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Master´s Thesis in Engineering Mathematics

Mathematical Investigation for the Estimation of Sound Noise from the Cylinder Pressure Noise for Direct Injection Diesel Engine.

Hany Gerges

Department of Mathematics CHALMERS UNIVERSITY OF TECHNOLOGY GÖTEBORG UNIVERSITY GÖTEBORG SWEDEN AUGUST 2006

Thesis for the degree of M.Sc. in Engineering Mathematics

Mathematical Investigation for the Estimation of Sound Noise from the Combustion Noise forDirect Injection Diesel Engine

Hany Gerges

Supervisors Prof.Ivar Gustafsson Mr.Sven Ahlinder

Department of Mathematics Chalmers University of Technology and Göteborg University SE-412 96 Göteborg,Sweden Göteborg,August 2006

ABSTRACT

New demands of noise reduction from engines have put focus on the demand of estimating noise in all engine measurements. The ultimate method would be to estimate the noise from the pressure curve of the engine.Ricardo´s method based on using multiple regression for estimation of noise components have been investigated and it has given some logical results for data sets from a certain optimised engine at speed 1500 rpm and speed 1800 rpm,however the measured noise components are not available to be able to compare them with the computed ones for this data set.Unfortunately the results show that Ricardo's method is not applicable to other kinds of engine data sets.

The companies AVL and D2T for the development of power train systems and test solutions have commercial software for this application and the AVL system is here evaluated. The work includes the trial to find Volvo specific transfer function between cylinder pressure and noise,in other words our own model.

A 13 litre US07 direct injection diesel engine has been run in a noise test room at Lundby. Both cylinder pressure and noise have been measured to be input datasets for the trial to investigate the correlation between the sound noise and cylinder pressure noise, i.e. the power spectrum of the measured pressure signal.

AVL has a software(Concerto) that filters the third octave spectrum. This means that the cylinder pressure is Fourier transformed,and the Fourier transformed data is gathered together at the third octave frequencies. Then there are correction terms at each of the third octave frequencies due to the engine damping,the room damping and the sensitivity of the human ear. In the end there is an estimated dB(A) value for the specific driving condition of the engine.

The AVL noise estimation has been compared to the measured noise.Indeed, AVL during the time delivered three versions of their software for the purpose of estimating sound noise from the cylinder pressure noise. None of them has been successful to estimate the measured noise. It is possible that other sources exist for noise,other than combustion, i.e. injection may be a cause of noise.

Acknowledgements

I am so grateful to my supervisor Prof.Ivar Gustafsson for allowing me to have this topic for my Master´s Thesis as well as Mr.Sven Ahlinder my Supervisor at Volvo Technology for his encouragements,support, and valuable remarks.

I would like to thank Prof.Stig Larsson and other teachers I have been engaged with in their courses in the Department of Mathematics at Chalmers University of Technology.

I wish to thank Mr.Johan Engström,the Head of the Simulation Group at Volvo Technology for his friendly motivation during our time of collaboration.

Finally,I wish to thank my family for their love and support during two years of study at Chalmers University of Technology in Göteborg Sweden.

Preface

This report is written for the fulfilment of the requirements for the masters´thesis of the International Master Program in Engineering Mathematics at Chalmers University of Technology

The report is handling the possibility of estimating the sound noise from the cylinder pressure noise for direct injection diesel engines.

A 13 litre US07 engine has been run in a noise test room at Lundby. Both cylinder pressure and sound noise has been measured.

The report is describing three methodologies used for the trial of estimating sound noise,and they are: using multiple regression for estimating radiated noise components,the commercial software AVL (Concerto),and finally my own model developed at Volvo Technology.

The report shows that the first and second methodology have not been successful for estimating the sound noise that explain the need for developing a new model for the data sets we have.

There is no real trend between cylinder pressure noise and sound noise for separate frequencies. This means that the AVL (Concerto) software method is not applicable since it subtracts dB values from cylinder pressure noise for each frequency.

Possibly injection pressure and turbo speed add noise. Such parameters are not taken into consideration here. Also different frequencies could be generated from a given frequency. Those questions are to be considered in the others´future thesis work.

There is however indication that the total sound noise is linear with the total cylinder pressure noise, which indicates that the AVL method should work anyway.

Contents

Preface.................................................................................................................................... i

1 Using multiple regression to estimate the Mechanical,Combustion and Load dependent noises 1.1 Introduction.......................................................................................................................... 1 1.2 Method................................................................................................................................. 2 1.3 Results.................................................................................................................................. 4 1.4 Discussions........................................................................................................................... 66 1.5 Conclusions and Recommendations..................................................................................... 68 1.6 Summary............................................................................................................................... 69

2 The AVL (Concerto) Software

2.1 Introduction.......................................................................................................................... 71 2.2 Method.................................................................................................................................. 72 2.3 Results.................................................................................................................................. 73 2.4 Discussions........................................................................................................................... 79 2.5 Conclusions and Recommendations.................................................................................... 83 2.6 Summary.............................................................................................................................. 84

3 Volvo Technology ´s Model

3.1 Introduction............................................................................................................................ 85 3.2 Method.................................................................................................................................... 85 3.3 Results..................................................................................................................................... 86 3.4 Discussions............................................................................................................................. 90 3.5 Conclusions and Recommendations....................................................................................... 90 3.6 Summary................................................................................................................................. 91

Chapter 1Using Multiple Regression Analysis to Estimate the Contributions of Engine-Radiated Noise Components.

1.1-IntroductionDuring the work on my thesis at Volvo Technology Corporation under the supervision of the noise and emission specialist Mr Sven Ahlinder within the simulation research group in the Energy Conversion and Physics Department at Volvo Technology Corporation,I have been asked by him to examine the paper entitled ”Using a multiple regression to estimate the contributions of engine-radiated noise components” (JSAE Review 20(1999) 363-368) published by the society of the Automotive Engineers of Japan and to try to apply this estimation to the measured data of the direct-injection diesel engine under test at Volvo Technology Corporation/AB Volvo as a part of my thesis report.

The input database have been collected from using a 2 litre per cylinder direct injection engine.we were able to vary the speed,torque,injection timing(tm),needle opening pressure(nop),exahaust gas recirculation (egr),and variable geometry turbine(vgt).The cylinder pressure is measured with a pizzo electric transducer.The noise is measured according to ISO 3744.

Four series were measured :

-Series O for optimized The settings were equal to an optimized engine

-Series S for star In all OICA speed torque combinations,tm, nop, EGR and VGT were varied as a star

-Series V for water coolingDuring series S, water cooling of the gearbox was added.

-Series V was a copy of serie Oto detect differences

-Series D for Dolfe Mr Dolfe asked us to run the engine with nop=0

The first part of the scope of my study is to calculate the decibels for approximately 1000 signals and some are fourier transformed data and some are in the time domain and I have checked the results in both domain,i.e. I have investigated a massive database of 10 Gbyte.The second part is to try to apply the multiple regression analysis to the data sets to estimate noise component factors, i.e. the combustion noise,mechanical noise,and the load-dependent noise in the direct-injection diesel engine under test. The only limitation in my investigation is that,as it is explained by the paper,the noise component factors must be measured through experimentation to be compared with the corresponding computed noise factors via the multiple regression analysis,and the data sets I have does not include the measurements of the noise components that do not allow me to make such comparison!

1

2.Method

The paper is assuming that the engine noise at a certain centre frequency consists of a mechanical noise,combustion noise and load-dependent noise,and it can be expressed as

SP=SPm+H*CP+G*L . (1)

Here SP is the engine noise,SPm is the mechanical power noise,CP is the cylinder pressure power,H is the transfer coefficient between the cylinder pressure power and the combustion noise power,L is the square of the engine torque and G is the transfer coefficient between torque and load-dependent noise.

In multivariate analysis terminology,SP is a criterion variable,CP and L are explanatory variables and SPm,H,G are partial regression coefficients.

Applying the above assumption to n data sets yield n equations forming a linear system that can be expressed in matrix form as follows:

Y=XA+E. (2)

Here Y is a noise vector,X is an excitation matrix,A is a coefficient vector that includes the partial regression coefficients,solving the above system by the Least Squares Method to minimize the square of error,i.e. ETE,where E is the error vector as explained above in (2),we can determine the coefficient vector A as follows:

A=(XTX)-1XTY (3)

By multiplying the excitation force matrix X by the transfer coefficient vector A thus found,the noise vector Y* is obtained as a calculated value,making it possible to separate mechanical noise,combustion noise and load dependent noise as follows:

Y*=XA (4)

The frequency characteristics of mechanical noise,combustion noise and load dependent noise are then found by performing this calculation for every centre frequency.

2

We validate the above multiple regression analysis by the following steps :

1-Examining the correlation between engine´s sound noise and cylinder pressure noise to be sure that a good linear correlation exists between cylinder pressure noise and engine´s sound noise and to determine the bandwidth over which a strong correlation exists.

2-Examining the correlation between the explanatory variables,i.e. the cylinder pressure and the engine torque trying to verify that the explanatory variables are uncorrelated to each other over wide bandwidth of frequencies.

3-The noise levels obtained by the multiple regression analysis must be compared with the results of the experiments in which the operation of various engine components were successively suspended.

