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Research On Active Control System Of Vehicle Noise Caused By Pavement Excitation
5th IFAC Conference on Engine Powertrain 5th IFACand Conference onControl, Simulation and Modeling 5th IFAC Conference on 5th IFACand Conference onControl,20-22, Changchun, China, September 2018 and online Engine Powertrain Simulation Modeling Available at www.sciencedirect.com Engine Powertrain Simulation and Modeling 5th IFACand Conference onControl, Engine and Powertrain Control,20-22, Simulation Changchun, China, September 2018 and Modeling Changchun, China, September 20-22, 2018 Engine and Powertrain Control,20-22, Simulation Changchun, China, September 2018 and Modeling Changchun, China, September 20-22, 2018
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PapersOnLine 51-31 (2018) 467–472 Research On IFAC Active Control System Of Vehicle Noise Caused Research On Active System Of Vehicle Noise Caused Research Control System Of ByControl Pavement Excitation Research On On Active Active Control System Of Vehicle Vehicle Noise Noise Caused Caused By Pavement Excitation Research On Active Control System Of Vehicle Noise Caused By Pavement Excitation By Pavement Excitation Hui LI*, Shuo Zhang* By Pavement Excitation Hui LI*, Shuo Zhang*
Hui LI*, LI*, Shuo Shuo Zhang* Zhang* Hui Engineering, Jilin University * College of Communications Hui LI*, Shuo Zhang* Jilin University Engineering, ** College of Communications China(e-mail:[email protected]) College of Communications Jilin Engineering, * College of Communications Engineering, Jilin University University China(e-mail:[email protected]) *College of electricalChina(e-mail:[email protected]) and Electronic Engineering, Changchun University of * College of Communications Engineering, Jilin University China(e-mail:[email protected]) *College of electrical and Electronic Engineering, Changchun University of Technology,Changchun, Jilin *College and Electronic Changchun China(e-mail:[email protected]) *College of of electrical electrical and Electronic Engineering, Engineering, Changchun University University of of Technology,Changchun, Jilin China(e-mail:[email protected]) Technology,Changchun, Jilin *College of electrical and Electronic Engineering, Changchun University of Technology,Changchun, Jilin China(e-mail:[email protected]) China(e-mail:[email protected]) Technology,Changchun, Jilin China(e-mail:[email protected]) China(e-mail:[email protected]) Abstract:In order to reduce the interior noise caused by road excitation,the finite element analysis Abstract:In order to reduce interior caused road finite element analysis method is used to analyze the the modal of thenoise interior space,by and thenexcitation,the get the frequency response curve of Abstract:In order to the interior noise caused by road excitation,the finite element analysis Abstract:In order to reduce reduce noise caused byand road excitation,the finite element analysis method is used to analyze modal of the interior space, then get frequency response curve of the corresponding nodes inthe thethe carinterior ear.Through the establishment of anthe adaptive neural network noise method is used to analyze the modal of the interior space, and then get the frequency response curve of Abstract:In order to reduce the interior noise caused byand road excitation,the finite response element curve analysis method is used to analyze the modal ofthe thesimulation interior space, then getanthe frequency of the corresponding nodes in the car ear.Through the establishment adaptive neural network noise active control system simulink model, experiment is of carried out.The experimental results the corresponding nodes in the car ear.Through the establishment of an adaptive neural network noise method is used to analyze the modal of the interior space, and thenof getanthe frequency response curve of the corresponding nodes in the car ear.Through the establishment adaptive neural network noise active control system simulink model, the simulation is carried results frequency band has show that the active control system of neural network experiment noise control in the 0out.The to 50 Hzexperimental active control system simulink the simulation experiment is carried out.The experimental the corresponding nodes in themodel, car ear.Through the establishment of an adaptive neural networkresults noise active control system simulink model, the simulation experiment is carried out.The experimental results frequency band has show active control neural network noise control 00 to dBthe . At 8650 , the maximum noise good that noisethe reduction effect system and theof average noise reduction is 4.3in Hz Hz frequency band has show the active system of neural network noise control the to 50 Hz activethat control systemcontrol simulink model, the simulation experiment is in carried experimental results frequency band has show that the active control system ofaverage neural network noise control in 0out.The to 50 Hz dBthe . At 86 , the maximum noise good noise reduction effect and the noise reduction is 4.3 Hzunder dB ,and the system can control the low frequency noise produced the excitation of reduction is 9.8 dBthe . At 86 , the maximum noise good noise reduction effect and the average noise reduction is 4.3 Hz Hz frequency band has show that the active control system of neural network noise control in 0 to 50 dB . At 86 , the maximum noise good noise reduction effect and the average noise reduction is 4.3 Hz dB ,and the system can control the low frequency noise produced under the excitation of reduction is 9.8 the road. ,and the can control the frequency noise excitation of reduction 9.8 dB produced . At 86 Hzunder , the the maximum noise good noiseis and the reduction is 4.3 dB ,andeffect the system system canaverage control noise the low low frequency noise produced under the excitation of reduction is reduction 9.8 dB the road. the road. dB ,and the system can control the low frequency noise produced under the excitation of reduction is 9.8 © 2018, (International Automaticadaptive Control) Hosting by Elsevier All rights reserved. the road.IFAC Keywords: Active control, Federation pavement ofexcitation, algorithm, acousticLtd. modal analysis, neural the road. Keywords: Active Active control, pavement pavement excitation, adaptive adaptive algorithm, acoustic acoustic modal analysis, analysis, neural network. Keywords: Keywords: Active control, control, pavement excitation, excitation, adaptive algorithm, algorithm, acoustic modal modal analysis, neural neural network. network. Keywords: Active control, pavement excitation, adaptive algorithm, acoustic modal analysis, neural network. network. and then provide the basis for the prediction of the car room 1. INTRODUCTION and then provide the basis for the prediction the car room noise. The adaptive neural network noise of active control and then provide the basis for the of the car room 1. INTRODUCTION and then provide the basis for the prediction prediction ofactive theestablished carcontrol room 1. INTRODUCTION noise. The adaptive neural network noise system is set up, and the simulation model is 1. automobile INTRODUCTION The characteristics of vibration and noise are the noise. The adaptive neural network noise active control and then provide the basis for network the prediction ofactive the carcontrol room noise. The adaptive neural noise system is set up, the simulation model established 1. automobile INTRODUCTION through simulink toand realize the active control ofis the noise in The characteristics of vibration and noise are the main factors that affect the comfort of the vehicle. With system is set up, and the simulation model is established noise. The adaptive neural network noise active control The characteristics of automobile vibration and noise are the system is set up, and the simulation model is established The characteristics of vibration and noise aremore the simulink to realize the active the noise in the vehicle. As a static system, neural control networkof noise control main factors that affect the comfort of the vehicle. With the through increasing demand forautomobile automobile comfort, more and through realize active control the noise in system issimulink set up,to the the simulation model of established main factors that affect the comfort of the vehicle. through simulink toand realize the active control ofisnoise the noise in The characteristics of automobile vibration and noiseWith are the main factors that affect the comfort of the vehicle. With the the vehicle. As a static system, neural network control system is difficult to realize the complex and changeable increasing demand for automobile comfort, more and more attention has been paid to the research on vibration noise the vehicle. As a static system, neural network noise control through simulink to realize the active control of the noise in increasing demand for automobile comfort, more and more the vehicle. As a static system, neural network noise control main factors that affect the comfortcomfort, of the vehicle. With the system increasing demand for automobile more and more difficult to complex and changeable workingis condition of realize vehicle.the Therefore, adding adaptive attention has paid to the research on vibration and reduction of been automobiles(see Wei et al.(2009)). One ofnoise the system is difficult to realize the complex and changeable the vehicle. As a static system, neural network noise control attention has been paid to the research on vibration and noise system iscondition difficult realize the complex andrequirement, changeable increasing demand for to automobile comfort, more and noise more control attention has been paid the research on vibration working of vehicle. Therefore, adding adaptive module can to not only meet the real-time reduction of automobiles(see Wei al.(2009)). Oneuneven of the sources of noise in the interior ofet the car is the working of vehicle. Therefore, adaptive system iscondition difficult to realize the complex adding and changeable reduction of automobiles(see Wei et al.(2009)). the working condition of vehicle. Therefore, adding adaptive attention has been paid to the research on vibrationOne andof noise reduction of automobiles(see Wei et al.(2009)). One of the control module can not only meet the real-time requirement, but also module improvecan the not effect of meet noise the reduction. sources of noise in the interior of the car is tothe 50uneven surface of the road. For the vehicle noise,30 Hz Hz is a control only real-time requirement, working condition of vehicle. Therefore, adding adaptive sources of noise in the interior of the car is the uneven control module can not only meet the real-time requirement, reductionofofnoise automobiles(see Wei ofet the al.