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Numerical Modeling of HCCI Combustion
Salvador M Aceves Daniel L Flowers J Ray Smith Joel Martinez-Frias Francisco Espinosa-Loza Tim Ross
Bruce Buchholz Nick Killingsworth Tom PiggottJonas Edman Charles Westbrook and William Pitz
Lawrence Livermore National Laboratory
Dennis Assanis Aris Babajimopoulos JY Chen Magnus Christensen John Dec Robert Dibble Russ Durrett Randy Hessel Bengt Johansson
Magnus Sjoberg Dick Steeper Axel Zur Loye
External Contributors
August 24 2006 Work performed under the auspices of the US Department of Energy by University of California Lawrence Livermore National Laboratory under Contract W-7405-ENG-48
Homogeneous charge compression ignition (HCCI) combustion is an autoignition process controlled by chemical kinetics
Courtesy of Professor Yuuji Ikeda Kyoto University
The physics of HCCI combustion can be well captured with a sequential fluid mechanics-chemical kinetics model
High resolution CFD Lower resolution chemical kinetics simulation (105-106 cells) discretization (10-100 zones)
Fluid mechanics sets the Combustion is very fast temperature distribution and therefore can be where autoignition occurs analyzed without
considering mixing or turbulence
Application of the multi-zone model Sandia engine running at low equivalence ratio with iso-octane
588 mm 523 mm 901 mm 0305 mm (02314 in) (0206 in) (0355 in) (00120 in)
102 mm
SAE Paper 2004-01-1910
(402 in)
Pre
ssur
e k
Pa
Multi-zone model accurately predicts pressure traces for multiple equivalence ratios from ultra lean to mid load
9000
Solid lines experimental
8000 Φ=026 Dashed lines numerical
Φ=024 Φ=022
7000 Φ=020Φ=018Φ=016
6000 Φ=014Φ=012Φ=0105000 Φ=008Φ=006
4000 Φ=004
3000
2000-30 -20 -10 0 10 20 30
crank angle degrees
Multi-zone model makes good predictions of emissions over the full range of operation
004 006 008 010 012 014 016 018 020 022 024 026 028 Equivalence ratio
0
10
20
30
40
50
60
70
80
90
100 Fu
el C
arbo
n in
to e
mis
sion
s (
) Solid lines experimental Dashed lines numerical
CO2 CO HC OHC
Model can tell us the location in the cylinder where different pollutants originate
Much interest exists on Premixed Charge Compression Ignition (PCCI) engines for high load and improved combustion control
PCCI through high EGR that does not PCCI through early mix well with fresh charge (VVT CAI) direct injection
Can we extend our sequential fluid mechanics-chemical kinetics model to model PCCI combustion
High resolution CFD Lower resolution chemical kinetics simulation (105 cells) discretization (10-100 zones)
Fluid mechanics sets the Combustion is very fast temperature distribution and therefore can be where autoignition occurs analyzed without
considering mixing or turbulence
We can try analyzing PCCI by doing a two-directional mapping from KIVA to CHEMKIN and from CHEMKIN back to KIVA
High resolution CFD Chemistry handled by multi-solver handles mixing zone detailed kinetics solveradvection and diffusion (10-100 zones)(~100k cells)
Solutions are mapped back and forth between solvers throughout the cycle
We have applied KIVA-MZ-MPI to analyze partially stratified combustion with a linear fuel distribution at intake valve closing
Head SAE Paper 2006-01-1363
Center line
Liner
Piston
Radial stratification imposed from centerline to liner ldquoUniformrdquo ldquoShallowrdquo and ldquoSteeprdquo
KIVA-MZ-MPI results compare well with
Pres
sure
(bar
)the results from a full integration of KIVA and Chemkin
90 350Phi = 026
Uniform (sim) 80 300Shallow (sim)
70 Steep (sim) 250
Rate of H
60 200
eat R
50 150
40 100
elease (JCA
D)
30 50
20 -20 -15 -10 -5 0 5 10 15
0 20
Crank Angle (Degree) φ=026
KIVA-MZ-MPI gives significant reduction in computational time but still requires 1-2 days in 50-100 processors
8192 CPUs 8 TB Memory 122 Tflopssec
160 Tons 6 MW
We have incorporated a neural network into KIVA3V for fast analysis of HCCI combustion and emissions (KIVA3V-ANN)
hellip
τ T
P
EGR
Ignition Delay Timeφ
Input Variables hellip
Input Hidden Output Layer Layers Layer
tk 1Ignition Condition I ( )t = intt (0) τ
dt = 1
KIVA3V-ANN greatly reduces computational intensity Firing cases take ~10 more time than a motored run
1 3
Is Ignition Criterion
met
No 4
Yes
Turn on global fuel conversion mechanism C8H18 + 85O2 rarr 8CO + 9H2O CO + frac12O2 rarr CO2
2 Calculate Ignition Integral in Each Cell
(0)
1( ) kt
t I t dt
τ = int
pres
sure
KPa
Results agree well with experiments and multi-zone model over a wide range of conditions (geometry φ CR intake pressure)
588 mm 523 mm 901 mm 0305 mm (02314 in) (0206 in) (0355 in) (00120 in)
8500
8000 Experimental Multi-zone
φ=026
7500 Neural network 024
022
7000 020
6500 018
102 mm (402 in) 6000 016
014 5500
φ=010 012 5000
4500
4000 -10 0 10
crank angle degrees Data from Sandia Livermore SAE 2003-01-0752
Scalloped piston iso-octane φ=01-026 CR=181 120 kPa intake
KIVA3V-ANN predicts similar spatial evolution of combustion as KIVA3V-MZ-MPI for ldquonormalrdquo HCCI
KIVA3V-MZ-MPI KIVA3V-ANN
φ=020 φ=020
KIVA3V-ANN predicts similar spatial evolution of CO during combustion as KIVA3V-MZ-MPI for ldquonormalrdquo HCCI
