Vehicle partition statistics noise source prediction method

Abstract

The invention relates to a vehicle partition statistical noise source prediction method, which comprises the following steps of: performing a wind tunnel test, synchronously performing whole vehicle wind tunnel noise source positioning and visualization test, and obtaining a vehicle wind noise source positioning result cloud picture under different working conditions and different frequencies; extracting 23 key parts from the wind noise source positioning result cloud picture according to a wind noise control strategy designed by a vehicle body; and performing partition statistics on the vehicle wind noise source positioning result cloud picture according to the divided key parts to form a unified standard table. According to the method, zoning statistics is carried out on a result cloud picture obtained by sound source localization and a visualization test in a wind tunnel test, so that generation parts of main noise sources and contribution of key parts to wind noise are made clear; noise sources can be accurately recognized, targeted measures can be taken to reduce noise, more importantly, control can be carried out in the automobile appearance design stage, the working efficiency can be greatly improved, time and resources are saved, the value in practical engineering application is high, and project development can be effectively supported.

Description

Technical Field

[0001] The invention belongs to the technical field of automobiles, and in particular relates to a vehicle partition statistical noise source prediction method. Background Art

[0002] With the development of my country's automobile industry and the popularity of passenger cars, the NVH performance of the whole vehicle has received more and more attention from consumers. Automobile noise is mainly divided into three categories: powertrain noise, tire road noise and wind noise. Among them, powertrain noise has been reduced to a negligible level in new energy vehicles. With the development of tire and chassis technology and the great improvement of road conditions, the control of tire road noise has also been greatly improved. Therefore, the problem of wind noise has become particularly important.

[0003] In the process of automobile development, it is necessary to control the appearance of the vehicle during the product development stage to achieve a low aerodynamic noise design. Wind tunnel testing has always played an indispensable role as a means of studying wind noise because the environment and test conditions are controllable, not affected by other noise sources, and the measurement accuracy is high and repeatable. Wind tunnel testing is even more indispensable in the process of vehicle development, and the location and visualization test of the wind tunnel noise source of the whole vehicle is also a very important test content. However, the existing technology has not yet formed a standardized and unified processing method for the test result - the cloud map of the vehicle wind noise source location result. Summary of the invention

[0004] The purpose of the present invention is to provide a method for processing a cloud map of vehicle wind noise source positioning results, so as to solve the problem of performing partition statistics on the cloud map of the results obtained by sound source positioning and visualization testing in a wind tunnel test.

[0005] The objective of the present invention is achieved through the following technical solutions:

[0006] A vehicle partition statistical noise source prediction method, the steps of which are as follows:

[0007] A. Conduct wind tunnel tests, and simultaneously conduct wind tunnel noise source location and visualization tests for the entire vehicle to obtain cloud maps of vehicle wind noise source location results under different working conditions and frequencies;

[0008] B. According to the wind noise control strategy of the vehicle body design, 23 key parts are extracted from the wind noise source location result cloud map, and the peak sound pressure values ​​of these key parts are counted;

[0009] C. Carry out statistical analysis on the cloud map of the vehicle wind noise source positioning results according to the divided key parts to form a unified and standardized table.

[0010] Further, step A is specifically as follows: when the frequencies are 400Hz, 500Hz, 630Hz, 800Hz, 1000Hz, 1250Hz, 1600Hz, 2000Hz, 2500Hz, 3150Hz, 4000Hz, 5000Hz, 6300Hz and 8000Hz, a cloud map of the vehicle model wind noise source positioning results at the corresponding frequencies is obtained.

[0011] Furthermore, in step A, three microphone arrays are placed on the left, right and top of the test vehicle respectively, and are kept at an appropriate distance from the test vehicle.

[0012] Furthermore, in step A, the controller controls the movable bracket to translate and adjust its position along the track so that the center of the microphone array can be aligned with the center position of test vehicles of various sizes; a dedicated camera is installed at the center of each microphone array to capture images of the left, right and upper surfaces of the vehicle.

[0013] Furthermore, in step A, the microphone array is driven to collect sound and vibration signals through the acquisition software installed on the computer, and the signal sizes collected by each microphone for the same sound event are compared. The noise distribution on the surface of the vehicle body is calculated based on the algorithm based on the beamforming principle, and the noise measurement results are mapped to the three-dimensional data model to obtain a cloud map of the wind noise source positioning results.

