CN110895622A - Noise reduction index decomposition method based on noise source identification in high-speed rail cabin - Google Patents
Noise reduction index decomposition method based on noise source identification in high-speed rail cabin Download PDFInfo
- Publication number
- CN110895622A CN110895622A CN201810974023.0A CN201810974023A CN110895622A CN 110895622 A CN110895622 A CN 110895622A CN 201810974023 A CN201810974023 A CN 201810974023A CN 110895622 A CN110895622 A CN 110895622A
- Authority
- CN
- China
- Prior art keywords
- noise
- area
- observation
- noise source
- noise reduction
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 39
- 238000000354 decomposition reaction Methods 0.000 title claims abstract description 15
- 238000012360 testing method Methods 0.000 claims abstract description 35
- 239000002245 particle Substances 0.000 claims abstract description 13
- 238000005457 optimization Methods 0.000 claims abstract description 6
- 238000012163 sequencing technique Methods 0.000 claims abstract description 4
- 238000012546 transfer Methods 0.000 claims description 9
- 238000005192 partition Methods 0.000 claims description 6
- 239000000126 substance Substances 0.000 claims description 4
- 238000000638 solvent extraction Methods 0.000 claims description 3
- 238000012935 Averaging Methods 0.000 claims description 2
- 238000013461 design Methods 0.000 abstract description 11
- 238000004364 calculation method Methods 0.000 abstract description 6
- 238000012545 processing Methods 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 239000000523 sample Substances 0.000 description 2
- 239000012814 acoustic material Substances 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000003137 locomotive effect Effects 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000013139 quantization Methods 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
- 238000010998 test method Methods 0.000 description 1
Images
Abstract
The invention discloses a noise reduction index decomposition method based on the recognition of a noise source in a high-speed rail cabin, which comprises the following steps: step 1) dividing a high-speed rail bulkhead into a plurality of noise source areas, dividing each area into a plurality of source sound pressure and air particle vibration velocity test points, dividing a carriage into a plurality of passenger observation areas, and arranging a plurality of observation points in each passenger observation area; step 2) identifying and testing noise source areas in the high-speed rail cabin in the passenger observation areas to obtain the noise contribution of each noise source area to all passenger observation areas; then, sequencing the noise source regions; and 3) establishing an optimal noise reduction optimization decomposition equation, and calculating an optimal noise reduction quantity value of each noise source region according to the total noise reduction index. The method can provide specific noise reduction values of all the cabin walls through an operable test calculation process, and provides a basis for the quantitative noise reduction design of the high-speed railway cabin.
Description
Technical Field
The invention relates to the field of noise reduction processing, in particular to a noise reduction index decomposition method based on the recognition of a noise source in a high-speed rail cabin.
Background
In recent years, along with the development of high-speed rail locomotive development and high-speed rail operation in China, high-speed rails become the main long-distance transportation mode for passengers in China, meanwhile, the high-speed rails are also continuously moving to the world, and the high-speed rail cabin is required to have low noise and also be capable of normally communicating and making calls, so that urgent needs are brought to the design of vibration reduction and noise reduction in the high-speed rail cabin.
Disclosure of Invention
The invention aims to meet the requirement of vibration and noise reduction design in a high-speed rail cabin, and provides a noise reduction index decomposition method based on the identification of a noise source transmission path of a wall plate in the high-speed rail cabin. In a high-speed rail closed cabin, noise usually comes from the noise transmitted by a wall plate caused by wall plate vibration or noise outside the cabin, the contribution of each wall plate to the cabin noise can be analyzed by analyzing the sound pressure and the surface air particle vibration speed of each area of the cabin wall, and then noise index decomposition design work is carried out by combining the noise contribution of each wall plate, so that a simple and easily-realized quantitative design method is provided for noise reduction of the high-speed rail cabin.
