research article application of cfd, taguchi...

Research ArticleApplication of CFD Taguchi Method andANOVA Technique to Optimize Combustion andEmissions in a Light Duty Diesel Engine

Senlin Xiao Wanchen Sun Jiakun Du and Guoliang Li

State Key Laboratory of Automotive Simulation and Control Jilin University Changchun 130022 China

Correspondence should be addressed to Wanchen Sun sunwcjlueducn

Received 9 April 2014 Accepted 11 July 2014 Published 24 July 2014

Academic Editor Zhijun Zhang

Copyright copy 2014 Senlin Xiao et al This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Some previous research results have shown that EGR (exhaust gas recirculation) rate pilot fuel quantity and main injection timingclosely associated with engine emissions and fuel consumption In order to understand the combined effect of EGR rate pilot fuelquantity and main injection timing on the NO

119909(oxides of nitrogen) soot and ISFC (indicated specific fuel consumption) in this

study CFD (computational fluid dynamics) simulation together with the Taguchi method and the ANOVA (analysis of variance)technique was applied as an effective research tool At first simulation model on combustion and emissions of a light duty dieselengine at original baseline condition was developed and the model was validated by test At last a confirmation experiment withthe best combination of factors and levels was implemented The study results indicated that EGR is the most influencing factoron NO

119909 In case of soot emission and ISFC the greatest influence parameter is main injection timing For all objectives pilot fuel

quantity is an insignificant factor Furthermore the engine with optimized combination reduces by at least 70 for NO119909 20 in

soot formation and 1 for ISFC in contrast to original baseline engine

1 Introduction

Because of robust design and high thermal efficiency dieselengines increasingly and widely are applied to many powerequipments including light duty medium duty and heavyduty vehicles [1 2] However under the pressure of environ-mental pollution and energy crisis diesel engine performanceis being continuously enhanced in order to pursue a higherpower density improve fuel economy and reduce emissionscharacteristics to meet more stringent emissions regulations[1 3ndash6]

There are many factors that will affect the combustionand emissions of diesel engines due to the fact that thecombustion process and emission formation are a processof complex physical and chemical action Taguchi method isone of the efficient and cost-effective methods to parametricanalysis and optimization performance of objectives with aremarkable decrease in number of trials and time involved[7ndash9] Thus the combination of test and Taguchi method isan effective way to investigate the effect of different factors

in controlling the emissions and fuel economy [1ndash3 10 11]However the decreased experimental numbers still spend alot of time and money

On the other hand current multidimensional CFD toolshave become sufficiently mature to guide the design anddevelopment of more efficient and cleaner internal com-bustion engines [4] Indeed simulation models can provideinsight into the fundamental processes occurring duringcombustion [12] For example Mobasheri et al [13] success-fully used a CFD simulation to explore the combined effectsof pilot post- and multiple-fuel injection strategies and EGRon engine performance and emission formation in a heavydutyDI-diesel engineThey found that injecting adequate fuelin postinjection at an appropriate EGR allows significant sootreduction without a NO

119909penalty rate

In addition numerous studies have shown that EGRrate pilot fuel quantity and main injection timing associatedwith engine NO

119909 soot emissions and fuel consumption [1ndash

3 10 11 13] It is necessary to conduct an in-depth study inorder to understand these three factors on the relationship

Hindawi Publishing CorporationMathematical Problems in EngineeringVolume 2014 Article ID 502902 9 pageshttpdxdoiorg1011552014502902

2 Mathematical Problems in Engineering

(+EGR)

Auto-ignition(oxidation)

Flame propagation

Turbulent mixing

Turbulent mixing

Burnt gases

Diffusion flamePremixed flame(oxidation + pollutant formation)

A = unmixed air Au Ab

Mu Mb

Fu Fb

M = mixed air and fuel

F = unmixed fuel

u = unburned gases b = burned gases

Figure 1 Principle of the ECFM3Z model

with emissions and fuel consumption Hence in this studyCFD simulation together with the Taguchi method wasused to investigate the combined effect of EGR rate pilotfuel quantity and main injection timing on the NO

119909 soot

and ISFC in a diesel engine at 2000 rmin 50 load withminimum number of experimental work Moreover forpurpose of determining the effects of influencing factors onthe objectives the ANOVA technique was applied to analyzesimulation results

2 Computational Procedure ModelValidation and Methodology

21 Computational Procedure The multidimensional CFDcalculations were carried out with the commercial CFDpackage AVL FIRE The turbulent combustion model isECFM-3Z (extended coherent flame model-3 zones) whichincludes a description of the local mixture stratification byconsidering three mixing zones (see Figure 1) the unmixedfuel zone F themixed zoneM containing fuel and air and theunmixed air zone A Each region is separated into two partsunburned gases region and burned gases region [14 15] Forturbulent combustion phenomena the ECFM-3Zmodel usesa 2-step chemistrymechanism for the fuel conversion like [16]

C120572H120573O120574+ (120572 +

120573

4

minus

120574

2

)O2997888rarr 120572CO

2+

120573

2

H2O (1)

C120572H120573O120574+ (

120572

2

minus

120574

2

)O2997888rarr 120572CO +

120573

2

H2

(2)

where 120572 120573 and 120574 are the atomic number of C H and Oelements respectively

The 119896-120577-119891 turbulentmodel is used formodeling turbulentflow in the combustion chamber Hanjalic et al developedthis model They proposed an eddy viscosity model basedon the concept of Durbinrsquos elliptic relaxation which solvesa transport equation for the velocity scales ratio 120577 = V2119896instead of V2 thus increasing the robustness of the modelreducing the sensitivity to nonuniform grids [17]

xy

z

Figure 2 Mesh of the simulation model on the TDC

Table 1 Computational submodels

Breakup model KH-RT modelEvaporation model Dukowicz modelCollision model Nordin modelWall interaction model Bai Gosman model

The extended Zeldovich mechanism is widely acceptedas a successful method for predicting NO concentrationChemical reactions are given by [6 17]

O + N2

1205811

larrrarr NO + N (3)

N +O2

1205812

larrrarr NO + N (4)