[0014] Further, in step B, the 23 key parts include the front wheel cavity, front fender, rearview mirror, A-pillar, front windshield, front door, front triangle window, grille, front bumper, headlight, hood, lower end of door, rear door fixed glass, rear wheel cavity, rear door, B-pillar, front windshield, rear windshield, rear fender, trunk rack / roof cover, rear side panel, trunk lid and roof radar.

[0015] Further, in step B, the wind noise control strategy of the vehicle body design is to combine the control of the wind noise source with the vehicle body design, which specifically includes the following steps:

[0016] a. Vehicle design, including the overall streamline design of the vehicle body, the design between the front grille and the engine hood, the design between the engine compartment and the front windshield, the design of the A-pillar area, the design of the transition area between the roof, rear windshield and luggage compartment, and the design of the outer floor;

[0017] b. Local design, including the design of rearview mirrors, antennas, sunroofs, luggage racks, radars, etc.

[0018] Furthermore, in step C, the partitioning is performed according to the names of the parts during automobile assembly, with a focus on 23 areas in the table.

[0019] Furthermore, the judgment of key parts is based on the conclusion drawn from statistics of various models. The sound pressure peaks of various models in these areas in the table are relatively high, which can be shown in the result cloud map. The sound pressure peaks of other parts are not large and are not included in the statistics.

[0020] Further, in step C, the statistics refer to that a three-dimensional color map at a certain frequency under a certain working condition can be displayed on the cloud map of the wind noise source positioning results obtained in the experiment, and according to the divided areas, the position of the maximum sound pressure in the area is manually selected for comparison with the legend to obtain the maximum sound pressure result.

[0021] Compared with the prior art, the present invention has the following beneficial effects:

[0022] The vehicle zoning statistical noise source prediction method of the present invention performs zoning statistics on the result cloud map obtained by sound source positioning and visualization test in the wind tunnel test, so as to clarify the generation location of the main noise source and the contribution of the key location to the wind noise; accurate identification of the noise source can not only take targeted measures to reduce the noise, but more importantly, it can be controlled in the automobile appearance design stage, which can greatly improve work efficiency, save time and resources, and is of high value in practical engineering applications, and can effectively support project development. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 A flow chart of the steps of the vehicle partition statistical noise source prediction method of the present invention. DETAILED DESCRIPTION

[0025] The present invention will be further described below in conjunction with embodiments:

[0026] The present invention proposes a method for processing a cloud map of vehicle wind noise source positioning results, and performs partition statistics on the cloud map of the results obtained by the test so as to clarify the generating position of the main noise source.

[0027] Among them, zoning means: classifying according to the names of parts during automobile assembly, focusing on the areas of multiple key parts. The judgment of key parts is as follows: the conclusion drawn from statistics of various models is that the sound pressure peaks in these areas in the table for various models are relatively high, which can be shown in the result cloud map. The sound pressure peaks of other parts are not large and are not included in the statistics.

[0028] Statistics means that a three-dimensional color map at a certain frequency under a certain working condition can be displayed on the cloud map of the wind noise source positioning results obtained in the test. According to the divided areas, the position of the maximum sound pressure in the area is manually selected and compared with the legend to obtain the maximum sound pressure result.

[0029] According to the statistical results, it can be seen which parts have high sound pressure values ​​under certain working conditions and at a specific frequency, indicating that this part is the main noise source.

[0030] The vehicle wind noise source positioning result cloud map processing method of the present invention comprises the following steps:

[0031] The first step is to conduct a wind tunnel test and simultaneously carry out wind tunnel noise source positioning and visualization testing for the entire vehicle to obtain a cloud map of the vehicle wind noise source positioning results under different working conditions and different frequencies.

[0032] In order to realize the noise source positioning and visualization test functions, and to ensure that the sound measurement range can fully cover the entire vehicle size, the three microphone arrays need to be placed on the left and right sides and above the test vehicle, and maintain an appropriate distance from the test vehicle. The controller can operate the movable bracket to adjust the position along the track so that the center of the microphone array can be aligned with the center position of various test vehicles of different sizes. A dedicated camera is installed in the center of each microphone array to capture images of the left, right and upper surfaces of the vehicle. The acquisition software installed on the computer drives the microphone array to collect sound and vibration signals, compares the different signal sizes collected by each microphone for the same sound event, and calculates the noise distribution on the vehicle body surface based on the algorithm of the beamforming principle. The noise measurement results are mapped to the three-dimensional data model to obtain a cloud map of wind noise source positioning results.