In order to achieve the above object, the present invention provides a method for decomposing a noise reduction index for identifying a noise source in a high-speed rail cabin, the method comprising:
step 1) dividing a high-speed rail bulkhead into a plurality of noise source regions, setting a plurality of source sound pressure and air particle vibration velocity test points on each region, partitioning a carriage into a plurality of passenger observation regions, and setting a plurality of observation points in each passenger observation region;
step 2) identifying and testing noise source areas in the high-speed rail cabin in the passenger observation areas to obtain the noise contribution of each noise source area to all passenger observation areas; then, sequencing the noise source regions;
and 3) establishing an optimal noise reduction optimization decomposition equation, and calculating an optimal noise reduction quantity value of each noise source region according to the total noise reduction index.
As an improvement of the above method, the step 1) is specifically:
step 1-1) the noise source area of the high-speed railway bulkhead comprises a top plate area, a side wall area, a compartment floor area and a front and rear partition door area; the top plate area is divided into a pantograph area and a non-pantograph area; the side wall area is divided into a window area and a non-window area; a plurality of sound pressure and air particle vibration speed test points are arranged in each noise source area;
step 1-2) dividing a passenger observation area into a carriage passenger middle area and a carriage passenger front and rear area; a plurality of observation points are provided on each passenger observation area.
As an improvement of the above method, the step 2) specifically includes:
step 2-1) placing a volume sound source at each observation point in the high-speed rail cabin for sounding, and synchronously measuring the surface air particle vibration velocity u at the first test point of the kth noise source area by using a reference signalk_lAnd sound pressure pk_l；
Step 2-2) obtaining the transfer function from each test point to each observation point by adopting a reciprocity method
And
wherein the content of the first and second substances,
to represent
To represent
k denotes the number of the noise source region, l denotes the number of the test point in the k-th noise source region, and i denotes the observationNumber of area, j denotes the number of observation point in the ith objective observation area, Qi_jRepresenting a volumetric sound source size for a jth viewpoint within an ith observation region;
step 2-3) calculating the noise of the noise source area of the high-speed rail bulkhead; obtaining weighted average noise contribution of each noise source region to all passenger observation regions; thereby ordering the noise source regions.
As an improvement of the above method, the step 2-3) specifically includes:
step 2-3-1) calculating the noise p generated by the k noise source region at the j observation point of the i observation regionk_i_jComprises the following steps:
wherein k is 1,2, … Nk，NkThe number of noise source regions;
total number of test points, Δ s, for the k-th noise source regionk_lThe area occupied by the l test point of the k noise source region;
the total noise of all noise source regions to the jth observation point of the i observation regions is calculated:
step 2-3-2) average contribution p of k-th noise source region with respect to all observation points of i-th observation regionk_iComprises the following steps:
wherein the content of the first and second substances,
the total number of observation points for the ith passenger observation area,<>representation space flatteningAll the above steps are carried out;
the average weighted contribution of the kth noise source region to all passenger observation regions is:
wherein N isiTotal number of observation point regions, wiFor the weighting coefficient of the ith observation region, the total weight should satisfy
Step 2-3-3) contributes to each noise source area<pkSorting is carried out to obtain the noise contribution of each noise source region.
As an improvement of the above method, the step 3) specifically includes:
step 3-1) obtaining noise reduction generalized cost weighted value c by applying analytic hierarchy processk；
Step 3-2) establishing an optimized noise reduction objective function:
the constraint is to make the overall region weighted average noise Δ dBtAnd (4) reaching the standard:
ΔdBt≤ΔdBr
wherein, Delta dBkFor optimizing variables, the noise reduction quantity of the kth noise source region is expressed, and is more than or equal to 0 and less than or equal to delta dBk≤dk，dkThe maximum vibration and noise reduction quantity expressed by decibel number is the kth noise source region, gamma is a constant greater than 0, and the noise reduction difficulty cost and the noise reduction decibel number form an exponential relationship; delta dBrA total weighted average noise reduction representing the desired total passenger viewing area obtained by subtracting the current noise level from the desired noise level;
and 3-3) according to the optimized noise reduction objective function established in the step 3-2), iteratively calculating to obtain the optimal noise reduction quantity value of the kth noise source area expressed by decibels.