N +OH1205813

larrrarr NO +H (5)

where 1205811 1205812 and 120581

3are the rate of reaction the unit is

cm3molsdotsThe Soot emission is calculated using the kinetic model

which contains a reduced number of species and reactionsand has been developed in order to provide a computationallyefficient kinetic overall soot model [16] Table 1 presentsseveral submodels used in present simulation model [13 18]

Because of the symmetry of the combustion chamber andthe injector at the center of the combustion chamber has 6nozzle holes the simulations were performed on one sixth ofthe combustion chamber for reducing calculation time Themesh of the simulation model on the top dead center wasshown in Figure 2

22 Model Validation The engine used in this study wasa light duty 4 cylinders four stroke turbocharging water-cooled diesel engine Specifications of simulated engine werelisted in Table 2 The experimental system was shown inFigure 3 The in-cylinder pressure was measured using aKistler 6125B quartz crystal pressure sensor and an ONOSOKKI DS-9100 Combustion analyzer was used to analyzethe in-cylinder pressure The NO

119909emissions were measured

with a HORIBA MEXA-7100D exhaust gas analyzer Smokewas measured by smoke meter

The simulated original baseline condition of light dutydiesel engine was actuated at 2000 rmin 50 load witha pilot injection included in injection strategy Table 3 listsinitial conditions and input parameters for simulation

Mathematical Problems in Engineering 3

2120

19

18

17

15

14

13

12

111098

7

4

1 2

3

5

6 16

Figure 3 Schematic of experimental setup (1)Dynamometer controller (2) dynamometer (3) charge amplifier (4) PC control (5) pressuresensor (6) engine (7) encoder (8) intake cooler (9) Compressor (10) gas flowmeter (11) air cleaner (12) exhaust gas analyzer (13) smokemeter (14) turbine (15) EGR valve (16) EGR cooler (17) ECU (18) PC control (19) common rail (20) fuel flow meter (21) fuel tank

Table 2 Engine specifications

Bore (mm) 93Stroke (mm) 102Compression ratio 172Swirl ratio 2Number of nozzle holes 6Number of cylinders 4Displacement (L) 2771Intake valve closure (CAD) 595Exhaust valve opening (CAD) 846

Table 3 Calculation conditions

Intake temperature (IVC) (K) 330Intake pressure (IVC) (MPa) 0151Engine speed (rmin) 2000EGR rate 7Start of pilot injection (∘BTDC) 19Start of main injection (∘ATDC) 1Fuel injection quantity (mgcycle) 25

TDC (top dead center) on the compression stroke wasdefined as 720CAD (crank angle degree) To ensure theintake air mass amount was equal to the actual quantity ofinlet air the intake air temperature obtained after severaladjustments was set to 330K at IVC (intake valve closure)The well-justified assumptions were applied such that anentire piston surface cylinder head and cylinder wall areat constant temperature Constant temperature of 500K wasused for the piston surface and cylinder head and 400K

was used for the cylinder wall From Figures 4 and 5 itcan be seen that good agreement was achieved betweenexperimental and simulation results Although there are stillsome differences between simulation and test Mobasheriet al [13] considered that these variances related to testinguncertainties in input parameters for the simulations forinstance the precise injection duration start of injectiontiming and gas temperature at intake valve closure

23 Taguchi Method Instead of having to test all possiblecombinations of design parameters the Taguchi methodtests the special combinations according to arrangementsof orthogonal arrays (OA) in order to obtain the optimalcombination and save time and expenses [9 19 20] Anotherimportant feature of Taguchi method is the signal-to-noise(119878119873) ratio employed as a measure of the impact of noisefactors on performance In this study EGR rate pilot fuelquantity and main injection timing were considered as theinfluencing factors In addition NO

119909emission soot emis-

sion and ISFC are the response variables of the influencingfactors Therefore the smaller-the-better characteristics of119878119873 ratio are used in the present study 119878119873 ratio is calculatedas depicted in the following formula

119878119873 = minus10 log 1

119899

(

119899

sum

119894=1

1199102

119894) (6)

where 119910119894is the value of the quality characteristic measured

from the test and 119899 is the number of experiments The unit of119878119873 ratio is dB [2 9 19 20]

In present study a L9orthogonal array for three factors

and three levels was chosen and given in Table 4 The detailsof three levels of the chosen factors are given in Table 5


Table 4 Taguchirsquos L9 orthogonal arrays

Trialnumber

Column 1(EGR rate)

Column 2(pilot fuelquantity)

Column 3(main injection

timing)1 1 1 12 1 2 23 1 3 34 2 1 25 2 2 36 2 3 17 3 1 38 3 2 19 3 3 2

Table 5 Factors with chosen levels

Factors Levels of factors1 2 3

EGR rate 7 17 27Pilot fuel quantity 72 172 272Main injection timing 4∘CA BTDC 1∘CA ATDC 6∘CA ATDC

InTable 4 columns 1 2 and 3 represent the EGR rate the pilotfuel quantity and main injection timing respectively

EGR rate used in this work was calculated by the concen-tration of carbon dioxide in intake and exhaust gas throughthe following equation

EGR =

[CO2]inlet

[CO2]exhaust

times 100 (7)

Definition of pilot fuel quantity was the proportion of anexisting fuel quantity in pilot injection which accounted forthe total cycle fuel injection quantity

24 ANOVA Technique ANOVA is an analytical methodthat identifies the factor which shows a large dispersion byanalyzing the dispersion of the characteristic value of eachfactor [20] In this study the data from CFD simulationresults based on the orthogonal arrays can be analyzed byperforming analysis of variance to illustrate the degree ofimportance of each factor that prominently influenced theresponse variables [11 19 20]The correspondingmathemati-cal statements established with ANOVA for present study aregiven in (8) (9) (10) and (11) Equation (8) expresses themean value of the overall 119878119873 where 119896 is the number of thetrials The sums of squares due to the variations of the overallmean (SS) is calculated by (9) The mean of the influencingfactors (SS

119894) is expressed as (10)The percentage contribution

of the individual factors on the chosen response variables will

0

3

6

9

690 720 750 780 810

TestSimulation

Pres

sure

(MPa

)

Crank angle (CAD)

Figure 4 Comparison of calculated and measured on cylinderpressure

0

30

60

90

690 720 750 780 810

TestSimulation

Hea

t rele

ase r

ate (

JCA

D)

Crank angle (CAD)