[0033] In the second step, according to the wind noise control strategy of the vehicle body design, 23 key parts are extracted from the cloud map of the wind noise source positioning results (the other parts have little influence and are not included in the statistics), and the peak sound pressure of these key parts is mainly counted. The 23 key parts include the front wheel cavity, front fender, rearview mirror, A-pillar, front windshield, front door, front triangle window, grille, front bumper, headlight, hood, lower end of door, rear door fixed glass, rear wheel cavity, rear door, B-pillar, front windshield, rear windshield, rear fender, luggage rack / roof cover, rear side wall, luggage cover and roof radar. The key parts that affect wind noise are shown in Table 1;

[0034] Table 1

[0035]

[0036]

[0037] In the present invention, the wind noise control strategy of the vehicle body design is to combine the control of the wind noise source with the vehicle body design, specifically including the following steps:

[0038] (1) Overall vehicle design, including the overall streamline design of the vehicle body, the design between the front grille and the engine hood, the design between the engine compartment and the front windshield, the design of the A-pillar area, the design of the transition area between the roof, rear windshield and luggage compartment, and the design of the exterior floor.

[0039] (2) Local design, including the design of rearview mirrors, antennas, sunroofs, luggage racks, radars, etc.

[0040] The third step is to perform partition statistics on the vehicle wind noise source positioning result cloud map according to the divided key parts to form a unified standard table, as shown in Table 2.

[0041] Among them, the divisions are: classified according to the names of parts during automobile assembly, focusing on 23 areas in the table (judgment of key parts: the conclusion drawn from statistics of various models, the sound pressure peaks of various models in these areas in the table are relatively high, which can be shown in the result cloud map, and the sound pressure peaks of other parts are not large and are not included in the statistics).

[0042] Statistics means that a three-dimensional color map at a certain frequency under a certain working condition can be displayed on the cloud map of the wind noise source positioning results obtained in the test. According to the divided areas, the position of the maximum sound pressure in the area is manually selected and compared with the legend to obtain the maximum sound pressure result.

[0043] Table 2

[0044]

[0045] Example 1

[0046] Taking a certain vehicle model as an example, the vehicle partition statistical noise source prediction method includes the following steps:

[0047] 1. Carry out wind tunnel tests, and simultaneously carry out wind tunnel noise source location and visualization tests for the entire vehicle, and obtain cloud maps of vehicle wind noise source location results under different working conditions and different frequencies.

[0048] At frequencies of 400Hz, 500Hz, 630Hz, 800Hz, 1000Hz, 1250Hz, 1600Hz, 2000Hz, 2500Hz, 3150Hz, 4000Hz, 5000Hz, 6300Hz and 8000Hz, the cloud map of the wind noise source positioning results of the vehicle model at the corresponding frequencies is obtained. After the wind tunnel noise source positioning and visualization test of the whole vehicle, the cloud map of the vehicle wind noise source positioning results at different frequencies is obtained.

[0049] 2. According to the wind noise control strategy of the vehicle body design, 23 key parts are extracted from the wind noise source location result cloud map, and the peak sound pressure values ​​of these key parts are counted.

[0050] Among them, 23 key parts include front wheel cavity, front fender, rearview mirror, A-pillar, front windshield, front door, front triangle window, grille, front bumper, headlight, hood, lower end of door, rear door fixed glass, rear wheel cavity, rear door, B-pillar, front windshield, rear windshield, rear fender, trunk rack / roof cover, rear side panel, trunk lid and roof radar.

[0051] 3. According to the key parts divided, the cloud map of the vehicle wind noise source positioning results is divided into zones and statistics to form a unified standard table, which is the wind noise source positioning result table of a certain vehicle model, as shown in Table 2. This result can help to clarify the location of the main noise source and the contribution of the key parts to the wind noise, which can effectively support project development.

[0052] Table 2

[0053]

[0054]

[0055] After the whole vehicle wind tunnel noise source positioning and visualization test, the vehicle wind noise source positioning results at different frequencies were obtained, as shown in Table 3.