KIVA3V-MZ-MPI KIVA3V-ANN
-4deg
-2deg
TDC
+4deg
+10 φ=020
-4deg
-2deg
TDC
+4deg
+10 φ=020
At φ=010 KIVA3V-ANN predicts high CO emissions because reaction mechanism misses the oxidation of CO in the central core
Kiva3v-MZ-MPI Kiva3v-ANN
φ=010 φ=010
Summary HCCI is dominated by chemical kinetics and can therefore be well characterized by a computationally efficient segregated fluid mechanics-multi-zone chemical kinetics model
Summary Our new fully integrated parallelized fluid mechanics-chemical kinetics code (KIVA3V-MZ-MPI) considers the effect of mixing and therefore applies to partially stratified combustion
High resolution CFD Chemistry handled by multi-solver handles mixing zone detailed kinetics solveradvection and diffusion (10-100 zones)(~100k cells)
Solutions are mapped back and forth between solvers throughout the cycle
Summary We are developing accurate HCCI and PCCI analysis techniques with greatly improved computational efficiency
bull Direct integration of KIVA and Chemkin
Years of computing time in single processor computer
bull Multi-zone KIVA-Chemkin Days of computing time in single processor computer
T
helliphellip
bull KIVA-Artificial neural networkP τ Ignition
Input Delayφ Time Hours of computing timeVariables
EGR
in single processor computerOutput Input Hidden
Layer Layers Layer
Homogeneous charge compression ignition (HCCI) combustion is an autoignition process controlled by chemical kinetics
Courtesy of Professor Yuuji Ikeda Kyoto University
The physics of HCCI combustion can be well captured with a sequential fluid mechanics-chemical kinetics model
High resolution CFD Lower resolution chemical kinetics simulation (105-106 cells) discretization (10-100 zones)
Fluid mechanics sets the Combustion is very fast temperature distribution and therefore can be where autoignition occurs analyzed without
considering mixing or turbulence
Application of the multi-zone model Sandia engine running at low equivalence ratio with iso-octane
588 mm 523 mm 901 mm 0305 mm (02314 in) (0206 in) (0355 in) (00120 in)
102 mm
SAE Paper 2004-01-1910
(402 in)
Pre
ssur
e k
Pa
Multi-zone model accurately predicts pressure traces for multiple equivalence ratios from ultra lean to mid load
9000
Solid lines experimental
8000 Φ=026 Dashed lines numerical
Φ=024 Φ=022
7000 Φ=020Φ=018Φ=016
6000 Φ=014Φ=012Φ=0105000 Φ=008Φ=006
4000 Φ=004
3000
2000-30 -20 -10 0 10 20 30
crank angle degrees
Multi-zone model makes good predictions of emissions over the full range of operation
004 006 008 010 012 014 016 018 020 022 024 026 028 Equivalence ratio
0
10
20
30
40
50
60
70
80
90
100 Fu
el C
arbo
n in
to e
mis
sion
s (
) Solid lines experimental Dashed lines numerical
CO2 CO HC OHC
Model can tell us the location in the cylinder where different pollutants originate
Much interest exists on Premixed Charge Compression Ignition (PCCI) engines for high load and improved combustion control
PCCI through high EGR that does not PCCI through early mix well with fresh charge (VVT CAI) direct injection
Can we extend our sequential fluid mechanics-chemical kinetics model to model PCCI combustion
High resolution CFD Lower resolution chemical kinetics simulation (105 cells) discretization (10-100 zones)
Fluid mechanics sets the Combustion is very fast temperature distribution and therefore can be where autoignition occurs analyzed without
considering mixing or turbulence
We can try analyzing PCCI by doing a two-directional mapping from KIVA to CHEMKIN and from CHEMKIN back to KIVA
High resolution CFD Chemistry handled by multi-solver handles mixing zone detailed kinetics solveradvection and diffusion (10-100 zones)(~100k cells)
Solutions are mapped back and forth between solvers throughout the cycle
We have applied KIVA-MZ-MPI to analyze partially stratified combustion with a linear fuel distribution at intake valve closing
Head SAE Paper 2006-01-1363
Center line
Liner
Piston
Radial stratification imposed from centerline to liner ldquoUniformrdquo ldquoShallowrdquo and ldquoSteeprdquo
KIVA-MZ-MPI results compare well with
Pres
sure
(bar
)the results from a full integration of KIVA and Chemkin
90 350Phi = 026
Uniform (sim) 80 300Shallow (sim)
70 Steep (sim) 250
Rate of H
60 200
eat R
50 150
40 100
elease (JCA
D)
30 50
20 -20 -15 -10 -5 0 5 10 15
0 20
Crank Angle (Degree) φ=026
KIVA-MZ-MPI gives significant reduction in computational time but still requires 1-2 days in 50-100 processors
8192 CPUs 8 TB Memory 122 Tflopssec
160 Tons 6 MW
We have incorporated a neural network into KIVA3V for fast analysis of HCCI combustion and emissions (KIVA3V-ANN)
hellip
τ T
P
EGR
Ignition Delay Timeφ
Input Variables hellip
Input Hidden Output Layer Layers Layer
tk 1Ignition Condition I ( )t = intt (0) τ
dt = 1
KIVA3V-ANN greatly reduces computational intensity Firing cases take ~10 more time than a motored run
1 3
Is Ignition Criterion
met
No 4
Yes
Turn on global fuel conversion mechanism C8H18 + 85O2 rarr 8CO + 9H2O CO + frac12O2 rarr CO2
2 Calculate Ignition Integral in Each Cell
(0)
1( ) kt
t I t dt
τ = int
pres
sure
KPa