(2009)). Oneuneven of the but also improve the effect of noise reduction. sources in the interior car is the 50 a but surface of the road. For the vehicle Hz to Hz isto. special frequency band which needs noise,30 to be paid attention also improve the effect of noise reduction. control module can not only meet the real-time requirement, to 50 surface of the road. For the vehicle noise,30 Hz Hz is a but also improve the effect of noise reduction. sources of noise in the interior of the car is the uneven to 50 surface of the road. For theofvehicle noise,30 Hz 2. ACOUSTICS PRINCIPLE ANALYSIS OF special frequency band which needs to be paid attention to.a but also The frequency band noise the frequency band is Hza issoimprove the effect of noiseAND reduction. special frequency band needs to be paid attention 50 Hz isto. a surface of the road. Forwhich the vehicle noise,30 Hz to special frequency band which needs to be paid attention to. 2. ACOUSTICS PRINCIPLE AND ANALYSIS OF PAVEMENT NOISE The noise of the frequency is aa socalledfrequency "roar" to band the people. Road roughness asband an important 2. PRINCIPLE AND The frequency band noise of the frequency is sospecial frequency band which needs to be paidband attention to. 2. ACOUSTICS ACOUSTICSPAVEMENT PRINCIPLENOISE AND ANALYSIS ANALYSIS OF OF The frequency band noise of the frequency band is a socalled "roar" to the people. Road roughness as an important source of excitation for vehicle vibration and noise, its main PAVEMENT NOISE 2. ACOUSTICS PRINCIPLE AND ANALYSIS OF called "roar" to the people. Road roughness as an important The frequency band noise of the frequency band is a soPAVEMENT NOISE called "roar" the than people. roughness asnoise, source of excitation for vehicle and its main .Therefore, it an is important of great frequency is toless 300Road Hzvibration 2.1 Acoustics principle source of excitation for vehicle vibration and its main PAVEMENT NOISE called "roar" to the people. Road roughness asnoise, an important source of excitation for vehicle vibration and noise, its main is of great frequency is than Hz .Therefore, significance toless study the300 influence of road it excitation on Acoustics principle .Therefore, it is frequency is than 300 Hzvibration source of excitation for vehicle and noise, its great main 2.1 2.1 Acoustics principle itexcitation is etof of great frequency is toless less than 300 Hz .Therefore, 2.1 Acoustics principle significance study the influence of road on vehicle vibration and noise performance(see Zhang al. Liu Active noise control (Active Noise Control) is the use of significance to study the influence of road excitation on itexcitation is of great frequency is toless thanthe300 Hz .Therefore, significance study influence of road on 2.1 Acoustics principle vehicle vibration and noise performance(see Zhang et al. Liu et al. Dan et al.(2010)).Chen Chang min and Sun Wei of Active noise (Active Noise use of acoustics "interference" principle. A Control) method is of the artificially vehicle vibration and noise performance(see Zhang et al. Liu significance to study the influence of roadZhang excitation on Active noise control control (Active Noise is the use vehicle vibration andstudied noise performance(see etWei al. et al. Dan et al.(2010)).Chen min and Sun of Active noise control (Active NoiseA Control) Control) is the use of of Tong ji University theChang instantaneous value of Liu the acoustics "interference" principle. method of artificially and purposely generating a secondary acoustic signal to et al. Dan et al.(2010)).Chen Chang min and Sun Wei of vehicle vibration and noise performance(see Zhang et al. Liu acoustics "interference" principle. method of artificially et al. ji Dan et al.(2010)).Chen Chang min and Sun Wei of Active noise control (Active NoiseA Control) is the use of Tong University studied the instantaneous value of the acoustics "interference" principle. A method of artificially to 250 in the two vehicle acoustic pressure from 20 Hz Hz and purposely generating a secondary acoustic signal to control the primary acoustic signal within a designated area. Tong ji University studied the instantaneous value of the et al. Dan et al.(2010)).Chen Chang min and Sun Wei of and purposely generating aa secondary acoustic signal to Tong ji on University studied theto instantaneous value of the acoustics "interference" principle. A method of artificially 250 the two vehicle acoustic pressure from 20 and purposely generating secondary acoustic signal to Hz Hz in et speeds the same road.(see Chen al. Sun et control the primary acoustic signal within a designated area. basic principle isacoustic the Yang's interference theory of sound 250 two vehicle acoustic pressure from 20 Hz Hz in the value Tong ji University studied theto instantaneous of the The control the primary signal within a designated area. to 250 in the two vehicle acoustic pressure from 20 Hz Hz and purposely generating a secondary acoustic signal to speeds on the same road.(see Chen et al. Sun et control the primary acoustic signal within a designated area. al.(2008)).The United States Ford Motor Co The basic principle is the Yang's interference theory of sound wave, that is, when the two rows of sound waves with the speeds on the same road.(see et the al. two Sun vehicle acoustic pressure from 20 Hz to 250Chen Hz in et The basic principle is the Yang's interference theory of sound speeds on the same road.(see Chen et al. Sun et control the primary acoustic signal within a designated area. al.(2008)).The United States Ford Motor Co The basic principle is the Yang's interference theory of sound AmiyaRMohanty using theStates finite element method and that is, when the opposite two rows of sound with the same frequency and the phase meet, waves the interference al.(2008)).The Ford speeds on the United same road.(see Chen al. Sun Co et wave, wave, that is, rows of waves with the al.(2008)).The States Ford et ofMotor Motor Co The basic isthe the two Yang's interference theory of sound AmiyaRMohanty using the finite element method wave, thatprinciple is, iswhen when the two rows of sound sound waves with the boundary element United method for the simulation a truck and car same frequency and the opposite phase meet, the interference superposition produced in the space(see MAkhtar et al. W AmiyaRMohanty using the finite element method and al.(2008)).The United States Ford Motor Co same frequency and the opposite phase meet, the interference AmiyaRMohanty using the finite element method and wave, that is, when the opposite two rows of sound waves with the boundary element method for the simulation of a truck car same frequency and the phase meet, the interference interior sound field, the vibration response was calculated superposition is produced in the space(see MAkhtar et al. W Mitsuhanshi et al.(2012)).When there are two coherent boundary element method for the simulation of a truck car AmiyaRMohanty using the element ofmethod superposition is produced in space(see MAkhtar et al. boundary element method for finite the simulation a calculated truck and car same frequency and the opposite phase meet, the interference interior sound the vibration response was superposition is in produced in the the space(see MAkhtar et sound al. W W according to field, the panel acoustic contribution, the Mitsuhanshi et al.(2012)).When there are two coherent S S and the sound field, the two phase dry sources interior sound field, the vibration response was calculated 1 2 boundary element method for the simulation of a truck car Mitsuhanshi al.(2012)).When there are two coherent interior sound field, the vibration response was calculated superposition et is produced in the space(see MAkhtar et al. W according to the panel acoustic contribution, the Mitsuhanshi et al.(2012)).When there are two coherent corresponding position in vehicle with sound-absorbing S1 and sound the two phase dry sound sources according to the panel acoustic contribution, the 2 in interior sound vibration response was calculated signals can beSSexpressed as: field, S1 and in the the field, the sound according to field, thecarthe panel acoustic contribution, the sources Mitsuhanshi two dry coherent corresponding position in with sound-absorbing S1 and Set22 inal.(2012)).When the sound sound field, there the two twoarephase phase dry sound sources material makes the noise isvehicle obviously reduced, but did not corresponding position in vehicle with sound-absorbing signals can be expressed as: according to the panel acoustic contribution, the corresponding position in vehicle with sound-absorbing S S and in the sound field, the two phase dry sound sources signals can be expressed as: 1 2 material makes the car noise is obviously reduced, but did not consider the incentive on the road(see AmiyaR et al. p p cos( t ) signals can be expressed as: material makes the car noise obviously reduced, but did not S 1 1 corresponding position in is vehicle with sound-absorbing material makes the car noise is obviously reduced, but did not consider the incentive on the road(see AmiyaR et al. (1) signals can be expressedppSas: Mohanty etthe al. BarryD et al.(2000)). p cos( t ) consider incentive on road(see AmiyaR et al. cos( pSS pp112cos( tt 1 )) material makes the car noise obviously reduced, but did consider the incentive on isthe the road(see AmiyaR et not al. pS p1 cos(t 112) (1) Mohanty et al. BarryD et al.(2000)). p p cos( t ) (1) Mohanty et al. BarryD et al.(2000)). consider incentive on noise the road(see AmiyaR et al. cos( tt 2)) (1) pSS p122cos( Mohanty etthe al. BarryD al.(2000)). You In this paper, theetroad is used as the source of pS p2 cos(t 122 ) (1) Mohanty etand al. BarryD etroad al.(2000)). You In this paper, the noise is used as the source of excitation the finite element analysis method is used to pS p2 cos(t 2 ) You In paper, the road noise is as source of You In this this paper, the road noise is used usedmethod as the theof source of excitation and the finite element analysis is used to analyze the acoustic modal of the interior space the car, excitation and the element used to You In this paper, the road noiseanalysis is usedmethod as the is source of excitation and the finite finite element analysis method is usedcar, to analyze the acoustic modal of the interior space of the analyze the acoustic modal of the interior space of the car, excitation and the finite element analysis method is used to analyze the acoustic modal of the interior space of the car, analyze the acoustic modal of the interior space of the car, Copyright © 2018 IFAC 506 1
1 12 1 2 12 2 2
2405-8963 © 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. Copyright 2018 responsibility IFAC 506Control. Peer review© of International Federation of Automatic Copyright ©under 2018 IFAC 506 Copyright © 2018 IFAC 506 10.1016/j.ifacol.2018.10.104 Copyright © 2018 IFAC 506
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is a point in the sound field, the point N distance S1 is r1 , and the distance S 2 is r2 ,so the total sound potential energy density at N point is as follows: N
E= p
x g 2 f0 xg 2 n0 Gq (n0 )v0 w
p2 (1 cos ) 2 2c2 p12 p22 2 p1 p2 cos
2 1 2
(2)
r2 r1
is the wavelength; is the density of sound propagation medium; c is the sound speed. When tends to the odd number of , E is close to 0, At this time, the noise of N point has been greatly reduced.