[0056] Table 3

[0057] Frequency / Hz Larger affected areas Frequency / Hz Larger affected areas 400 Door bottom 2000 Front triangular window 500 Door bottom 2500 Front fender 630 Front triangular window 3150 Front wheel cavity 800 Front wheel cavity 4000 Qianbao 1000 rearview mirror 5000 Hair cover 1250 Qianbao 6300 Qianbao 1600 Front wheel cavity 8000 Front triangular window

[0058] The present invention extracts 23 key parts of the vehicle body from the result cloud map and performs a statistical processing on the result cloud map by partitioning. It is also not limited to the above 23 key parts of the vehicle body, and other key parts that have a great impact on wind noise can be added.

[0059] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A vehicle partition statistical noise source prediction method, characterized in that: The steps are as follows: A. Conduct wind tunnel tests, and simultaneously conduct wind tunnel noise source location and visualization tests for the entire vehicle to obtain cloud maps of vehicle wind noise source location results under different working conditions and frequencies; B. According to the wind noise control strategy of the vehicle body design, 23 key parts are extracted from the wind noise source location result cloud map, and the peak sound pressure values ​​of these key parts are counted; C. Carry out statistical analysis on the cloud map of the vehicle wind noise source positioning results according to the divided key parts to form a unified and standardized table.

2. A vehicle partition statistical noise source prediction method according to claim 1, characterized in that: Step A, specifically: when the frequencies are 400Hz, 500Hz, 630Hz, 800Hz, 1000Hz, 1250Hz, 1600Hz, 2000Hz, 2500Hz, 3150Hz, 4000Hz, 5000Hz, 6300Hz and 8000Hz, obtain a cloud map of the vehicle model wind noise source positioning results at the corresponding frequencies.

3. The vehicle partition statistical noise source prediction method according to claim 1, characterized in that: Step A: Place the three microphone arrays on the left, right and top of the test vehicle respectively, and keep an appropriate distance from the test vehicle.

4. The vehicle partition statistical noise source prediction method according to claim 3, characterized in that: Step A, the controller controls the movable bracket to translate and adjust the position along the track so that the center of the microphone array can be aligned with the center position of test vehicles of various sizes; a dedicated camera is installed in the center of each microphone array to capture images of the left, right and upper surfaces of the vehicle.

5. A vehicle partition statistical noise source prediction method according to claim 4, characterized in that: Step A: Drive the microphone array to collect sound and vibration signals through the acquisition software installed on the computer, compare the differences in the signal sizes collected by each microphone for the same sound event, calculate the noise distribution on the vehicle body surface based on the algorithm based on the beamforming principle, and map the noise measurement results to the three-dimensional data model to obtain a cloud map of wind noise source positioning results.

6. The vehicle partition statistical noise source prediction method according to claim 1, characterized in that: Step B, 23 key parts include front wheel cavity, front fender, rearview mirror, A-pillar, front windshield, front door, front triangle window, grille, front bumper, headlight, hood, lower end of door, rear door fixed glass, rear wheel cavity, rear door, B-pillar, front windshield, rear windshield, rear fender, trunk rack / roof cover, rear side panel, trunk lid and roof radar.

7. The vehicle partition statistical noise source prediction method according to claim 1, characterized in that: Step B, the wind noise control strategy of the vehicle body design is to combine the control of the wind noise source with the vehicle body design, which specifically includes the following steps: a. Vehicle design, including the overall streamline design of the vehicle body, the design between the front grille and the engine hood, the design between the engine compartment and the front windshield, the design of the A-pillar area, the design of the transition area between the roof, rear windshield and luggage compartment, and the design of the outer floor; b. Local design, including the design of rearview mirrors, antennas, sunroofs, luggage racks, radars, etc.

8. The vehicle partition statistical noise source prediction method according to claim 1, characterized in that: Step C, the partitioning is to classify according to the names of the parts during automobile assembly, focusing on 23 areas in the table.

9. A vehicle partition statistical noise source prediction method according to claim 8, characterized in that: The judgment of key parts is based on the conclusion drawn from statistics of various models. The sound pressure peaks of various models in these areas in the table are relatively high, which can be shown in the result cloud map. The sound pressure peaks of other parts are not large and are not included in the statistics.

10. The vehicle partition statistical noise source prediction method according to claim 1, characterized in that: Step C, the statistics means that a three-dimensional color map under a certain working condition and a certain frequency can be displayed on the cloud map of the wind noise source positioning result obtained in the experiment. According to the divided area, the position of the maximum sound pressure in the area is manually selected for comparison with the legend to obtain the maximum sound pressure result.

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