An adaptive multi-grid chemistry (AMC) model for efficient simulation of HCCI and DI engine...

University of Wisconsin– Engine Research Center 1

An adaptive multi-grid chemistry (AMC) model for efficient

simulation of HCCI and DI engine combustion

Yu Shi and Rolf D. ReitzSponsor: Department of Energy

Engine Research Center 2

Outline

• Background and model description• Parametric study of the AMC model• Application in HCCI engine combustion• Application in DI engine combustion• Conclusions


Part 1



Background

• There is a need to reduce the computational expense of practical multidimensional combustion simulationsa) Simulation of HCCI engine requires detailed chemistry, although the grid resolution can be relatively coarse.b) Simulation of DI engine requires fine meshes to achieve the resolution required by the spray and mixing models.c) The increasing application of CFD for engine design optimization is pushing the demand to reduce computation time.


Background

• For the current version of KIVA3v2-Chemkin, most of computing time is spent on the chemistry solver.

• Efforts of reducing computing time need to be placed on reducing the calling frequency to the chemkin solver. This can be achieved by multi-grid technique.


Model Description – Global MappingMapping cells to a group(2-D illustration)

Chemkin Solver

Remapping back to individual cells

Center cell

Thermodynamicallysimilar cells

Computingdomain


Model Description – Adaptive Neighbor MappingCenter cell

First level neighbors

Second level neighbors

Third level neighbors

Fourth level neighbors

A maximum 129 cells canbe grouped in 3-D meshes

Mapping cells to a group(2-D illustration)

Chemkin Solver

Remapping back to Individual cells

Search level is adaptively determined by temperatureinhomogeneity


Model Description – Mapping related parameters

• In-cylinder temperature reaches a specified temperature during the injection events and thereafter (eg. 1000K)

• Neighbor cells (the level is adaptively determined by the inhomgeneity of in-cylinder temperature)

• Cells within a specified temperature difference (eg. 20K)

• Cells within a specified equivalence ratio difference (eg. 10%)


Model Description – Remapping Techniques

• Averaged remappingRemap group information to individual cells

• Gradient-preserving remapping *

Based on C, H numbers of cells

* Adopted from Babajimopoulos et al. International Journal of Engine Research 2005


Part 2



Engine specification and operating conditions

130Spary Angle

8Holes

760Inj.Pre(bar)

142EVO

-142IVC

15.5CR

90.4Stroke (mm)

82Bore (mm)

GM-FiatEngine

Pilot-50 to-30

Main0

-30 to 0-30 to 0-30 to 0N/ASOI

(ATDC)

0.750.750.750.250.25Equivalence ratio

552555550EGR(%)

1114.5115.55.5IMEP(bar)

Dur. Inj.High-loadMid-loadLow-loadLow-loadSpeed

(rev/min)

DIHCCI

45° sector mesh with ~14000 cells at BDC


Global Mapping VS Neighbor Mapping - HCCI

-50 -40 -30 -20 -10 0 10 20 30 40 500

2

4

6

8

10

12

14

HR

R(J

/CA

)

Pre

ssur

e(M

Pa)

Crank Angle

Full Chemistry Group - Neighbor Mapping Group - Global Mapping

0

50

100

150

200

250

300

350

400

0 50 100 15010

100

1000

10000

Global Mapping

Neighbor Mapping

Num

ber o

f Cel

ls (g

roup

s)

Crank Angle

Full Chemistry

1.530E-021.270E-021.470E-02CO (g/kg.fuel)2.899E-022.951E-023.015E-02NOx (g/kg.fuel)3.677E-053.603E-053.645E-05Soot (g/kg.fuel)2.021.519.59Comp. Time (hr)NeighborGlobalFull Chem.Methods


Global Mapping VS Neighbor Mapping - DI

-30 -20 -10 04

6

8

10

12

14

16

18

Full Chemistry Global Mapping

Com

puta

tiona

l Tim

e (h

r)

SOI

Adaptive Mapping Fixed 1 level Mapping Fixed 2 level Mapping Fixed 3 level Mapping Fixed 4 level Mapping

-30 -20 -10 00.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Soo

t (g/

kg.fu

el)

SOI

-30 -20 -10 00.0

0.5

1.0

1.5

2.0

NO

x (g

/kg.

fuel

)

SOI-30 -20 -10 0

195

200

205

210

215

220

225

230

GIS

FC (g

/kW

.hr)