Finally as an example of the application of multiple regression analysis,this text presents the results of an investigation concerning the directions to take in trying to reduce noise levels according to different engine operating conditions,as was seen in case of a direct-injection diesel engine,the contributions of different noise components vary substantially depending on the engine operating conditions.Tendency is indicated more concretely in the paper which shows the contribution of each noise component as a percentage under partial load and full load operating conditions. These results indicate that reducing combustion noise and mechanical noise at low load and load-dependent noise at high loads would be effective approaches to take.

My ideas for series S

For series S,I have divided all measured data sets (116 states of series s,each state is a different combination of torque,speed,injection timing(tm),needle opening pressure(nop),exhaust gas recirculation,..etc)into three groups corresponding to speeds 1200 rpm, 1500 rpm,1800 rpm. Within each group I found that measurements have been done at four torque levels or three depends on the group under test,in other words there were 10 measurements at the same torque level,i.e. torque point,for instance for speed 1800 rpm there are four torque points each one has measurements 10 times ,in addition two points have been measured at very low torque level. Based on the fact that multiple regression analysis may avoid the repeated measurements and the need to have a frequency bandwidth of no correlation between engine sound noise and the cylinder pressure noise,I have automated the Matlab code to consider all possible combinations and to search on the most suitable combination that satisfies our requirements and I have been successful to get some logical results under the above assumptions. However,I have been needing the measured values of the noise factors to be able to compare them with the computed ones. Although my ideas have enabled me to get some near results from the paper and it is the only logical way I can see to apply multiple regression analysis according to the paper they have been rejected by the technical supervisor.

3

3.Results

I have applied the above estimation to the data sets I have for series Vand series O,and in my trial to apply the above estimation I have done all of my best to get the results expected by the paper.However,I have had to add my own ideas to be able to get some results complying with the paper as for the case in series S. Indeed,I have faced many mistakes and contradictions by the paper and by the results and I shall mentioned them later.

1- at speed 1200 rpm/series v

Figure 1,Multiple correlation at speed 1200 rpm/series v

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Figure 2,Correlation between cylinder pressure and engine torque at speed 1200 rpm/series v

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Figure 3,Correlation between engine noise and cylinder pressure and engine torque at speed 1200 rpm/series v

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Figure 4,Sound pressure at speed 1200 rpm/series v

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Figure 5,Cylinder pressure at speed 1200 rpm/series v

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Figure 6,Mechanical&Load and Combustion noises at speed 1200 rpm/series v

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Table-1 Partial regression coefficients for series v at speed 1200 rpm

16 Hz 20 HZ 25 HZ 32 HZ 40 HZ

SPm 3.2182339e - 06 4.5215838e - 06 2.4153609e - 06 8.7884706e - 06 3.1095143e - 06

H 2.9352122e - 12 - 5.446064e - 16 - 8.922918e - 13 - 3.384726e - 15 - 1.807630e - 15

G - 3.170752e - 13 1.2045793e - 12 2.6187636e - 13 3.7602000e - 12 1.8241992e - 12

50 Hz 63 HZ 80 HZ 100 HZ 125 HZ

SPm 1.2162713e - 06 - 9.4067779e06 - 9.4067779e06 4.2123938e - 06 3.0120198e05

H 1.9763430e - 16 2.2561900e - 14 2.9040849e - 15 2.9300908e - 14 1.6079639e - 14

G - 7.353948e - 14 1.6347216e - 11 7.7292320e - 12 7.6260856e - 12 1.5499168e - 11

160 Hz 200 HZ 250 HZ 315 HZ 400 HZ

SPm 1.3593725e - 05 3.7986768e - 06 2.3646576e - 05 1.6938143e - 05 1.0504971e - 05

H - 2.143502e - 13 2.2697049e - 12 2.3201241e - 12 3.7858236e - 12 1.4831605e - 11

G 7.6964104e - 12 - 5.478686e - 12 - 4.439791e - 12 - 4.733745e - 13 1.0717932e - 11

500 Hz 630 HZ 800 HZ 1000 HZ 1250 HZ

SPm 1.8408713e - 04 2.5526975e - 04 1.3563709e - 04 1.4627484e - 04 2.0006147e - 04

H - 1.735147e - 11 - 1.210228e - 10 - 2.329096e - 11 - 1.153569e - 10 - 3.522940e - 10

G 5.5097233e - 12 - 1.028773e - 11 - 2.434893e - 12 5.1605133e - 12 6.8431206e - 12

10

1600 Hz 2000 HZ 2500 HZ 3150 HZ 4000 HZ

SPm 1.7001557e - 04 1.1890267e - 04 6.0288722e - 05 3.6280661e - 05 1.2010242e - 05

H - 2.339028e - 10 - 4.22980e - 10 - 1.611411e - 10 - 8.748255e - 11 2.1045326e - 10

G - 6.308853e - 12 2.1492465e - 12 2.9990873e - 12 5.0222905e - 12 2.3476984e - 12

5000 Hz 6300 HZ 8000 HZ 10000 HZ 12500HZ

SPm 1.1461733e - 05 1.6147062e - 05 5.6432588e - 06 3.6441994e - 06 - 1.034897e - 05

H 3.3724833e - 10 - 7.770195e - 11 - 3.225430e - 12 - 1.308155e - 10 7.4061826e - 10

G 4.1235982e - 13 1.3275612e - 12 2.4361447e - 12 1.1381678e - 11 3.1295332e - 12

2-at speed 1500 rpm/series v

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12


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Figure 9,Correlation between engine noise and cylinder pressure and engine torque at speed 1500 rpm/series v

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15


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Figure 12, Noise components versus frequency

Table-2 Partial regression coefficients for series v at speed 1500 rpm

16 Hz 20 HZ 25 HZ 32 HZ 40 HZ

SPm 2.0310966e - 06 3.9561777e - 06 1.3309942e - 06 7.1624318e - 06 1.8213351e - 06

H 6.9385939e - 14 - 3.098498e - 12 6.4532340e - 16 - 1.313455e - 14 3.9186865e - 16

G - 3.319764e - 12 - 3.616407e - 14 - 9.973458e - 13 4.7617846e - 13 - 4.326139e - 13

50 Hz 63 HZ 80 HZ 100 HZ 125 HZ

SPm 4.6715647e - 06 1.2182356e - 06 1.0247918e - 05 1.4590841e - 05 2.3417033e - 05

H - 3.181817e - 15 - 3.543246e - 16 1.1177027e - 13 2.7298719e - 14 4.3952938e - 14

G 5.5577298e - 12 3.2173022e - 13 4.7315378e - 11 3.8268594e - 12 1.2759095e - 11

17

160 Hz 200 HZ 250 HZ 315 HZ 400 HZ

SPm 6.6677750e - 05 5.3155141e - 05 - 6.225603e - 05 7.2169471e - 06 5.1489716e - 05

H - 1.816671e - 13 - 1.027844e - 12 2.1258737e - 11 7.5262413e - 12 1.1320307e - 11

G 7.9255690e - 12 3.0553272e - 11 5.9127993e - 12 2.4939931e - 12 1.9378508e - 11

500 Hz 630 HZ 800 HZ 1000 HZ 1250 HZ

SPm 2.1021033e - 04 3.8747348e - 04 3.8410051e - 04 3.9695802e - 04 2.7491759e - 04

H 2.3999383e - 13 - 1.498307e - 10 - 6.548979e - 10 - 9.960006e - 10 - 3.610239e - 10

G 2.5826704e - 11 - 9.931568e - 12 - 2.395588e - 12 - 3.471397e - 12 - 6.828263e - 13

1600 Hz 2000 HZ 2500 HZ 3150 HZ 4000 HZ

SPm 2.6675063e - 04 1.1441311e - 04 8.7979470e - 05 9.2371853e - 05 2.1186984e - 05

H - 4.619287e - 10 - 1.958556e - 10 - 4.155293e - 10 - 8.638662e - 10 3.9375537e - 10

G 3.8203226e - 12 1.0330317e - 11 2.8299334e - 12 9.0575415e - 13 3.6600672e - 12

5000 Hz 6300 HZ 8000 HZ 10000 HZ 12500 HZ

SPm 2.0811589e - 05 1.9295280e - 05 8.2740504e - 06 1.0672805e - 05 4.4752309e - 06

H 2.6464445e - 10 2.3562915e - 10 7.0785379e - 11 2.0697375e - 11 - 1.384173e - 10

G 3.0270301e - 13 - 9.128339e - 14 2.0833146e - 12 3.5845047e - 12 1.3560188e - 11

3-at speed 1800 rpm/series v

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Figure 15 Correlation between engine noise and cylinder pressure and engine torque at speed 1800 rpm/series v

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Figure 18,Noise components versus frequencyTable-3 Partial regression coefficients for series v at speed 1800 rpm

16Hz 20HZ 25 HZ 32HZ 40HZ

SPm 5.6756719e - 06 3.3433331e - 06 2.6657604e - 06 1.0385132e - 05 2.7381773e - 06

H - 1.954443e - 16 2.7088528e - 11 - 2.416803e - 12 2.3517576e - 15 - 2.095536e - 16

G 1.6493075e - 13 - 3.974355e - 13 4.3990059e - 14 - 3.283751e - 12 5.3467713e - 13