describe its statistical characteristics(MA et al.VA et al(2007)).A large number of practical measurements show that the power spectrum of the pavement displacement can be made up of a standard form. GB7031-86 recommends the use of the power spectral density of the pavement displacement. w is the spatial frequency; n0 is The n in G（n）= G（n q q 0）(n/n 0 )
Table 1.The pavement constant related to the pavement grade of a Pavement grade
the reference spatial frequency( n0 =0.1m1 ); Gq (n) is the displacement spectrum density; Gq (n0 ) is the pavement
a(1/ m)
spectrum value under the reference spatial frequency n0 ,it is called the road roughness coefficient; W is the fitting index, which determines the frequency structure of the pavement spectrum(He Xuefeng et al. Yan et al.(2000)).GB7031-86 divides the road surface into 8 levels according to the road roughness coefficient Gq (n0 ) , and the fitting index is W =2 .
A
0.132
B
C
D
E
0.1303
0.12
0.1007
0.09
3.ANALYSIS OF ACTIVE NOISE REDUCTION CONTROL SYSTEM Before determining the parameters of adaptive filter, we first need to clarify the principle of adaptive noise active control system, so we establish the principle of adaptive noise active control as shown in Fig.1.
Therefore, considering the influence of vehicle speed, the spatial spectral density can be transformed into the time spectrum density Gq ( f ) . The results are as follows:
output signal
p ( n) H 2 ( z)
d ( n)
+
(3)
e( n )
Where the f is the time frequency.
H1 ( z )
(4) (5)
From the formula (4), it can be seen that the power spectrum of the road surface input speed is a constant over the entire frequency range, which is a "white noise". So the method of generating random road roughness profile is made of white noise through a filter(Chen et al.(2013)).so the wheel received road excitation can use differential equation representation, the following is as follows:
x ( n)
Adaptive filter
y ( n)
|
Gq ( f ) (2 f )4 Gq ( f ) 16 4n02Gq (n0 ) f 2v
(7)
In the formula, qi (t ) is the time sample of the road incentive process in i , u is the speed of the car （m/s）. i (t ) is a random signal with zero mean white noise, and its coequation must satisfy E[i (t )i ( )] 2au 2 (t ) . is the road surface roughness constant; (t ) is the Dirac generalized function. a is the road constant (1/ m) related to the pavement grade The specific parameters corresponding to the pavement grade are shown in Table 1：
The change in the height of the road to the height of the base plane q along the road to the length of I is usually called the q i）.As the road surface level pavement unevenness function （ of vehicle vibration input, the statistical characteristics of the pavement power spectral density Gq (n) are mainly used to
Gq ( f ) (2 f )2 Gq ( f ) 4 2 n02Gq (n0 )v
Where, the x g is the vertical displacement of the pavement; the f 0 is the lower cut-off frequency of the road input, and the w is the white noise with a mean of 0 of the power of 20 dB . White noise is a random signal or random process with constant power spectrum density. White noise can be changed to a certain system by specific system. The road excitation noise is a typical band limited noise. When driving at i , the road excitation is qi (t ) ,then a single point excitation mathematical model with white noise as input is established (Yin et al. Chen et al. Wu et al.(2017))： qi (t ) auqi (t ) i (t )
2.2 Road Roughness Excitation Noise
Gq ( f ) n02Gq (n0 )v / f 2
(6)
LMS
Fig.1. Principle diagram of adaptive noise active control As shown in the diagram, p(n) is the output signal of the neural network subsystem, H 2 ( z) is the primary acoustic channel transfer function, and H1 ( z ) is the acoustic channel transfer function of primary signal to error sensor. d (n) is the signal obtained from the output signal p(n) through the
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469
in the modeling aspect respectively deal with all the mesh, then assembled into a white body grid structure model(Bai et al.(2013)).Due to the complexity of vehicle body structure, the establishment of finite element model is very heavy. Therefore, the body structure is simplified to some extent under the condition of ensuring the accuracy of calculation results.
primary acoustic channel transfer function, y(n) is the output signal of the adaptive filter, and e(n) is the error signal after the noise elimination of the active control system. The determination of the parameters of the adaptive filtering noise control system includes primary and secondary acoustic channel transfer functions H1 ( z ) and H 2 ( z) ,the filter order M and the convergence factor .
The body model is a complex space grid plate and shell structure. As shown in the figure, the structure mode of the body is shown in Fig.2.
The acoustic channel transfer function has been identified by parameter identification in the Adaptive IIR Fileter-v Algorithms for ANC in the paper of David H.C and Robert W.S(David H.C et al. Robert W.S et al.(1997)).among: H 2 ( z )=0.05-0.001z 1 0.001z 2 0.8 z 3 0.6 z 4 0.2 z 5 0.5 z 6 0.8 z 7 0.4 z 8 0.005z 9 H1 ( z )=0.05-0.01z 1 0.95 z 2 0.01z 3 0.9 z 4
(8)
The two parameters that affect the performance of the adaptive filter are the filter order and the convergence factor. The higher the order of the filter, the longer the delay introduced in the digital filter system, and the greater the possibility of steady-state misalignment, the smaller the range of the convergence factor and the more complex the calculation. But not blindly reduce the order, because of the order is too low can not fully reflect the correlation matrix. The greater the convergence rate is, the faster the convergence rate. and the greater the imbalance of the steady state. The smaller the convergence factor and the slower the convergence rate(Kuraya et al. Nishimura et al. Usagawa et al. Ebata et al. Okda et al.(1988)).The convergence factor =0.005 is selected through experimental simulation, and the filter order is suitable for selecting M =16 . 4. ACOUSTIC MODAL ANALYSIS IN CAR CHARMBER
The 3D model is built by CATIA, and the grid is imported into the Hypermesh format in IGES format. Then, the grid is imported into the LMS Virtual Lab by BDF format, and the modal analysis is done. When dividing meshes in Hypermesh, in order to ensure the reliability of acoustic mode simulation, cell size must be at least six acoustic units in every 1 1 c tmax min 6 6 f max
Acoustic modal analysis of the acoustic modality in the vehicle cavity is performed to obtain its mode shape and modal frequencies. In general, the longer the cavity, the lower the frequency. According to relevant literature(Deng et al. Chen et al.(2013)).Under normal driving conditions on the road to the car is not greater than 150km/h speed for the car, road excitation frequency is less than 21 Hz , and because the wheel imbalance caused by the incentive is usually below 11 Hz , the above frequency is far lower than the lowest order where the tune is not equal to zero modal frequency, namely 82.76 Hz , so the vehicle interior resonance excitation not within the scope of consideration cannot cause. This paper uses LMS Virtual Lab to the tune of finite element model for the acoustic modal analysis, As shown in Fig 3-8.
