profiles, which is why they are not shown and discussed in more detail.
AAlC -COSMO - ÄMeteo, LC ~ ÄMeteo, COSMO (40)
ΔΑιρ-cOSMO - ÄMeteo, IP ~ ÄMeteo, COSMO (41)
Results Fig. 32 is a graph showing an exemplary variation in air absorption of a homogeneous atmosphere with shift to current conditions at 2 m to a uniform atmosphere with averaged conditions.
Results of the propagation from S1 to R1 at a distance of 500 m. Distribution of the calculated differences in air absorption are presented for the 223 exemplary prints and their associated weather situations. For each third octave band, the differences are shown as box whisker plots (see legend in Fig. 32). Because it provides the most reliable data, COSMO-2 is used as a reference for comparing results.
Differences between homogeneous atmospheres In a first step, the weakening of a homogeneous atmosphere based on current conditions on the ground is compared with the weakening of a homogeneous atmosphere with averaged values of 8 ° C and 76% for Switzerland. The results for the point of greatest approximation (CPA) in FIG. 32 show only slight fluctuations of less than 0.6 dB below 500 Hz. Between 500 Hz and 1.6 kHz there were mean values of approximately +0.5 dB with maximum values of +1.9 dB and positive minimum values (except at 1.6 kHz). Thus, the current conditions always led to higher weakenings than the averaged atmosphere. At high frequencies, the trend towards negative differences changed, but showed significantly higher fluctuations in both directions. The mean value of the 5 kHz band is -4.3 dB with a minimum value of -10.3 dB and a maximum value of +15.0 dB.
Fig. 33 is a graph showing an exemplary variation in air absorption of a homogeneous atmosphere with shift to current conditions at 2 m to a uniform atmosphere with averaged conditions. Results of the spread from S3 to R ² at a distance of 1,118 m. The attenuation spectra for the greatest propagation distance of 1,118 m can hereby be derived from FIG. 33. The same trend as for the CPA can be observed with a turn to negative values at 2 kHz. The mean values for 500 Hz to 1.6 kHz ranged from +0.5 dB to +1.0 dB with maximum values from +2.0 dB to +4.3 dB and also positive minimum values. The high frequencies showed a higher negative mean dissipation for an atmosphere based on current conditions, again with large deviations between -23 dB and +34 dB.
Differences in Profile Data Sources Figure 34 shows two graphs that exemplify differences in air absorption between a homogeneous atmosphere under current conditions and COSMO-2 profiles. Results of the propagation from s1 to R1 (500 m) and S3 to R ² (1,118 m). In accordance with the presently exemplary embodiments of the present invention, results are presented only for 250 Hz to 5 kHz because the fluctuations in low frequencies are negligible. Fig. 34 (left) shows the fluctuations in air absorption between the homogeneous atmosphere under current conditions and the COSMO-2 profiles for S1 to R1. The mean values were slightly positive below 2 kHz. In contrast, above 2 kHz the averaged differences increased up to -3.6 dB for 5 kHz with strong fluctuations from -17.4 dB to +5.7 dB. Fig. 34 (right) compares the spread from S3 to R ² . The trend is similar, but shows higher fluctuations at medium and high frequencies as a result of the longer distance.
Fig. 35 shows two diagrams which exemplify differences in air absorption between idealized profiles and COSMO-2 profiles. Results of the propagation from S1 to R1 (500 m) and S3 to R ² (1,118 m). The variance of the idealized profiles compared to the COSMO-2 profiles is presented in Fig. 35. For both spectra there were no fluctuations of more than 0.5 dB below 1 kHz. At higher frequencies, the mean values for Fig. 35 (left) dropped to 4.2 dB at 5 kHz with a fluctuation between -17.3 dB and 2.3 dB. In Fig. 35 (right) the mean values also decreased to 3.4 dB, but fluctuated from -15.1 dB to 8.1 dB.
CH 713 630 A1
Influence on the resulting sound exposure level. The results discussed above clearly represent significant fluctuations in air absorption for frequencies above 250 Hz. Thus, the question arises how relevant these fluctuations are for the resulting situation. Therefore, noise emission directivities of 35 Airbus A320s can be averaged, and the resulting A-weighted spectra and the total L _AE at receiver R1 were calculated (Fig. 10). Then the differences to L _ae for the mean difference and the minimum and maximum fluctuations can be determined as described above.