24

50Hz 63HZ 80HZ 100HZ 125HZ

SPm 1.5792351e - 06 2.4856004e - 06 3.0823044e - 05 4.9172498e - 05 4.1682590e - 06

H 6.4058811e - 18 - 1.139217e - 15 - 2.062298e - 18 - 1.415710e - 14 9.7764022e - 14

G 3.8602287e - 13 1.8126178e - 12 7.1403944e - 12 1.5790519e - 11 1.0445743e - 11

160 Hz 200HZ 250HZ 315HZ 400HZ

SPm 3.3775007e - 05 5.9877194e - 05 1.0644728e - 04 4.1269007e - 05 1.0591146e - 04

H 7.9991588e - 15 2.0244440e - 13 - 3.089880e - 12 7.4253943e - 12 9.3711282e - 12

G 2.2131788e - 11 5.1574417e - 11 2.3198603e - 11 1.8794898e - 11 3.9844291e - 11

500Hz 630HZ 800HZ 1000HZ 1250HZ

SPm 2.1977175e - 04 4.2461225e - 04 2.7502550e - 04 1.2508136e - 04 1.7541900e - 04

H - 5.867989e - 12 - 2.228241e - 10 - 1.064827e - 10 1.1638037e - 09 1.1031236e - 09

G 1.2910017e - 10 9.0170734e - 11 3.7531722e - 11 1.1009851e - 11 5.3276684e - 11

1600Hz 2000HZ 2500HZ 3150HZ 4000HZ

SPm 2.6608438e - 04 1.3532871e - 04 1.7462516e - 05 6.3988045e - 05 1.4322333e - 05

H - 1.152691e - 10 1.6255251e - 10 1.8357873e - 09 2.0082091e - 10 1.8451204e - 09

G 1.9772364e - 11 3.4798476e - 11 2.1710464e - 11 1.7625947e - 11 - 8.489182e - 12

5000Hz 6300HZ 8000HZ 10000HZ 12500HZ

SPm 1.7464220e - 05 - 5.676781e - 08 7.5795238e - 06 6.9142583e - 06 1.9901017e - 05

H 1.0366767e - 09 1.3613032e - 09 1.0013343e - 10 6.4054654e - 11 - 1.514002e - 10

G - 9.554095e - 13 - 3.910685e - 12 3.1975623e - 12 1.1756568e - 12 1.0297198e - 11

Series O

1-at speed 1200 rpm/series o

25

Figure 19,Multiple correlation at speed 1200 rpm/series o

26

Figure 20,Correlation between cylinder pressure and engine torque

27

Figure 21,Correlation between engine noise and cylinder pressure and engine torque at speed 1200 rpm/series o

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Figure 22,Sound pressure at speed 1200 rpm/series o

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Figure 23,Cylinder pressure at speed 1200 rpm/series o

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Figure 24,Noise components versus frequency.Table-4 partial regression coefficients at speed 1200 rpm/series o

25Hz 32HZ 40HZ 50HZ 63HZ

SPm 4.6341877e - 06 7.3614618e - 06 3.4200583e - 06 1.2805152e - 06 3.6159190e - 06

H - 5.882078e - 15 - 1.772400e - 15 - 2.322412e - 15 5.3627985e - 16 - 9.507555e - 15

G 1.5934255e - 12 2.4690496e - 12 2.1626519e - 12 - 2.166788e - 13 3.0564218e - 11

80Hz 100HZ 125HZ 160HZ 200HZ

SPm 5.1242161e - 06 1.1354177e - 06 3.7938645e - 05 1.3948336e - 05 7.1861851e - 06

H - 3.195738e - 15 3.4179376e - 14 - 2.020492e - 13 - 3.278277e - 13 2.1050973e - 12

G 3.9202406e - 12 3.7056785e - 12 3.2372099e - 11 1.1511683e - 11 - 5.058072e - 12

31

250Hz 315HZ 400HZ 500HZ 630HZ

SPm 1.7192997e - 05 2.0719315e - 05 1.8146530e - 06 1.5461617e - 04 2.4508902e - 04

H 2.6456816e - 12 3.2509638e - 12 1.6411309e - 11 1.0061714e - 11 - 7.982198e - 11

G - 3.832337e - 12 - 1.523121e - 12 1.0974046e - 11 9.0204641e - 12 - 1.134215e - 11

800Hz 1000HZ 1250HZ 1600HZ 2000HZ

SPm 1.4561840e - 04 1.3326552e - 04 2.1012422e - 04 1.7351820e - 04 9.7221904e - 05

H - 3.569052e - 11 - 3.395378e - 11 - 3.257750e - 10 - 1.886194e - 10 - 1.629396e - 10

G - 6.830754e - 12 4.5703982e - 12 2.8358114e - 12 - 4.696706e - 12 8.4594107e - 12

2500Hz 3150HZ 4000HZ 5000HZ 6300HZ

SPm 5.8125812e - 05 3.7189128e - 05 9.5888868e - 06 9.0717825e - 06 1.9471131e - 05

H - 9.024305e - 11 - 9.765580e - 11 2.6139893e - 10 5.8560831e - 10 - 1.736967e - 10

G 3.5387316e - 12 5.1772712e - 12 2.7779087e - 12 5.1454043e - 13 7.0464897e - 13

8000Hz 10000HZ 12500HZ 16000HZ 20000HZ

SPm 8.2273738e - 06 4.5922076e - 06 - 5.545274e - 06 1.4685554e - 06 3.2048945e - 07

H - 2.442338e - 11 - 1.389578e - 10 5.5743608e - 10 - 1.265621e - 11 1.4851965e - 11

G 2.1497704e - 12 1.0207791e - 11 4.4766618e - 12 1.7830897e - 12 7.4228181e - 13


32


33

Figure 26,Correlation between cylinder pressure and engine torque at speed 1500 rpm/series o

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35


36


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Figure 30,Noise components versus frequency.Table-5 partial regression coefficients at speed 1500 rpm/series o


SPm 2.643450e - 06 9.6146913e - 06 - 3.502922e - 05 - 4.102199e - 05 2.1180756e - 06

H 1.1434255e - 15 - 1.201276e - 14 3.1276116e - 14 5.5606588e - 14 - 1.574138e - 15

G - 1.816627e - 12 2.7707079e - 12 - 3.414932e - 11 - 3.777882e - 11 9.9010088e - 13

38

80Hz 100HZ 125HZ 160HZ 200HZ

SPm - 5.351908e - 05 3.3960217e - 05 3.9432942e - 05 4.1673685e - 05 4.7498486e - 05

H 2.1578200e - 13 6.7970693e - 15 - 2.013934e - 14 - 2.553632e - 13 - 1.071087e - 12

G - 3.702781e - 11 1.6751260e - 11 3.0958201e - 11 1.2764820e - 11 3.1787896e - 11

250Hz 315HZ 400HZ 500HZ 630HZ

SPm - 4.698076e - 05 1.0372966e - 05 5.9608698e - 05 1.5796346e - 04 3.3130308e - 04

H 1.9555355e - 11 1.9555355e - 11 6.3149220e - 12 1.9103434e - 11 - 1.827530e - 10

G 6.1353337e - 12 1.3788011e - 12 1.6980422e - 11 3.1744097e - 11 2.5438485e - 12

800Hz 1000HZ 1250HZ 1600HZ 2000HZ

SPm 2.9996598e - 04 2.9744716e - 04 2.1469230e - 04 2.3404743e - 04 1.1575899e - 04

H - 5.136469e - 10 - 6.797151e - 10 - 3.419396e - 10 - 5.449314e - 10 2.087939e - 10

G - 4.323580e - 13 7.2303394e - 12 1.5565121e - 11 5.8121656e - 12 1.1555175e - 11

2500Hz 3150HZ 4000HZ 5000HZ 6300HZ

SPm 1.0129167e - 04 8.0604142e - 05 6.2254936e - 06 1.9070357e - 05 1.8523191e - 05

H - 7.303403e - 10 - 7.026466e - 10 9.9451926e - 10 1.1134731e - 10 9.2027640e - 11

G 2.8703574e - 12 2.9996347e - 12 3.2535088e - 12 1.1846489e - 12 2.7506966e - 13

8000Hz 10000HZ 12500HZ 16000HZ 20000HZ

SPm 8.1867319e - 06 7.2428792e - 06 2.4028096e - 07 1.1181912e - 05 1.0624579e - 06

H 4.3942435e-12 5.7180171e-11 2.2207683e-10 - 4.573889e - 10 2.0501185e - 11

G 2.0888108e - 12 8.0792516e - 13 6.7189208e - 12 6.9427548e - 12 6.5106795e - 13

Look at the positive partial regression coefficients we got over wide frequency band from 4kHZ to 12.5 kHZ,so the paper may be applicable here but I cannot decide without the measured values of the noise factors!.


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43

Figure 36,Noise components versus frequency.