Figure 5 Comparison of calculated and measured on heat releaserate

be considered to decide the optimized combination and it iscalculated through (11) as follows [2 9 19 20]

119878119873 =

1

9

9

sum

119896=1

(119878119873)119896 (8)

SS =

9

sum

119894=1

((119878119873)119894119895minus 119878119873)

2

(9)

SS119894=

3

sum

119895=1

((119878119873)119894119895minus 119878119873)

2

(10)

Contribution =

SS119894

SStimes 100 (11)

3 Results and Discussion

31 CFD Computational Results After conducting the CFDsimulation with the combination of influencing factors fromL9OA the computational results of response variables were

shown inTable 1The results derived from the calculatedmass


Table 6 Calculated normalized results of response variables

Trial number NO119909

Soot ISFC1 10876 05770 098222 06593 08876 100003 03268 18455 102514 05788 10485 100425 03426 18547 103566 05966 06473 099427 02403 22587 105928 02637 07622 099069 01129 15820 10079

0

4

8

12

16

1 2 3Levels

SN

ratio

(dB)

EGR ratePilot injection quantity

Main injection timing

Figure 6 119878119873 response curves for NO119909

formation divided by the calculated IMEP (indicated meaneffective pressure) then they were normalized as discussedby Hajireza et al [21] in respect to the corresponding valuefor original baseline case in order to see a comparable result

32 Analysis of 119878119873 Response Curves Table 7 shows the119878119873 ratios for each factor computed in the Taguchi processand based on the results of Table 6 From the Table 7the combination which has the maximum 119878119873 ratio wasconsidered as the best combination in achieving the optimumobjective since as the assessment of quality characteristics thelarger 119878119873 value and the better results of response variables[11] The 119878119873 response curves are graphical expressions ofchange in response variables with the variation in influencingfactor level The Figures 6 7 and 8 show the response curvesof Taguchi experiment In fact 119878119873 ratio in Figures 6 7 and 8is the value of corresponding (119878119873)

119894119895 which have considered

the mean effect of each levels in each factor 119880 119881 and 119882

in Tables 7 and 8 represent EGR rate pilot fuel quantity andmain injection timing respectively

As it can be seen from Figure 6 the peak point of eachfactor occurs at the EGR rate of 27 pilot fuel quantityof 272 and main injection timing of 6∘CA ATDC (aftertop dead center) respectively It is obvious that the 119878119873

ratio of factor EGR rate varies largely and three factorsshow a consistent trend in affecting the NO

119909varying with

the level The reason is that EGR has been proved to be avery effective NO

119909reduction technique and retarded main

1

4

7

1 2 3Levels

SN

ratio

(dB)

minus2

minus5

minus8



Figure 7 119878119873 response curves for soot

0

1 2 3Levels

SN

ratio

(dB)

015

minus015

minus03

minus045



Figure 8 119878119873 response curves for ISFC

injection timing decreases in-cylinder peak heat release rateand peak temperature which lead to a reduction inNO

119909[22]

As for pilot injection it can shorten the ignition delay andraise the in-cylinder temperature before main combustionthus avoiding the rapid heat release rate and increasing thegas temperature too quickly Accordingly theNO

119909emissions

which have a close relationship with temperature can bereduced [23] For further analysis and discussionwewill drawon ANOVA technique in the following sections

It is observed from Figure 7 that the optimum factors areat 7 EGR rate 172 pilot fuel quantity and 4∘CA BTDC(Before Top Dead Center) main injection timing For pilotfuel quantity the maximum 119878119873 ratio in Figures 7 and 8appears in corresponding point that at level 2 This illustratesthat it is necessary to choose proper pilot fuel quantityfor optimizing combustion process in-cylinder Since thechange in pilot fuel quantity changes the fuel concentrationdistribution this may result in two different effects eitherenhancing or deteriorating fuel-air mixing [24] In additionit is found that EGR rate and main injection timing havethe same effect for soot emissions In other words withEGR rate increasing and main injection timing retardingthe soot emissions increased It is also illustrated that EGRand main injection timing have a contrary influence forsoot emissions as compared to NO

119909emissions This effect is

attributed to the introduced high level EGR in combustion


Table 7 119878119873 ratio results

Factors Levels Trial number 119878119873 ratio (119878119873)119894119895

(119894) (119895) (119896) NO119909

Soot ISFC NO119909

Soot ISFC

119880

71 minus07297 47766 01561 42013 01633 minus00199

2 36189 10356 00000

3 97148 minus53225 minus02157

174 47500 minus04114 minus00363 61800 minus06665 minus00967

5 93041 minus53657 minus03040

6 44859 37776 00502


8 115794 23591 00823

9 189458 minus39839 minus00680

119881

7201 minus07297 47766 01561 54679 minus09040 minus01266

4 47500 minus04114 minus00363

7 123834 minus70773 minus04995

17202 36189 10356 00000 81674 minus06570 minus00739

5 93041 minus53657 minus03040

8 115794 23591 00823


6 44859 37776 00502

9 189458 minus39839 minus00680

119882

4∘CA BTDC1 minus07297 47766 01561 51118 36378 00962

6 44859 37776 00502

8 115794 23591 00823

1∘CA ATDC2 36189 10356 00000 91049 minus11199 minus00348

4 47500 minus04114 minus00363

9 189458 minus39839 minus00680

4∘CA ATDC3 97148 minus53225 minus02157 104674 minus59218 minus03397

5 93041 minus53657 minus03040

7 123834 minus70773 minus04995

Table 8 Results of ANOVA

Factors 119878119873 SS SS119894

Contribution NO119909

Soot ISFC NO119909

Soot ISFC119880 8228 27432 573127 50226 00101 209 323 288

119881 minus11347 15539 155789 0783 00017 57 05 049

119882 minus00928 03506 154943 45693 01001 56 2941 2854

chamber giving rise to the reduction in oxygen availabilityfor fuel combustion and the retarded main injection wouldweaken atomization and vaporization of the injected fuelfurthermore those will lead to incomplete combustion andincrease in soot emission [25]

Figure 8 shows plots of the 119878119873 ratio of ISFC against EGRrate pilot fuel quantity andmain injection timing As is seenthe results show the same trend as Figure 7 This means thatthe soot emissions and ISFC can be reduced simultaneouslyby selecting the same factors and levels