Fluctuations in the frequencies above 2.5 kHz have almost no influence on L _AE (<0.1 dB (A)), particularly due to the high absolute atmospheric absorption. The same applies to frequencies below 125 Hz, which are greatly weakened by the A weighting. The influence between a homogeneous atmosphere under local and averaged conditions on L _AE for 500 m (S1R1) is -0.2 dB (A) as the mean, but varies between -1.4 dB (A) and 0.3 dB (A ) for single flights. At 1,000 m (S3R1) the mean fluctuation of the L _AE is -0.6 dB (A) with a range of -2.6 dB (A) to 0.2 dB (A).
In the same way, the influence of the differences in air absorption between a homogeneous atmosphere under current conditions and COSMO-2 profiles is -0.6 dB (A) to 1.3 dB (A) at 500 m and -0.9 dB (A) to 0.4 dB (A) at 1,000 m. The differences in the calculated air absorption of idealized and COSMO-2 profiles change the L _AE from -0.2 dB (A) to 1.5 dB (A) at 500 m and from -0.5 dB (A) to 0, 7 dB (A) at 1,000 m.
As FIG. 35 shows, the differences between idealized and COSMO-2 profiles at frequencies above 1 kHz are in the same size range as the variance between the homogeneous atmosphere under current conditions and COSMO-2. At medium frequencies below 1 kHz, which strongly influence the L _AE , the fluctuations are small.
36 shows a diagram which shows an exemplary influence on the variance of the dissipation in the A-weighted spectrum at the receiver R1 of the A320 for the occurrence of S1 (90 °) and from the front on S3 (30 °). The indicators represent the minimum and maximum fluctuations between the homogeneous atmospheres under current or averaged conditions.
As a result, a significant dependence of the sound level on higher frequencies on varying conditions of temperature and humidity can be seen. The differences are smaller for the frequency bands that significantly influence the resulting A-weighted level, but still have a range of several decibels. A look at the mean values reveals that the deviations between the different data sets show a clear trend and thus also have a systematic influence on the long-term averages that result.
[0239] Prediction models like COSMO-2 appear to be the most reliable for comparing different sources of weather data. While black box data show good agreement with COSMO-2 for wind and temperature, they do not provide moisture data. In addition to the fact that flight data recorders cannot be used as a single data source, the availability of flight recorder data is usually restricted and the accuracy of the input data also depends on the aircraft type. Therefore, flight recorder data alone is not an adequate source of weather data.
The use of standardized atmospheric profiles normalized to ground conditions, as shown above, has a beneficial effect on the accuracy of the sound propagation calculation compared to the assumptions of a homogeneous atmosphere. The advantage of this approach is that the profiles can be generated with very little, easily accessible input data.
A variance in air absorption can be expected to be of the same order of magnitude as the results presented here. In contrast, the fluctuations between the homogeneous atmosphere under current conditions and idealized profiles are not expected to change significantly compared to the COSMO-2 profiles, and the following conclusions can also be considered as generally applicable.
The use of current soil conditions of temperature and humidity is an important improvement over a uniform atmosphere with averaged conditions for the calculation of single flights. In addition, correct reproduction of the stratification in the spread calculation also has a beneficial effect on the accuracy of the resultant L _AE on. In particular, the use of data from weather forecast models such as COSMO-2 is believed to be the most accurate solution. Especially for the development of an emission model according to the present invention, in which individual flights are converted back to the source, it is recommended to use such detailed input data. Although high frequencies above 2.5 kHz are negligible for the L _AE above 500 m, the strong fluctuation in air absorption could lead to large errors in this band.
[0243] It is believed that idealized profiles cannot replace the more detailed profiles from COSMO-2. The assumptions, especially for moisture, but also for the temperature above 100 m, often lead to an extrapolation of the soil conditions, which is not valid at higher altitudes. Thus, the differences in air absorption compared to the COSMO-2 profiles, which are significant at frequencies above 1 kHz, change the L _AE and lead to a less accurate forecast for individual flights.
CH 713 630 A1