As an improvement of the above method, when the noise of a certain noise source region is made to meet the requirement, the constraint conditions of the noise reduction optimization equation of step 3-2) are:
weighted average noise Δ dB for a passenger viewing areat_iAnd (4) reaching the standard:
ΔdBt_i≤ΔdBr_i
wherein, Delta dBr_iThe amount of noise reduction required for the total average of the passenger viewing area is obtained by subtracting the current noise level and the required noise level for the viewing area.
The invention has the advantages that: specific values of the noise reduction of each bulkhead can be provided through an operable test calculation process, and a basis is provided for the quantitative noise reduction design of the high-speed railway cabin.
Drawings
Fig. 1 is a flowchart of a noise reduction index decomposition method based on the recognition of a noise source in a high-speed railway cabin according to the present invention.
Detailed Description
The present invention will be described in detail with reference to the accompanying drawings.
Aiming at the requirement of noise reduction in a high-speed rail cabin, the invention provides a noise reduction index decomposition method based on the recognition of a noise source transmission path of a wallboard in the cabin, and the noise reduction quantization design of the cabin is realized.
According to the principle of acoustic reciprocity, the formula for calculating the noise of an observation point is as follows:
by applying the principle of reciprocity,
and
in order to place a volume sound source at an observation point, the transfer function of the bulkhead sound pressure and the air molecule vibration velocity to the volume sound source is measured, and u 'and p' are the actual working condition bulkhead surface vibration velocity and sound pressure, and can be measured by a pu probe.
Partitioning the bulkhead, such as: the roof can be divided there are pantograph district and non-pantograph district, and the lateral wall divides window district and non-window district etc. also can the subregion to the passenger observation point, if: the passenger intermediate zone, the passenger fore-and-aft zone. And for the source region, according to a discretization principle, two three measuring points are arranged according to each wavelength of sound waves, and a plurality of measuring points are arranged in each region. Measuring the transfer function from source area to observation point, and transferring the transfer function from each source partition point to each passenger observation point
And
the discretization is expressed as
And
to represent
To represent
The superscript k _ l, where k denotes the source partition number, l denotes the measurement point number in the source region of k, and the subscript i _ j, where i denotes the receiverPosition partition number, j, indicates the number of stations in the i receiving area.
The sound pressure contribution of the kth source region to the jth point of the ith observation region is discretized by a calculation formula:
Δsk_lthe areas near the measuring points divided for the source areas are added to form the total source area,
the total number of probes for the kth source partition.
Computing
Much larger than the air impedance, indicating that the surface is very hard, neglecting the influence of the surface sound pressure, the formula can also be simplified as:
and simultaneously calculating the total noise of all wall noise source regions to the j observation point of the i observation regions:
the value calculated by the above formula can be compared with the sound pressure measured by the jth observation point of the ith observation area to analyze whether the source is lost or not or other test analysis error problems exist.
As shown in fig. 1, the present invention provides a noise reduction index decomposition method based on the recognition of a noise source in a high-speed railway cabin, which specifically includes:
1. testing and analysis of in-cabin noise identification
1.1 method for testing noise transfer function in cabin
The test of the cabin wall to the transfer function of the observation area is carried out by adopting a reciprocal method, namely, a volume sound source is placed at each observation point to sound,testing the sound pressure and air particle velocity near the wall surface of each region to obtain the transfer function from each source point to observation point
And
1.2 bulkhead noise and particle velocity test method
The high-speed rail runs according to the actual working condition, sound pressure and air particle vibration test points are arranged in each area, the corresponding parameter values of each area are measured, and when the surface impedance is higher, a simplified calculation formula for neglecting the sound pressure can be adopted, so that the vibration speed of the surface air particles can be measured.