Results agree well with experiments and multi-zone model over a wide range of conditions (geometry φ CR intake pressure)
588 mm 523 mm 901 mm 0305 mm (02314 in) (0206 in) (0355 in) (00120 in)
8500
8000 Experimental Multi-zone
φ=026
7500 Neural network 024
022
7000 020
6500 018
102 mm (402 in) 6000 016
014 5500
φ=010 012 5000
4500
4000 -10 0 10
crank angle degrees Data from Sandia Livermore SAE 2003-01-0752
Scalloped piston iso-octane φ=01-026 CR=181 120 kPa intake
KIVA3V-ANN predicts similar spatial evolution of combustion as KIVA3V-MZ-MPI for ldquonormalrdquo HCCI
KIVA3V-MZ-MPI KIVA3V-ANN
φ=020 φ=020
KIVA3V-ANN predicts similar spatial evolution of CO during combustion as KIVA3V-MZ-MPI for ldquonormalrdquo HCCI
KIVA3V-MZ-MPI KIVA3V-ANN
-4deg
-2deg
TDC
+4deg
+10 φ=020
-4deg
-2deg
TDC
+4deg
+10 φ=020
At φ=010 KIVA3V-ANN predicts high CO emissions because reaction mechanism misses the oxidation of CO in the central core
Kiva3v-MZ-MPI Kiva3v-ANN
φ=010 φ=010
Summary HCCI is dominated by chemical kinetics and can therefore be well characterized by a computationally efficient segregated fluid mechanics-multi-zone chemical kinetics model
Summary Our new fully integrated parallelized fluid mechanics-chemical kinetics code (KIVA3V-MZ-MPI) considers the effect of mixing and therefore applies to partially stratified combustion
High resolution CFD Chemistry handled by multi-solver handles mixing zone detailed kinetics solveradvection and diffusion (10-100 zones)(~100k cells)
Solutions are mapped back and forth between solvers throughout the cycle
Summary We are developing accurate HCCI and PCCI analysis techniques with greatly improved computational efficiency
bull Direct integration of KIVA and Chemkin
Years of computing time in single processor computer
bull Multi-zone KIVA-Chemkin Days of computing time in single processor computer
T
helliphellip
bull KIVA-Artificial neural networkP τ Ignition
Input Delayφ Time Hours of computing timeVariables
EGR
in single processor computerOutput Input Hidden
Layer Layers Layer
The physics of HCCI combustion can be well captured with a sequential fluid mechanics-chemical kinetics model
High resolution CFD Lower resolution chemical kinetics simulation (105-106 cells) discretization (10-100 zones)
Fluid mechanics sets the Combustion is very fast temperature distribution and therefore can be where autoignition occurs analyzed without
considering mixing or turbulence
Application of the multi-zone model Sandia engine running at low equivalence ratio with iso-octane
588 mm 523 mm 901 mm 0305 mm (02314 in) (0206 in) (0355 in) (00120 in)
102 mm
SAE Paper 2004-01-1910
(402 in)
Pre
ssur
e k
Pa
Multi-zone model accurately predicts pressure traces for multiple equivalence ratios from ultra lean to mid load
9000
Solid lines experimental
8000 Φ=026 Dashed lines numerical
Φ=024 Φ=022
7000 Φ=020Φ=018Φ=016
6000 Φ=014Φ=012Φ=0105000 Φ=008Φ=006
4000 Φ=004
3000
2000-30 -20 -10 0 10 20 30
crank angle degrees
Multi-zone model makes good predictions of emissions over the full range of operation
004 006 008 010 012 014 016 018 020 022 024 026 028 Equivalence ratio
0
10
20
30
40
50
60
70
80
90
100 Fu
el C
arbo
n in
to e
mis
sion
s (
) Solid lines experimental Dashed lines numerical
CO2 CO HC OHC
Model can tell us the location in the cylinder where different pollutants originate
Much interest exists on Premixed Charge Compression Ignition (PCCI) engines for high load and improved combustion control
PCCI through high EGR that does not PCCI through early mix well with fresh charge (VVT CAI) direct injection
Can we extend our sequential fluid mechanics-chemical kinetics model to model PCCI combustion
High resolution CFD Lower resolution chemical kinetics simulation (105 cells) discretization (10-100 zones)
Fluid mechanics sets the Combustion is very fast temperature distribution and therefore can be where autoignition occurs analyzed without
considering mixing or turbulence
We can try analyzing PCCI by doing a two-directional mapping from KIVA to CHEMKIN and from CHEMKIN back to KIVA
High resolution CFD Chemistry handled by multi-solver handles mixing zone detailed kinetics solveradvection and diffusion (10-100 zones)(~100k cells)
Solutions are mapped back and forth between solvers throughout the cycle
We have applied KIVA-MZ-MPI to analyze partially stratified combustion with a linear fuel distribution at intake valve closing
Head SAE Paper 2006-01-1363
Center line
Liner
Piston
Radial stratification imposed from centerline to liner ldquoUniformrdquo ldquoShallowrdquo and ldquoSteeprdquo
KIVA-MZ-MPI results compare well with
Pres
sure
(bar
)the results from a full integration of KIVA and Chemkin
90 350Phi = 026
Uniform (sim) 80 300Shallow (sim)
70 Steep (sim) 250
Rate of H
60 200
eat R
50 150
40 100
elease (JCA
D)
30 50
20 -20 -15 -10 -5 0 5 10 15
0 20
Crank Angle (Degree) φ=026
KIVA-MZ-MPI gives significant reduction in computational time but still requires 1-2 days in 50-100 processors
8192 CPUs 8 TB Memory 122 Tflopssec
160 Tons 6 MW
We have incorporated a neural network into KIVA3V for fast analysis of HCCI combustion and emissions (KIVA3V-ANN)
