Airport aircraft noise prediction method fusing ellipsoid and nonlinear function
By integrating ellipsoidal models and nonlinear functions, and combining the Doppler effect and atmospheric absorption attenuation, an aircraft noise prediction model is constructed. This solves the dynamic adaptability problem of noise propagation in existing technologies and enables accurate prediction of the spatiotemporal evolution of noise in a single flight event.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- THE SECOND RES INST OF CIVIL AVIATION ADMINISTRATION OF CHINA
- Filing Date
- 2025-12-25
- Publication Date
- 2026-05-19
AI Technical Summary
Existing aircraft noise prediction methods rely too heavily on static databases, making it difficult to accurately depict the propagation process of noise in real dynamic environments, resulting in inaccurate prediction of the spatiotemporal evolution characteristics of noise in a single flight event.
By employing a method that integrates ellipsoids and nonlinear functions, and by correlating aircraft flight paths and ground-based noise monitoring data, a noise level isosurface based on an ellipsoidal model is constructed. Furthermore, by combining the Doppler effect and atmospheric absorption attenuation, a mathematical model for noise attenuation is established, and the model parameters are optimized using a genetic algorithm.
It achieves essential modeling of the noise propagation process, improves the adaptability and reliability of prediction, and can accurately simulate and predict the complete propagation process of noise from the sound source to the receiving point. It is especially suitable for characterizing the dynamic temporal noise features of a single flight event.
Smart Images
Figure CN122065641A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of environmental monitoring and noise control technology, specifically to a method for predicting airport aircraft noise by integrating ellipsoids and nonlinear functions. Background Technology
[0002] The rapid development of the air transport industry, while promoting regional economic growth and urban prosperity, has also made aircraft noise problems in airport surrounding areas increasingly prominent. Scientifically assessing and managing aircraft noise has become a crucial and unavoidable issue in the sustainable operation of airports and the rational planning of surrounding land. However, accurate prediction of aircraft noise faces many complex challenges in practical applications. Dynamic changes in flight trajectories, differences in aircraft performance parameters, and uncertainties in meteorological conditions all contribute to the generation, propagation, and attenuation of noise, resulting in significant fluctuations in sound levels over time and space. Traditional theoretical models are often based on simplified assumptions and cannot fully and accurately reflect the actual dynamic changes, thus predictions under complex environmental conditions often contain significant deviations.
[0003] To improve the reliability of predictions, the industry currently widely adopts models based on the noise-power-sound-level relationship and their accompanying simulation tools. These methods rely on a pre-built static database under specific operating conditions, estimating noise levels through interpolation and other methods. Although the overall framework is relatively mature, when dealing with specific single flight events, deviations between the actual flight trajectory and the database's preset conditions, as well as the complex variations in noise attenuation during propagation in the real atmosphere, can still lead to significant errors in predictions. To address this limitation, researchers have proposed various improvement approaches, such as attempting to combine databases containing aircraft performance parameters to obtain a more realistic thrust state, or constructing thrust correction models based on dynamic equations, aiming to establish a more accurate correlation between thrust and noise level.
[0004] However, these methods are still subject to many interferences in practical applications: aerodynamic parameters change nonlinearly with flight conditions, and there are uncertainties in the actual mass and configuration of the aircraft. These factors make it very difficult to accurately map the noise level of the sound source, resulting in such improvements being more suitable for comparing the relative noise impact between different flight procedures rather than for accurate prediction of the absolute sound level. In addition, even if performance parameters are introduced into the noise source modeling, the noise propagation process usually still uses the original static model or traditional geometric attenuation theory, failing to fundamentally solve the problem of dynamic adaptability in the propagation process.
[0005] In recent years, machine learning methods have been introduced into the field of noise prediction and have shown a certain fitting ability on specific datasets. However, these methods usually have inherent limitations such as strong data dependence, poor interpretability of the internal mechanism of the model, and weak transferability between different airports or operating environments, which limit their widespread application in engineering practices that require high reliability and universality.
