VVT optimization with GT-POWER and Genetic Algorithms

Research Institute of Automotive Engineering and Vehicle Engines Stuttgart VVT optimization with GT-POWER and Genetic Algorithms Dipl.-Ing. R. Kuberc...
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Research Institute of Automotive Engineering and Vehicle Engines Stuttgart

VVT optimization with GT-POWER and Genetic Algorithms Dipl.-Ing. R. Kuberczyk Prof. Dr.-Ing. M. Bargende

Slide 1 ;

Research Institute of Automotive Engineering and Vehicle Engines Stuttgart

Overview 1. Introduction 2. GT-Power Model and Combustion Model 3. Model Calibration 4. GA Optimization 5. Conclusion

Slide 2 ;

Research Institute of Automotive Engineering and Vehicle Engines Stuttgart

Introduction 11 10 9 8

Variable valve train (VVT):

H u b [m m ]

7 6

• Advantages in efficiency by lower throttling losses 5 4 3

• Complexity raises exponentially 2 1

• Great challenge for simulation

0 0

30

60

90

120

150

180

210

240

270

300

Kurbelw inkel [°KW]

• 3D-CFD is still time consuming 1.2

in testing a high number of possible valve1.1

timing combinations 1

0.9

• 1-D engine simulation tools best solution for these tasks

Druck [bar]

0.8 0.7 0.6 0.5 0.4

How to optimize a VVT in a 1-D simulation ? 0.3 0.2 0

0.1

0.2

0.3

0.4

0.5

Volumen [l]

Slide 3 ;

Research Institute of Automotive Engineering and Vehicle Engines Stuttgart

Overview 1. Introduction 2. GT-Power Model and Combustion Model 3. Model Calibration 4. GA Optimization 5. Conclusion

Slide 4 ;

Research Institute of Automotive Engineering and Vehicle Engines Stuttgart

GT-Power Model 4-Cylinder SDI-Engine (2198 cm³)

External Cylinder Model used !

How does it work ? Slide 5 ;

Research Institute of Automotive Engineering and Vehicle Engines Stuttgart

Combustion Model External Cylinder Model/ Entrainment Model

flame burnt zone unburnt zone

phenomenological model is taking into account: - residual gas fraction (laminar flame speed) - shape of the combustion chamber (flame surface) important for VVT simulation ! Slide 6 ;

Research Institute of Automotive Engineering and Vehicle Engines Stuttgart

Overview 1. Introduction 2. GT-Power Model and Combustion Model 3. Model Calibration 4. GA Optimization 5. Conclusion

Slide 7 ;

Model Calibration

Research Institute of Automotive Engineering and Vehicle Engines Stuttgart

Entrainment Model Interval of Calculations: Inlet Valve max. lift 4.5mm C

355 350

10 Ventilhub [mm]

345 340 EVO

335

late EVO

330 325 320 310 305 490

8

500

505

510 IVO

515

520

525

2

360

450

540

630

720

810

900

810

900

Kurbelwinkel [°KW]

B

530

8

B

6

…the calibartion 4results ?

4 2

2 0

0

Slide 8

4

10

A

6

270

6

270

Ventilhub [mm]

Ventilhub [mm]

10

495

C

0

late IVO

315 A

8

360

450

540

630

720

Kurbelwinkel [°KW]

810

900

270

360

450

540

630

720

Kurbelwinkel [°KW] ;

Model Calibration/ Results

Research Institute of Automotive Engineering and Vehicle Engines Stuttgart

Entrainment Model 10

355

n=3000 1/min 1/min pmi=4.06imep=4bar bar rpm=3000

350

8

345

7

340

6

335

EVO EVO

pmi [bar] [bar] imep

9

5 4

330 325

3

320

2

315

1

310

0

0

500

305 490

1000 1500 2000 2500 3000 3500 4000 Drehzahl [1/min]

495

500

505

Slide 9

burn rate [J/° CA] Brennverlauf [J/° KW]

pressure [bar] Druck [bar]

3000_4.06pmi_4.5mm_in070_ex095

Messung measured GT

GT Power

90

120

150 180 210 240 crank angle [°KW] [°CA] Kurbelwinkel

270

515

520

525

530

IVO

rpm [1/min]

40 35 30 25 20 15 10 5 0

510 IVO

45 40 35 30 25 20 15 10 5 0 150

DVA measured GT

GT Power

165

180 195 210 225 crank angle [° CA] Kurbelwinkel [°KW]

240 ;