Table-6 partial regression coefficients at speed 1800 rpm/series o


SPm 6.0365858e - 06 1.5246201e - 05 1.3958320e - 06 1.6382617e - 06 1.7561699e - 06

H - 3.356548e - 15 2.7379277e - 15 5.2769139e - 16 - 3.903053e - 19 - 4.845585e - 16

G 7.628612e - 13 - 5.433846e - 12 - 7.553445e - 14 6.1028465e - 13 1.5608811e - 12

80Hz 100HZ 125HZ 160HZ 200HZ

SPm 5.6828641e - 06 1.9452280e - 05 1.2286238e - 05 3.8695922e - 05 5.6191772e - 05

H 1.8400283e - 14 1.4762764e - 14 9.9263803e - 14 7.4813603e - 15 1.5439800e - 13

G - 2.213597e - 12 9.4171784e - 12 6.4698388e - 12 1.7170634e - 11 3.5924184e - 11

44

250Hz 315HZ 400HZ 500HZ 630HZ

SPm 7.8880661e - 05 3.6516285e - 05 1.0963761e - 04 2.2810689e - 04 3.8745300e - 04

H - 2.324280e - 12 9.9819208e - 12 1.1033952e - 11 - 1.117992e - 11 - 1.923968e - 10

G 3.0963385e - 11 2.0874578e - 11 4.3818349e - 11 1.2976953e - 10 6.4070473e - 11

800 Hz 1000HZ 1250HZ 1600HZ 2000HZ

SPm 2.4409295e - 04 9.6030036e - 05 1.5608834e - 04 2.5117465e - 04 1.2943001e - 04

H - 1.318966e - 10 1.2010078e - 09 1.2031174e - 09 1.7696983e - 10 3.2477294e - 10

G 3.3616192e - 11 1.9536555e - 11 5.4088116e - 11 2.7497813e - 11 3.6288849e - 11

2500Hz 3150HZ 4000HZ 5000HZ 6300HZ

SPm 6.9281058e - 05 8.6094102e - 05 - 4.978116e - 06 1.6787005e - 05 4.2213978e - 06

H 3.4169307e - 10 - 6.845321e - 10 2.9864716e - 09 1.2813327e - 09 1.3156320e - 09

G 2.0726968e - 11 1.4354393e - 11 - 8.139568e - 12 - 1.023360e - 12 - 1.871973e - 12

8000Hz 10000HZ 12500HZ 16000HZ 20000HZ

SPm 7.1168602e - 06 6.1333864e - 06 5.4464382e - 06 3.1244794e - 06 3.6762979e - 06

H 1.0109896e - 10 5.8013578e - 11 3.5567618e - 10 2.6622200e - 11 - 1.812055e - 11

G 2.9209401e - 12 1.1559489e - 12 1.3859876e - 12 1.5394210e - 12 6.7330735e - 13

Look at the positive partial regression coefficients we got over wide frequency band from 8kHZ to 16 kHZ,so the paper may be applicable here but I can not decide without the measured values for the noise factors!.

Series S

It would be rather excellent to try to find if the above estimation can be applicable under certain assumptions,and if so what are the assumptions?,I want now to attract your attention regarding my ideas I had to manage on series S to try to get some results according to the paper,and to be able to estimate the mechanical noise,combustion noise,and load-dependent noise. I need to recall my ideas at this moment :

45

The idea can be applied to series S because we have enough data sets,the idea is to divide the data sets we have for series s into three groups corresponding to the successive speeds 1200rpm,1500rpm,and 1800rpm,I have seen that each group has four torque levels or three depends on the group under test,and each level consists of 10 measurements approximately at the same torque point.Consequently,I have considered each 10 measurements as one point,then search among all the possible combinations about the best case that satisfy the two following conditions:

1-Very low correlation between engine noise and cylinder pressure over certain bandwidth.

2-A good multiple correlation coefficient over the whole bandwidth under examination.

Let us assume the above assumptions are true and apply my idea to series S and check the partial regression coefficients under the above assumptions.

1-at speed 1200rpm/series s

Figure 37,Multiple correlation at speed 1200 rpm/series s

46

Figure 38,Correlation between cylinder pressure and engine torque at speed 1200 rpm/series s

47

Figure 39,Correlation between engine noise and cylinder pressure and engine torque at speed 1200 rpm/series s

48

Figure 40,Sound pressure at speed 1200 rpm/series s

49

Figure 41,Cylinder pressure at speed 1200 rpm/series s

50

Figure 42,Noise components versus frequency

Table-7 partial regression coefficients at speed 1200 rpm/series s


SPm 3.4412823e - 06 3.9457766e - 06 2.1512757e - 06 5.2774004e - 06 2.4776010e - 06

H 5.7744493e - 12 - 5.058681e - 16 - 1.543590e - 13 1.6164396e - 15 - 4.795304e - 16

G - 9.544512e - 13 1.6423439e - 12 3.3948824e - 13 - 1.525731e - 12 7.7057512e - 13

51

50Hz 63HZ 80HZ 100HZ 125HZ

SPm 1.398783e - 06 1.6495925e - 06 4.0141526e - 06 6.9417405e - 06 2.2027212e - 05

H - 3.484131e - 16 - 7.490686e - 16 8.3349145e - 16 1.5847171e - 14 3.5525988e - 14

G 1.2693375e - 13 2.0349846e - 11 4.7692445e - 12 9.9907793e - 12 2.1221436e - 11

160 Hz 200HZ 250HZ 315HZ 400HZ

SPm 7.3007942e - 07 1.2128837e - 06 5.3134404e - 06 6.2445472e - 06 2.7698987e - 05

H 3.6246230e - 13 7.6061418e - 13 9.7933632e - 13 3.0867718e - 12 2.6687401e - 12

G 1.6677204e - 11 1.3830280e - 11 1.1107223e - 11 4.0504869e - 14 1.2114138e - 11

500 Hz 630 HZ 800HZ 1000HZ 1250HZ

SPm 6.2721214e - 05 9.9046221e - 05 1.2032911e - 04 7.8926441e - 05 1.1500325e - 04

H 2.3804481e - 11 - 1.401083e - 11 - 1.052493e - 10 - 2.809493e - 11 - 7.375823e - 11

G 5.8546351e - 11 3.6009618e - 11 1.5564113e - 12 2.2514091e - 11 2.5413343e - 11

1600Hz 2000HZ 2500HZ 3150HZ 4000HZ

SPm 6.5442142e - 05 2.4908180e - 05 1.7960474e - 05 2.1567420e - 05 5.5535623e - 06

H 3.0920062e - 10 3.5269000e - 10 3.4428068e - 10 5.5449979e - 11 3.1451287e - 10

G 2.8532920e - 11 3.1993135e - 11 1.3196098e - 11 8.9969572e - 12 2.8812349e - 12

5000 Hz 6300 HZ 8000 HZ 10000HZ 12500HZ

SPm 4.6314602e - 06 4.3205043e - 06 2.2182020e - 06 1.3923223e - 06 - 8.321185e - 06

H 4.7060169e - 10 - 4.622647e - 124.3983021e - 11 2.3583256e - 13 5.4228951e - 10

G 1.5737636e - 12 5.0811427e - 12 2.9781058e - 12 5.6828385e - 12 4.2488741e - 12

52

Look at the positive partial regression coefficients we got over wide frequency band from 80 HZ to 500 HZ and1.6kHZ to 5kHZ,check also the figures you would find them near to the figures in the paper.

2- at speed 1500 rpm/series s


53

Figure 44,Correlation between cylinder pressure and engine and engine torque at speed 1500 rpm/series s

54

Figure 45,Correlation between engine noise and cylinder pressure&engine torque at speed 1500 rpm/series s

55


56

Figure 48,Noise components versus frequencyTable-8 partial regression coefficients at speed 1500rpm/series s


SPm 3.3959958e - 06 3.7228986e - 06 2.4780721e - 06 4.9166225e - 06 2.6391674e - 06

H 1.7345290e - 14 - 1.864418e - 11 6.9214010e - 17 9.4418540e - 15 - 3.409367e - 16

G - 7.773971e - 13 5.3316324e - 13 - 4.033018e - 14 - 2.110804e - 14 5.3326672e - 13

50Hz 63HZ 80 HZ 100HZ 125HZ

SPm - 3.365659e - 06 5.0075641e - 07 1.6182398e - 05 1.9360522e - 05 1.4897618e - 05

H 5.8451243e - 15 9.0060850e - 16 1.2084490e - 13 2.1351542e - 14 8.7620690e - 14

G - 1.160921e - 13 - 1.946232e - 13 2.9441053e - 11 1.3593399e - 11 1.5943961e - 11

57

160Hz 200HZ 250HZ 315HZ 400HZ

SPm 4.1960793e - 05 2.7607951e - 05 1.5272264e - 05 1.3906510e - 05 5.5365175e - 05

H - 9.752961e - 14 4.1539804e - 13 1.9687554e - 12 3.4582085e - 12 1.0179896e - 11

G 8.5729964e - 12 2.1860804e - 11 2.0982926e - 11 3.6591086e - 12 1.8496508e - 11

500Hz 630HZ 800HZ 1000HZ 1250HZ

SPm 1.5572612e - 04 2.6937205e - 04 1.8656404e - 04 1.4536599e - 04 1.1599572e - 04

H 1.1822406e - 11 - 1.534301e - 10- 1.889647e - 10 - 8.630957e - 11 2.2467825e - 10

G 3.4524564e - 11 3.3575601e - 11 1.9387314e - 11 2.9150656e - 11 3.6241087e - 11

1600Hz 2000HZ 2500HZ 3150HZ 4000HZ

SPm 1.1211686e - 04 7.0264409e - 05 3.7856471e - 05 4.8983350e - 05 1.4422365e - 05

H 3.8721380e - 10 6.4055287e - 11 3.0930367e - 10 2.7891323e - 11 6.3678702e - 10

G 4.0546231e - 11 2.7518187e - 11 1.4795767e - 11 1.1487982e - 11 4.0516340e - 12

5000Hz 6300HZ 8000HZ 10000HZ 12500HZ

SPm 7.1747653e - 06 4.9397045e - 06 3.8841265e - 06 7.2669221e - 06 3.2052278e - 06

H 7.7073267e - 10 4.5828370e - 10 9.9848053e - 11 1.5248918e - 11 - 9.536199e - 11

G 1.2805811e - 12 1.4019688e - 12 2.1950619e - 12 3.8776369e - 12 1.1028736e - 11

Look at the positive partial regression coefficients we got over wide frequency band from 1.25kHZ to 10 kHZ,check also the figures you would find them near to the figures in the paper .