[0006] Existing aircraft noise prediction methods generally focus on static estimation of noise sources and prediction of several macroscopic integral evaluation indicators. In contrast, they fail to adequately characterize the dynamic characteristics of noise energy as it continuously evolves over time and space during propagation. Current models generally lack the ability to accurately predict the dynamic temporal structure of noise generated by a single flight event, and they also fail to systematically integrate the physical mechanisms by which the Doppler effect and complex atmospheric conditions (such as temperature, humidity, and wind speed gradients) affect sound wave propagation paths and attenuation patterns. Therefore, developing a noise prediction method that can break free from excessive reliance on static databases, directly identify noise attenuation patterns based on actual operational data, and naturally encompass the influence of various dynamic environmental factors is of significant practical importance and urgent need for improving the accuracy of noise assessments, the targeted nature of mitigation measures, and the scientific rigor of airport perimeter spatial planning. Summary of the Invention
[0007] The purpose of this invention is to address the problem that existing aircraft noise prediction methods rely excessively on static databases, making it difficult to accurately depict the propagation process of noise in real dynamic environments, thus hindering the precise prediction of the spatiotemporal evolution characteristics of noise in a single flight event. To address this, this invention proposes an airport aircraft noise prediction method that integrates ellipsoidal and nonlinear functions. This method correlates and matches data such as aircraft flight tracks and ground-monitored noise, describes the aircraft noise sound level isosurface based on an ellipsoidal model, and uses a nonlinear function to fit the sound level attenuation law with distance, thereby achieving physical simulation and prediction of the entire noise propagation process from aircraft to ground. This invention improves the existing noise prediction technology system and can accurately assess the impact of aircraft noise in the area surrounding airports.
[0008] The present invention employs the following technical solutions to achieve its objective: An airport aircraft noise prediction method integrating ellipsoids and nonlinear functions, comprising the following steps: S1. Acquire the aircraft's trajectory data and the coordinates and noise data of ground monitoring points, and transform the trajectory data and coordinates to a three-dimensional rectangular coordinate system based on the Gauss-Kruger projection coordinate system; S2. Filter valid noise events based on the noise data of the ground monitoring points, and associate and match the valid noise events with the flight track data of the aircraft; S3. Based on Morse theory of moving sound sources, construct an ellipsoidal model to characterize the isosurface of aircraft noise level. S4. Based on the aircraft noise attenuation characteristics and the ellipsoidal model, combined with the Doppler effect and atmospheric absorption attenuation, a nonlinear function of noise level attenuation with distance is constructed, and a mathematical model of noise attenuation is established. S5. Construct the objective function, use a genetic algorithm to optimize the parameters in the ellipsoidal model and the noise attenuation mathematical model, obtain the optimal parameters of the model, and output the noise prediction results.
[0009] Specifically, in step S1, the track data originates from the aircraft's ADS-B data or radar data, and the track data includes at least the aircraft's latitude and longitude coordinates, altitude, speed, heading, and time information; the conversion to a three-dimensional rectangular coordinate system specifically includes: based on the Gauss-Kruger projection, converting the aircraft's latitude and longitude coordinates, the latitude and longitude coordinates of the ground monitoring points, and the latitude and longitude coordinates of the runway center point into a unified plane rectangular coordinate system with the projection plane as the reference, and then combining the altitude information to construct a three-dimensional rectangular coordinate system.
[0010] Specifically, in step S2, filtering valid noise events based on the noise data from the ground monitoring points includes: sorting the noise data by sound level and taking a preset decibel value as the noise event judgment threshold; filtering out data sequences whose sound level continuously exceeds the preset value of the judgment threshold and whose duration is greater than a preset duration as valid noise events. The association and matching of the effective noise events with the aircraft's flight track data specifically includes: performing time interpolation smoothing on the aircraft's flight track data to align its timestamp with the time sequence of the effective noise events; and matching flight track data sequences and noise data sequences that are within the same time range and whose three-dimensional spatial distance between the aircraft and the ground monitoring point is within a preset distance threshold.