33 Application of ANOVA In order to determine the effectsof influencing factors (pilot fuel quantity EGR rate and

main injection timing) on theNO119909emissions soot emissions

and ISFC of a light duty diesel engine the ANOVA wascarried out and the results were shown in Table 8 Note thatthe calculation results of Table 8 depended on the results inTable 7

As shown in the Table 8 percentage contributions ofEGR rate pilot fuel quantity and main injection timing were209 57 and 56 respectively for NO

119909emissions In

the case of soot emissions the percentage contributions ofEGR rate pilot fuel quantity and main injection timing were323 05 and 2941 respectively In addition with regardto ISFC percentage contributions of EGR rate pilot fuelquantity and main injection timing were 288 049 and2854 respectively


3

2427

211815120906030

Specification

Specification

SpecificationEquivalence ratio at end of the main Equivalence ratio at end of the maininjection (optimum) injection (original)

In-cylinder soot distributions at 55 ATDC (optimum) In-cylinder soot distributions at 55 ATDC (original)

000035

0000315

000028

0000245

000021

0000175

000014

0000105

0

35e minus 005

7e minus 005

000025

0000225

00002

0000175

000015

0000125

00001

0

75e minus 005

5e minus 005

25e minus 005

In-cylinder NOx distributions at 55 ATDC (optimum) In-cylinder NOx distributions at 55 ATDC (original)

Figure 9 Comparison of equivalence ratio and emission distributions between optimized and original case

The main objective of percentage contributions is toanalyze engine influencing parameters which significantlyaffect the performance characteristics The larger value ofpercentage contributions means that the factor has a greaterinfluence on the response variable It can be observed thatEGR is the most influencing factor on NO

119909 When compared

with EGR the other influencing parameters had slight influ-ence for NO

119909control though retarded injection has been

recognized as an effective way to reduce NO119909emissions by

retarding the combustion phase which reduces the peak tem-perature in-cylinder resulting in suppressing the moleculesof oxygen and nitrogen combine Temperature and oxygenconcentration play a crucial role in the formation of oxidesof nitrogen On the other hand EGR can reduce the peakflame temperature and oxygen concentration at the sametimeThose factors can further illustrate that as compared toother influencing parameters EGR is a very effective controltechnique for decreasing NO

119909[1 26] In addition the two

parameters of pilot fuel quantity and main injection timing

have approximately the same level of influence on the NO119909

emissions as shown in Table 8Based on Table 8 it also can be seen that the greatest

influence of the soot and ISFC is exhibited main injectiontiming then EGR rate and pilot fuel quantity In view of thefact that the percentage contributions of pilot fuel quantityis almost close to zero it can be considered that pilot fuelquantity is almost insignificant in case of soot and ISFCcompared to main injection timing and EGRThis is becausethe pilot fuel quantity is smaller than the injected fuel inmaininjection so the emissions caused by the pilot injection can beignored inmost cases and the total emissions simply relied onthe main combustion [27]

34 Optimum Combination Based on the analysis of 119878119873

response curves and results of ANOVA it is concludedthat in the case of NO

119909 EGR is the most influencing

factor the other influencing parameters have slight influence


and the best level is at level 3 However main injection timingis the greatest effect in regard to soot and ISFC the peak point119878119873 ratio of this factor occurs at 4∘CA BTDC On the otherhand pilot fuel quantity is an insignificant influencing factorfor all response variables especially in terms of soot and ISFCHence the optimum combination of factors and levels for thechosen response variables are at 27 EGR rate 272 pilotfuel quantity and 4∘CA BTDC main injection timing

35 Confirmation Experiment The last step in Taguchi opti-mization method is to conduct confirmation experimentswith the optimum combination of influencing factors forvalidating the improvement in the response variable bycomparing it with the original baseline condition Afterconducting the confirmation experiment optimized enginereduces by at least 70 for NO

119909and 20 in soot formation

As for ISFC a slight reduction of 1 compared to originalbaseline engine is shown

Figure 9 shows in-cylinder equivalence ratio and emis-sion distributions with respect to optimized and originalbaseline case by simulations As is seen in the optimizedcase after the spray impinges on the top of the piston bowlat end of the main injection equivalence ratio distributionsindicate a more homogeneous mixture in-cylinder than fuelconcentration distributions in original baseline case It is alsoillustrated that the fuel-air mixing was enhanced as a resultof the application of optimum parameter combination Inthe case of comparison of emission distributions betweenoptimized and original case as shown in Figure 9 it is foundthat significant concentrations of NO

119909and soot emissions are

found in regions corresponding to emission formation zonesin original baseline case However most of NO

119909and soot

emissions are eliminated in the optimized case because ofimproved mixture formation and combustion processes [28]

4 Conclusions

In the present work CFD simulation together with theTaguchi and the ANOVA technique was used to investigatethe combined effect of EGR rate pilot fuel quantity andmain injection timing on the NO

119909 soot and ISFC in a diesel

engine at 2000 rmin 50 load with minimum numberof experimental work The following conclusions are drawnfrom the investigation

(1) According to the analysis of 119878119873 curves it is con-cluded that the 119878119873 ratio of factor EGR rate varieslargely and three influencing factors show a consistenttrend in affecting the NO

119909varying with the levels

In addition EGR and main injection timing have acontrary influence for soot as compared to NO

119909 In

the case of soot emissions and ISFC they can bereduced simultaneously by choosing the same factorsand levels for each other

(2) The ANOVA results illustrated that EGR is the mostinfluencing factor on NO

119909 For soot emission and

ISFC the greatest influence factor is main injectiontiming However pilot fuel quantity does not haveimportant effects on all objectives

(3) The optimum combination of factors and levels forthe chosen response variables are at 27 EGR rate272 pilot fuel quantity and 4∘CABTDCmain injec-tion timing Confirmation experiment was carriedout to validate the accuracy of the CFD simulationresults And the engine with optimized combinationreduces by at least 70 for NO

119909and 20 in soot

formation As for ISFC a slight reduction of 1compared to original baseline engine was shown

(4) The combination of the CFD simulation the Taguchimethod and the ANOVA technique is found to be agood method which can significantly save time andcost to find optimum combination for lowNO