58


59

Figure 50,Correlation between cylinder pressure and engine noise at speed 1800 rpm/series s

60

Figure 51,Correlation between engine noise and cylinder pressure&engine torque at speed 1800 rpm/series s

61

Figure 52,Sound pressure at speed 1800 rpm/series s

62


63

Figure 54,Noise components versus frequencyTable-9 partial regression coefficients at speed 1800 rpm/series s


SPm 5.407505e - 06 3.2984606e - 06 2.0171813e - 06 4.0344994e - 06 2.4101607e - 06

H - 2.020927e - 16 - 3.315564e - 12- 4.369648e - 13 3.4258739e - 15 1.0135801e - 16

G 5.4597465e - 13 8.9726820e - 14 1.0414636e - 13 - 5.094107e - 12 2.2664665e - 13

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50 Hz 63HZ 80HZ 100HZ 125HZ

SPm 1.3944981e - 06 3.9080629e - 07 3.9083426e - 05 4.0916809e - 05 1.3832239e - 05

H 1.6405667e - 16 4.9958969e - 16 - 8.460924e - 15 - 1.295425e - 14 8.3523671e - 14

G 2.4190790e - 13 3.0142738e - 13 1.5334336e - 11 1.9526656e - 11 2.4160068e - 12

160Hz 200HZ 250HZ 315HZ 400HZ

SPm 2.8567490e - 05 8.1751356e - 05 9.6462354e - 05 1.8719501e - 05 1.1305188e - 04

H 6.3741861e - 14 7.5512488e - 14 - 2.944072e - 12 1.7764051e - 11 1.0567781e - 11

G 1.4831145e - 11 4.5898676e - 11 2.5945376e - 11 5.6774308e - 12 5.6328953e - 11

500Hz 630HZ 800HZ 1000HZ 1250HZ

SPm 2.8219336e - 04 3.8283312e - 04 3.0939065e - 04 9.8710571e - 05 1.9908751e - 04

H - 4.065396e - 11 - 1.241751e - 10 - 2.964239e - 10 1.4298066e - 09 4.2069026e - 10

G 1.3457753e - 10 8.1457608e - 11 2.6307779e - 11 2.1043651e - 11 6.2622922e - 11

1600Hz 2000HZ 2500 HZ 3150HZ 4000HZ

SPm 2.3054010e - 04 1.0746582e - 04 5.8062304e - 05 6.6977000e - 05 2.9820024e - 05

H 2.2253624e - 10 5.8946018e - 10 6.6433004e - 10 2.3120581e - 11 3.6102251e - 10

G 3.4596504e - 11 3.7707078e - 11 1.9796491e - 11 1.3039905e - 11 7.7839805e - 12

Look at the positive partial regression coefficients we got over wide frequency band from 1kHZ to 4000 HZ,check also the figures you would find them near to the figures in the paper.

5000Hz 6300HZ 8000HZ 10000HZ 12500HZ

SPm 1.1349281e - 05 7.4042721e - 08 4.9253671e - 06 7.0568582e - 06 4.2632504e - 05

H 1.5373212e - 09 1.5630698e - 09 1.8582220e - 10 3.9366464e - 11 - 8.759656e - 10

G - 2.722937e - 12 - 6.498433e - 12 2.5469171e - 12 2.3939527e - 12 1.9180094e - 11

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4.Discussion

I have faced many mistakes and contradictions in the paper under consideration and many confusing results in the experiments done. Firstly I list my comments on the paper and secondly my comments on the results.

My comments on the paper:

1-The paper starts with a graph Fig.1 showing a strong correlation between cylinder pressure and engine noise above 1.25 kHz to below 10 kHz,while Fig.8 shows a contradicting behaviour;a no virtually correlation between the engine noise and cylinder pressure over the same bandwidth.

2-The paper does not distinguish at all between the coefficient of correlation and the coefficient of determination,which is the square of the coefficient of correlation like most excellent statistical articles and lectures, i.e. It is stated in the paper that all figures are examining the correlation coefficients but what is really drawn in figures is the square of the correlation coefficient,which is known as the coefficient of determination. May be the japanese school has its own terminology.

3-Equation(5) explains how we can compute the coefficient of determination,which is the square of the correlation coefficient,but there is a mistake in equation(5);the mean of the fitted values in the numerator should be replaced by the mean of the measured values

4-Correlation coefficient has a meaning only when fitting linear model,and the engine noise is expressed as a linear function in the square of torque,so it is not right to write “we need to examine the correlation of engine noise with torque”,but the right is to write that “we need to examine correlation of engine noise with the square of torque”!!.

5-the paper says that the correlation between the explanatory variables over the bandwidth from 800 Hz to 4 kHz is low so they can be regarded as independent variables over this bandwidth,and I want to explain here that uncorrelation between two variables does not imply that they are independent. Independence is much stronger than uncorrelation.We can say that two variables are independent if and only if any function of the first variable is uncorrelated with any function in the second variable. Correlation implies there is some linear relation between the two variables.However,it is desirable to fit multiple regression linear model in orthogonal predictors(the correlation between every two predictors is zero)but that does not imply they are independent(see hierarchical regression analysis)

My comments on the results:

1-I always get at least one negative value among the values of the partial regression coefficients over most third octave frequencies when I have tried to apply this estimation to series V at different speeds 1200 rpm,1500 rpm,1800 rpm and series O at the same different speed which contradicts the physical meaning of power.

2-The paper says we should get a contribution of the load-dependent noise over wide frequency band under full load and should get a contribution of the combustion noise over a wide frequency band under partial load

66

3-The noise component curves I got for the mechanical noise,load-dependent noise,and combustion noise are not behaving like the ones in the paper under full or partial load,

4-The correlation between the explanatory variables does not show a bandwidth of no virtually correlation with each other which is one of the assumption to validate the accuracy of calculation

5-The multiple correlation coefficient does not show a strong correlation as estimated by the paper over the whole width under correlation,

6-Comparison the two curves I have got with the two curves correlating the engine noise with cylinder pressure and torque (the right the square of torque) in the paper they do not behave the same,and I have had to manage some ideas to get some logical results for series s but my ideas were rejected by the technical supervisor of my thesis work.

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1.5 Conclusions and recommendations

I need firstly to repeat that the paper assumes that we measured the contributed factors of noise in engine and we must compare between the measured noise factors and the computed ones because a strong correlation between engine noise and combustion excitation forces does not always exist in case of direct-injection diesel engine and I do not have the measured noise components to be able to do that!

My conclusion is that the results contradicts the logic because a logical thought would say that the partial regression coefficients calculated by the LSM must be positive simultaneously to verify the assumption about the noise power that assumed that consists of three components,any negative value for any factor contradicts our assumption!

Finally,I recommend that this estimation of contributing factors in engine noise by multiple regression analysis may be applicable only to the data sets of series o at speed 1500 rpm and speed 1800 rpm where I have got logical results,in other words the cut answer is possible if and only if we have measured the noise components,then I can compare them with the calculated ones for series O.

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1.6 SummaryDuring my thesis work at Volvo Technology Corporation/AB Volvo I have considered carefully the paper published by the society of the Automotive Engineers of Japan entitled “Using multiple regression analysis to estimate the contributions of engine-radiated noise components”(JSAE Review 20(1999) 363-368)and have applied this estimation to the data sets measured on Direct Injection Diesel Engine at Volvo Technology Corporation trying to estimate the contributions of combustion noise,mechanical noise,load-dependent noise,however at this stage it has become clear to me that the paper may be applicable to our data sets of series o at speed 1500 rpm and speed 1800 rpm,in other words I can not offer a cut answer due to the lack of the measured noise components of our DI diesel engine under test.

I shall mention my comments on the paper and the results :

1-I always get at least one negative value among the values of the partial regression coefficients over most third octave frequencies when I have tried to apply this estimation to series v at different speeds 1200 rpm,1500 rpm,1800 rpm and series O at the same different speed but series o has better results at speed 1500rpm I got positive partial regression coefficients from 4kHz to10kHz,and at speed 1800 from 8kHz to 16kHz but any negative value contradicts the physical meaning of power. The paper also says we should get a contribution of the load-dependent noise over wide frequency band under full load and should get a contribution of the combustion noise over a wide frequency band under partial load contrary to the noise component curves I have got for the mechanical noise,load-dependent noise,and combustion noise. They are not behaving like the ones in the paper under full or partial load. The correlation between the explanatory variables does not show a bandwidth of no virtually correlation with each other which is the main assumption to validate the accuracy of calculation. The multiple correlation coefficient does not show a strong correlation as estimated by the paper over the whole width under correlation. Comparison OF the two curves I have got with the two curves correlating the engine noise with cylinder pressure and torque (the right the square of torque)they do not behave the same.I have had to manage some ideas to get some logical results for series S but my ideas has been rejected rejected by the technicalsupervisor of my thesis work.