[0011] Furthermore, in step S3, an ellipsoidal model characterizing the noise level isosurface of an aircraft is constructed, specifically including: taking the aircraft as a moving sound source, with its instantaneous position as the center, the noise level isosurface is an ellipsoid, and determining the expression of the ellipsoid in a vertical plane containing the direction of aircraft motion and the direction of gravity.
[0012] Specifically, in the constructed ellipsoid model, the axis length With axis length The relationship satisfies: in the direction of motion In the opposite direction .
[0013] Furthermore, in step S4, when constructing the nonlinear function based on the aircraft noise attenuation characteristics, the relationship between sound level attenuation and vertical distance is first established without considering atmospheric attenuation; the isosurface of the noise sound level of the ellipsoidal model constructed in step S3 is an ellipsoid, and the vertical distance is the axial length of the ellipsoid perpendicular to the aircraft's direction of motion and in the horizontal plane. .
[0014] Specifically, in step S4, corrections are made based on the Doppler effect, adjusting the distance in the vertical direction. sound level at the location Distance along the direction of aircraft movement sound level at the location Establish the corresponding relationship; by combining the relationship corrected for the Doppler effect with the relationship that does not consider atmospheric attenuation, derive the relationship for a given... Solve the corresponding The cubic equation of one variable.
[0015] Specifically, in step S4, an atmospheric absorption attenuation factor is further introduced. The solution to the current situation The attenuation model is corrected; the atmospheric absorption attenuation factor It is distance The function; or the atmospheric absorption attenuation factor. Simplified to polynomial form; under the condition of equal sound levels, for the model determined by neglecting atmospheric attenuation... and corresponding value The correspondence is obtained by iterative calculation or direct solution of the correction equation that introduces the atmospheric attenuation difference, thus yielding the actual distance correspondence after considering atmospheric attenuation. As a result, the construction of the mathematical model for noise attenuation is completed.
[0016] Specifically, in step S5, the objective function is the mean square error function between the predicted sound level and the measured sound level; the genetic algorithm is used to globally optimize the major and minor axis parameters of the ellipsoidal surface in the ellipsoidal model and multiple coefficient parameters in the noise attenuation mathematical model, thereby minimizing the objective function, obtaining the optimal parameter set of the model, and using it to calculate the noise level at any point in the prediction area.
[0017] In summary, due to the adoption of this technical solution, the beneficial effects of this invention are as follows: This invention breaks through the reliance of traditional noise prediction methods on static databases and preset attenuation models. By directly linking real-time aircraft flight paths with ground noise monitoring data and constructing a prediction model based on a data-driven approach, it effectively avoids prediction biases caused by discrepancies between model assumptions and actual conditions or limited database coverage, significantly improving the model's adaptability and reliability in real dynamic environments.
[0018] This invention achieves an essential modeling of complex noise propagation processes. By integrating a sound level isoelastic geometric model based on a moving sound source with a nonlinear function characterizing sound level attenuation with distance, and introducing iterative corrections for atmospheric attenuation, this invention comprehensively covers the influence of key dynamic factors such as individual differences in aircraft performance, the Doppler effect, and complex atmospheric conditions from a physical perspective. This modeling approach is not a simple superposition of factors, but rather an intrinsic characterization of their combined mechanisms, thus enabling more accurate simulation and prediction of the complete noise propagation process from the sound source to the receiver, and is particularly adept at characterizing the dynamic temporal noise characteristics of a single flight event.