119909 soot

and ISFC in a light duty diesel engine

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgment

This work is supported by the National Natural ScienceFoundation of China (Grant no 51176064)

References

[1] S Saravanan G Nagarajan and S Sampath ldquoMulti responseoptimization of NOx emission of a stationary diesel enginerdquoFuel vol 89 no 11 pp 3235ndash3240 2010

[2] H Wu and Z Wu ldquoCombustion characteristics and optimalfactors determination with Taguchi method for diesel enginesport-injecting hydrogenrdquo Energy vol 47 no 1 pp 411ndash4202012

[3] C Krishnan Y Vinod and C Sudhindra ldquoOptimization offuel injection parameters for meeting euro III exhaust emissionnorms on a heavy duty diesel engine using Taguchi techniquerdquoSAE Technical Paper 2008-01-1610 2008

[4] R D Reitz ldquoDirections in internal combustion engineresearchrdquo Combustion and Flame vol 160 no 1 pp 1ndash8 2013

[5] W Park J Y Lee K Min J Yu S Park and S H CholdquoPrediction of real-time NO based on the in-cylinder pressurein Diesel enginesrdquo Proceedings of the Combustion Institute vol34 no 2 pp 3075ndash3082 2013

[6] S Kumar and M K Chauhan ldquoNumerical modeling of com-pression ignition engine a reviewrdquo Renewable and SustainableEnergy Reviews vol 19 pp 517ndash530 2013

[7] S Z Rezaei F Zhang H Xu A Ghafourian J MHerreros andS Shuai ldquoInvestigation of two-stage split-injection strategies fora Dieseline fuelled PPCI enginerdquo Fuel vol 107 pp 299ndash3082013

[8] Q-C Hsu and A T Do ldquoMinimum porosity formation in pres-sure die casting by Taguchi methodrdquoMathematical Problems inEngineering vol 2013 Article ID 920865 9 pages 2013

[9] S Thipprakmas ldquoApplication of Taguchi technique to inves-tigation of geometry and position of V-ring indenter in fine-blanking processrdquoMaterials and Design vol 31 no 5 pp 2496ndash2500 2010


[10] A Diez and H Zhao ldquoInvestigation of split injection in a singlecylinder optical diesel enginerdquo SAE Technical Paper 2010-01-0605 2010

[11] S Saravanan G Nagarajan and S Sampath ldquoCombined effectof injection timing EGR and injection pressure in NO

119909control

of a stationary diesel engine fuelled with crude rice bran oilmethyl esterrdquo Fuel vol 104 pp 409ndash416 2013

[12] N P Komninos and C D Rakopoulos ldquoModeling HCCIcombustion of biofuels a reviewrdquo Renewable and SustainableEnergy Reviews vol 16 no 3 pp 1588ndash1610 2012

[13] R Mobasheri Z Peng and S M Mirsalim ldquoAnalysis the effectof advanced injection strategies on engine performance andpollutant emissions in a heavy duty DI-diesel engine by CFDmodelingrdquo International Journal of Heat and Fluid Flow vol 33no 1 pp 59ndash69 2012

[14] O Colin and A Benkenida ldquoThe 3-zones Extended CoherentFlame Model (ECFM3Z) for computing premixeddiffusioncombustionrdquo Oil and Gas Science and Technology vol 59 no6 pp 593ndash609 2004

[15] K Truffin and O Colin ldquoAuto-ignition model based ontabulated detailed kinetics and presumed temperature PDFmdashapplication to internal combustion engine controlled by ther-mal stratificationsrdquo International Journal of Heat and MassTransfer vol 54 no 23-24 pp 4885ndash4894 2011

[16] AVL LIST GmbH AVL FIRE Users Guide-Version 20130 AVLLIST GmbH Graz Austria 2013

[17] K Hanjalic M Popovac and M Hadziabdic ldquoA robust near-wall elliptic-relaxation eddy-viscosity turbulence model forCFDrdquo International Journal of Heat and Fluid Flow vol 25 no6 pp 1047ndash1051 2004

[18] Z Peng B Liu W Wang and L Lu ldquoCFD investigationinto diesel PCCI combustion with optimized fuel injectionrdquoEnergies vol 4 no 3 pp 517ndash531 2011

[19] SThipprakmas andW Phanitwong ldquoProcess parameter designof spring-back and spring-go in V-bending process usingTaguchi techniquerdquo Materials and Design vol 32 no 8-9 pp4430ndash4436 2011

[20] D H Lee J S Park M R Ryu and J H Park ldquoDevelopmentof a highly efficient low-emission diesel engine-powered co-generation system and its optimization using Taguchi methodrdquoApplied Thermal Engineering vol 50 no 1 pp 491ndash495 2013

[21] S Hajireza G Regner A Christie M Egert and H Mit-termaier ldquoApplication of CFD modeling in combustion bowlassessment of diesel engines using DoE methodologyrdquo SAETechnical Paper 2006-01-3330 2006

[22] H E Saleh ldquoEffect of exhaust gas recirculation on diesel enginenitrogen oxide reduction operating with jojoba methyl esterrdquoRenewable Energy vol 34 no 10 pp 2178ndash2186 2009

[23] J Zhang Particle matter emission control and related issuesfor diesel engines [PhD thesis] University of BirminghamBirmingham UK 2011

[24] K Okude K Mori S Shiino K Yamada and Y MatsumotoldquoEffects of multiple injections on diesel emission and combus-tion characteristicsrdquo SAE Technical Paper 2007-01-4178 2007

[25] M Gomaa A J Alimin and K A Kamaruddin ldquoTrade-offbetween NOx soot and EGR rates for an IDI diesel enginefuelled with JB5rdquo Journal of Applied Sciences vol 11 no 11 pp1987ndash1993 2011

[26] D Agarwal S K Singh and A K Agarwal ldquoEffect of ExhaustGas Recirculation (EGR) on performance emissions depositsand durability of a constant speed compression ignition enginerdquoApplied Energy vol 88 no 8 pp 2900ndash2907 2011

[27] S N Soid and Z A Zainal ldquoSpray and combustion characteri-zation for internal combustion engines using optical measuringtechniquesmdasha reviewrdquo Energy vol 36 no 2 pp 724ndash741 2011