And for the test area which is large, all sensors may not be laid at one time, the test is carried out in blocks, and a reference signal synchronization-based method is adopted. Assuming that a region has a reference signal and the signal is tested in the subsequent block test, the reference signal first tests the frequency domain signal as
Thereafter, each test signal can obtain the synchronization signal by using the following formula:
yifor the ith time of the test frequency domain signal,
for the ith test of the frequency-domain reference signal, yi' is a synchronized signal, which can be sound pressure or surface particle velocity.
1.3 method for analyzing and calculating noise multipoint contribution quantity in cabin
Average contribution p of k-th noise source region relative to all observation points of i-th observation regionk_iComprises the following steps:
wherein, in the step (A),
the total number of observation points for the i observation region,<>representing spatial averaging.
The average weighted contribution of the kth noise source region to all observation regions is:
wherein N isiTotal number of observation point regions, wiFor the weighting coefficient of the ith observation region, the total weight should satisfy
Can be analyzed by the above formula<pk_i＞，<pk>(i＝1...Ni，k＝1...Nk,NiAnd NkRepresenting the observation region and the total number of source regions, respectively), to derive a weighted noise contribution of each source region to the i observation region or all observation regions. Therefore, the noise source contributions of the wall plates are sequenced, and a reference is provided for vibration reduction and noise reduction of the wall plates.
2. Noise index decomposition method
2.1 regional noise reduction results in total noise reduction calculations
Let a certain region reduce noise by delta dBk(k＝1...Nk) Assuming that the region variation does not have too great an influence on the transfer function, the contribution of each source region to the observation point conforms to the energy addition, and practice proves that the approximation is feasible for high-frequency higher than 200Hz, such as high-speed rail. The formula for calculating the noise reduction value is as follows:
(1) a certain observation area
(2) Integral average noise reduction
2.2 noise indicator decomposition
Noise reduction generally involves a cost problem, namely, the most effective method is adopted to obtain noise reduction effect, the noise contribution of each bulkhead is reduced by processing each bulkhead of a high-speed rail, and the noise reduction processing cost is different in different areas due to different area contributions. Therefore, the noise reduction cost weighted value c of each bulkhead source area is analyzed by an analytic hierarchy processkThe cost may be a generalized cost value that takes into account noise reduction feasibility, periodicity, and cost.
And finally, establishing the following optimization equation and solving the noise reduction value.
Optimizing an equation objective function:
ΔdBkfor optimizing variables, the noise reduction quantity of the kth noise source region is expressed, and is more than or equal to 0 and less than or equal to delta dBk≤dk，dkThe maximum vibration and noise reduction quantity expressed by decibel number for a single wallboard, gamma is a constant larger than 0, and the index relation between the noise reduction difficulty cost and the noise reduction decibel number can be generally 1.
The constraint conditions are as follows:
(1) reducing noise in a certain area only to make it meet the standard
ΔdBt_i≤ΔdBr_i
Or
(2) Qualifying global area weighted average noise
ΔdBt≤ΔdBr
ΔdBt_iAnd Δ dBtThe calculation method is shown in 2.1, delta dBr_iAnd Δ dBrThe amount of noise reduction required for a certain observation area and the total average can be obtained by subtracting the current noise level and the required noise level.
3. Iterative design
After the first test and design are finished, the acoustic material can be laid on a real vehicle, the test and the analysis are carried out again, whether the noise reaches the standard or not is compared, the next cycle design is carried out, and the proposed design flow chart is as follows.
The method can be carried out for each frequency band in sequence and can also be carried out for the total noise, and the method does not depart from the essential requirements of the method.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention and are not limited. Although the present invention has been described in detail with reference to the embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (6)
1. A noise reduction index decomposition method based on noise source identification in a high-speed rail cabin comprises the following steps:
step 1) dividing a high-speed rail bulkhead into a plurality of noise source regions, setting a plurality of source sound pressure and air particle vibration velocity test points on each region, partitioning a carriage into a plurality of passenger observation regions, and setting a plurality of observation points in each passenger observation region;
step 2) identifying and testing noise source areas in the high-speed rail cabin in the passenger observation areas to obtain the noise contribution of each noise source area to all passenger observation areas; then, sequencing the noise source regions;
and 3) establishing an optimal noise reduction optimization decomposition equation, and calculating an optimal noise reduction quantity value of each noise source region according to the total noise reduction index.