Model Calibration/ Results

Research Institute of Automotive Engineering and Vehicle Engines Stuttgart

Entrainment Model 10

355

n=3000 1/min pmi=7.01 bar

rpm=3000 1/min imep=7bar

350

8

345

7

340

6

335 EVO

pmi[bar] [bar] imep

9

5 4

325

3 2 1 0

330

0

320 there is only one set of parameters for the 315 310 combustion model for all operating points and 305 500 1000 1500 2000 2500 3000 3500 4000 490 495 500 505 510 515 520 valve timings Drehzahl [1/min] ! IVO IVO

525

530

rpm [1/min]

Slide 10

40 35 30 25 20 15 10 5 0

burn rate [J/° CA] Brennverlauf [J/° KW]

pressure [bar] Druck [bar]

3000_7.01pmi_4.5mm_in080_ex095

Messung measured GT GT Power

90

120

150 180 210 240 Kurbelwinkel crank angle [°KW] [°CA]

270

45 40 35 30 25 20 15 10 5 0 150

DVA GT

165

measured GT Power

180 195 210 225 Kurbelwinkel [°KW] crank angle [° CA]

240 ;

Research Institute of Automotive Engineering and Vehicle Engines Stuttgart

Overview 1. Introduction 2. GT-Power Model and Combustion Model 3. Model Calibration 4. GA Optimization 5. Conclusion

Slide 11 ;

Research Institute of Automotive Engineering and Vehicle Engines Stuttgart

GA Optimization Why is it useful to use genetic algorithms ?

BSFC [g/kWh] Col5

2000 rpm, 0,62 bar bmep 10mm valve lift 765 760 755 750 745 740 735 730

540

global Minimum

530

520

510

IVO Slide 12

local Minimum

500

490

480

290 300 310 320 330 O 340 EV 350

Optimizer has to deal with more than one minimum ! ;

Research Institute of Automotive Engineering and Vehicle Engines Stuttgart

GA Optimization Optimizer is coupled with the GT-Power Model for automatic optimization

Optimizer

GT-Model

Aim: Optimizing IVO, EVO, Inlet valve lift to find the global minimum of the ISFC [g/kWh] How does the GA Optimizer work ? Slide 13 ;

Start Population

IVO EVO IV_lift

Research Institute of Automotive Engineering and Vehicle Engines Stuttgart

1 0 1 0 0 1

0 1 1 1 1 1

1

1 0 1 1 0 0

1

2 7

3

6

9

Selection 4

5

8

Ind. efficiency

Calculation of the efficiency

next population

Ind. efficiency

completion 9

2

4

8

Crossover

1 0 1 0 0 1

1

2

3

6 4

5

0 1 1 1 1 1

1 0 1 1 0 0

...

1

7 8

9

Slide 14 ;

GA Optimization

Research Institute of Automotive Engineering and Vehicle Engines Stuttgart

Result

270

ISFC Distribution: 2000 rpm, 3bar imep

bi [g/kWh] ISFC

260

250 Best Point after 450 calculations

240

………

230 Slide 15

~42h calculation time (3GHz)

0

200

400

600 800 number of calculations Aufruf [-]

1000

1200

1400 ;

Research Institute of Automotive Engineering and Vehicle Engines Stuttgart

Overview 1. Introduction 2. GT-Power Model and Combustion Model 3. Model Calibration 4. GA Optimization 5. Conclusion

Slide 16 ;

Research Institute of Automotive Engineering and Vehicle Engines Stuttgart

Conclusion VVT optimization: • 1-D simulation in connection with an phenomenological combustion Model (Entrainment Model) is necessary to optimize VVT • Optimization of IVC, EVO, IV lift with Genetic Algorithms to increase the Ind. Efficiency is useful because local and global minima exists • Powerful combination of Entrainment Model and automatic optimizer based on Genetic Algorithms • results are time consuming, but if only “good” results are necessary: these are received quickly ! (result could be used for beginning optimization

Slide 17

by hand then) ;

Research Institute of Automotive Engineering and Vehicle Engines Stuttgart

END

Slide 18 ;

Research Institute of Automotive Engineering and Vehicle Engines Stuttgart

dQb dmv dt = Hu ⋅ ⋅ dt dϕ dϕ

...burn rate

dmv mE − mv = dt τ

lT τ= sL

lT = 15 ⋅

u' =

Dissipationskoeffizient = 2,157 Turbulenzkoeffizient = 0,08

k ES = C k ⋅ Slide 19

2 c m ⋅ d Zyl

nEV ⋅ d EV ⋅ hEV

2

υT ⋅ l u'

s L = f ( Tuv , p, x r

dmE = ρ uv ⋅ AFl ⋅ ( u '+s L dt

l =3

6

π

V

)

) 2 k 3

dk = ( P −E dt

)

k1.5 E = εd ⋅ l 2 k dVZyl ⋅ P=− ⋅ dt 3 VZyl ;

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