[28] D Kim and S Park ldquoOptimization of injection strategy toreduce fuel consumption for stoichiometric diesel combustionrdquoFuel vol 93 pp 229ndash237 2012

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of


Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of


Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of


Mathematical PhysicsAdvances in

Complex AnalysisJournal of


OptimizationJournal of


CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of


Operations ResearchAdvances in

Journal of


Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences


The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014


Algebra

Discrete Dynamics in Nature and Society



Decision SciencesAdvances in

Discrete MathematicsJournal of


Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of


(+EGR)

Auto-ignition(oxidation)

Flame propagation

Turbulent mixing

Turbulent mixing

Burnt gases

Diffusion flamePremixed flame(oxidation + pollutant formation)

A = unmixed air Au Ab

Mu Mb

Fu Fb

M = mixed air and fuel

F = unmixed fuel

u = unburned gases b = burned gases

Figure 1 Principle of the ECFM3Z model

with emissions and fuel consumption Hence in this studyCFD simulation together with the Taguchi method wasused to investigate the combined effect of EGR rate pilotfuel quantity and main injection timing on the NO

119909 soot

and ISFC in a diesel engine at 2000 rmin 50 load withminimum number of experimental work Moreover forpurpose of determining the effects of influencing factors onthe objectives the ANOVA technique was applied to analyzesimulation results

2 Computational Procedure ModelValidation and Methodology

21 Computational Procedure The multidimensional CFDcalculations were carried out with the commercial CFDpackage AVL FIRE The turbulent combustion model isECFM-3Z (extended coherent flame model-3 zones) whichincludes a description of the local mixture stratification byconsidering three mixing zones (see Figure 1) the unmixedfuel zone F themixed zoneM containing fuel and air and theunmixed air zone A Each region is separated into two partsunburned gases region and burned gases region [14 15] Forturbulent combustion phenomena the ECFM-3Zmodel usesa 2-step chemistrymechanism for the fuel conversion like [16]

C120572H120573O120574+ (120572 +

120573

4

minus

120574

2

)O2997888rarr 120572CO

2+

120573

2

H2O (1)

C120572H120573O120574+ (

120572

2

minus

120574

2

)O2997888rarr 120572CO +

120573

2

H2

(2)

where 120572 120573 and 120574 are the atomic number of C H and Oelements respectively

The 119896-120577-119891 turbulentmodel is used formodeling turbulentflow in the combustion chamber Hanjalic et al developedthis model They proposed an eddy viscosity model basedon the concept of Durbinrsquos elliptic relaxation which solvesa transport equation for the velocity scales ratio 120577 = V2119896instead of V2 thus increasing the robustness of the modelreducing the sensitivity to nonuniform grids [17]

xy

z

Figure 2 Mesh of the simulation model on the TDC

Table 1 Computational submodels

Breakup model KH-RT modelEvaporation model Dukowicz modelCollision model Nordin modelWall interaction model Bai Gosman model

The extended Zeldovich mechanism is widely acceptedas a successful method for predicting NO concentrationChemical reactions are given by [6 17]

O + N2

1205811

larrrarr NO + N (3)

N +O2

1205812

larrrarr NO + N (4)

N +OH1205813

larrrarr NO +H (5)

where 1205811 1205812 and 120581

3are the rate of reaction the unit is

cm3molsdotsThe Soot emission is calculated using the kinetic model

which contains a reduced number of species and reactionsand has been developed in order to provide a computationallyefficient kinetic overall soot model [16] Table 1 presentsseveral submodels used in present simulation model [13 18]

Because of the symmetry of the combustion chamber andthe injector at the center of the combustion chamber has 6nozzle holes the simulations were performed on one sixth ofthe combustion chamber for reducing calculation time Themesh of the simulation model on the top dead center wasshown in Figure 2

22 Model Validation The engine used in this study wasa light duty 4 cylinders four stroke turbocharging water-cooled diesel engine Specifications of simulated engine werelisted in Table 2 The experimental system was shown inFigure 3 The in-cylinder pressure was measured using aKistler 6125B quartz crystal pressure sensor and an ONOSOKKI DS-9100 Combustion analyzer was used to analyzethe in-cylinder pressure The NO

119909emissions were measured

with a HORIBA MEXA-7100D exhaust gas analyzer Smokewas measured by smoke meter

The simulated original baseline condition of light dutydiesel engine was actuated at 2000 rmin 50 load witha pilot injection included in injection strategy Table 3 listsinitial conditions and input parameters for simulation


2120

19

18

17

15

14

13

12

111098

7

4

1 2

3

5

6 16











119878119873 = minus10 log 1

119899

(

119899

sum

119894=1

1199102

119894) (6)







Trialnumber

Column 1(EGR rate)



timing)1 1 1 12 1 2 23 1 3 34 2 1 25 2 2 36 2 3 17 3 1 38 3 2 19 3 3 2






EGR =

[CO2]inlet

[CO2]exhaust

times 100 (7)





0

3

6

9

690 720 750 780 810

TestSimulation

Pres

sure

(MPa

)

Crank angle (CAD)


0

30

60

90

690 720 750 780 810

TestSimulation

Hea

t rele

ase r

ate (

JCA

D)

Crank angle (CAD)



119878119873 =

1

9

9

sum

119896=1

(119878119873)119896 (8)

SS =

9

sum

119894=1

((119878119873)119894119895minus 119878119873)

2

(9)

SS119894=

3

sum

119895=1

((119878119873)119894119895minus 119878119873)

2

(10)

Contribution =

SS119894

SStimes 100 (11)







Soot ISFC1 10876 05770 098222 06593 08876 100003 03268 18455 102514 05788 10485 100425 03426 18547 103566 05966 06473 099427 02403 22587 105928 02637 07622 099069 01129 15820 10079

0

4

8

12

16

1 2 3Levels

SN

ratio

(dB)











119909varying with



1

4

7

1 2 3Levels

SN

ratio

(dB)

minus2

minus5

minus8




0

1 2 3Levels

SN

ratio

(dB)

015

minus015

minus03

minus045





119909[22]