hellip
τ T
P
EGR
Ignition Delay Timeφ
Input Variables hellip
Input Hidden Output Layer Layers Layer
tk 1Ignition Condition I ( )t = intt (0) τ
dt = 1
KIVA3V-ANN greatly reduces computational intensity Firing cases take ~10 more time than a motored run
1 3
Is Ignition Criterion
met
No 4
Yes
Turn on global fuel conversion mechanism C8H18 + 85O2 rarr 8CO + 9H2O CO + frac12O2 rarr CO2
2 Calculate Ignition Integral in Each Cell
(0)
1( ) kt
t I t dt
τ = int
pres
sure
KPa
Results agree well with experiments and multi-zone model over a wide range of conditions (geometry φ CR intake pressure)
588 mm 523 mm 901 mm 0305 mm (02314 in) (0206 in) (0355 in) (00120 in)
8500
8000 Experimental Multi-zone
φ=026
7500 Neural network 024
022
7000 020
6500 018
102 mm (402 in) 6000 016
014 5500
φ=010 012 5000
4500
4000 -10 0 10
crank angle degrees Data from Sandia Livermore SAE 2003-01-0752
Scalloped piston iso-octane φ=01-026 CR=181 120 kPa intake
KIVA3V-ANN predicts similar spatial evolution of combustion as KIVA3V-MZ-MPI for ldquonormalrdquo HCCI
KIVA3V-MZ-MPI KIVA3V-ANN
φ=020 φ=020
KIVA3V-ANN predicts similar spatial evolution of CO during combustion as KIVA3V-MZ-MPI for ldquonormalrdquo HCCI
KIVA3V-MZ-MPI KIVA3V-ANN
-4deg
-2deg
TDC
+4deg
+10 φ=020
-4deg
-2deg
TDC
+4deg
+10 φ=020
At φ=010 KIVA3V-ANN predicts high CO emissions because reaction mechanism misses the oxidation of CO in the central core
Kiva3v-MZ-MPI Kiva3v-ANN
φ=010 φ=010
Summary HCCI is dominated by chemical kinetics and can therefore be well characterized by a computationally efficient segregated fluid mechanics-multi-zone chemical kinetics model
Summary Our new fully integrated parallelized fluid mechanics-chemical kinetics code (KIVA3V-MZ-MPI) considers the effect of mixing and therefore applies to partially stratified combustion
High resolution CFD Chemistry handled by multi-solver handles mixing zone detailed kinetics solveradvection and diffusion (10-100 zones)(~100k cells)
Solutions are mapped back and forth between solvers throughout the cycle
Summary We are developing accurate HCCI and PCCI analysis techniques with greatly improved computational efficiency
bull Direct integration of KIVA and Chemkin
Years of computing time in single processor computer
bull Multi-zone KIVA-Chemkin Days of computing time in single processor computer
T
helliphellip
bull KIVA-Artificial neural networkP τ Ignition
Input Delayφ Time Hours of computing timeVariables
EGR
in single processor computerOutput Input Hidden
Layer Layers Layer
Application of the multi-zone model Sandia engine running at low equivalence ratio with iso-octane
588 mm 523 mm 901 mm 0305 mm (02314 in) (0206 in) (0355 in) (00120 in)
102 mm
SAE Paper 2004-01-1910
(402 in)
Pre
ssur
e k
Pa
Multi-zone model accurately predicts pressure traces for multiple equivalence ratios from ultra lean to mid load
9000
Solid lines experimental
8000 Φ=026 Dashed lines numerical
Φ=024 Φ=022
7000 Φ=020Φ=018Φ=016
6000 Φ=014Φ=012Φ=0105000 Φ=008Φ=006
4000 Φ=004
3000
2000-30 -20 -10 0 10 20 30
crank angle degrees
Multi-zone model makes good predictions of emissions over the full range of operation
004 006 008 010 012 014 016 018 020 022 024 026 028 Equivalence ratio
0
10
20
30
40
50
60
70
80
90
100 Fu
el C
arbo
n in
to e
mis
sion
s (
) Solid lines experimental Dashed lines numerical
CO2 CO HC OHC
Model can tell us the location in the cylinder where different pollutants originate
Much interest exists on Premixed Charge Compression Ignition (PCCI) engines for high load and improved combustion control
PCCI through high EGR that does not PCCI through early mix well with fresh charge (VVT CAI) direct injection
Can we extend our sequential fluid mechanics-chemical kinetics model to model PCCI combustion
High resolution CFD Lower resolution chemical kinetics simulation (105 cells) discretization (10-100 zones)
Fluid mechanics sets the Combustion is very fast temperature distribution and therefore can be where autoignition occurs analyzed without
considering mixing or turbulence
We can try analyzing PCCI by doing a two-directional mapping from KIVA to CHEMKIN and from CHEMKIN back to KIVA
High resolution CFD Chemistry handled by multi-solver handles mixing zone detailed kinetics solveradvection and diffusion (10-100 zones)(~100k cells)
Solutions are mapped back and forth between solvers throughout the cycle
We have applied KIVA-MZ-MPI to analyze partially stratified combustion with a linear fuel distribution at intake valve closing
Head SAE Paper 2006-01-1363
Center line
Liner
Piston
Radial stratification imposed from centerline to liner ldquoUniformrdquo ldquoShallowrdquo and ldquoSteeprdquo
KIVA-MZ-MPI results compare well with
Pres
sure
(bar
)the results from a full integration of KIVA and Chemkin
90 350Phi = 026
Uniform (sim) 80 300Shallow (sim)
70 Steep (sim) 250
Rate of H
60 200
eat R
50 150
40 100
elease (JCA
D)
30 50
20 -20 -15 -10 -5 0 5 10 15
0 20
Crank Angle (Degree) φ=026
KIVA-MZ-MPI gives significant reduction in computational time but still requires 1-2 days in 50-100 processors
8192 CPUs 8 TB Memory 122 Tflopssec