4.1 Body Modeling And Acoustic Modal Analysis Of Chamber Cavity
wavelength range. In
Fig.2. the finite element model of the body structure
, in the formula, tmax
is the maximum unit length; min is the least sound wave wavelength; c is sound speed, ( c=340m / s ); f max is the maximum frequency. Considering the frequency range of acoustic simulation modal simulation for 0~300 Hz , combining with the car room size acoustic model size and simulation reliability requirements set model unit size is 60mm. The network model is shown by Hypermesh partition. White body as the basis of the vehicle model and the bearing body is made up of hundreds of pieces of sheet metal complex system, main nacelle assembly and a trunk assembly, body floor assembly, side wall assembly and cover assembly, 508
As shown in the figure, the first order modal frequency is 3.959e-6 Hz and the acoustic mode is to the front window as the center of the diffusion distribution, second order modal frequency is 79.947 Hz , the pitch line position is located in the center of the car, the car rear cavity pressure large, abdominal pressure located inside the vehicle rear. The third order modal frequency is 129.455 Hz , and the sound pressure increases from the left rear side to the right rear side home, and reaches the maximum in the right rear part of the car cavity, which is the sound pressure abdomen area. The fifth order modal frequency is 157.907 Hz , and the longitudinal line is located at the front row position. The transverse section line is located in the transverse symmetry plane, and the two sections are perpendicular to each other. Under this mode, the driver and front row crew environment is better. The Tenth - order acoustic modal frequency is 202.637 Hz , and the sound pressure varies laterally. The two sections are located in the front windshield, along the rear windshield, and the maximum sound pressure is located under the rear windshield. The fifteenth order modal frequency is 266.626 Hz , and the driver and the front row occupant are in
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the abdominal space of sound pressure, and the rear occupants are near the sound pressure line.
5. MODELING AND SIMULATION ANALYSIS OF ROAD SURFACE EXCITATION 5.1 Modeling and simulation analysis of road surface excitation For unstable random vibration, the power spectral density function is used to describe the vibration in the frequency domain. Therefore, when studying the vibration of a car driving on an uneven road surface, the frequency domain method is used to describe the degree of unevenness of the road surface. That is, a mathematical model of the power spectrum density of road irregularities with respect to time is established. It has the following two characteristics: (1)the roughness model of the pavement varies with the road grade and the speed of the car. (2)The rear wheel unevenness model has the effect of delay in the model relative to the front wheel unevenness. In the vehicle modeling analysis, not only need to consider the road input wheelbase lag, but also consider the degree of correlation between left and right wheels.
Fig.3-8. acoustic modal analysis in a vehicle cavity
The model of wheel single wheel withdrawal is as follows: 4.2 Frequency Response Analysis Of Sound Pressure Near The Ear
au q1 au 2 / 2 q2
Put the car in the head cavity grid four sites were selected for drivers and passengers head on both sides of a total of four nodes, respectively, the driver's left ear, right ear of the driver and passengers and crew left ear, and the freedom degree is set to S, after completion the sound pressure frequency response function at the field point (human ear) can be calculated. Finally, you can see the sound pressure at the four nodes. As shown in Fig 9.
0 q1 (t ) 2 / 2 q2 (t )
(9)
In the formula 2 =1/ u ; q is the wheelbase of the front and rear wheels of the car. It has been proved that there is a certain spatial correlation between the trajectory of the left and right wheels. It is assumed that the 1 and 2 wheel model is derived from the white noise x according to the previous method, and the other round of the input white noise is y , and the two wheel correlation equation of state can be derived. b1 x1 b 2 x2 1
y a1 a2
Fig.9. sound pressure at four nodes
b1 b2
b0 1 x b2 1 b2 x x 0 2 0
a0 a2
b0 x1 a2 x b2 x2 b2
(10)
(11)
In which a0、a1、a2、b0、b1、b2 is constant, according to the coherent function and processing method cited in the literature, x1、x2 are intermediate state variables; Combined with (9.10.11), the simulation model of the time history of Simulink four wheel pavement excitation is set up, as shown in Fig 10.
Four from the end point by X and Z on the left, and the law in the Y coordinates of the symmetrical arrangement, the frequency response curve is similar. The scope of this unit the applied excitation frequency is 0~190 Hz ,the maximum sound pressure at 130 Hz is 136 dB . It shows that the body's vibration energy is the highest at this frequency, and the driver and occupant's ear excitation is the largest. The next few frequency response peaks are concentrated at several points near 190 Hz . The ear noise caused by these peak frequencies is more prominent. It affects the ride comfort and should be the main subject of research in the simulation.
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Fig.10. simulation model of time history of four wheel pavement excitation Where: b1 A3= b2 1
a A6= 2 b2
b0 b2 0
au A2 A8 au 2 2
,
1 A4= b2 0
,
0 2 2
Fig.13. adaptive neural network noise active control system The parameters needed in the system are input into each module and subsystem, and the frequency response contrast curve before and after control is shown as shown in Figure 14.
，
75
，
b A5= a1 a2 1 b 2
b a0 a2 0 b2
Before Eliminating Noise After Eliminating Noise
，
70
Sound Pressure Level（dB）
1 A1 A7 1
471
.
65
60
55
The simulation results are shown in Fig.11-12.
50 0
0.03
10
20
30
40
50 Frequency（Hz）
60
70
80
90
100
Fig.14. frequency response contrast curve before and after control
left-front wheel left-rear wheel
0.02
qx(n) /m
0.01
The noise control effect can be seen from the graph. The noise reduction characteristics of the active noise control system in the 0 to 100 Hz frequency band can be divided into 0 to 50 Hz and 50 to 100 Hz two stages. 0 to 50 Hz neural network noise active control system has good noise reduction effect and the average noise reduction is 4.3 dB . The effect of noise reduction in the 50 to 100 Hz frequency band is preferably, and the maximum noise reduction is 9.8 dB at 86 Hz . About 40 Hz and 80 Hz , the noise level corresponding to the first and two order disturbance frequencies of the engine is relatively large. From the simulation curve, it can be judged that the adaptive neural network active control system can well control the low-frequency noise generated by the road excitation.
0
-0.01
-0.02
-0.03 0
2
4
6
8
10 t/s
12
14
16
18
20
Fig.11. simulation result diagram a 0.03
right-front wheel right-rear wheel
0.02
qx(n) /m
0.01
0
-0.01
-0.02
-0.03 0
2
4
6
8
10 t/s
12
14
16
18
20
Fig.12. simulation result diagram b
6. CONCLUSIONS
Compared with the diagram and graph, it can be found that the corresponding characteristics of the front wheel and rear wheel are almost the same under road excitation, but the response of rear wheel is delayed for the front wheel, and the
In this paper, based on the characteristics of high sound pressure level and prominent frequency characteristics of a domestic vehicle. Combined with finite element analysis method and simulation modeling analysis method, adaptive noise active control system is adopted, and on this basis, neural network is used to improve noise active control system .The noise active control system was verified by simulation analysis. Simulation analysis shows that the noise active control system can greatly improve the noise environment inside the vehicle, But the control system also has the insufficiency, system operation in 70 to 80 Hz bands in a short period of silence after rather than before the sound pressure level greater noise, increased the noise inside the car. I will be in the follow-up work on the noise reduction system will compensate for this lack of improvement.
L v
delay time is = .
5.2 Simulation and analysis of active noise reduction An adaptive neural network noise active control system is set up based on the principle of adaptive neural network noise reduction, As shown in Figure 13.