119909emissions








(119894) (119895) (119896) NO119909

Soot ISFC NO119909

Soot ISFC

119880

71 minus07297 47766 01561 42013 01633 minus00199

2 36189 10356 00000

3 97148 minus53225 minus02157


5 93041 minus53657 minus03040

6 44859 37776 00502


8 115794 23591 00823

9 189458 minus39839 minus00680

119881


4 47500 minus04114 minus00363

7 123834 minus70773 minus04995

17202 36189 10356 00000 81674 minus06570 minus00739

5 93041 minus53657 minus03040

8 115794 23591 00823


6 44859 37776 00502

9 189458 minus39839 minus00680

119882

4∘CA BTDC1 minus07297 47766 01561 51118 36378 00962

6 44859 37776 00502

8 115794 23591 00823


4 47500 minus04114 minus00363

9 189458 minus39839 minus00680


5 93041 minus53657 minus03040

7 123834 minus70773 minus04995


Factors 119878119873 SS SS119894


Soot ISFC NO119909

Soot ISFC119880 8228 27432 573127 50226 00101 209 323 288

119881 minus11347 15539 155789 0783 00017 57 05 049

119882 minus00928 03506 154943 45693 01001 56 2941 2854







119909emissions In



3

2427

211815120906030

Specification

Specification



000035

0000315

000028

0000245

000021

0000175

000014

0000105

0

35e minus 005

7e minus 005

000025

0000225

00002

0000175

000015

0000125

00001

0

75e minus 005

5e minus 005

25e minus 005


























119909and soot


4 Conclusions







119909 In









119909 soot




Acknowledgment


References













119909control


























Volume 2014




Journal of











Journal of


Function Spaces






Algebra










2120

19

18

17

15

14

13

12

111098

7

4

1 2

3

5

6 16











119878119873 = minus10 log 1

119899

(

119899

sum

119894=1

1199102

119894) (6)







Trialnumber

Column 1(EGR rate)



timing)1 1 1 12 1 2 23 1 3 34 2 1 25 2 2 36 2 3 17 3 1 38 3 2 19 3 3 2






EGR =

[CO2]inlet

[CO2]exhaust

times 100 (7)





0

3

6

9

690 720 750 780 810

TestSimulation

Pres

sure

(MPa

)

Crank angle (CAD)


0

30

60

90

690 720 750 780 810

TestSimulation

Hea

t rele

ase r

ate (

JCA

D)

Crank angle (CAD)



119878119873 =

1

9

9

sum

119896=1

(119878119873)119896 (8)

SS =

9

sum

119894=1

((119878119873)119894119895minus 119878119873)

2

(9)

SS119894=

3

sum

119895=1

((119878119873)119894119895minus 119878119873)

2

(10)

Contribution =

SS119894

SStimes 100 (11)







Soot ISFC1 10876 05770 098222 06593 08876 100003 03268 18455 102514 05788 10485 100425 03426 18547 103566 05966 06473 099427 02403 22587 105928 02637 07622 099069 01129 15820 10079

0

4

8

12

16

1 2 3Levels

SN

ratio

(dB)











119909varying with



1

4

7

1 2 3Levels

SN

ratio

(dB)

minus2

minus5

minus8




0

1 2 3Levels

SN

ratio

(dB)

015

minus015

minus03

minus045





119909[22]


119909emissions








(119894) (119895) (119896) NO119909

Soot ISFC NO119909

Soot ISFC

119880

71 minus07297 47766 01561 42013 01633 minus00199

2 36189 10356 00000

3 97148 minus53225 minus02157


5 93041 minus53657 minus03040

6 44859 37776 00502


8 115794 23591 00823

9 189458 minus39839 minus00680

119881


4 47500 minus04114 minus00363

7 123834 minus70773 minus04995

17202 36189 10356 00000 81674 minus06570 minus00739

5 93041 minus53657 minus03040

8 115794 23591 00823


6 44859 37776 00502

9 189458 minus39839 minus00680

119882

4∘CA BTDC1 minus07297 47766 01561 51118 36378 00962

6 44859 37776 00502

8 115794 23591 00823


4 47500 minus04114 minus00363

9 189458 minus39839 minus00680


5 93041 minus53657 minus03040

7 123834 minus70773 minus04995


Factors 119878119873 SS SS119894


Soot ISFC NO119909

Soot ISFC119880 8228 27432 573127 50226 00101 209 323 288

119881 minus11347 15539 155789 0783 00017 57 05 049

119882 minus00928 03506 154943 45693 01001 56 2941 2854







119909emissions In



3

2427

211815120906030

Specification

Specification



000035

0000315

000028

0000245

000021

0000175

000014

0000105

0

35e minus 005

7e minus 005

000025

0000225

00002

0000175

000015

0000125

00001

0

75e minus 005

5e minus 005

25e minus 005


























119909and soot


4 Conclusions







119909 In









119909 soot




Acknowledgment


References













119909control


























Volume 2014




Journal of











Journal of


Function Spaces






Algebra











Trialnumber

Column 1(EGR rate)



timing)1 1 1 12 1 2 23 1 3 34 2 1 25 2 2 36 2 3 17 3 1 38 3 2 19 3 3 2






EGR =

[CO2]inlet

[CO2]exhaust

times 100 (7)





0

3

6

9

690 720 750 780 810

TestSimulation

Pres

sure

(MPa

)

Crank angle (CAD)


0

30

60

90

690 720 750 780 810

TestSimulation

Hea

t rele

ase r

ate (

JCA

D)

Crank angle (CAD)



119878119873 =

1

9

9

sum

119896=1

(119878119873)119896 (8)

SS =

9

sum

119894=1

((119878119873)119894119895minus 119878119873)

2

(9)

SS119894=

3

sum

119895=1

((119878119873)119894119895minus 119878119873)

2

(10)

Contribution =

SS119894

SStimes 100 (11)







Soot ISFC1 10876 05770 098222 06593 08876 100003 03268 18455 102514 05788 10485 100425 03426 18547 103566 05966 06473 099427 02403 22587 105928 02637 07622 099069 01129 15820 10079

0

4

8

12

16

1 2 3Levels

SN

ratio

(dB)











119909varying with



1

4

7

1 2 3Levels

SN

ratio

(dB)

minus2

minus5

minus8




0

1 2 3Levels

SN

ratio

(dB)

015

minus015

minus03

minus045





119909[22]