2. The method for decomposing the noise reduction index for identifying the noise source in the high-speed rail cabin according to claim 1, wherein the step 1) specifically comprises:
step 1-1) the noise source area of the high-speed railway bulkhead comprises a top plate area, a side wall area, a compartment floor area and a front and rear partition door area; the top plate area is divided into a pantograph area and a non-pantograph area; the side wall area is divided into a window area and a non-window area; a plurality of sound pressure and air particle vibration speed test points are arranged in each noise source area;
step 1-2) dividing a passenger observation area into a carriage passenger middle area and a carriage passenger front and rear area; a plurality of observation points are provided on each passenger observation area.
3. The method for decomposing the noise reduction index for identifying the noise source in the high-speed rail cabin according to claim 2, wherein the step 2) specifically comprises:
step 2-1) placing a volume sound source at each observation point in the high-speed rail cabin for sounding, and synchronously measuring the surface air particle vibration velocity u at the first test point of the kth noise source area by using a reference signalk_lAnd sound pressure pk_l；
Step 2-2) obtaining the transfer function from each test point to each observation point by adopting a reciprocity method
And
wherein the content of the first and second substances,
to represent
To represent
k denotes the number of the noise source region, l denotes the number of the test point in the k-th noise source region, i denotes the number of the observation region, j denotes the number of the observation point in the i-th multiplication objective observation region, Qi_jRepresenting a volumetric sound source size for a jth viewpoint within an ith observation region;
step 2-3) calculating the noise of the noise source area of the high-speed rail bulkhead; obtaining weighted average noise contribution of each noise source region to all passenger observation regions; thereby ordering the noise source regions.
4. The method for decomposing the noise reduction index based on the identification of the noise source in the high-speed railway according to claim 3, wherein the step 2-3) specifically comprises the following steps:
step 2-3-1) calculating the noise p generated by the k noise source region at the j observation point of the i observation regionk_i_jComprises the following steps:
wherein k is 1,2, … Nk，NkThe number of noise source regions;
total number of test points, Δ s, for the k-th noise source regionk_lThe area occupied by the l test point of the k noise source region;
the total noise of all noise source regions to the jth observation point of the i observation regions is calculated:
step 2-3-2) average contribution p of k-th noise source region with respect to all observation points of i-th observation regionk_iComprises the following steps:
wherein the content of the first and second substances,
the total number of observation points for the ith passenger observation area,<>representing spatial averaging;
the average weighted contribution of the kth noise source region to all passenger observation regions is:
wherein N isiTotal number of observation point regions, wiFor the weighting coefficient of the ith observation region, the total weight should satisfy
Step 2-3-3) contributes to each noise source area<pk>And sequencing to obtain the noise contribution of each noise source region.
5. The method for optimizing and decomposing the noise reduction index for identifying the noise source in the high-speed rail cabin according to claim 4, wherein the step 3) specifically comprises the following steps:
step 3-1) obtaining noise reduction generalized cost weighted value c by applying analytic hierarchy processk；
Step 3-2) establishing an optimized noise reduction objective function:
the constraint is to make the overall region weighted average noise Δ dBtAnd (4) reaching the standard:
ΔdBt≤ΔdBr
wherein, Delta dBkFor optimizing variables, the noise reduction quantity of the kth noise source region is expressed, and is more than or equal to 0 and less than or equal to delta dBk≤dk，dkThe maximum vibration and noise reduction quantity expressed by decibel number is the kth noise source region, gamma is a constant greater than 0, and the noise reduction difficulty cost and the noise reduction decibel number form an exponential relationship; delta dBrRepresenting the total weighted average noise reduction required for all passenger viewing zones based on the current noise floorThe sum and the required noise level are obtained by subtracting;
and 3-3) according to the optimized noise reduction objective function established in the step 3-2), iteratively calculating to obtain the optimal noise reduction quantity value of the kth noise source area expressed by decibels.