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REFERENCES Akhtar, M., & Mitsuhashi, W. (2012). On family of fractional lower order moment (flom)-based algorithms for active noise control of impulsive noise sources. Journal of the Acoustical Society of America, 131(4), 3435-3435. Changming,C. and Wei,S.(2008).Simulation and analysis of interior noise caused by road excitation. vibration and shock. 27 (8): 102-105. David H,C. and RobertW,S.(1997). Adaptive IIR Fileter-v Algorithms for ANC.Journal of the Acoustical Society of America.101(4):2097-2103. Deng, S., & Chen, J. (2013). A model to describe the dynamic influence length of the vehicles on the main road at the upstream sections of off-street parking access. Advances in Mechanical Engineering,2013,(2013-12-29), 2013(4), 1-10. Dongyun,C.(2013).based on vehicle vibration comfort method to calculate the energy balance study. Zhejiang University of Technology. Garciaromeumartinez, M. A., Rouillard, V., & Cloquellballester, V. A. (2007). A model for the statistical distribution of road vehicle vibrations. Lecture Notes in Engineering & Computer Science, 2166(1), 451–467. Jie,B.(2013). based on sound solid coupling in vehicle noise analysis.Wuhan University of Technology. Kuraya, S., Nishimura, Y., Usagawa, T., Ebata, M., & Okda, J. (1988). Application of a digital filter to active noise control in a duct. Journal of the Acoustical Society of America, 84(1), S182-S182. Mohanty, A. R., Pierre, B. D. S., & Suruli-Narayanasami, P. (2000). Structure-borne noise reduction in a truck cab interior using numerical techniques. Applied Acoustics, 59(1), 1-17. Wei,L.(2012). Research on comprehensive evaluation of riding comfort. Chang'an University. Wei,Z.,Xiandong,L., and Yingchun D.(2010). Simulation of noise in vehicle caused by road surface excitation based on sound transfer vector method. 2010 International Symposium on vehicle NVH control technology. XueFeng,H. and Yongfu,Y.(2000). mathematical model and computer simulation study. the roughness of Tang Guangwu.13(1):114-117. Yin,J.,Chen,J. and Liu,Y.(2017). Simulation Method of Road Excitation in Time Domain Using Filtered White Noise and Dynamic Analysis of Suspension. Journal of Tongji University.
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PapersOnLine 51-31 (2018) 467–472 Research On IFAC Active Control System Of Vehicle Noise Caused Research On Active System Of Vehicle Noise Caused Research Control System Of ByControl Pavement Excitation Research On On Active Active Control System Of Vehicle Vehicle Noise Noise Caused Caused By Pavement Excitation Research On Active Control System Of Vehicle Noise Caused By Pavement Excitation By Pavement Excitation Hui LI*, Shuo Zhang* By Pavement Excitation Hui LI*, Shuo Zhang*
Hui LI*, LI*, Shuo Shuo Zhang* Zhang* Hui Engineering, Jilin University * College of Communications Hui LI*, Shuo Zhang* Jilin University Engineering, ** College of Communications China(e-mail:[email protected]) College of Communications Jilin Engineering, * College of Communications Engineering, Jilin University University China(e-mail:[email protected]) *College of electricalChina(e-mail:[email protected]) and Electronic Engineering, Changchun University of * College of Communications Engineering, Jilin University China(e-mail:[email protected]) *College of electrical and Electronic Engineering, Changchun University of Technology,Changchun, Jilin *College and Electronic Changchun China(e-mail:[email protected]) *College of of electrical electrical and Electronic Engineering, Engineering, Changchun University University of of Technology,Changchun, Jilin China(e-mail:[email protected]) Technology,Changchun, Jilin *College of electrical and Electronic Engineering, Changchun University of Technology,Changchun, Jilin China(e-mail:[email protected]) China(e-mail:[email protected]) Technology,Changchun, Jilin China(e-mail:[email protected]) China(e-mail:[email protected]) Abstract:In order to reduce the interior noise caused by road excitation,the finite element analysis Abstract:In order to reduce interior caused road finite element analysis method is used to analyze the the modal of thenoise interior space,by and thenexcitation,the get the frequency response curve of Abstract:In order to the interior noise caused by road excitation,the finite element analysis Abstract:In order to reduce reduce noise caused byand road excitation,the finite element analysis method is used to analyze modal of the interior space, then get frequency response curve of the corresponding nodes inthe thethe carinterior ear.Through the establishment of anthe adaptive neural network noise method is used to analyze the modal of the interior space, and then get the frequency response curve of Abstract:In order to reduce the interior noise caused byand road excitation,the finite response element curve analysis method is used to analyze the modal ofthe thesimulation interior space, then getanthe frequency of the corresponding nodes in the car ear.Through the establishment adaptive neural network noise active control system simulink model, experiment is of carried out.The experimental results the corresponding nodes in the car ear.Through the establishment of an adaptive neural network noise method is used to analyze the modal of the interior space, and thenof getanthe frequency response curve of the corresponding nodes in the car ear.Through the establishment adaptive neural network noise active control system simulink model, the simulation is carried results frequency band has show that the active control system of neural network experiment noise control in the 0out.The to 50 Hzexperimental active control system simulink the simulation experiment is carried out.The experimental the corresponding nodes in themodel, car ear.Through the establishment of an adaptive neural networkresults noise active control system simulink model, the simulation experiment is carried out.The experimental results frequency band has show active control neural network noise control 00 to dBthe . At 8650 , the maximum noise good that noisethe reduction effect system and theof average noise reduction is 4.3in Hz Hz frequency band has show the active system of neural network noise control the to 50 Hz activethat control systemcontrol simulink model, the simulation experiment is in carried experimental results frequency band has show that the active control system ofaverage neural network noise control in 0out.The to 50 Hz dBthe . At 86 , the maximum noise good noise reduction effect and the noise reduction is 4.3 Hzunder dB ,and the system can control the low frequency noise produced the excitation of reduction is 9.8 dBthe . At 86 , the maximum noise good noise reduction effect and the average noise reduction is 4.3 Hz Hz frequency band has show that the active control system of neural network noise control in 0 to 50 dB . At 86 , the maximum noise good noise reduction effect and the average noise reduction is 4.3 Hz dB ,and the system can control the low frequency noise produced under the excitation of reduction is 9.8 the road. ,and the can control the frequency noise excitation of reduction 9.8 dB produced . At 86 Hzunder , the the maximum noise good noiseis and the reduction is 4.3 dB ,andeffect the system system canaverage control noise the low low frequency noise produced under the excitation of reduction is reduction 9.8 dB the road. the road. dB ,and the system can control the low frequency noise produced under the excitation of reduction is 9.8 © 2018, (International Automaticadaptive Control) Hosting by Elsevier All rights reserved. the road.IFAC Keywords: Active control, Federation pavement ofexcitation, algorithm, acousticLtd. modal analysis, neural the road. Keywords: Active Active control, pavement pavement excitation, adaptive adaptive algorithm, acoustic acoustic modal analysis, analysis, neural network. Keywords: Keywords: Active control, control, pavement excitation, excitation, adaptive algorithm, algorithm, acoustic modal modal analysis, neural neural network. network. Keywords: Active control, pavement excitation, adaptive algorithm, acoustic modal analysis, neural network. network. and then provide the basis for the prediction of the car room 1. INTRODUCTION and then provide the basis for the prediction the car room noise. The adaptive neural network noise of active control and then provide the basis for the of the car room 1. INTRODUCTION and then provide the basis for the prediction prediction ofactive theestablished carcontrol room 1. INTRODUCTION noise. The adaptive neural network noise system is set up, and the simulation model is 1. automobile INTRODUCTION The characteristics of vibration and noise are the noise. The adaptive neural network noise active control and then provide the basis for network the prediction ofactive the carcontrol room noise. The adaptive neural noise system is set up, the simulation model established 1. automobile INTRODUCTION through simulink toand realize the active control ofis the noise in The characteristics of vibration and noise are the main factors that affect the comfort of the vehicle. With system is set up, and the simulation model is established noise. The adaptive neural network noise active control The characteristics of automobile vibration and noise are the system is set up, and the simulation model is established The characteristics of vibration and noise aremore the simulink to realize the active the noise in the vehicle. As a static system, neural control networkof noise control main factors that affect the comfort of the vehicle. With the through increasing demand forautomobile automobile comfort, more and through realize active control the noise in system issimulink set up,to the the simulation model of established main factors that affect the comfort of the vehicle. through simulink toand realize the active control ofisnoise the noise in The characteristics of automobile vibration and noiseWith are the main factors that affect the comfort of the vehicle. With the the vehicle. As a static system, neural network control system is difficult to realize the complex and changeable increasing demand for automobile comfort, more and more attention has been paid to the research on vibration noise the vehicle. As a static system, neural network noise control through simulink to realize the active control of the noise in increasing demand for automobile comfort, more and more the vehicle. As a static system, neural network noise control main factors that affect the comfortcomfort, of the vehicle. With the system increasing demand for automobile more and more difficult to complex and changeable workingis condition of realize vehicle.the Therefore, adding adaptive attention has paid to the research on vibration and reduction of been automobiles(see Wei et al.