119909emissions








(119894) (119895) (119896) NO119909

Soot ISFC NO119909

Soot ISFC

119880

71 minus07297 47766 01561 42013 01633 minus00199

2 36189 10356 00000

3 97148 minus53225 minus02157


5 93041 minus53657 minus03040

6 44859 37776 00502


8 115794 23591 00823

9 189458 minus39839 minus00680

119881


4 47500 minus04114 minus00363

7 123834 minus70773 minus04995

17202 36189 10356 00000 81674 minus06570 minus00739

5 93041 minus53657 minus03040

8 115794 23591 00823


6 44859 37776 00502

9 189458 minus39839 minus00680

119882

4∘CA BTDC1 minus07297 47766 01561 51118 36378 00962

6 44859 37776 00502

8 115794 23591 00823


4 47500 minus04114 minus00363

9 189458 minus39839 minus00680


5 93041 minus53657 minus03040

7 123834 minus70773 minus04995


Factors 119878119873 SS SS119894


Soot ISFC NO119909

Soot ISFC119880 8228 27432 573127 50226 00101 209 323 288

119881 minus11347 15539 155789 0783 00017 57 05 049

119882 minus00928 03506 154943 45693 01001 56 2941 2854







119909emissions In



3

2427

211815120906030

Specification

Specification



000035

0000315

000028

0000245

000021

0000175

000014

0000105

0

35e minus 005

7e minus 005

000025

0000225

00002

0000175

000015

0000125

00001

0

75e minus 005

5e minus 005

25e minus 005


























119909and soot


4 Conclusions







119909 In









119909 soot




Acknowledgment


References













119909control


























Volume 2014




Journal of











Journal of


Function Spaces






Algebra












Soot ISFC1 10876 05770 098222 06593 08876 100003 03268 18455 102514 05788 10485 100425 03426 18547 103566 05966 06473 099427 02403 22587 105928 02637 07622 099069 01129 15820 10079

0

4

8

12

16

1 2 3Levels

SN

ratio

(dB)











119909varying with



1

4

7

1 2 3Levels

SN

ratio

(dB)

minus2

minus5

minus8




0

1 2 3Levels

SN

ratio

(dB)

015

minus015

minus03

minus045





119909[22]


119909emissions








(119894) (119895) (119896) NO119909

Soot ISFC NO119909

Soot ISFC

119880

71 minus07297 47766 01561 42013 01633 minus00199

2 36189 10356 00000

3 97148 minus53225 minus02157


5 93041 minus53657 minus03040

6 44859 37776 00502


8 115794 23591 00823

9 189458 minus39839 minus00680

119881


4 47500 minus04114 minus00363

7 123834 minus70773 minus04995

17202 36189 10356 00000 81674 minus06570 minus00739

5 93041 minus53657 minus03040

8 115794 23591 00823


6 44859 37776 00502

9 189458 minus39839 minus00680

119882

4∘CA BTDC1 minus07297 47766 01561 51118 36378 00962

6 44859 37776 00502

8 115794 23591 00823


4 47500 minus04114 minus00363

9 189458 minus39839 minus00680


5 93041 minus53657 minus03040

7 123834 minus70773 minus04995


Factors 119878119873 SS SS119894


Soot ISFC NO119909

Soot ISFC119880 8228 27432 573127 50226 00101 209 323 288

119881 minus11347 15539 155789 0783 00017 57 05 049

119882 minus00928 03506 154943 45693 01001 56 2941 2854







119909emissions In



3

2427

211815120906030

Specification

Specification



000035

0000315

000028

0000245

000021

0000175

000014

0000105

0

35e minus 005

7e minus 005

000025

0000225

00002

0000175

000015

0000125

00001

0

75e minus 005

5e minus 005

25e minus 005


























119909and soot


4 Conclusions







119909 In









119909 soot




Acknowledgment


References













119909control


























Volume 2014




Journal of











Journal of


Function Spaces






Algebra












(119894) (119895) (119896) NO119909

Soot ISFC NO119909

Soot ISFC

119880

71 minus07297 47766 01561 42013 01633 minus00199

2 36189 10356 00000

3 97148 minus53225 minus02157


5 93041 minus53657 minus03040

6 44859 37776 00502


8 115794 23591 00823

9 189458 minus39839 minus00680

119881


4 47500 minus04114 minus00363

7 123834 minus70773 minus04995

17202 36189 10356 00000 81674 minus06570 minus00739

5 93041 minus53657 minus03040

8 115794 23591 00823


6 44859 37776 00502

9 189458 minus39839 minus00680

119882

4∘CA BTDC1 minus07297 47766 01561 51118 36378 00962

6 44859 37776 00502

8 115794 23591 00823


4 47500 minus04114 minus00363

9 189458 minus39839 minus00680


5 93041 minus53657 minus03040

7 123834 minus70773 minus04995


Factors 119878119873 SS SS119894


Soot ISFC NO119909

Soot ISFC119880 8228 27432 573127 50226 00101 209 323 288

119881 minus11347 15539 155789 0783 00017 57 05 049

119882 minus00928 03506 154943 45693 01001 56 2941 2854







119909emissions In



3

2427

211815120906030

Specification

Specification



000035

0000315

000028

0000245

000021

0000175

000014

0000105

0

35e minus 005

7e minus 005

000025

0000225

00002

0000175

000015

0000125

00001

0

75e minus 005

5e minus 005

25e minus 005


























119909and soot


4 Conclusions







119909 In









119909 soot




Acknowledgment


References













119909control


























Volume 2014




Journal of











Journal of


Function Spaces






Algebra










3

2427

211815120906030

Specification

Specification



000035

0000315

000028

0000245

000021

0000175

000014

0000105

0

35e minus 005

7e minus 005

000025

0000225

00002

0000175

000015

0000125

00001

0

75e minus 005

5e minus 005

25e minus 005


























119909and soot


4 Conclusions







119909 In









119909 soot




Acknowledgment


References













119909control


























Volume 2014




Journal of











Journal of


Function Spaces






Algebra

















119909and soot


4 Conclusions







119909 In









119909 soot




Acknowledgment


References













119909control


























Volume 2014




Journal of











Journal of


Function Spaces






Algebra












119909control


























Volume 2014




Journal of











Journal of


Function Spaces






Algebra
















Volume 2014




Journal of











Journal of


Function Spaces






Algebra









research article application of cfd, taguchi...

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