160 Tons 6 MW
We have incorporated a neural network into KIVA3V for fast analysis of HCCI combustion and emissions (KIVA3V-ANN)
hellip
τ T
P
EGR
Ignition Delay Timeφ
Input Variables hellip
Input Hidden Output Layer Layers Layer
tk 1Ignition Condition I ( )t = intt (0) τ
dt = 1
KIVA3V-ANN greatly reduces computational intensity Firing cases take ~10 more time than a motored run
1 3
Is Ignition Criterion
met
No 4
Yes
Turn on global fuel conversion mechanism C8H18 + 85O2 rarr 8CO + 9H2O CO + frac12O2 rarr CO2
2 Calculate Ignition Integral in Each Cell
(0)
1( ) kt
t I t dt
τ = int
pres
sure
KPa
Results agree well with experiments and multi-zone model over a wide range of conditions (geometry φ CR intake pressure)
588 mm 523 mm 901 mm 0305 mm (02314 in) (0206 in) (0355 in) (00120 in)
8500
8000 Experimental Multi-zone
φ=026
7500 Neural network 024
022
7000 020
6500 018
102 mm (402 in) 6000 016
014 5500
φ=010 012 5000
4500
4000 -10 0 10
crank angle degrees Data from Sandia Livermore SAE 2003-01-0752
Scalloped piston iso-octane φ=01-026 CR=181 120 kPa intake
KIVA3V-ANN predicts similar spatial evolution of combustion as KIVA3V-MZ-MPI for ldquonormalrdquo HCCI
KIVA3V-MZ-MPI KIVA3V-ANN
φ=020 φ=020
KIVA3V-ANN predicts similar spatial evolution of CO during combustion as KIVA3V-MZ-MPI for ldquonormalrdquo HCCI
KIVA3V-MZ-MPI KIVA3V-ANN
-4deg
-2deg
TDC
+4deg
+10 φ=020
-4deg
-2deg
TDC
+4deg
+10 φ=020
At φ=010 KIVA3V-ANN predicts high CO emissions because reaction mechanism misses the oxidation of CO in the central core
Kiva3v-MZ-MPI Kiva3v-ANN
φ=010 φ=010
Summary HCCI is dominated by chemical kinetics and can therefore be well characterized by a computationally efficient segregated fluid mechanics-multi-zone chemical kinetics model
Summary Our new fully integrated parallelized fluid mechanics-chemical kinetics code (KIVA3V-MZ-MPI) considers the effect of mixing and therefore applies to partially stratified combustion
High resolution CFD Chemistry handled by multi-solver handles mixing zone detailed kinetics solveradvection and diffusion (10-100 zones)(~100k cells)
Solutions are mapped back and forth between solvers throughout the cycle
Summary We are developing accurate HCCI and PCCI analysis techniques with greatly improved computational efficiency
bull Direct integration of KIVA and Chemkin
Years of computing time in single processor computer
bull Multi-zone KIVA-Chemkin Days of computing time in single processor computer
T
helliphellip
bull KIVA-Artificial neural networkP τ Ignition
Input Delayφ Time Hours of computing timeVariables
EGR
in single processor computerOutput Input Hidden
Layer Layers Layer
Pre
ssur
e k
Pa
Multi-zone model accurately predicts pressure traces for multiple equivalence ratios from ultra lean to mid load
9000
Solid lines experimental
8000 Φ=026 Dashed lines numerical
Φ=024 Φ=022
7000 Φ=020Φ=018Φ=016
6000 Φ=014Φ=012Φ=0105000 Φ=008Φ=006
4000 Φ=004
3000
2000-30 -20 -10 0 10 20 30
crank angle degrees
Multi-zone model makes good predictions of emissions over the full range of operation
004 006 008 010 012 014 016 018 020 022 024 026 028 Equivalence ratio
0
10
20
30
40
50
60
70
80
90
100 Fu
el C
arbo
n in
to e
mis
sion
s (
) Solid lines experimental Dashed lines numerical
CO2 CO HC OHC
Model can tell us the location in the cylinder where different pollutants originate
Much interest exists on Premixed Charge Compression Ignition (PCCI) engines for high load and improved combustion control
PCCI through high EGR that does not PCCI through early mix well with fresh charge (VVT CAI) direct injection
Can we extend our sequential fluid mechanics-chemical kinetics model to model PCCI combustion
High resolution CFD Lower resolution chemical kinetics simulation (105 cells) discretization (10-100 zones)
Fluid mechanics sets the Combustion is very fast temperature distribution and therefore can be where autoignition occurs analyzed without
considering mixing or turbulence
We can try analyzing PCCI by doing a two-directional mapping from KIVA to CHEMKIN and from CHEMKIN back to KIVA
High resolution CFD Chemistry handled by multi-solver handles mixing zone detailed kinetics solveradvection and diffusion (10-100 zones)(~100k cells)
Solutions are mapped back and forth between solvers throughout the cycle
We have applied KIVA-MZ-MPI to analyze partially stratified combustion with a linear fuel distribution at intake valve closing
Head SAE Paper 2006-01-1363
Center line
Liner
Piston
Radial stratification imposed from centerline to liner ldquoUniformrdquo ldquoShallowrdquo and ldquoSteeprdquo
KIVA-MZ-MPI results compare well with
Pres
sure
(bar
)the results from a full integration of KIVA and Chemkin
90 350Phi = 026
Uniform (sim) 80 300Shallow (sim)
70 Steep (sim) 250
Rate of H
60 200
eat R
50 150
40 100
elease (JCA
D)
30 50
20 -20 -15 -10 -5 0 5 10 15
0 20
Crank Angle (Degree) φ=026
KIVA-MZ-MPI gives significant reduction in computational time but still requires 1-2 days in 50-100 processors
8192 CPUs 8 TB Memory 122 Tflopssec
160 Tons 6 MW
We have incorporated a neural network into KIVA3V for fast analysis of HCCI combustion and emissions (KIVA3V-ANN)