[0019] The model constructed in this invention possesses both clear physical meaning and good engineering applicability. Compared to purely empirical formulas or "black box" machine learning models, the model in this invention has a clear structure, interpretable parameters, and does not rely on massive amounts of historical data for training, thus exhibiting stronger transferability and generalization ability. Furthermore, by designing an optimal objective function that considers predictive equilibrium and employing efficient optimization algorithms to solve for the parameters, the robustness and computational feasibility of the model in practical applications are ensured, providing a more scientific and reliable technical tool for the accurate assessment of airport noise, the delineation of its impact range, and subsequent planning and mitigation. Attached Figure Description
[0020] The present invention is described in detail with reference to the following figures, which include four figures as follows: Figure 1 This is a schematic diagram illustrating the overall process of the airport aircraft noise prediction method of the present invention. Figure 2 This is a schematic diagram of the noise level isosurface of the ellipsoidal model in this invention; Figure 3 This is a schematic diagram of the sound level attenuation curve considering atmospheric attenuation during the iterative calculation in this invention; Figure 4 This is a schematic diagram illustrating the verification of the prediction results of this invention through aircraft noise calibration. Detailed Implementation
[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.
[0022] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.
[0023] An airport aircraft noise prediction method that integrates ellipsoids and nonlinear functions. Figure 1 This document provides a brief overview of the overall process of the method, which can be viewed concurrently. The key steps of the method can be summarized as follows: S1. Acquire the aircraft's trajectory data and the coordinates and noise data of ground monitoring points, and transform the trajectory data and coordinates to a three-dimensional rectangular coordinate system based on the Gauss-Kruger projection coordinate system; S2. Filter valid noise events based on noise data from ground monitoring points, and associate and match valid noise events with aircraft flight track data; S3. Based on Morse theory of moving sound sources, construct an ellipsoidal model to characterize the isosurface of aircraft noise level. S4. Based on the noise attenuation characteristics and ellipsoidal model of aircraft, combined with the Doppler effect and atmospheric absorption attenuation, a nonlinear function of noise level attenuation with distance is constructed, and a mathematical model of noise attenuation is established. S5. Construct the objective function, use the genetic algorithm to optimize the parameters in the ellipsoidal model and the noise attenuation mathematical model, obtain the optimal parameters of the model, and output the noise prediction results.
[0024] This implementation method will describe the details and preferred methods of each step in the order described above.
[0025] In step S1, the track data comes from the aircraft's ADS-B data or radar data. The track data includes at least the aircraft's latitude and longitude coordinates, altitude, speed, heading, and time information. The conversion to a three-dimensional rectangular coordinate system specifically includes: based on the Gauss-Kruger projection, the latitude and longitude coordinates of the aircraft, the ground monitoring points, and the runway center point are uniformly converted into plane rectangular coordinates with the projection plane as the reference, and then combined with the altitude information to construct a three-dimensional rectangular coordinate system.
[0026] In step S2, valid noise events are screened based on noise data from ground monitoring points. Specifically, this includes: sorting the noise data by sound level and taking 90 decibels as the noise event judgment threshold; and selecting data sequences whose sound level continuously exceeds the judgment threshold by more than 10 decibels and whose duration is greater than 10 seconds as valid noise events.
[0027] In step S2, the effective noise events are associated and matched with the aircraft track data. Specifically, this includes: performing time interpolation smoothing on the aircraft track data to align its timestamp with the time series of the effective noise events; and matching the track data series and noise data series within the same time range and within 3km of the three-dimensional spatial distance between the aircraft and the ground monitoring point.