SOI

Computational time Soot

NOx GISFC


Averaged Remapping VS Gradient-preserving Remapping HCCI

-50 -40 -30 -20 -10 0 10 20 30 40 500

2

4

6

8

10

12

14 Full Chemistry Group - Gradient-preserving Remapping Group - Averaged Remapping

Pre

ssur

e (M

Pa)

Crank Angle0

50

100

150

200

250

300

350

400

1.530E-020.998E-021.470E-02CO (g/kg.fuel)2.899E-022.916E-023.015E-02NOx (g/kg.fuel)3.677E-053.602E-053.645E-05Soot (g/kg.fuel)2.021.799.59Comp. Time (hr)Gradient-pre.AveragedFull Chem.Methods


Averaged Remapping VS Gradient-preserving Remapping DI

-30 -20 -10 00.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Soo

t (g/

kg.fu

el)

SOI

-30 -20 -10 00.0

0.5

1.0

1.5

2.0

NO

x (g

/kg.

fuel

))

SOI-30 -20 -10 0

180

185

190

195

200

205

210

215

220

225

230

GIS

FC (g

/kW

.hr)

SOI

-30 -20 -10 06

8

10

12

14

16

18

Com

puta

tiona

l Tim

e (h

r)

SOI

Full Chemistry Gradient-preserving remapping Averaged remapping


Parametric study ∆T and ∆φ

-30 -20 -10 00.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Soot

(g/k

g.fu

el)

SOI

-30 -20 -10 00.0

0.5

1.0

1.5

2.0

NO

x (g

/kg.

fuel

)

SOI-30 -20 -10 0

195

200

205

210

215

220

225

230

GIS

FC (g

/kW

.hr)

SOI

-30 -20 -10 06

8

10

12

14

16

18

Full Chemistry Group ∆T = 30 K, ∆φ = 15% Group ∆T = 20 K, ∆φ = 10% Group ∆T = 10 K, ∆φ = 10% Group ∆T = 20 K, ∆φ = 5% Group ∆T = 10 K, ∆φ = 5%

Com

puta

tiona

l Tim

e (h

r)

SOI


Switching temperature

-30 -20 -10 00

2

4

6

8

10

12

14

16

18

20C

ompu

tatio

nal T

ime

(hr)

SOI

Full Chemistry Group 1000K Group 900K

-30 -20 -10 00.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Soot

(g/k

g.fu

el)

SOI

-30 -20 -10 00.0

0.5

1.0

1.5

2.0

NO

x(g/

kg.fu

el)

SOI-30 -20 -10 0

195

200

205

210

215

220

225

230

GIS

FC(g

/kW

.hr)

SOI


DI simulations – low load

-30 -20 -10 05

6

7

8

9

10

11

12

Full Chemistry Group

Com

puta

tiona

l Tim

e (h

r)

SOI-30 -20 -10 0

0.0000

0.0005

0.0010

0.0015

0.0020

0.0025

0.0030

0.0035

Soot

(g/k

g.fu

el)

SOI

-30 -20 -10 0

10

15

20

25

30

35

40

45

50

NO

x(g/

kg.fu

el)

SOI-30 -20 -10 0

160

165

170

175

180

185

GIS

FC(g

/kW

.hr)

SOI


DI simulations – high load

-30 -20 -10 05

10

15

20

25C

ompu

tatio

nal T

ime

(hr)

SOI


-30 -20 -10 00.0

0.5

1.0

1.5

2.0

Soot

(g/k

g.fu

el)

SOI

-30 -20 -10 00

5

10

15

20

25

NO

x(g/

kg.fu

el)

SOI-30 -20 -10 0

200

205

210

215

220

225

230

235

240

GIS

FC(g

/kW

.hr)

SOI


DI simulations – medium load, double injection

-50 -40 -300

5

10

15

20

Com

puta

tiona

l Tim

e (h

r)

Pilot injection SOI


-50 -40 -300.0

0.5

1.0

1.5

2.0

Soot

(g/k

g.fu

el)

Pilot injection SOI

-50 -40 -300.0

0.5

1.0

1.5

2.0

NO

x(g/

kg.fu

el)

Pilot injection SOI-50 -40 -30

150155160165170175180185190195200205210215220225230

GIS

FC(g

/kW

.hr)

Pilot injection SOI


Part 3



Engine specification and experimental setup

153EVO

n-heptaneFuel

-158IVC

8.9CR

86Stroke (mm)

86Bore (mm)

HondaEngine

512459IVC Temp. (K)

42.736.7A/F

00EGR (%)

2.5972.503IMEP (bar)

1500600Speed (rev/min)