6. The method for decomposing noise reduction indexes for noise source identification in a high-speed rail cabin according to claim 5, wherein when the noise in a certain noise source region is made to meet the requirement, the constraints of the noise reduction optimization equation of step 3-2) are as follows:
weighted average noise Δ dB for a passenger viewing areat_iAnd (4) reaching the standard:
ΔdBt_i≤ΔdBr_i
wherein, Delta dBr_iThe amount of noise reduction required for the total average of the passenger viewing area is obtained by subtracting the current noise level and the required noise level for the viewing area.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810974023.0A CN110895622A (en) | 2018-08-24 | 2018-08-24 | Noise reduction index decomposition method based on noise source identification in high-speed rail cabin |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810974023.0A CN110895622A (en) | 2018-08-24 | 2018-08-24 | Noise reduction index decomposition method based on noise source identification in high-speed rail cabin |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110895622A true CN110895622A (en) | 2020-03-20 |
Family
ID=69785200
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810974023.0A Pending CN110895622A (en) | 2018-08-24 | 2018-08-24 | Noise reduction index decomposition method based on noise source identification in high-speed rail cabin |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110895622A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111899710A (en) * | 2020-07-10 | 2020-11-06 | 广东电网有限责任公司 | Method and device for actively reducing noise of power distribution room based on analytic hierarchy process |
| CN111931291A (en) * | 2020-07-27 | 2020-11-13 | 中国第一汽车股份有限公司 | Noise index decomposition method considering cost |
| CN113012672A (en) * | 2020-12-31 | 2021-06-22 | 江苏南高智能装备创新中心有限公司 | Noise reduction control method and system for multiple noise sources and network side server |
| CN114313100A (en) * | 2021-12-17 | 2022-04-12 | 中国人民解放军海军工程大学 | Bulkhead spacing arrangement method of vibration localization pressure hull |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103175699A (en) * | 2011-12-23 | 2013-06-26 | 南车青岛四方机车车辆股份有限公司 | Noise transmission route testing method for railway vehicle |
| CN103630232A (en) * | 2013-10-29 | 2014-03-12 | 南车青岛四方机车车辆股份有限公司 | Noise source identifying and testing method for high speed train |
| WO2014202286A1 (en) * | 2013-06-21 | 2014-12-24 | Brüel & Kjær Sound & Vibration Measurement A/S | Method of determining noise sound contributions of noise sources of a motorized vehicle |
| US20150110276A1 (en) * | 2012-04-03 | 2015-04-23 | Budapesti Muszaki Es Gazdasagtudomanyi Egyetem | Method and system for source selective real-time monitoring and mapping of environmental noise |
| CN106153185A (en) * | 2016-06-15 | 2016-11-23 | 铁道第三勘察设计院集团有限公司 | Source Spectrum computational methods are divided in high-speed railway noise source |
-
2018
- 2018-08-24 CN CN201810974023.0A patent/CN110895622A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103175699A (en) * | 2011-12-23 | 2013-06-26 | 南车青岛四方机车车辆股份有限公司 | Noise transmission route testing method for railway vehicle |
| US20150110276A1 (en) * | 2012-04-03 | 2015-04-23 | Budapesti Muszaki Es Gazdasagtudomanyi Egyetem | Method and system for source selective real-time monitoring and mapping of environmental noise |
| WO2014202286A1 (en) * | 2013-06-21 | 2014-12-24 | Brüel & Kjær Sound & Vibration Measurement A/S | Method of determining noise sound contributions of noise sources of a motorized vehicle |
| CN105473988A (en) * | 2013-06-21 | 2016-04-06 | 布鲁尔及凯尔声音及振动测量公司 | Method of determining noise sound contributions of noise sources of a motorized vehicle |
| CN103630232A (en) * | 2013-10-29 | 2014-03-12 | 南车青岛四方机车车辆股份有限公司 | Noise source identifying and testing method for high speed train |