[0028] According to Morse's theory of moving sound sources, due to the Doppler effect of the moving sound source, the isosurface of the aircraft noise level will transform from a concentric sphere to a concentric ellipsoid, gradually decreasing outward from the sound source. Therefore, in step S3 of this embodiment, an ellipsoidal model characterizing the isosurface of the aircraft noise level is constructed, specifically including: assuming the aircraft is a moving sound source, with its instantaneous position as the center, the isosurface of its noise level is an ellipsoid. In a vertical plane containing the aircraft's motion direction and the direction of gravity, the expression for the ellipsoid is as follows:
[0029] In the formula, , , These represent the position coordinates of the ground monitoring point relative to the sound source in a three-dimensional rectangular coordinate system, used to characterize the relative distance between the aircraft and the ground monitoring point; Indicates the angle of ascent or descent of an aircraft's trajectory; This represents the axial length of the ellipsoid in the direction of aircraft motion and its opposite direction, and due to the Doppler effect, the lengths in both directions are... Different values; The length of the ellipsoid is the length of the axis of the ellipsoid that is perpendicular to the direction of motion and lies in the horizontal plane.
[0030] In this embodiment, as Figure 2 As shown, the axial length of the ellipsoid It can be further divided into those that proceed forward along the track. and backward The expression for the corresponding ellipsoid is as follows:
[0031] The analysis in this embodiment is not detailed enough. and To what extent, and then use A consistent terminology. In the constructed ellipsoidal model, the axis length... With axis length The relationship satisfies: in the direction of motion In the opposite direction .
[0032] In step S4, referring to the NPD curve and based on the aircraft noise attenuation characteristics, when constructing the nonlinear function, the relationship between sound level attenuation and vertical distance is first established without considering atmospheric attenuation; the isosurface of the noise sound level of the ellipsoidal model constructed in step S3 is an ellipsoid, and the vertical distance is the axis length of the ellipsoid perpendicular to the direction of aircraft motion and in the horizontal plane. The established relation is as follows:
[0033] In the formula, This indicates that at a distance perpendicular to the direction of aircraft movement, the distance is... The predicted A-weighted sound pressure level; , , , , These are all parameters characterizing the shape of the sound level attenuation curve; Parameters used to characterize the intensity of an aircraft's sound source.
[0034] In this embodiment, based on Morse moving sound source theory and the 1 / 3 octave sound level calculation method, the Doppler effect of aircraft noise is corrected, and the vertical distance is adjusted. sound level at the location Distance along the direction of aircraft movement sound level at the location Establish the following relation:
[0035]
[0036] In the formula, Mach number, For aircraft speed, Speed of sound; Let be the angle between the direction of sound source motion and the direction of the line connecting the sound source and the receiver. By combining the Doppler effect-corrected relation and the relation that does not consider atmospheric attenuation, we derive the following for a given... Solve the corresponding A cubic equation in one variable is given by the following formula:
[0037] In the formula, the coefficients , , , According to parameters , , , , , , , and The determination is then made. The specific derivation and solution process can be illustrated as follows: The established relation is directly converted into a relation about When solving a cubic equation in one variable, the following equation applies:
[0038]
[0039] Due to the function It is monotonically decreasing, therefore the equation has only one real root:
[0040] in:
[0041]
[0042] , , , This completes the derivation and solution process.
[0043] In step S4, an atmospheric absorption attenuation factor is further introduced. The solution to the current situation The attenuation model is corrected. Atmospheric absorption attenuation factor It is distance The function is as follows:
[0044] Or atmospheric absorption attenuation factor Simplified to polynomial form, as follows:
[0045] In the formula, , , These are all coefficients corresponding to atmospheric attenuation parameters. Under the condition of equal sound levels, for models determined without considering atmospheric attenuation... and corresponding value The correspondence is obtained by iterative calculation or direct solution of the correction equation that introduces the atmospheric attenuation difference, thus yielding the actual distance correspondence after considering atmospheric attenuation. As a result, the mathematical model for noise attenuation was constructed.
[0046] In this embodiment, the iterative calculation specifically involves: assuming that, without considering atmospheric attenuation, a distance value exists. corresponding for Calculate the atmospheric attenuation difference between the vertical direction and the aircraft's direction of motion. ; with point Starting from the current point, the slope of the tangent line to the decay function is used for iteration to obtain the following values sequentially. , … until and The difference meets the preset accuracy requirement, at this time Recorded as the corrected version, and The distance along the direction of aircraft movement corresponding to the same total sound level.