(2009)). One ofnoise the system is difficult to realize the complex and changeable the vehicle. As a static system, neural network noise control attention has been paid to the research on vibration and noise system iscondition difficult realize the complex andrequirement, changeable increasing demand for to automobile comfort, more and noise more control attention has been paid the research on vibration working of vehicle. Therefore, adding adaptive module can to not only meet the real-time reduction of automobiles(see Wei al.(2009)). Oneuneven of the sources of noise in the interior ofet the car is the working of vehicle. Therefore, adaptive system iscondition difficult to realize the complex adding and changeable reduction of automobiles(see Wei et al.(2009)). the working condition of vehicle. Therefore, adding adaptive attention has been paid to the research on vibrationOne andof noise reduction of automobiles(see Wei et al.(2009)). One of the control module can not only meet the real-time requirement, but also module improvecan the not effect of meet noise the reduction. sources of noise in the interior of the car is tothe 50uneven surface of the road. For the vehicle noise,30 Hz Hz is a control only real-time requirement, working condition of vehicle. Therefore, adding adaptive sources of noise in the interior of the car is the uneven control module can not only meet the real-time requirement, reductionofofnoise automobiles(see Wei ofet the al.(2009)). Oneuneven of the but also improve the effect of noise reduction. sources in the interior car is the 50 a but surface of the road. For the vehicle Hz to Hz isto. special frequency band which needs noise,30 to be paid attention also improve the effect of noise reduction. control module can not only meet the real-time requirement, to 50 surface of the road. For the vehicle noise,30 Hz Hz is a but also improve the effect of noise reduction. sources of noise in the interior of the car is the uneven to 50 surface of the road. For theofvehicle noise,30 Hz 2. ACOUSTICS PRINCIPLE ANALYSIS OF special frequency band which needs to be paid attention to.a but also The frequency band noise the frequency band is Hza issoimprove the effect of noiseAND reduction. special frequency band needs to be paid attention 50 Hz isto. a surface of the road. Forwhich the vehicle noise,30 Hz to special frequency band which needs to be paid attention to. 2. ACOUSTICS PRINCIPLE AND ANALYSIS OF PAVEMENT NOISE The noise of the frequency is aa socalledfrequency "roar" to band the people. Road roughness asband an important 2. PRINCIPLE AND The frequency band noise of the frequency is sospecial frequency band which needs to be paidband attention to. 2. ACOUSTICS ACOUSTICSPAVEMENT PRINCIPLENOISE AND ANALYSIS ANALYSIS OF OF The frequency band noise of the frequency band is a socalled "roar" to the people. Road roughness as an important source of excitation for vehicle vibration and noise, its main PAVEMENT NOISE 2. ACOUSTICS PRINCIPLE AND ANALYSIS OF called "roar" to the people. Road roughness as an important The frequency band noise of the frequency band is a soPAVEMENT NOISE called "roar" the than people. roughness asnoise, source of excitation for vehicle and its main .Therefore, it an is important of great frequency is toless 300Road Hzvibration 2.1 Acoustics principle source of excitation for vehicle vibration and its main PAVEMENT NOISE called "roar" to the people. Road roughness asnoise, an important source of excitation for vehicle vibration and noise, its main is of great frequency is than Hz .Therefore, significance toless study the300 influence of road it excitation on Acoustics principle .Therefore, it is frequency is than 300 Hzvibration source of excitation for vehicle and noise, its great main 2.1 2.1 Acoustics principle itexcitation is etof of great frequency is toless less than 300 Hz .Therefore, 2.1 Acoustics principle significance study the influence of road on vehicle vibration and noise performance(see Zhang al. Liu Active noise control (Active Noise Control) is the use of significance to study the influence of road excitation on itexcitation is of great frequency is toless thanthe300 Hz .Therefore, significance study influence of road on 2.1 Acoustics principle vehicle vibration and noise performance(see Zhang et al. Liu et al. Dan et al.(2010)).Chen Chang min and Sun Wei of Active noise (Active Noise use of acoustics "interference" principle. A Control) method is of the artificially vehicle vibration and noise performance(see Zhang et al. Liu significance to study the influence of roadZhang excitation on Active noise control control (Active Noise is the use vehicle vibration andstudied noise performance(see etWei al. et al. Dan et al.(2010)).Chen min and Sun of Active noise control (Active NoiseA Control) Control) is the use of of Tong ji University theChang instantaneous value of Liu the acoustics "interference" principle. method of artificially and purposely generating a secondary acoustic signal to et al. Dan et al.(2010)).Chen Chang min and Sun Wei of vehicle vibration and noise performance(see Zhang et al. Liu acoustics "interference" principle. method of artificially et al. ji Dan et al.(2010)).Chen Chang min and Sun Wei of Active noise control (Active NoiseA Control) is the use of Tong University studied the instantaneous value of the acoustics "interference" principle. A method of artificially to 250 in the two vehicle acoustic pressure from 20 Hz Hz and purposely generating a secondary acoustic signal to control the primary acoustic signal within a designated area. Tong ji University studied the instantaneous value of the et al. Dan et al.(2010)).Chen Chang min and Sun Wei of and purposely generating aa secondary acoustic signal to Tong ji on University studied theto instantaneous value of the acoustics "interference" principle. A method of artificially 250 the two vehicle acoustic pressure from 20 and purposely generating secondary acoustic signal to Hz Hz in et speeds the same road.(see Chen al. Sun et control the primary acoustic signal within a designated area. basic principle isacoustic the Yang's interference theory of sound 250 two vehicle acoustic pressure from 20 Hz Hz in the value Tong ji University studied theto instantaneous of the The control the primary signal within a designated area. to 250 in the two vehicle acoustic pressure from 20 Hz Hz and purposely generating a secondary acoustic signal to speeds on the same road.(see Chen et al. Sun et control the primary acoustic signal within a designated area. al.(2008)).The United States Ford Motor Co The basic principle is the Yang's interference theory of sound wave, that is, when the two rows of sound waves with the speeds on the same road.(see et the al. two Sun vehicle acoustic pressure from 20 Hz to 250Chen Hz in et The basic principle is the Yang's interference theory of sound speeds on the same road.(see Chen et al. Sun et control the primary acoustic signal within a designated area. al.(2008)).The United States Ford Motor Co The basic principle is the Yang's interference theory of sound AmiyaRMohanty using theStates finite element method and that is, when the opposite two rows of sound with the same frequency and the phase meet, waves the interference al.(2008)).The Ford speeds on the United same road.(see Chen al. Sun Co et wave, wave, that is, rows of waves with the al.(2008)).The States Ford et ofMotor Motor Co The basic isthe the two Yang's interference theory of sound AmiyaRMohanty using the finite element method wave, thatprinciple is, iswhen when the two rows of sound sound waves with the boundary element United method for the simulation a truck and car same frequency and the opposite phase meet, the interference superposition produced in the space(see MAkhtar et al. W AmiyaRMohanty using the finite element method and al.(2008)).The United States Ford Motor Co same frequency and the opposite phase meet, the interference AmiyaRMohanty using the finite element method and wave, that is, when the opposite two rows of sound waves with the boundary element method for the simulation of a truck car same frequency and the phase meet, the interference interior sound field, the vibration response was calculated superposition is produced in the space(see MAkhtar et al. W Mitsuhanshi et al.(2012)).When there are two coherent boundary element method for the simulation of a truck car AmiyaRMohanty using the element ofmethod superposition is produced in space(see MAkhtar et al. boundary element method for finite the simulation a calculated truck and car same frequency and the opposite phase meet, the interference interior sound the vibration response was superposition is in produced in the the space(see MAkhtar et sound al. W W according to field, the panel acoustic contribution, the Mitsuhanshi et al.(2012)).When there are two coherent S S and the sound field, the two phase dry sources interior sound field, the vibration response was calculated 1 2 boundary element method for the simulation of a truck car Mitsuhanshi al.(2012)).When there are two coherent interior sound field, the vibration response was calculated superposition et is produced in the space(see MAkhtar et al. W according to the panel acoustic contribution, the Mitsuhanshi et al.(2012)).When there are two coherent corresponding position in vehicle with sound-absorbing S1 and sound the two phase dry sound sources according to the panel acoustic contribution, the 2 in interior sound vibration response was calculated signals can beSSexpressed as: field, S1 and in the the field, the sound according to field, thecarthe panel acoustic contribution, the sources Mitsuhanshi two dry coherent corresponding position in with sound-absorbing S1 and Set22 inal.