Models for Reflections on Forests, Cliffs, Buildings, Walls and Other Rigid Surfaces A method according to the present invention can also include models for reflections on buildings, walls and other rigid surfaces, as well as diffuse reflections on forest edges and cliffs. For a detailed description and references to such extensions of an expansion model on which a method according to the present invention is based, reference is made to the “Documentation of the sonX model” of September 12, 2016 and to scientific publications that are published in ACTA ACUSTICA UNITED WITH ACUSTICA have appeared, in particular “An Extended Model to Predict Reflections from Forests” (Jean Marc Wunderli; Voi. 98 (2012) 263-278; DO! 10.3813 / AAA.918510); "A Model to Predict Sound Reflections from Cliffs" (Reto Pieren, Jean Marc Wunderli; Vol. 97 (2011) 243 - 253; DO110.3813 / AAA.918404); "Calculation of Reflections in an Urban Environment" (Kurt Heutschi; Voi. 95 (2009) 644 - 652; DOI 10.3813 / AAA.918193); and “An Engineering Model for Sound Pressure in Shadow Zones Based on Numerical Simulations” (Jan Hofmann, Kurt Heutschi; Voi. 91 (2005) 661 - 670), which are included here with reference , referred.
Partial space concept Fig. 37 shows a schematic perspective view of a vehicle 111, for example an aircraft, in particular an aircraft, for which sound emissions and emissions are to be calculated in accordance with a method according to the present invention. An air space that the vehicle 111 traverses is divided into subspaces 112 that form cells. Each of these subspaces 112 can have a cubic shape, so that it provides eight corners 113.
[0246] Each of these subspaces 113 represents a potential source location. From each corner 113 of one of the subspaces 113 through which the vehicle 111 travels to each receiver point, an attenuation is stored in a database. During individual flights, attenuations are looked up from corners 113 in the database. The resulting attenuation is calculated as a linear interpolation of the attenuations of the corners 113 compared to the effective source position, i.e. the position of the vehicle 111.
FIG. 37 shows a schematic perspective view of an air space which is divided into subspaces 112 in accordance with a method according to the present invention. Source locations 114 of a sound source such as a vehicle 111 form an expected trajectory 115 or a defined trajectory 116. The airspace and thus the subspaces 112 extend along a longitudinal direction X, a transverse direction Y and a height direction Z, which together form a Cartesian coordinate system. Preferably, each of the subspaces 112 has a square shape in a projection along the height direction Z. In particular, vertical edges of the subspaces that extend parallel to the height direction Z can, however, have different and / or changing lengths and coordinates along the height direction Z.
By dividing the air space into the subspaces 112, considerably fewer attenuations and thus corresponding calculations have to be carried out than in this simulation in full format. This enables an efficient calculation of different traffic scenarios in a method according to the present invention. Maximum level calculations and simulations of individual flights as well as real-time simulations of noise pollution are possible. This enables precise calculations with a method according to the present invention, which integrates spectral noise propagation models, considerations of weather influences, coherent and incoherent reflections on buildings, shielding effects by buildings and / or considerations of ground reflection 7 noise propagation influences depending on earth types as described above. Furthermore, a precise emission model is made possible, which can be integrated into a method according to the present invention for the simulation of low-noise landing and departure procedures of vehicles to and from the airport or the like.
Computing devices, systems and methods According to at least one aspect of a method and system according to the present invention, interfaces for noise calculations can be provided on computing devices that work in parallel with one another. Central data storage for easier administration can be implemented. Interfaces for data import can be configured flexibly. Methods and systems according to the present invention can provide functionalities for data preparation and homogenization. Furthermore, modules for evaluation and reporting for generating reports and standard file formats can be provided in relation to the noise exposure of people, buildings, apartments, workplaces, areas, etc.
Figure 39 is a schematic diagram illustrating a system 117 for performing a method in accordance with the present invention. System 117 comprises an external module 118 and an internal module 119. In external module 118, external data such as cockpit data, radar data, transponder data, emission data and evaluation data as well as statistics can be provided and processed. The statistics include in particular certain numbers of flight movements for certain aircraft types and routes. Based on such statistics, webs are overlaid,