| CN106153185A (en) * | 2016-06-15 | 2016-11-23 | 铁道第三勘察设计院集团有限公司 | Source Spectrum computational methods are divided in high-speed railway noise source |
Non-Patent Citations (3)
| Title |
|---|
| 纳跃跃 等: "高速列车透射噪声与结构噪声的分离", 北京交通大学学报 * |
| 胡文林 等: "高速铁路噪声源区划及各区域声源贡献量分析", 铁道标准设计 * |
| 鞠龙华 等: "基于多声源模式的高速铁路噪声预测模型", 同济大学学报(自然科学版) * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111899710A (en) * | 2020-07-10 | 2020-11-06 | 广东电网有限责任公司 | Method and device for actively reducing noise of power distribution room based on analytic hierarchy process |
| CN111931291A (en) * | 2020-07-27 | 2020-11-13 | 中国第一汽车股份有限公司 | Noise index decomposition method considering cost |
| CN111931291B (en) * | 2020-07-27 | 2022-10-28 | 中国第一汽车股份有限公司 | Noise index decomposition method considering cost |
| CN113012672A (en) * | 2020-12-31 | 2021-06-22 | 江苏南高智能装备创新中心有限公司 | Noise reduction control method and system for multiple noise sources and network side server |
| CN114313100A (en) * | 2021-12-17 | 2022-04-12 | 中国人民解放军海军工程大学 | Bulkhead spacing arrangement method of vibration localization pressure hull |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110895622A (en) | Noise reduction index decomposition method based on noise source identification in high-speed rail cabin | |
| CN105836560B (en) | Elevator comfort test system | |
| CN103175699B (en) | Rail vehicle noise transmission approach method of testing | |
| CN101650221B (en) | Method for measuring and analyzing noise in train and system thereof | |
| CN105590003A (en) | Interior noise analysis and prediction method of high speed train | |
| CN105300706A (en) | NVH performance test method for automobile acoustic package | |
| CN105021275A (en) | Time domain dynamic characteristic analysis-based intra-vehicle sound quality prediction method | |
| Zhang et al. | An acoustic design procedure for controlling interior noise of high-speed trains | |
| EP2879423A1 (en) | Radio frequency spectrum management apparatus, method, system and secondary user apparatus | |
| Zhang et al. | Sound source localisation for a high-speed train and its transfer path to interior noise | |
| CN107084851A (en) | Method based on psychoacoustic parameter in statistics Energy Flow Analysis prediction high-speed train | |
| CN107179201A (en) | The recognition methods of bullet train internal noise source and system | |
| CN111521406A (en) | High-speed wind noise separation method for passenger car road test | |
| He et al. | Channel characterization and hybrid modeling for millimeter-wave communications in metro train | |
| CN114363808B (en) | Indoor positioning method based on RSSI ranging | |
| CN115758289B (en) | Rail wave mill identification method based on multitask learning neural network | |
| CN113203464A (en) | Sensor fault detection method of dynamic vehicle-mounted weighing system | |
| CN112019999A (en) | Interference detection and positioning method for rail transit wireless network | |
| CN116090526A (en) | Structural vibration noise inversion method based on embedded physical information neural network | |
| CN109307608B (en) | Method for plugging and repairing train sound leakage part in whole vehicle semi-anechoic chamber environment | |
| CN113515808B (en) | Noise prediction method of power assembly | |
| CN105372080B (en) | A kind of tramcar and its embedded tracks Coupled Dynamics test device and method | |
| CN109307609B (en) | Method for testing acoustic absorption coefficient in passenger room of rail train based on whole-vehicle semi-anechoic chamber | |
| CN110143217B (en) | Track state measuring method, system and device | |
| CN113761798B (en) | Construction method and prediction method of vehicle acoustic sound-wrapping and insulation performance prediction model |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| AD01 | Patent right deemed abandoned |
Effective date of abandoning: 20240312 |
|
| AD01 | Patent right deemed abandoned |