[0047] Combination Figure 3 The illustration further explains this part in this embodiment. When in front of the aircraft, based on the ellipsoidal model, it is obvious... Compared to the vertical trajectory direction, atmospheric attenuation is greater; assuming When atmospheric effects are not considered The corresponding value, Figure 3 middle As a sound level attenuation curve, This represents the difference in atmospheric attenuation between the two. After considering the sound level attenuation curve under atmospheric attenuation, the point... Draw a tangent line and set its slope as . It can be found that ,but Similarly, passing the point Draw a tangent line and set its slope as . , ,but A little later By drawing tangents and repeating this process multiple times, we can obtain the results when the sound levels are the same. Corresponding corrections When the distance change is small, atmospheric attenuation is minimal, so the solution can be obtained several times and approximate values can be taken. Similarly, the value can be calculated when the distance is behind the aircraft. Correction value. To simplify the process, atmospheric attenuation can be set as the aforementioned cubic function form, i.e. Directly ask about The roots of the cubic equation, i.e. The root is obtained after correction. .
[0048] This implementation method, by considering the Doppler effect and atmospheric absorption attenuation, constructs a corresponding nonlinear function, thus obtaining the relationship and calculation method between the axis lengths of the aircraft's sound level isosphere.
[0049] In step S5, the objective function is the mean square error function between the predicted sound level and the measured sound level. A genetic algorithm is used to globally optimize the major and minor axis parameters of the ellipsoidal surface in the ellipsoidal model, as well as multiple coefficient parameters in the noise attenuation mathematical model, thereby minimizing the objective function and obtaining the optimal parameter set of the model. This optimal parameter set is then used to calculate the noise level at any point within the prediction region.
[0050] In this embodiment, when using mean squared error (MSE) as the objective function, considering the balance of prediction errors, the noise sequence data of a single flight event is divided into two parts from the point of maximum A-weighted sound level. The goal is to minimize the prediction errors of both parts and ensure that the error levels are comparable. Let the average error of the first half be:
[0051] Let the average error of the second half be:
[0052] In addition, a maximum noise limit for aircraft is added. The objective function is as follows:
[0053]
[0054] In the formula, Represent the objective function; and These represent the actual value and the predicted value, respectively. and These represent the quantity of data in the preceding and following segments, respectively. Indicates the parameters that need to be calibrated; express The One linear constraint; express The One nonlinear constraint; and These represent the number of linear constraints and nonlinear constraints, respectively.
[0055] In this implementation, model parameters are solved using optimization algorithms such as genetic algorithms. It is then determined whether the constraints of the model in step S4 are met, and chromosomes that meet the conditions are grouped into an initial population. Based on the constructed constraints and objective function, the objective function value for each individual is calculated. Individuals with smaller objective function values are selected as parents, and crossover and mutation are performed with a certain probability to generate new individuals and a new population. This process is iterated until the termination condition is met. Finally, the optimal parameter solution that meets the conditions is output, and aircraft noise is predicted based on this solution, with the prediction result output.
[0056] The following is an example of what happens after applying the method of this embodiment.
[0057] Two sets of monitoring points were set up at one end of an airport runway. The first set, monitoring point #1, was 4 km from the runway, and the second set, monitoring point #2, was 6 km from the runway, maintaining a lateral distance of approximately 200 meters from the runway centerline. Monitoring point #1 was used as the model parameter calibration group, and monitoring point #2 as the model validation group. Takeoff trajectory data and noise data collected from monitoring point #1 were input into the model to calibrate the optimal model parameters and predict the results at monitoring point #2.