2-The paper is inaccurate in many details for instance:the paper starts with a graph Fig.1 showing a strong correlation between cylinder pressure and engine noise above 1.25 kHz to below 10 kHz,while Fig.8 shows a contradicting behavior;a no virtually correlation between the engine noise and cylinder pressure over the same band width.All excellent statistical articles and lectures distinguish apparently between the coefficient of correlation and the coefficient of determination,which is the square of the coefficient of correlation,all figures in the paper are examining the correlation coefficients but what is really drawn in figures is the square of the correlation coefficient,which is known as the coefficient of determination but may be the Japanese school has its own definitions. Correlation coefficient has a meaning as explained in the paper when fitting linear model,and the engine noise is expressed as a linear function in the square of torque,so it is not right to write “we need to examine the correlation of engine noise with torque”,but the right is to write that “we need to examine correlation of engine noise with the square of torque”. Equation(5) explains how we can compute the coefficient of determination which is the square of the coefficient of correlation,but there is a mistake in equation(5);the mean of the fitted values in the numerator should be replaced by the mean of measured values.

69

It is stated in the paper that the correlation between the explanatory variables over the bandwidth from 800 HZ to 4 kHz is low so they can be regarded as independent variables over this bandwidth.I want to explain here that uncorrelation between two variables does not imply that they are independent. Independence is much stronger than uncorrelation.We can say that two variables are independent if and only if any function of the first variable is uncorrelated to any function in the second variable.Correlation imply there is some linear relation between the two variables however it is desirable to fit multiple regression linear model in orthogonal predictors(the correlation between every two predictors is zero) but that does not imply they are independent(see hierarchical regression analysis).

3-For series S,I have divided all measured data sets (116 states of series s,each state is a different combination of torque,speed,injection timing(tm),needle opening pressure(nop),exhaust gas recirculation,..etc) into three groups corresponding to 1200 rpm,1500rpm,1800 rpm,within each group I found that measurements have been done at four or three torque levels depends on the group under test,in other words there are 10 measurements at the same torque point,for instance for speed 1800 rpm there are four points each one has repeated measurements 10 times,in addition two points have measured at very low torque. Based on the fact that multiple regression analysis may avoid the repeated measurements and the need to find bandwidth of virtually no correlation between cylinder pressure noise and engine sound noise,I have automated the MATLAB code to consider all possible combinations and to search for the most suitable combination that satisfies our requirements and I have been successful to get some logical results under the above assumption.However,I have been needing the measured values of the noise components to be able to compare them with the computed ones.I think my idea has enabled me to get some near results to the paper and it is the only logical way to apply multiple regression analysis according to the paper.

4-The paper assumes that we have measured the contributed factors of noise in engine and we must compare between the measured noise factors and the computed ones because a strong correlation between engine noise and combustion excitation forces does not always exist in case of direct-injection diesel engine and I do not have the measured noise components to be able to do that as the case of the results of series o at speed 1500 rpm and 1800 rpm as the paper may be applicable here!

My conclusion is that the results contradicts the logic because a logical thought would say that the partial regression coefficients calculated by the LSM must be positive simultaneously to verify the assumption about the noise power that is assumed It consists of three components,any negative value for any factor contradicts our assumption!

Finally,I recommend that this estimation of contributing factors in engine noise by multiple regression analysis may be applicable to the data sets of series o at speed 1500 rpm and 1800 rpm where I have applied it to them,in other words the paper and the methodology may be applicable to our DI diesel engine that is under test and noise components should be measured in a future thesis work to enable us to compare them with the computed noise components.

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Chapter 2The AVL(Concerto)Software

2.1 IntroductionCommercial softwares like AVL and D2T have systems for estimasion of sound noise from the pressure signal and the AVL system is here evaluated. AVL during the time delivered three versions of their software upon my request to try to make their system applicable to the massive data set I have had.The AVL system assumes that we have the speed signal,i.e. the change of speed versus time,but what I have had in the data sets is the average value of speed.Furthermore,the AVL system assumes pressure signal versus crank angle in the data set,but what I have had is pressure signal versus time.However,none of them has been successful to estimate the measured noise. It is possible that other sources exist for noise,other than combustion, i.e. injection may be a cause of noise.

Some research work have shown that there are many patterns of sound energy propagation through the engine block,whose response is a time-variant and nonlinear phenomenon. This behaviour complicates the study of the combustion noise mechanisms by means of a block response approach.Furthermore, despite the fact that noise emission due to the block vibration is mostly a linear phenomenon, nonlinearities become relevant again when the sound quality of noise is assessed.What is assumed by AVL software (Concerto) is that it filters the third octave spectrum. This means that the cylinde pressure is Fourier transformed,and the Fourier transformed data is gathered together at certain frequencies. Then there are correction terms for the different frequencies due to engine damping,the room damping and the sensitivity of the human air. In the end there is an estimated dB(A) value for the specific driving condition of the engine

According to the comments exposed above,combustion noise analysis of modern diesel engines seems to be unapproachable by means of the traditional objective criteria based on the estimation of the attenuation curve of the block and the overall in-cylinder noise,whose level and frequency repartition is not necessarily representative of the level and frequency repartition of the radiated noise,and less of sound quality that is contrary to AVL assumption.

71

2.2-Method

AVL has a software (Concerto) that assumes there are correction terms in dB for the different frequencies for damping from the engine and the sensitivity of the human ear.

The AVL noise estimation has been compared to the measured noise.

Tabel 10 explains the estimated values by AVL in dB for attenuation for each frequency in the third octave band

Tabel 10

f 80 100 125 160 200 250 315 400 500 630 800

dB 0 -143.0 -137.5 -132.4 -127.8 -122.3 -117.2 -112.3 -108.2 -103.8 -100

f 1000 1250 1600 2000 2500 3150 4000 5000 6300 8000 10000

dB -96.5 -93.3 -90.8 -89.7 -89.5 -90.6 -94.2 -99.2 -105.6 -116.0 -127.8

Tabel 11 explains the estimated values by AVL in dB for the sensitvity of the human air

f 80 100 125 160 200 250 315 400 500 630 800

dB -22.5 -19.1 -16.1 -13.4 -10.9 -8.6 -6.6 -4.8 -3.2 -1.9 -0.8

f 1000 1250 1600 2000 2500 3150 4000 5000 6300 8000 10000

dB 0 +0.6 +1.0 +1.2 +1.3 +1.2 +1.0 +0.5 -0.1 -1.1 -2.5

The method of AVL software is to subtracts the shown value in each table at each frequency from the cylinder pressure noise at that frequency.

72

2.3-ResultsSee tables for the calculated noises based on AVL and based on the Fourier transformed data given to me. Tables for calculations for series v and series o (third version of AVL software)

Tabel 12 Series v

State Cylinder Noise/AVL

Sound Noise/AVL Cylinder Noise/LMS

Sound Noise/LMS

v02 196.06 89.093 204 77.4

v03 197.74 76.413 208 81.7

v04 197.74 74.539 210 84.88

v05 202.41 77.958 213 86.8

v06 205.02 79.636 216 88.2

v07 207.32 80.488 218 89.4

v08 200.16 75.495 219 90

v09 200.34 76.121 219 91

v10 203.56 79.626 220 92

v11 205.69 82.091 221 92.8

v12 207.29 82.240 222 93.5

v13 201.95 96.053 222 93.8

v14 200.71 94.063 222 94.3

v15 203.76 97.357 223 94.8

v16 205.3 98.404 224 95.36

v17 205.97 99.189 224 95.88

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Tabel 13 Series o

State Cylinder Noise/AVL

Sound Noise/AVL Cylinder Noise/LMS

Sound Noise/LMS

O-02 193.15 64.287 202.27 77.8

O-03 195.30 66.619 206.44 81.9

O-04 196.24 70.647 208.53 85

O-05 200.83 77.254 211.46 86.9

O-06 203.90 81.475 214.42 83.6

O-07 206.56 82.899 216.88 89

O-08 198.03 68.991 217.39 90

O-09 198.53 69.894 217.83 90.8

O-10 202.44 72.589 218.68 91.7

O-11 205.07 78.122 219.79 92.5

O-12 206.77 81.599 221 93.27

O-13 200.59 68.442 221.38 93.3

O-14 199.37 69.025 221.64 94

O-15 202.81 73.821 222.11 94.5

O-16 204.84 75.379 222.71 95.1

O-17 205.72 76.242 223.33 95.67

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Figure 55,The correlation between the estimated cylinder pressure noise by AVL(Concerto) and the estimated cylinder pressure noise through LMS for series v.

75

Figure 56,The correlation between the estimated sound noise by AVL(Concerto) and the estimated sound noise through LMS for series v.

76

Figure 57, The correlation between the estimated cylinder pressure noise and the estimated cylinder pressure noise through LMS for series o.