hellip
τ T
P
EGR
Ignition Delay Timeφ
Input Variables hellip
Input Hidden Output Layer Layers Layer
tk 1Ignition Condition I ( )t = intt (0) τ
dt = 1
KIVA3V-ANN greatly reduces computational intensity Firing cases take ~10 more time than a motored run
1 3
Is Ignition Criterion
met
No 4
Yes
Turn on global fuel conversion mechanism C8H18 + 85O2 rarr 8CO + 9H2O CO + frac12O2 rarr CO2
2 Calculate Ignition Integral in Each Cell
(0)
1( ) kt
t I t dt
τ = int
pres
sure
KPa
Results agree well with experiments and multi-zone model over a wide range of conditions (geometry φ CR intake pressure)
588 mm 523 mm 901 mm 0305 mm (02314 in) (0206 in) (0355 in) (00120 in)
8500
8000 Experimental Multi-zone
φ=026
7500 Neural network 024
022
7000 020
6500 018
102 mm (402 in) 6000 016
014 5500
φ=010 012 5000
4500
4000 -10 0 10
crank angle degrees Data from Sandia Livermore SAE 2003-01-0752
Scalloped piston iso-octane φ=01-026 CR=181 120 kPa intake
KIVA3V-ANN predicts similar spatial evolution of combustion as KIVA3V-MZ-MPI for ldquonormalrdquo HCCI
KIVA3V-MZ-MPI KIVA3V-ANN
φ=020 φ=020
KIVA3V-ANN predicts similar spatial evolution of CO during combustion as KIVA3V-MZ-MPI for ldquonormalrdquo HCCI
KIVA3V-MZ-MPI KIVA3V-ANN
-4deg
-2deg
TDC
+4deg
+10 φ=020
-4deg
-2deg
TDC
+4deg
+10 φ=020
At φ=010 KIVA3V-ANN predicts high CO emissions because reaction mechanism misses the oxidation of CO in the central core
Kiva3v-MZ-MPI Kiva3v-ANN
φ=010 φ=010
Summary HCCI is dominated by chemical kinetics and can therefore be well characterized by a computationally efficient segregated fluid mechanics-multi-zone chemical kinetics model
Summary Our new fully integrated parallelized fluid mechanics-chemical kinetics code (KIVA3V-MZ-MPI) considers the effect of mixing and therefore applies to partially stratified combustion
High resolution CFD Chemistry handled by multi-solver handles mixing zone detailed kinetics solveradvection and diffusion (10-100 zones)(~100k cells)
Solutions are mapped back and forth between solvers throughout the cycle
Summary We are developing accurate HCCI and PCCI analysis techniques with greatly improved computational efficiency
bull Direct integration of KIVA and Chemkin
Years of computing time in single processor computer
bull Multi-zone KIVA-Chemkin Days of computing time in single processor computer
T
helliphellip
bull KIVA-Artificial neural networkP τ Ignition
Input Delayφ Time Hours of computing timeVariables
EGR
in single processor computerOutput Input Hidden
Layer Layers Layer
Multi-zone model makes good predictions of emissions over the full range of operation
004 006 008 010 012 014 016 018 020 022 024 026 028 Equivalence ratio
0
10
20
30
40
50
60
70
80
90
100 Fu
el C
arbo
n in
to e
mis
sion
s (
) Solid lines experimental Dashed lines numerical
CO2 CO HC OHC
Model can tell us the location in the cylinder where different pollutants originate
Much interest exists on Premixed Charge Compression Ignition (PCCI) engines for high load and improved combustion control
PCCI through high EGR that does not PCCI through early mix well with fresh charge (VVT CAI) direct injection
Can we extend our sequential fluid mechanics-chemical kinetics model to model PCCI combustion
High resolution CFD Lower resolution chemical kinetics simulation (105 cells) discretization (10-100 zones)
Fluid mechanics sets the Combustion is very fast temperature distribution and therefore can be where autoignition occurs analyzed without
considering mixing or turbulence
We can try analyzing PCCI by doing a two-directional mapping from KIVA to CHEMKIN and from CHEMKIN back to KIVA
High resolution CFD Chemistry handled by multi-solver handles mixing zone detailed kinetics solveradvection and diffusion (10-100 zones)(~100k cells)
Solutions are mapped back and forth between solvers throughout the cycle
We have applied KIVA-MZ-MPI to analyze partially stratified combustion with a linear fuel distribution at intake valve closing
Head SAE Paper 2006-01-1363
Center line
Liner
Piston
Radial stratification imposed from centerline to liner ldquoUniformrdquo ldquoShallowrdquo and ldquoSteeprdquo
KIVA-MZ-MPI results compare well with
Pres
sure
(bar
)the results from a full integration of KIVA and Chemkin
90 350Phi = 026
Uniform (sim) 80 300Shallow (sim)
70 Steep (sim) 250
Rate of H
60 200
eat R
50 150
40 100
elease (JCA
D)
30 50
20 -20 -15 -10 -5 0 5 10 15
0 20
Crank Angle (Degree) φ=026
KIVA-MZ-MPI gives significant reduction in computational time but still requires 1-2 days in 50-100 processors
8192 CPUs 8 TB Memory 122 Tflopssec
160 Tons 6 MW
We have incorporated a neural network into KIVA3V for fast analysis of HCCI combustion and emissions (KIVA3V-ANN)
hellip
τ T
P
EGR
Ignition Delay Timeφ
Input Variables hellip
Input Hidden Output Layer Layers Layer
tk 1Ignition Condition I ( )t = intt (0) τ
dt = 1
KIVA3V-ANN greatly reduces computational intensity Firing cases take ~10 more time than a motored run
1 3
Is Ignition Criterion
met
No 4
Yes
Turn on global fuel conversion mechanism C8H18 + 85O2 rarr 8CO + 9H2O CO + frac12O2 rarr CO2