[0058] Optimal parameters for a certain aircraft flight noise event to The calibration results are as follows: [0.07, 91.31, 264.98, 272.67, 13.75, 106.63, -0.54, 7.28], Using the default values as the preset parameters, predictions are made on noisy event data using these optimal parameters. The prediction results are as follows: Figure 4 As shown in the figure, the curves of the A-weighted sound level after fitting and smoothing the actual data as a function of time, and the curves of the predicted A-weighted sound level as a function of time, are respectively. From Figure 4 It can be seen that the fitting prediction results of calibration group #1 are good, basically reflecting the time series change process of noise events received by ground monitoring points. Due to the certain distance between verification group #2 and calibration group #1, the aircraft's operating state being takeoff and departure, and environmental noise interference, the sound level of the aircraft source will change to some extent, resulting in a certain prediction deviation. However, the effective perceived noise level calculation errors of the two groups are 0.13 dB and 0.66 dB, respectively, proving that the noise prediction method proposed in this embodiment is scientific and effective, and can achieve the prediction of aircraft noise around the monitoring point. Based on this, by combining noise data from multiple ground monitoring points, a more accurate noise attenuation curve calibration is performed, and the sound level change of the sound source during the entire flight process is reversed using the aircraft noise attenuation model, the noise distribution of the entire flight noise event around the airport can be predicted.
Claims
1. A method for predicting airport aircraft noise by integrating ellipsoids and nonlinear functions, characterized in that, The method includes the following steps: S1. Acquire the aircraft's trajectory data and the coordinates and noise data of ground monitoring points, and transform the trajectory data and coordinates to a three-dimensional rectangular coordinate system based on the Gauss-Kruger projection coordinate system; S2. Filter valid noise events based on the noise data of the ground monitoring points, and associate and match the valid noise events with the flight track data of the aircraft; S3. Based on Morse theory of moving sound sources, construct an ellipsoidal model to characterize the isosurface of aircraft noise level. S4. Based on the aircraft noise attenuation characteristics and the ellipsoidal model, combined with the Doppler effect and atmospheric absorption attenuation, a nonlinear function of noise level attenuation with distance is constructed, and a mathematical model of noise attenuation is established. S5. Construct the objective function, use a genetic algorithm to optimize the parameters in the ellipsoidal model and the noise attenuation mathematical model, obtain the optimal parameters of the model, and output the noise prediction results.
2. The airport aircraft noise prediction method according to claim 1, characterized in that: In step S1, the track data is derived from the aircraft's ADS-B data or radar data. The track data includes at least the aircraft's latitude and longitude coordinates, altitude, speed, heading, and time information. The conversion to a three-dimensional rectangular coordinate system specifically includes: based on the Gauss-Kruger projection, converting the aircraft's latitude and longitude coordinates, the latitude and longitude coordinates of the ground monitoring points, and the latitude and longitude coordinates of the runway center point into a plane rectangular coordinate system with the projection plane as the reference, and then combining the altitude information to construct a three-dimensional rectangular coordinate system.
3. The airport aircraft noise prediction method according to claim 1, characterized in that, In step S2, the effective noise events are screened based on the noise data of the ground monitoring points. Specifically, this includes: sorting the noise data by sound level and taking a preset decibel value as the noise event judgment threshold; and selecting data sequences whose sound level continuously exceeds the preset value of the judgment threshold and whose duration is greater than a preset duration as effective noise events. The association and matching of the effective noise events with the aircraft's flight track data specifically includes: performing time interpolation smoothing on the aircraft's flight track data to align its timestamp with the time sequence of the effective noise events; and matching flight track data sequences and noise data sequences that are within the same time range and whose three-dimensional spatial distance between the aircraft and the ground monitoring point is within a preset distance threshold.