77

Figure 58,The correlation between the estimated sound noise by AVL(Concerto) and the estimated sound noise through LMS for series o.

78

2.4-DiscussionsThere is no real trend between cylinder pressure and noise for separate frequencies.This means that the AVL(Concerto) is not applicable since it subtracts dB values for each frequency.

Possibly injection pressure and turbo speed add noise.Such parameters are not taken into consideration here.Also different frequencies could be generated from a given frequency.Those questions are to be considered in the future thesis work.

Figures below demonstrate that there is no mathematical relation between cylinder pressure noise and sound noise for each frequency.

Figure 59,Relation between cylinder pressure noise and sound noise at 1000 HZ.

79

Figure 60,Relation between cylinder pressure noise and sound noise at 2000 HZ

80


81


82

2.5-Conclusions and Recommendations

It is clear that from our previous discussions that the agreement between AVL noise and measurement is really poor.All figures show that the noise estimated by AVL has weak correlation with the measured one.

We can see that

1-Different versions of the software gives very diffeent results

2-There is no even monotonic trend in the AVL values

The second pint indicates that the AVL method is not even useful for indicate trends in noise.

83

2.6-Summary

Commercial softwares like the AVL(Concerto) and D2T have systems for estimation of the sound noise from the cylinder pressure noise.Here we have studies the applicability of the AVL (Concerto) software to the measured data set of the engine under test.

AVL during the time delivered three versions of their software.None of them has been successful to estimate the measured noise.It is possible that other sources exist for noise,other than combustion.i.e. injection may be a cause of noise.

AVL has a software (Concerto) that filters the third octave spectrum.This means that the cylinder pressure is Fourier transformed,and the Fourier transformed data is gathered together at certain frequencies.Then there are correction terms for the different frequencies due to the engine damping,the room damping and the sensitivity of the human ear.In the end there is an estimated dB(A) value for the specific driving condition of the engine.

The AVL noise estimation has been compared to the measured noise,and we have found the following:

1-The agrement between AVL noise and measured noise is really poor.

2-Different versions of the software gives very different results.

3-There is no even monotonic trend in the AVL values.

84

Chapter 3The Volvo Technology ´s Model

3.1-IntroductionIt has become clear for us that we should develop a new model based on our data set,as we have seen neither multiple regression for estimation of radiated noise components nor the Commercial AVL (Concerto) software has been successful to estimate the sound noise from the cylinder pressure noise according to the measured data we have.

It has been an excellent idea to try to study the possible mathematical relation between the average noise from the cylinder noise spectrum with the average sound noise from the sound noise spectrum.

The idea may be successful to find the correlation between the average cylinder noise with the average sound noise,and the transfer function should be the same for all series V,O,D,and S which we are looking for.

3.2-Method

Our technique is based on the following steps:

1-Calculation of the average value of the cylinder noise from the cylinder pressue noise spectrum.

2-Calculation of the average value of the sound noise from the sound noise spectrum.

3-Studying the correlation between the average value of the sound noise and the average value of the Cylinder noise

4-Trying establishing one transfer function for all the series O,S,V and D.

85

3.3-ResultsThe results are explained in the figures below:

Figure 63,The correlation between the average sound noise and the average cylinder noise for series s

86

Figure 64,The corelation between the average sound noise and the average cylinder noise for series v

87

Figure 65,Correlation between the average sound noise and the average cylinder noise for series o

88

Figure 66,The correlation between the average sound noise and the average cylinder noise for series d

89

3.4-Discussions

We have seen a linear relation between the average sound noise and the average cylinder noise for all series O,S,V and D.The transfer function,i.e.the coefficients of the straight line equation tends to beconstant,If we denote the sound noise by SP and the cylinder noise by CP we can write the following relations:

SP=0.5 *CP-24 for series s (1)

SP=0.53* CP -32 for series v (2)

SP=0.51 *CP -27 for series o (3)

SP=0.94*CP -120 for series d (4)

On average,we can assume a straight line equation for all series S,V,D,and O

SP=0.62*CP -50.75 for the engine under test (5)

The above equation the transfer function between the average cylinder noise and the average sound noise.

3.5-Conclusions and recommendations

A linear relation between the average cylinder noise and the average sound noise has been established for all series S,D,V and O which represents the transfer function between CP and SP for the engine under test.

90

3.6-SummaryIt has become clear for us that we should develop a new model based on our data set,as we have seen neither multiple regression for estimation of radiated noise components nor the commercial AVL (Concerto) software has been successful to estimate the sound noise from the cylinder noise according to the measured data we have.

It has been an excellent idea to try to study the possible mathematical relation between the average noise from the cylinder noise spectrum with the average sound noise from the sound noise spectrum.

Indeed the idea has been successful to find a correlation between the average cylinder noise with the average sound noise,and the transfer function seems to be the same for all series V,O,D,and S which what we are looking for.

91

Bibliography[1] F Payri,A Broatch,B Tormos and V Marant 2004 New methodology for in-cylinder pressure analysis in direct injection diesel engines-Application to combustion noise,Institute of Physics Publishing,Mea.Sci.Technol.16(2005)540-547

[2] Anderton D 1979 Relation between combustion system and noise SAE pAPER 790270

[3] Heywood J B 1986 Fluid motion within the cylinder of internal combustion engines-The 1986 Freeman Scholar Lecture J.Fluids Eng.109 3-35

[4] Osawa H and Nakada T 1999 Pseudo cylinder pressure excitation for analysis the noise characteristics of the engine structure JSAE Rev.20 67-72

[5] Stankovic L and Böhme J F 1999 Time-frequency analysis of multiple resonances in combustion engine signals Signal Process.79 15-28

[6]Saad AAA,EL-Sebai NA.Combustion noise prediction inside diesel engine.SAE paper 1999-01-1774.1999

[7] Ren,Y.,Randall,R.B.,Milton,b.e. Influence of the resonant frequency on control of knock in Diesel Engines.IMechE paper D04767.1999

[8] Russel MF,Haworth R.Combustion noise from high speed direct injection diesel engine.SAE paper850973.1985

[9] F.Payri,J. Benajes,X.Nargot,A.Gil.CFD modeling of the in-cylinder flow in direct-injection Diesel Engine.Computers&Fluid 33(2004)995-1021.2004

[10] LJ.Stankovic:”A method for time-frequency signal analysis”IEEE Trans.SP,Vol.42,Jan.1994,PP. 225-229

[11] LJ.Stankovic:”An analysis of some time-frequency and time-scale distribution”Ann.Telec.,Vol.49,No.9-10,Sep./Oct.1994,pp.505-517.

[12] LJ.Stankovic,J.F.Böhme: ”Time-frequency analysis of multiple resonances in combustion engine signals”Sig.Proc.,Vol.79,No.1,pp.15-28,Nov.1999.

[13] O. Boubal:”Knock detection in automotive engines” IEEE Inst.Meas.Mag.,Vol.3,No.3,2000,pp.24-28

[14] Robert Hickling,Douglas A. Feldmaier,Francis H.K.Chen,and Josette S.Morel,”Cavity resonances in engine combustion chamber and some applications”J.Acoust.Soc.Am.vol.73(4),pp.1170-1178,1983.

[15] Tony Gustafsson,Subspace Methods for System Identification and Signal Processing,Ph.D.thesis, Chalmers University of Technology,Göteborg,1999

[16] Chui,C.K.,1992,An Introduction to wavelets,volume 1:Wavelets Analysis and Its Applications, Academic Press,San Diego,CA

[17] Cohen L.1989,”Time-FRequency Distributions-A Review” Proc.IEEE,77 No.7,pp.941-981

[18] G.T.Zheng,A.Y.T.Leung:”Internal Combustion Engine Noise Analysis With Time-Frequency Distribution” July 2002,Vol.124

[19] Izuho Hirano,Masahiko Kondo,Youichi Uraki,Yasuyuki Asahara:” Using multiple regression analysis to estimate the contributions of engine-radiated noise components” JSAE Review 20(1999)363-368

[20] Yanagii,H .et al.,Multivariate Analysis Handbook,Gendai Sugakusha,Tokyo(1986)(in Japanese)

[21] Ozawa,K. Et al.,evaluation of the noise characteristics of an engine structure by pseudo cylinder pressure excitation,Preprint of Scientific Lecture Series of JSAE,976(October1997) (in Japanese).