2 Calculate Ignition Integral in Each Cell
(0)
1( ) kt
t I t dt
τ = int
pres
sure
KPa
Results agree well with experiments and multi-zone model over a wide range of conditions (geometry φ CR intake pressure)
588 mm 523 mm 901 mm 0305 mm (02314 in) (0206 in) (0355 in) (00120 in)
8500
8000 Experimental Multi-zone
φ=026
7500 Neural network 024
022
7000 020
6500 018
102 mm (402 in) 6000 016
014 5500
φ=010 012 5000
4500
4000 -10 0 10
crank angle degrees Data from Sandia Livermore SAE 2003-01-0752
Scalloped piston iso-octane φ=01-026 CR=181 120 kPa intake
KIVA3V-ANN predicts similar spatial evolution of combustion as KIVA3V-MZ-MPI for ldquonormalrdquo HCCI
KIVA3V-MZ-MPI KIVA3V-ANN
φ=020 φ=020
KIVA3V-ANN predicts similar spatial evolution of CO during combustion as KIVA3V-MZ-MPI for ldquonormalrdquo HCCI
KIVA3V-MZ-MPI KIVA3V-ANN
-4deg
-2deg
TDC
+4deg
+10 φ=020
-4deg
-2deg
TDC
+4deg
+10 φ=020
At φ=010 KIVA3V-ANN predicts high CO emissions because reaction mechanism misses the oxidation of CO in the central core
Kiva3v-MZ-MPI Kiva3v-ANN
φ=010 φ=010
Summary HCCI is dominated by chemical kinetics and can therefore be well characterized by a computationally efficient segregated fluid mechanics-multi-zone chemical kinetics model
Summary Our new fully integrated parallelized fluid mechanics-chemical kinetics code (KIVA3V-MZ-MPI) considers the effect of mixing and therefore applies to partially stratified combustion
High resolution CFD Chemistry handled by multi-solver handles mixing zone detailed kinetics solveradvection and diffusion (10-100 zones)(~100k cells)
Solutions are mapped back and forth between solvers throughout the cycle
Summary We are developing accurate HCCI and PCCI analysis techniques with greatly improved computational efficiency
bull Direct integration of KIVA and Chemkin
Years of computing time in single processor computer
bull Multi-zone KIVA-Chemkin Days of computing time in single processor computer
T
helliphellip
bull KIVA-Artificial neural networkP τ Ignition
Input Delayφ Time Hours of computing timeVariables
EGR
in single processor computerOutput Input Hidden
Layer Layers Layer
Model can tell us the location in the cylinder where different pollutants originate
Much interest exists on Premixed Charge Compression Ignition (PCCI) engines for high load and improved combustion control
PCCI through high EGR that does not PCCI through early mix well with fresh charge (VVT CAI) direct injection
Can we extend our sequential fluid mechanics-chemical kinetics model to model PCCI combustion
High resolution CFD Lower resolution chemical kinetics simulation (105 cells) discretization (10-100 zones)
Fluid mechanics sets the Combustion is very fast temperature distribution and therefore can be where autoignition occurs analyzed without
considering mixing or turbulence
We can try analyzing PCCI by doing a two-directional mapping from KIVA to CHEMKIN and from CHEMKIN back to KIVA
High resolution CFD Chemistry handled by multi-solver handles mixing zone detailed kinetics solveradvection and diffusion (10-100 zones)(~100k cells)
Solutions are mapped back and forth between solvers throughout the cycle
We have applied KIVA-MZ-MPI to analyze partially stratified combustion with a linear fuel distribution at intake valve closing
Head SAE Paper 2006-01-1363
Center line
Liner
Piston
Radial stratification imposed from centerline to liner ldquoUniformrdquo ldquoShallowrdquo and ldquoSteeprdquo
KIVA-MZ-MPI results compare well with
Pres
sure
(bar
)the results from a full integration of KIVA and Chemkin
90 350Phi = 026
Uniform (sim) 80 300Shallow (sim)
70 Steep (sim) 250
Rate of H
60 200
eat R
50 150
40 100
elease (JCA
D)
30 50
20 -20 -15 -10 -5 0 5 10 15
0 20
Crank Angle (Degree) φ=026
KIVA-MZ-MPI gives significant reduction in computational time but still requires 1-2 days in 50-100 processors
8192 CPUs 8 TB Memory 122 Tflopssec
160 Tons 6 MW
We have incorporated a neural network into KIVA3V for fast analysis of HCCI combustion and emissions (KIVA3V-ANN)
hellip
τ T
P
EGR
Ignition Delay Timeφ
Input Variables hellip
Input Hidden Output Layer Layers Layer
tk 1Ignition Condition I ( )t = intt (0) τ
dt = 1
KIVA3V-ANN greatly reduces computational intensity Firing cases take ~10 more time than a motored run
1 3
Is Ignition Criterion
met
No 4
Yes
Turn on global fuel conversion mechanism C8H18 + 85O2 rarr 8CO + 9H2O CO + frac12O2 rarr CO2
2 Calculate Ignition Integral in Each Cell
(0)
1( ) kt
t I t dt
τ = int
pres
sure
KPa
Results agree well with experiments and multi-zone model over a wide range of conditions (geometry φ CR intake pressure)
588 mm 523 mm 901 mm 0305 mm (02314 in) (0206 in) (0355 in) (00120 in)
8500
8000 Experimental Multi-zone
φ=026
7500 Neural network 024
022
7000 020
6500 018
102 mm (402 in) 6000 016
014 5500
φ=010 012 5000
4500
4000 -10 0 10
crank angle degrees Data from Sandia Livermore SAE 2003-01-0752
Scalloped piston iso-octane φ=01-026 CR=181 120 kPa intake
KIVA3V-ANN predicts similar spatial evolution of combustion as KIVA3V-MZ-MPI for ldquonormalrdquo HCCI
KIVA3V-MZ-MPI KIVA3V-ANN
φ=020 φ=020