4. The airport aircraft noise prediction method according to claim 1, characterized in that, In step S3, an ellipsoidal model representing the noise level isosurface of the aircraft is constructed. Specifically, this includes: assuming the aircraft is a moving sound source, and centering on its instantaneous position, the noise level isosurface is an ellipsoid. Within a vertical plane containing the aircraft's motion direction and the direction of gravity, the expression for the ellipsoid is as follows: In the formula, , , These represent the position coordinates of the ground monitoring point relative to the sound source in a three-dimensional rectangular coordinate system, used to characterize the relative distance between the aircraft and the ground monitoring point; Indicates the angle of ascent or descent of an aircraft's trajectory; This represents the axial length of the ellipsoid in the direction of aircraft motion and its opposite direction, and due to the Doppler effect, the lengths in both directions are... Different values; The length of the ellipsoid is the length of the axis of the ellipsoid that is perpendicular to the direction of motion and lies in the horizontal plane.
5. The airport aircraft noise prediction method according to claim 4, characterized in that, In the constructed ellipsoid model, the axis length With axis length The relationship satisfies: in the direction of motion In the opposite direction .
6. The airport aircraft noise prediction method according to claim 1, characterized in that: In step S4, when constructing the nonlinear function based on the aircraft noise attenuation characteristics, the relationship between sound level and vertical distance attenuation is first established without considering atmospheric attenuation; the isosurface of the noise sound level of the ellipsoidal model constructed in step S3 is an ellipsoid, and the vertical distance is the axial length of the ellipsoid perpendicular to the aircraft's direction of motion and in the horizontal plane. The established relation is as follows: In the formula, This indicates that at a distance perpendicular to the direction of aircraft movement, the distance is... The predicted A-weighted sound pressure level; , , , , These are all parameters characterizing the shape of the sound level attenuation curve; Parameters used to characterize the intensity of an aircraft's sound source.
7. The airport aircraft noise prediction method according to claim 6, characterized in that: In step S4, corrections are made based on the Doppler effect, adjusting the distance in the vertical direction. sound level at the location Distance along the direction of aircraft movement sound level at the location Establish the following relation: In the formula, Mach number, For aircraft speed, Speed of sound; Let be the angle between the direction of sound source motion and the direction of the line connecting the sound source and the receiver; by combining the relationship corrected for the Doppler effect and the relationship neglecting atmospheric attenuation, the following can be derived for a given... Solve the corresponding A cubic equation in one variable is given by the following formula: In the formula, the coefficients , , , According to parameters , , , , , , , and To be confirmed.
8. The airport aircraft noise prediction method according to claim 7, characterized in that: In step S4, an atmospheric absorption attenuation factor is further introduced. The solution to the current situation The attenuation model is corrected; the atmospheric absorption attenuation factor It is distance The function is as follows: Or atmospheric absorption attenuation factor Simplified to polynomial form, as follows: In the formula, , , These are all coefficients corresponding to atmospheric attenuation parameters; under the condition of equal sound levels, for models determined without considering atmospheric attenuation... and corresponding value The correspondence is obtained by iterative calculation or direct solution of the correction equation that introduces the atmospheric attenuation difference, thus yielding the actual distance correspondence after considering atmospheric attenuation. As a result, the construction of the mathematical model for noise attenuation is completed.
9. The airport aircraft noise prediction method according to claim 8, characterized in that, The iterative calculation specifically involves: assuming that, without considering atmospheric attenuation, there exists a distance value. corresponding for Calculate the atmospheric attenuation difference between the vertical direction and the aircraft's direction of motion. ; with point Starting from the current point, the slope of the tangent line to the decay function is used for iteration to obtain the following values sequentially. , … until and The difference meets the preset accuracy requirement, at this time Recorded as the corrected version, and The distance along the direction of aircraft movement corresponding to the same total sound level.
10. The airport aircraft noise prediction method according to claim 1, characterized in that: In step S5, the objective function is the mean square error function between the predicted sound level and the measured sound level; the genetic algorithm is used to globally optimize the major and minor axis parameters of the ellipsoidal surface in the ellipsoidal model and multiple coefficient parameters in the noise attenuation mathematical model, thereby minimizing the objective function, obtaining the optimal parameter set of the model, and using it to calculate the noise level at any point in the prediction area.