High-speedLow-speedOperatingconditions

FTIR (Fourier transform infrared spectroscopy ) measurementCell averaged temperature and H2Oconcentration are available


Comparison of experimental and simulated results – 1500 rev/min

-50 -40 -30 -20 -10 0 10 20 30 40 500.0

0.5

1.0

1.5

2.0

2.5

3.0 Experiment Simulation-AMC Simulation-Full Chemistry

Pres

sure

(MP

a)

Crank Angle

-100 -80 -60 -40 -20 0 20 40 60 80 1000.00

0.02

0.04

0.06

0.08

0.10 Experiment Simulation-AMC (laser path) Simulation-Full Chemistry (laser path)

H2O

(mol

e fra

ctio

n)

Crank Angle

-100 -80 -60 -40 -20 0 20 40 60 80 100400

600

800

1000

1200

1400

1600

1800

2000

Experiment Simulation-AMC

(cyl. avg.) Simulation-AMC

(laser path) Simulation-FULL

(cyl. avg.) Simulation-FULL

(laser path)

Tem

pera

ture

(K)

Crank Angle

Computer time was reduced by anorder of magnitude using AMC model with the global mapping method (50 hours to 5 hours)

ERC Primary Reference Fuel (PRF) Mech.(Ra et al. Combustion and Flame)

Cyl. avg.

laser pathlaser pathlaser path


Comparison of experimental and simulated results – 600 rev/min

-50 -40 -30 -20 -10 0 10 20 30 40 500.0

0.5

1.0

1.5

2.0

2.5

3.0

Pres

sure

(MP

a)

Crank Angle

Experiment Simulation-AMC Simulation-Full Chemistry

-100 -80 -60 -40 -20 0 20 40 60 80 100

400

600

800

1000

1200

1400

1600

1800

2000

2200

Tem

pera

ture

(K)

Crank Angle





(laser path)

-100 -80 -60 -40 -20 0 20 40 60 80 1000.00

0.02

0.04

0.06

0.08

0.10

0.12 Experiment Simulation-AMC (laser path) Simulation-Full Chemistry (laser path)

H2O

(mol

e fra

ctio

n)

Crank Angle

Cyl. avg.

laser path


Boundary layer development

-100 -80 -60 -40 -20 0 20 40 60 80 100

400

600

800

1000

1200

1400

1600

1800

2000

2200

Tem

pera

ture

(K)

Crank Angle





(laser path)

Courtesy of Honda R&D Co., Ltd,Prof. Sanders, Andrew Caswell


Part 4



Engine specification and operating conditions

155Spary Angle

7Holes

860Inj.Pre(bar)

112EVO

-132IVC

16.6CR

90.4Stroke (mm)

82Bore (mm)

GM-FiatEngine

SOI

A/F

EGR (%)

IMEP (bar)

Speed (rev/min)

Operatingconditions

-39 to -21

0.95

67

5.5

2000

GM-Fiat

51.4° sector mesh with ~32000 cells at BDC


Comparison of experimental and simulated results

-40 -36 -32 -28 -24 -20

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14 Experiment Simulation-AMC Simulation-Full Chemistry

Soot

(g/k

g/fu

el)

Crank Angle-40 -36 -32 -28 -24 -20

0.02

0.03

0.04

0.05

0.06

0.07

0.08

NO

x (g

/kg/

fuel

)

Crank Angle

-40 -36 -32 -28 -24 -205

10

15

20

25

30

35

40

45

Unb

urne

d H

C (g

/kg.

fuel

)

Crank Angle-40 -36 -32 -28 -24 -20

100

150

200

250

300

CO

(g/k

g.fu

el)

Crank Angle

Opat et al.SAE Paper2007-01-0193CO ‘sweet spot’

Computer timewas reduced from 40 hours to 14.5 hours

Soot NOx

UHC CO


Part 5



Conclusions• The AMC model is able to reduce computer

time by a factor of ten for HCCI cases and two to three for DI cases without losing prediction accuracy.

• Both global and neighbor grouping methods were satisfactory to predict HCCI engine combustion, but the simpler global grouping method led to inaccuracies in the predicted emissions for DI cases.


Conclusions (cont.)• An averaged remapping method was

explored but not suggested in the AMC model due to increased numerical diffusion. Instead, a gradient-preserving method was found to be applicable for both HCCI and DI cases.

• The present AMC model predictions were shown to be fairly insensitive to convergence tolerance parameters in parametric studies.

An adaptive multi-grid chemistry (AMC) model for efficient simulation of HCCI and DI engine...

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