(2012)).When the sound sound field, there the two twoarephase phase dry sound sources material makes the noise isvehicle obviously reduced, but did not corresponding position in vehicle with sound-absorbing signals can be expressed as: according to the panel acoustic contribution, the corresponding position in vehicle with sound-absorbing S S and in the sound field, the two phase dry sound sources signals can be expressed as: 1 2 material makes the car noise is obviously reduced, but did not consider the incentive on the road(see AmiyaR et al. p p cos( t ) signals can be expressed as: material makes the car noise obviously reduced, but did not S 1 1 corresponding position in is vehicle with sound-absorbing material makes the car noise is obviously reduced, but did not consider the incentive on the road(see AmiyaR et al. (1) signals can be expressedppSas: Mohanty etthe al. BarryD et al.(2000)). p cos( t ) consider incentive on road(see AmiyaR et al. cos( pSS pp112cos( tt 1 )) material makes the car noise obviously reduced, but did consider the incentive on isthe the road(see AmiyaR et not al. pS p1 cos(t 112) (1) Mohanty et al. BarryD et al.(2000)). p p cos( t ) (1) Mohanty et al. BarryD et al.(2000)). consider incentive on noise the road(see AmiyaR et al. cos( tt 2)) (1) pSS p122cos( Mohanty etthe al. BarryD al.(2000)). You In this paper, theetroad is used as the source of pS p2 cos(t 122 ) (1) Mohanty etand al. BarryD etroad al.(2000)). You In this paper, the noise is used as the source of excitation the finite element analysis method is used to pS p2 cos(t 2 ) You In paper, the road noise is as source of You In this this paper, the road noise is used usedmethod as the theof source of excitation and the finite element analysis is used to analyze the acoustic modal of the interior space the car, excitation and the element used to You In this paper, the road noiseanalysis is usedmethod as the is source of excitation and the finite finite element analysis method is usedcar, to analyze the acoustic modal of the interior space of the analyze the acoustic modal of the interior space of the car, excitation and the finite element analysis method is used to analyze the acoustic modal of the interior space of the car, analyze the acoustic modal of the interior space of the car, Copyright © 2018 IFAC 506 1
1 12 1 2 12 2 2
2405-8963 © 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. Copyright 2018 responsibility IFAC 506Control. Peer review© of International Federation of Automatic Copyright ©under 2018 IFAC 506 Copyright © 2018 IFAC 506 10.1016/j.ifacol.2018.10.104 Copyright © 2018 IFAC 506
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is a point in the sound field, the point N distance S1 is r1 , and the distance S 2 is r2 ,so the total sound potential energy density at N point is as follows: N
E= p
x g 2 f0 xg 2 n0 Gq (n0 )v0 w
p2 (1 cos ) 2 2c2 p12 p22 2 p1 p2 cos
2 1 2
(2)
r2 r1
is the wavelength; is the density of sound propagation medium; c is the sound speed. When tends to the odd number of , E is close to 0, At this time, the noise of N point has been greatly reduced.
describe its statistical characteristics(MA et al.VA et al(2007)).A large number of practical measurements show that the power spectrum of the pavement displacement can be made up of a standard form. GB7031-86 recommends the use of the power spectral density of the pavement displacement. w is the spatial frequency; n0 is The n in G（n）= G（n q q 0）(n/n 0 )
Table 1.The pavement constant related to the pavement grade of a Pavement grade
the reference spatial frequency( n0 =0.1m1 ); Gq (n) is the displacement spectrum density; Gq (n0 ) is the pavement
a(1/ m)
spectrum value under the reference spatial frequency n0 ,it is called the road roughness coefficient; W is the fitting index, which determines the frequency structure of the pavement spectrum(He Xuefeng et al. Yan et al.(2000)).GB7031-86 divides the road surface into 8 levels according to the road roughness coefficient Gq (n0 ) , and the fitting index is W =2 .
A
0.132
B
C
D
E
0.1303
0.12
0.1007
0.09
3.ANALYSIS OF ACTIVE NOISE REDUCTION CONTROL SYSTEM Before determining the parameters of adaptive filter, we first need to clarify the principle of adaptive noise active control system, so we establish the principle of adaptive noise active control as shown in Fig.1.
Therefore, considering the influence of vehicle speed, the spatial spectral density can be transformed into the time spectrum density Gq ( f ) . The results are as follows:
output signal
p ( n) H 2 ( z)
d ( n)
+
(3)
e( n )
Where the f is the time frequency.
H1 ( z )
(4) (5)
From the formula (4), it can be seen that the power spectrum of the road surface input speed is a constant over the entire frequency range, which is a "white noise". So the method of generating random road roughness profile is made of white noise through a filter(Chen et al.(2013)).so the wheel received road excitation can use differential equation representation, the following is as follows:
x ( n)
Adaptive filter
y ( n)
|
Gq ( f ) (2 f )4 Gq ( f ) 16 4n02Gq (n0 ) f 2v
(7)
In the formula, qi (t ) is the time sample of the road incentive process in i , u is the speed of the car （m/s）. i (t ) is a random signal with zero mean white noise, and its coequation must satisfy E[i (t )i ( )] 2au 2 (t ) . is the road surface roughness constant; (t ) is the Dirac generalized function. a is the road constant (1/ m) related to the pavement grade The specific parameters corresponding to the pavement grade are shown in Table 1：
The change in the height of the road to the height of the base plane q along the road to the length of I is usually called the q i）.As the road surface level pavement unevenness function （ of vehicle vibration input, the statistical characteristics of the pavement power spectral density Gq (n) are mainly used to
Gq ( f ) (2 f )2 Gq ( f ) 4 2 n02Gq (n0 )v
Where, the x g is the vertical displacement of the pavement; the f 0 is the lower cut-off frequency of the road input, and the w is the white noise with a mean of 0 of the power of 20 dB . White noise is a random signal or random process with constant power spectrum density. White noise can be changed to a certain system by specific system. The road excitation noise is a typical band limited noise. When driving at i , the road excitation is qi (t ) ,then a single point excitation mathematical model with white noise as input is established (Yin et al. Chen et al. Wu et al.(2017))： qi (t ) auqi (t ) i (t )
2.2 Road Roughness Excitation Noise
Gq ( f ) n02Gq (n0 )v / f 2
(6)
LMS
Fig.1. Principle diagram of adaptive noise active control As shown in the diagram, p(n) is the output signal of the neural network subsystem, H 2 ( z) is the primary acoustic channel transfer function, and H1 ( z ) is the acoustic channel transfer function of primary signal to error sensor. d (n) is the signal obtained from the output signal p(n) through the
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469
in the modeling aspect respectively deal with all the mesh, then assembled into a white body grid structure model(Bai et al.(2013)).Due to the complexity of vehicle body structure, the establishment of finite element model is very heavy. Therefore, the body structure is simplified to some extent under the condition of ensuring the accuracy of calculation results.
primary acoustic channel transfer function, y(n) is the output signal of the adaptive filter, and e(n) is the error signal after the noise elimination of the active control system. The determination of the parameters of the adaptive filtering noise control system includes primary and secondary acoustic channel transfer functions H1 ( z ) and H 2 ( z) ,the filter order M and the convergence factor .
The body model is a complex space grid plate and shell structure. As shown in the figure, the structure mode of the body is shown in Fig.2.
The acoustic channel transfer function has been identified by parameter identification in the Adaptive IIR Fileter-v Algorithms for ANC in the paper of David H.C and Robert W.S(David H.C et al. Robert W.S et al.(1997)).among: H 2 ( z )=0.05-0.001z 1 0.001z 2 0.8 z 3 0.6 z 4 0.2 z 5 0.5 z 6 0.8 z 7 0.4 z 8 0.005z 9 H1 ( z )=0.05-0.01z 1 0.95 z 2 0.01z 3 0.9 z 4
(8)
The two parameters that affect the performance of the adaptive filter are the filter order and the convergence factor. The higher the order of the filter, the longer the delay introduced in the digital filter system, and the greater the possibility of steady-state misalignment, the smaller the range of the convergence factor and the more complex the calculation. But not blindly reduce the order, because of the order is too low can not fully reflect the correlation matrix. The greater the convergence rate is, the faster the convergence rate. and the greater the imbalance of the steady state. The smaller the convergence factor and the slower the convergence rate(Kuraya et al. Nishimura et al. Usagawa et al. Ebata et al. Okda et al.(1988)).The convergence factor =0.005 is selected through experimental simulation, and the filter order is suitable for selecting M =16 . 4. ACOUSTIC MODAL ANALYSIS IN CAR CHARMBER
The 3D model is built by CATIA, and the grid is imported into the Hypermesh format in IGES format. Then, the grid is imported into the LMS Virtual Lab by BDF format, and the modal analysis is done. When dividing meshes in Hypermesh, in order to ensure the reliability of acoustic mode simulation, cell size must be at least six acoustic units in every 1 1 c tmax min 6 6 f max
Acoustic modal analysis of the acoustic modality in the vehicle cavity is performed to obtain its mode shape and modal frequencies. In general, the longer the cavity, the lower the frequency. According to relevant literature(Deng et al. Chen et al.(2013)).Under normal driving conditions on the road to the car is not greater than 150km/h speed for the car, road excitation frequency is less than 21 Hz , and because the wheel imbalance caused by the incentive is usually below 11 Hz , the above frequency is far lower than the lowest order where the tune is not equal to zero modal frequency, namely 82.76 Hz , so the vehicle interior resonance excitation not within the scope of consideration cannot cause. This paper uses LMS Virtual Lab to the tune of finite element model for the acoustic modal analysis, As shown in Fig 3-8.