CH 713 630 A1 in order to generate the sound profiles and to derive and / or calculate total sound immission values for the at least one reception point R or generally a number of reception points arranged along the tracks. In the internal module 119, calculations are carried out according to models integrated in a method according to the present invention, which generate the generation of flight events and geometries, source locations, reception locations and a sound propagation based thereon, and attenuation data derived therefrom and a simulation of individual flights and the generation of Sound profiles that include a bundle of estimated and / or defined tracks and finally noise pollution maps. The internal module 119 comprises a central computer and / or computer network 120 and at least one client device 121. The most complex computing operations for simulating models according to the present invention are carried out in the computer network 120 as described above. For this purpose, the computer network 120 preferably comprises a plurality of digital processors for performing the calculations, which central computers, servers or the like can be implemented. The client device 121 can be a stand-alone computer or the like on which less demanding calculations and operations are performed. The system 117 according to the present invention is adapted to perform a method according to the present invention, which comprises the following steps:
On the side of the external module 118, for example by external data providers and providers of social services, location data such as cockpit data, radar data, transponder data and other related data are provided in a first step S1. In a second step S2, the location data are processed in order to be input into the internal module 119.
In the internal module 119, in particular in the computer network 120, flight events including geometries are processed in a third step S3. In a fourth step S4, sound source positions are calculated based on the flight events. In a fourth step s4, reception points are calculated in parallel or following the third step S3. The source positions and reception points then leave the computer network 120 in order to be displayed on the client device 121. In the client device 121, initial calculations of the actual sound propagation can be carried out in a sixth step S6 in such a way that they can be adapted and alternated by a user of the client device 121. The spreading calculations are then fed back from the client device 121 to the computer network 120.
In a computer network 120, the attenuation is calculated in a seventh step S7. The weakening data are returned to the client device 121, where individual flight simulations are carried out in an eighth step S8. Results of at least one single flight simulation, preferably a plurality of single flight simulations, are fed back to the computer network in order to generate sound profiles with clean simulation data that relate to several lanes, taking into account emission data that are provided by the external module 118 in a ninth step S9. to generate.
Based on the generation of sound profiles in a tenth step S10, which can be carried out by the computer network 120 and / or the client device 121, in combination with movement statistics, a population is obtained from the external module 118 in an eleventh step S11 are generated in a twelfth step S12 noise pollution maps. Based on the noise pollution maps, a discussion of railways, in particular flight routes, can be objectified using a method according to the present invention, so that all parties involved, such as operators, airlines, the public and politicians, implement feasible and acceptable solutions for planning and controlling traffic, in particular Air traffic.
REFERENCE SIGNS [0256]
Location microphone one
Location microphone two
Location microphone three
Location microphone four
Location microphone five
Location microphone six
Location microphone seven
Location microphone eight
Location microphone nine
Location microphone ten
CH 713 630 A1
16 First runway (departures) 28 First runway (landings) 34 Second runway 111 vehicle 112 subspace 113 Subspace corner 114 Location of the source 115 Expected trajectory 116 Defined trajectory 117 system 118 External module 119 Internal module 120 Computer network / host computer 121 Client device S source R receiver X longitudinal direction J transversely z height direction θ polar angle <PP azimuth anglemedium density