[22] Nakajima,k.et al.,Measurement of structural attenuation of a diesel engine and its application for reduction of noise and vibration,J.JSAE,Vol.46,No.6,pp.82-87(1992)(in japanese)

Appendix A_________________________________________________The Matlab codes used are:

clear allclose alldiary off %for l=1:4%TypeOfSerie=['serie v','serie o','serie d','serie s'] %Which cataloge should be evaluated %RUN=TypeOfSerie(7*l-6:7*l); RUN='serie v' m=1;Nl=5; %minimum number of lines per third octave band Np=1000; %time window (in samples) in which you are sure that only one combustion occurs %correction for the Aweighting fm_values = [16 20 25 31.5 40 50 63 80 100 125 160 200 250 315 400 500 630 800 1000 1250 1600 2000 2500 3150 4000 5000 6300 8000 10000 12500 16000 20000]; % third oc-tave band midvalues (sorry for the scale model factor)DL_A=[56.7 50.5 44.7 39.4 34.6 30.2 26.2 22.2 19.1 16.1 13.4 10.9 8.6 6.6 4.8 3.2 1.9 0.8 0 -0.6 -1 -1.2 -1.3 -1.2 -1 -0.5 0.1 1.1 2.5 4.3 6.6 9.3]; sep='\'; %depends on the computermain1=pwd; %check the cataloge and find the different sets D=dir(['\\vcn\vtec-got\Assignments\SAP10400174\Workmaterial and data\Sven\Ljudkorr\Data',sep,RUN]);fname={D.name};index_dir_set=find(strncmp(fname,'.',1)==0) mark=num2str(clock);j=1;%y=zeros(16,2);n=1;for idir_set=1:length(index_dir_set) diary off STATE=fname{index_dir_set(idir_set)}; STATE_dir=['\\vcn\vtec-got\Assignments\SAP10400174\Workmaterial and data\Sven\Ljudkorr\Data',sep,RUN,sep,STATE]; goal_dir=['result_',RUN,'_',STATE];

mkdir(['\\vcn\vtec-got\Assignments\SAP10400174\Workmaterial and data\Sven\Ljud-korr\Data',sep,goal_dir]) diary(['\\vcn\vtec-got\Assignments\SAP10400174\Workmaterial and data\Sven\Ljud-korr\Data',sep,goal_dir,sep,'run_evaluate_third_octav3.txt']) disp(['date/time: ',mark]) disp(['evaluate_third_octav3.m']) disp(['cataloge: ',RUN]) disp(['State: ',STATE]) disp(['results in:\\vcn\vtec-got\Assignments\SAP10400174\Workmaterial and data\Sven\Ljudkorr\Data',sep,goal_dir]) D=dir(STATE_dir); fname_files ={D.name}; Nfiles=length(fname_files); iblocks=0; i=1; for ifile=1:Nfiles dummy=fname_files{ifile}; if strcmp(dummy(1),RUN(7)) load([STATE_dir,sep,fname_files{ifile}]) disp(['evaluate file: ', STATE_dir,sep,fname_files{ifile}]) [l,k]=size(thisData); Amp=thisData(:,2:3:k); seta=thisData(:,3:3:k); f=thisData(:,1); N1=5; Amp1=Amp.^2; %P=Amp1.* cos(seta); %P=Amp1.*Amp1+Amp2.*Amp2; X(:,i)=mean(Amp1,2); i=i+1; if i==12 % evaluate the records of length Ilength [N,Nchannel]=size(X); iblocks=iblocks+1; if iblocks==1 [Lm,fm]=third_octave_f(X(:,1),f,Nl); Nf=length(fm); power=zeros(Nchannel,Nf); for ichannel=1:Nchannel; [Lm,fm]=third_octave_f(X(:,ichannel),f,Nl); power(ichannel,:)=power(ichannel,:)+10.^(Lm/10) end else for ichannel=1:Nchannel; [Lm,fm]=third_octave_f(X(:,ichannel),f,Nl); power(ichannel,:)=power(ichannel,:)+10.^(Lm/10); end end end end end

% -20*log10(20*10^-6)=93.9794 Lthird=10*log10(power/iblocks)+93.9794; %Calibration C(1)=0; C(2)=0; C(3)=0; C(4)=0; C(5)=0; C(6)=0; C(7)=0; C(8)=0; C(9)=0; C(10)=0; C(11)=0; %C(12)=52.3; %C(13)=26; %C(14)=0; %C(15)=0; %C(16)=49.6; ii_fm1=find(fm(1)==fm_values); ii_fm2=find(fm(end)==fm_values); Correct=DL_A(ii_fm1:ii_fm2); for ichannel=1:Nchannel Lthird(ichannel,:)=Lthird(ichannel,:)+C(ichannel); LthirdA(ichannel,:)=Lthird(ichannel,:)-Correct; Ltot(ichannel)=10*log10(sum(10.^(Lthird(ichannel,:)/10))); LtotA(ichannel)=10*log10(sum(10.^(LthirdA(ichannel,:)/10))); end % Z(m,:)=LtotA; % m=m+1; %ind=[1 2 3 4 7 10]; %figure(j);semilogx(fm,Lthird(ind,:)) %legend('Mic 1', 'Mic 10','Mic 2','Mic 3','Mic 6','Mic 9',4) %xlabel('Frequency') %ylabel('Decibels') %title(['STATE: ',STATE]) s1=0; for i=1:10 s1=s1+10.^(0.1*Lthird(i,:)); end m1=length(s1); for j1=1:m1 AverageLthird(j1)=10*log10(s1(j1)/10); end Y1(m,:)=AverageLthird; A(m,:)=Lthird(11,:); m=m+1; ind=[11]; figure(j+5);semilogx(fm,LthirdA(ind,:)) legend('cylinder P 1',4)

xlabel('Frequency') ylabel('Decibels') title(['STATE: ',STATE]) ind=[1 10 2 3 4 5 6]; figure(j+6);semilogx(fm,LthirdA(ind,:));hold on semilogx(fm,LthirdA(7,:),'g');hold on semilogx(fm,LthirdA(8,:),'c');hold on semilogx(fm,LthirdA(9,:),'m') legend('Mic 1', 'Mic 10','Mic 2','Mic 3','Mic 4','Mic 5','Mic 6','Mic 7','Mic 8','Mic 9',4) xlabel('Frequency') ylabel('Decibels') title(['STATE: ',STATE]) disp(['Ltot: ',num2str(Ltot)]) disp(['LtotA: ',num2str(LtotA)]) s=0; for i=1:10 s=s+10^(0.1*LtotA(i)); end AverageLtotA=10*log10(s/10); AverageLtotAWithoutMicrophone5=10*log10((s-10^(0.1*LtotA(5)))/9); disp(['AverageLtotAWithoutMicrophone5: ',num2str(AverageLtotAWithoutMicro-phone5)]) disp(['AverageLtotA: ',num2str(AverageLtotA)]) y(n,1)=AverageLtotA; y(n,2)=AverageLtotAWithoutMicrophone5; n=n+1; disp(['end of calculation']) %eval(['save ',sep,goal_dir,sep,'third_oct fm Lthird LthirdA C mark Ltot LtotA']) j=j+7; end % calculation of the average value for all frequency levels in serie v[t,u]=size(Y1);

for i=1:ts=0;for j=1:u s=s+10^(0.1*Y1(i,j));endTheAverage2(i,1)=10*log10(s/u);enddisp(['TheAverage: ',num2str(TheAverage)])y;A;Y1;E=A\Y1;

And the following two functions:

function [Lm,fm]= third_octave_f(ps,f,Nl);% caluclates the third octave band values "Lm" at the frequencies "fm" for a% given time signal "signal"%% returns Lm and fm on for such third octave bands where at least Nl% values are contained in the band% % % it applies a hanning window% Nl=5;% Fs=50000;% N=1024*8;% t=(0:N-1)/fs;% amp=3.2% signal=amp*(rand(1,N)-0.5)*2;%sin(5000*2*pi*t);% %amp^2 fm = [16 20 25 31.5 40 50 63 80 100 125 160 200 250 315 400 500 630 800 1000 1250 1600 2000 2500 3150 4000 5000 6300 8000 10000 12500 16000 20000]; % third octave band midvalues (sorry for the scale model factor)sig=fm*0;N=length(ps); ep=2^(1/5); %f=(0:N-1)*df; % calculation of the Schroedercurves using a butterworth filterfor jj = 1:length(fm) flow=fm(jj)/ep;fup=fm(jj)*ep; ii=find(f>flow & f<fup); if length(ii)>=Nl power=sum(ps(ii)); Lm(jj)=10*log10(power); sig(jj)=1; endendiii=find(sig==1);fm=fm(iii);Lm=Lm(iii);

function [Lm,fm]= third_octave(signal,Fs,N1)% calculates the third octave band values "Lm" at the frequencies "fm" for% a given time signal "signal"%% returns Lm and fm on for such third octave bands where at least N1% values are contained in the band%%% it applies a hanning window% N1=5;% Fs=50000;% N=1024*8;% t=(0:N-1)/Fs;% amp=3.2% N=100;% signal=amp*(rand(1,N)-0.5)*2;%sin (5000*2*pi*t);% % amp^2 fm = [16 20 25 31.5 40 50 63 80 100 125 160 200 250 315 400 500 630 800 ... 1000 1250 1600 2000 2500 3150 4000 5000 6300 8000 10000 12500 16000 20000];% third octave band midvalues (sorry for the scale model factor)sig=fm*0;N=length(signal);window_h = hanning(N)';s1=signal'.* window_h;w1=mean(signal.^2)/mean(s1.^2);sp=fft(s1);ps=2*w1*(abs(sp)/N).^2; ep=2^(1/6);df=Fs/N; f=(0:round(N/2)+1)*df; % calculation of the Schroeder curves using a Butterworth filter.for jj=1:length(fm) flow=fm(jj)/ep;fup=fm(jj)*ep; ii=find(f>flow&f<fup); if length(ii)>=N1 power=sum(ps(ii)); Lm(jj)=10*log10(power); sig(jj)=1; endendiii=find(sig==1);fm=fm(iii);Lm=(iii);

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