4.1 Body Modeling And Acoustic Modal Analysis Of Chamber Cavity
wavelength range. In
Fig.2. the finite element model of the body structure
, in the formula, tmax
is the maximum unit length; min is the least sound wave wavelength; c is sound speed, ( c=340m / s ); f max is the maximum frequency. Considering the frequency range of acoustic simulation modal simulation for 0~300 Hz , combining with the car room size acoustic model size and simulation reliability requirements set model unit size is 60mm. The network model is shown by Hypermesh partition. White body as the basis of the vehicle model and the bearing body is made up of hundreds of pieces of sheet metal complex system, main nacelle assembly and a trunk assembly, body floor assembly, side wall assembly and cover assembly, 508
As shown in the figure, the first order modal frequency is 3.959e-6 Hz and the acoustic mode is to the front window as the center of the diffusion distribution, second order modal frequency is 79.947 Hz , the pitch line position is located in the center of the car, the car rear cavity pressure large, abdominal pressure located inside the vehicle rear. The third order modal frequency is 129.455 Hz , and the sound pressure increases from the left rear side to the right rear side home, and reaches the maximum in the right rear part of the car cavity, which is the sound pressure abdomen area. The fifth order modal frequency is 157.907 Hz , and the longitudinal line is located at the front row position. The transverse section line is located in the transverse symmetry plane, and the two sections are perpendicular to each other. Under this mode, the driver and front row crew environment is better. The Tenth - order acoustic modal frequency is 202.637 Hz , and the sound pressure varies laterally. The two sections are located in the front windshield, along the rear windshield, and the maximum sound pressure is located under the rear windshield. The fifteenth order modal frequency is 266.626 Hz , and the driver and the front row occupant are in
IFAC E-CoSM 2018 470 Changchun, China, September 20-22, 2018
Hui Li et al. / IFAC PapersOnLine 51-31 (2018) 467–472
the abdominal space of sound pressure, and the rear occupants are near the sound pressure line.
5. MODELING AND SIMULATION ANALYSIS OF ROAD SURFACE EXCITATION 5.1 Modeling and simulation analysis of road surface excitation For unstable random vibration, the power spectral density function is used to describe the vibration in the frequency domain. Therefore, when studying the vibration of a car driving on an uneven road surface, the frequency domain method is used to describe the degree of unevenness of the road surface. That is, a mathematical model of the power spectrum density of road irregularities with respect to time is established. It has the following two characteristics: (1)the roughness model of the pavement varies with the road grade and the speed of the car. (2)The rear wheel unevenness model has the effect of delay in the model relative to the front wheel unevenness. In the vehicle modeling analysis, not only need to consider the road input wheelbase lag, but also consider the degree of correlation between left and right wheels.
Fig.3-8. acoustic modal analysis in a vehicle cavity
The model of wheel single wheel withdrawal is as follows: 4.2 Frequency Response Analysis Of Sound Pressure Near The Ear
au q1 au 2 / 2 q2
Put the car in the head cavity grid four sites were selected for drivers and passengers head on both sides of a total of four nodes, respectively, the driver's left ear, right ear of the driver and passengers and crew left ear, and the freedom degree is set to S, after completion the sound pressure frequency response function at the field point (human ear) can be calculated. Finally, you can see the sound pressure at the four nodes. As shown in Fig 9.
0 q1 (t ) 2 / 2 q2 (t )
(9)
In the formula 2 =1/ u ; q is the wheelbase of the front and rear wheels of the car. It has been proved that there is a certain spatial correlation between the trajectory of the left and right wheels. It is assumed that the 1 and 2 wheel model is derived from the white noise x according to the previous method, and the other round of the input white noise is y , and the two wheel correlation equation of state can be derived. b1 x1 b 2 x2 1
y a1 a2
Fig.9. sound pressure at four nodes
b1 b2
b0 1 x b2 1 b2 x x 0 2 0
a0 a2
b0 x1 a2 x b2 x2 b2
(10)
(11)
In which a0、a1、a2、b0、b1、b2 is constant, according to the coherent function and processing method cited in the literature, x1、x2 are intermediate state variables; Combined with (9.10.11), the simulation model of the time history of Simulink four wheel pavement excitation is set up, as shown in Fig 10.
Four from the end point by X and Z on the left, and the law in the Y coordinates of the symmetrical arrangement, the frequency response curve is similar. The scope of this unit the applied excitation frequency is 0~190 Hz ,the maximum sound pressure at 130 Hz is 136 dB . It shows that the body's vibration energy is the highest at this frequency, and the driver and occupant's ear excitation is the largest. The next few frequency response peaks are concentrated at several points near 190 Hz . The ear noise caused by these peak frequencies is more prominent. It affects the ride comfort and should be the main subject of research in the simulation.
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Hui Li et al. / IFAC PapersOnLine 51-31 (2018) 467–472
Fig.10. simulation model of time history of four wheel pavement excitation Where: b1 A3= b2 1
a A6= 2 b2
b0 b2 0
au A2 A8 au 2 2
,
1 A4= b2 0
,
0 2 2
Fig.13. adaptive neural network noise active control system The parameters needed in the system are input into each module and subsystem, and the frequency response contrast curve before and after control is shown as shown in Figure 14.
，
75
，
b A5= a1 a2 1 b 2
b a0 a2 0 b2
Before Eliminating Noise After Eliminating Noise
，
70
Sound Pressure Level（dB）
1 A1 A7 1
471
.
65
60
55
The simulation results are shown in Fig.11-12.
50 0
0.03
10
20
30
40
50 Frequency（Hz）
60
70
80
90
100
Fig.14. frequency response contrast curve before and after control
left-front wheel left-rear wheel
0.02
qx(n) /m
0.01
The noise control effect can be seen from the graph. The noise reduction characteristics of the active noise control system in the 0 to 100 Hz frequency band can be divided into 0 to 50 Hz and 50 to 100 Hz two stages. 0 to 50 Hz neural network noise active control system has good noise reduction effect and the average noise reduction is 4.3 dB . The effect of noise reduction in the 50 to 100 Hz frequency band is preferably, and the maximum noise reduction is 9.8 dB at 86 Hz . About 40 Hz and 80 Hz , the noise level corresponding to the first and two order disturbance frequencies of the engine is relatively large. From the simulation curve, it can be judged that the adaptive neural network active control system can well control the low-frequency noise generated by the road excitation.
0
-0.01
-0.02
-0.03 0
2
4
6
8
10 t/s
12
14
16
18
20
Fig.11. simulation result diagram a 0.03
right-front wheel right-rear wheel
0.02
qx(n) /m
0.01
0
-0.01
-0.02
-0.03 0
2
4
6
8
10 t/s
12
14
16
18
20
Fig.12. simulation result diagram b
6. CONCLUSIONS
Compared with the diagram and graph, it can be found that the corresponding characteristics of the front wheel and rear wheel are almost the same under road excitation, but the response of rear wheel is delayed for the front wheel, and the
In this paper, based on the characteristics of high sound pressure level and prominent frequency characteristics of a domestic vehicle. Combined with finite element analysis method and simulation modeling analysis method, adaptive noise active control system is adopted, and on this basis, neural network is used to improve noise active control system .The noise active control system was verified by simulation analysis. Simulation analysis shows that the noise active control system can greatly improve the noise environment inside the vehicle, But the control system also has the insufficiency, system operation in 70 to 80 Hz bands in a short period of silence after rather than before the sound pressure level greater noise, increased the noise inside the car. I will be in the follow-up work on the noise reduction system will compensate for this lack of improvement.
L v
delay time is = .
5.2 Simulation and analysis of active noise reduction An adaptive neural network noise active control system is set up based on the principle of adaptive neural network noise reduction, As shown in Figure 13.
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Hui Li et al. / IFAC PapersOnLine 51-31 (2018) 467–472
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