权利要求:
Claims (42)
[1]
claims
1. Method for calculating a noise level generated by a sound source (S, 111), in particular an aircraft, which moves along a defined trajectory (116) with respect to the at least one reception point (R), for at least one reception point ( R), the method comprising the following steps: providing a sound propagation model for an air space which extends between the at least one reception point and at least one expected trajectory (115) along which the sound source is likely to move with respect to the reception point;
Calculating the noise level by entering the defined trajectory (116) in the sound propagation model and calculating a sound propagation between the sound source (S, 111) and the at least one reception point (R) for each of a number of different sound source locations (114) that run along the defined one Trajectory are arranged.
[2]
2. The method according to claim 1, wherein the sound propagation is calculated based on at least one sound emission value of the sound source (S, 111).
[3]
3. The method according to claim 2, wherein the at least one sound emission value in dependence on a sound radiation pattern, the type of sound source (S, 111) for each of a number of different sound source locations (114), which are arranged along the defined trajectory (116), is assigned, is calculated.
[4]
4. The method according to claim 3, wherein the sound radiation pattern is based on a sound power level associated with the type of sound source (S, 111).
[5]
5. The method according to claim 4, wherein the sound power level is estimated based on course parameters of the sound source (s, 111), which are derived from the defined trajectory (116).
CH 713 630 A1
[6]
6. The method according to at least one of claims 3 to 5, wherein the sound radiation pattern is based on a directional characteristic which is assigned to the type of sound source (S, 111).
[7]
7. The method according to at least one of claims 1 to 6, wherein the sound propagation model comprises a direct sound propagation scenario and a complex propagation scenario;
wherein the direct sound propagation scenario assumes direct sound propagation between the sound source (S, 111) and the at least one reception point (R); and in the complex propagation scenario, a complex sound propagation between the sound source (S, 111) and the at least one reception point (R) is assumed.
[8]
8. The method of claim 7, wherein for each of the number of different sound source locations (114) a line of sight angle between the sound source (S, 111) and a horizon is determined;
wherein the direct sound propagation model is applied to line-of-sight angles exceeding a de minimis threshold; and assuming that at line-of-sight angles exceeding the de minimis threshold, complex sound propagation becomes negligible.
[9]
9. The method of claim 7 or 8, wherein the direct sound propagation scenario assumes a homogeneous atmosphere within the airspace.
[10]
10. The method of claim 9, wherein the direct sound propagation scenario comprises at least one of a geometric deviation model, a dissipation model, a barrier effect model, an anti-fouling model and a ground effect model for airspace.
[11]
11. The method of claim 10, wherein the ground effect model comprises at least one of a spherical wave propagation determination, a model for uneven terrain, a surface texture variation and a coherence loss model for modeling a coherence loss of different sound paths between the sound source (S, 111) and the reception point (R) ,
[12]
12. The method according to at least one of claims 7 to 11, wherein the complex propagation scenario comprises at least one of a weather influence correction model, an obstacle reflection model and a forest diffusion model for airspace.
[13]
13. The method according to claim 12, wherein the weather influence correction model adapts a calculation of sound dissipation within the air space to local temperature and humidity values of the air space.
[14]
14. The method of claim 12 or 13, wherein the weather influence correction model adapts a calculation of barrier effects within the air space to vertical gradients of at least one of wind speed values and temperature values of the air space.
[15]
15. The method according to claim 14, wherein the barrier effects are a sound shadow zone effect for at least one sound shadow region within the air space, in which sound beams emitted directly and / or indirectly from the sound source identify the at least one reception point on the basis of updraft conditions, which are based on the vertical gradients of the at least one wind speed value not reach directly, include.
[16]
16. The method of claim 15, wherein a residual sound exposure of the at least one reception point located within the at least one sound shadow region is based on at least one of a diffraction effect model and a scattering effect model applied to a sound beam passing along the at least one sound shadow region , is calculated.
[17]
17. The method according to at least one of claims 1 to 16, further comprising the following steps
Dividing the air space into adjoining subspaces (112);
Computing a subspace model for each of the subspaces (112) to determine finite sound propagation within each of the subspaces (112);
Assembling the sound propagation model from the subspace models, wherein at least one border area between adjoining subspaces (112) represents at least one of a virtual sound source and a virtual reception point for a virtual sound transmission value, which represents a virtual sound power level transmitted between at least two adjoining subspaces (112) ,
[18]
18. The method according to claim 17, wherein a transmission of virtual sound power values is calculated for a number of possible combinations of virtual sound sources and virtual reception points.
[19]
19. The method according to claim 17 or 18, wherein a virtual attenuation is calculated for each or each of the virtual sound sources and / or virtual reception points, wherein the virtual attenuation represents an attenuation of the virtual sound power value during transmission between the virtual sound sources and / or virtual reception points ,
[20]
20. The method of claim 19, wherein virtual attenuations are stored in a subspace database.
CH 713 630 A1
[21]
21. The method according to claim 20, wherein the virtual attenuations are read out of the subspace database during the calculation of the noise level.
[22]
22. The method according to at least one of claims 17 to 21, wherein the propagation model includes an intermediate weakening that is interpolated between the defined trajectory (116) and the at least one boundary region.
[23]
23. The method according to at least one of claims 17 to 22, wherein the air space is divided along a homogeneous horizontal grid into the subspaces and the subspaces have fixed distances in a longitudinal direction (X) and a transverse direction (Y) of the air space.
[24]
24. The method according to at least one of claims 17 to 24, wherein the air space is divided into a partial space along a heterogeneous vertical grid and the height of the partial spaces increases along a height direction (Z) of the air space.
[25]
25. The method according to at least one of claims 17 to 24, wherein the subspaces (112) have a cuboid shape.
[26]
26. The method according to claim 25, wherein the boundary regions are formed by eight corners (113) of each of the cuboid subspaces (112).
[27]
27. The method according to at least one of claims 1 to 26, wherein a sound propagation between the sound source (S, 111) and the at least one reception point (R) is calculated both in the time domain and in the frequency domain.
[28]
28. The method according to claim 27, wherein a frequency spectrum of the sound propagation in the frequency domain is divided into frequency bands and the sound propagation is calculated for each of the frequency bands.
[29]
29. The method according to at least one of claims 1 to 28, wherein the sound propagation is calculated with unweighted sound pressure levels and the noise level is displayed with A-weighted sound pressure levels.
[30]
30. The method according to at least one of claims 1 to 29, wherein the noise level is weighted with a population value that is an estimated population of the at least one reception point with a number of people at a predefined time along which the sound source (S, 111) the defined trajectory (116) moves, reproduces.
[31]
31. The method according to at least one of claims 1 to 30, wherein calculating the sound propagation between a client device (121), which provides a number of client FLOPs per cycle, and a computer network (120), which provides a number of cluster Provides FLOPs per cycle, is distributed and the number of client FLOPs per cycle is smaller than the number of cluster FLOPs per cycle.
[32]
32. The method according to claim 31, wherein at least one total sound attenuation data record, which represents an attenuation of sound along at least one sound path that was created between the sound source (S, 111) and the at least one reception point (R), from a number Partial attenuation value data sets, which were generated by the computer network and which reproduce different sound attenuation characteristics of the airspace for a respective scenario, are calculated.
[33]
33. The method according to claim 32, wherein the total attenuation value data record is calculated by the computer network (120) and transmitted to the client device (121) or is calculated on the client device (121).
[34]
34. The method according to at least one of claims 31 to 33, wherein a sound profile is provided which comprises at least an average noise level at the at least one reception point; and wherein the at least one mean noise level is associated with a bundle of sound source tracks.
[35]
35. The method of claim 34, wherein the sound profile is calculated by the computer network (120) and transmitted to the client device (121).
[36]
36. The method according to claim 34, wherein an overlay of at least two sound profiles is calculated by the client device (121).
[37]
37. The method according to at least one of claims 1 to 36, further comprising a data preparation step in which a number of source points corresponding to the different sound source locations (114) along the defined trajectory (115) are defined.
[38]
38. The method according to claim 37, wherein the source points (114) are derived from at least one transponder data obtained from a radar system and from a transponder system, the radar system and the transponder system for locating and / or tracking the at least one sound source (S , 111) are designed.
[39]
39. The method of claim 37 or 39, wherein the sound propagation model is calculated based on at least the number of source points, a number of reception points and a geodata set that represents a geological environment of the air space.
[40]
40. The method according to at least one of claims 1 to 39, wherein the at least one sound source is an aircraft.
CH 713 630 A1
[41]
41. Computer-readable medium which comprises computer-readable instructions which enable a computer system to carry out a method according to at least one of claims 1 to 40.
[42]
42. Computer system (117), which is designed to carry out a method according to at least one of claims 1 to 40.
CH 713 630 A1

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同族专利:

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WO2018178809A1|2018-10-04|

引用文献:

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公开号 | 申请日 | 公开日 | 申请人 | 专利标题

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EP3667266A1|2018-12-12|2020-06-17|Airbus Defence and Space|Environment information system|

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CN109981577B|2019-02-22|2022-01-28|维沃移动通信（深圳）有限公司|Content processing method, terminal equipment and server|

法律状态:

优先权:

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申请号 | 申请日 | 专利标题

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CH00411/17A|CH713630B1|2017-03-28|2017-03-28|Computer-implemented noise level simulation method and computer-readable medium and computer system therefor.|CH00411/17A| CH713630B1|2017-03-28|2017-03-28|Computer-implemented noise level simulation method and computer-readable medium and computer system therefor.|

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PCT/IB2018/051866| WO2018178809A1|2017-03-28|2018-03-20|Noise level simulation method as well as computer readable medium and system therefore|