KIVA3V-ANN predicts similar spatial evolution of CO during combustion as KIVA3V-MZ-MPI for ldquonormalrdquo HCCI
KIVA3V-MZ-MPI KIVA3V-ANN
-4deg
-2deg
TDC
+4deg
+10 φ=020
-4deg
-2deg
TDC
+4deg
+10 φ=020
At φ=010 KIVA3V-ANN predicts high CO emissions because reaction mechanism misses the oxidation of CO in the central core
Kiva3v-MZ-MPI Kiva3v-ANN
φ=010 φ=010
Summary HCCI is dominated by chemical kinetics and can therefore be well characterized by a computationally efficient segregated fluid mechanics-multi-zone chemical kinetics model
Summary Our new fully integrated parallelized fluid mechanics-chemical kinetics code (KIVA3V-MZ-MPI) considers the effect of mixing and therefore applies to partially stratified combustion
High resolution CFD Chemistry handled by multi-solver handles mixing zone detailed kinetics solveradvection and diffusion (10-100 zones)(~100k cells)
Solutions are mapped back and forth between solvers throughout the cycle
Summary We are developing accurate HCCI and PCCI analysis techniques with greatly improved computational efficiency
bull Direct integration of KIVA and Chemkin
Years of computing time in single processor computer
bull Multi-zone KIVA-Chemkin Days of computing time in single processor computer
T
helliphellip
bull KIVA-Artificial neural networkP τ Ignition
Input Delayφ Time Hours of computing timeVariables
EGR
in single processor computerOutput Input Hidden
Layer Layers Layer
Much interest exists on Premixed Charge Compression Ignition (PCCI) engines for high load and improved combustion control
PCCI through high EGR that does not PCCI through early mix well with fresh charge (VVT CAI) direct injection
Can we extend our sequential fluid mechanics-chemical kinetics model to model PCCI combustion
High resolution CFD Lower resolution chemical kinetics simulation (105 cells) discretization (10-100 zones)
Fluid mechanics sets the Combustion is very fast temperature distribution and therefore can be where autoignition occurs analyzed without
considering mixing or turbulence
We can try analyzing PCCI by doing a two-directional mapping from KIVA to CHEMKIN and from CHEMKIN back to KIVA
High resolution CFD Chemistry handled by multi-solver handles mixing zone detailed kinetics solveradvection and diffusion (10-100 zones)(~100k cells)
Solutions are mapped back and forth between solvers throughout the cycle
We have applied KIVA-MZ-MPI to analyze partially stratified combustion with a linear fuel distribution at intake valve closing
Head SAE Paper 2006-01-1363
Center line
Liner
Piston
Radial stratification imposed from centerline to liner ldquoUniformrdquo ldquoShallowrdquo and ldquoSteeprdquo
KIVA-MZ-MPI results compare well with
Pres
sure
(bar
)the results from a full integration of KIVA and Chemkin
90 350Phi = 026
Uniform (sim) 80 300Shallow (sim)
70 Steep (sim) 250
Rate of H
60 200
eat R
50 150
40 100
elease (JCA
D)
30 50
20 -20 -15 -10 -5 0 5 10 15
0 20
Crank Angle (Degree) φ=026
KIVA-MZ-MPI gives significant reduction in computational time but still requires 1-2 days in 50-100 processors
8192 CPUs 8 TB Memory 122 Tflopssec
160 Tons 6 MW
We have incorporated a neural network into KIVA3V for fast analysis of HCCI combustion and emissions (KIVA3V-ANN)
hellip
τ T
P
EGR
Ignition Delay Timeφ
Input Variables hellip
Input Hidden Output Layer Layers Layer
tk 1Ignition Condition I ( )t = intt (0) τ
dt = 1
KIVA3V-ANN greatly reduces computational intensity Firing cases take ~10 more time than a motored run
1 3
Is Ignition Criterion
met
No 4
Yes
Turn on global fuel conversion mechanism C8H18 + 85O2 rarr 8CO + 9H2O CO + frac12O2 rarr CO2
2 Calculate Ignition Integral in Each Cell
(0)
1( ) kt
t I t dt
τ = int
pres
sure
KPa
Results agree well with experiments and multi-zone model over a wide range of conditions (geometry φ CR intake pressure)
588 mm 523 mm 901 mm 0305 mm (02314 in) (0206 in) (0355 in) (00120 in)
8500
8000 Experimental Multi-zone
φ=026
7500 Neural network 024
022
7000 020
6500 018
102 mm (402 in) 6000 016
014 5500
φ=010 012 5000
4500
4000 -10 0 10
crank angle degrees Data from Sandia Livermore SAE 2003-01-0752
Scalloped piston iso-octane φ=01-026 CR=181 120 kPa intake
KIVA3V-ANN predicts similar spatial evolution of combustion as KIVA3V-MZ-MPI for ldquonormalrdquo HCCI
KIVA3V-MZ-MPI KIVA3V-ANN
φ=020 φ=020
KIVA3V-ANN predicts similar spatial evolution of CO during combustion as KIVA3V-MZ-MPI for ldquonormalrdquo HCCI
KIVA3V-MZ-MPI KIVA3V-ANN
-4deg
-2deg
TDC
+4deg
+10 φ=020
-4deg
-2deg
TDC
+4deg
+10 φ=020
At φ=010 KIVA3V-ANN predicts high CO emissions because reaction mechanism misses the oxidation of CO in the central core
Kiva3v-MZ-MPI Kiva3v-ANN
φ=010 φ=010
Summary HCCI is dominated by chemical kinetics and can therefore be well characterized by a computationally efficient segregated fluid mechanics-multi-zone chemical kinetics model
Summary Our new fully integrated parallelized fluid mechanics-chemical kinetics code (KIVA3V-MZ-MPI) considers the effect of mixing and therefore applies to partially stratified combustion
High resolution CFD Chemistry handled by multi-solver handles mixing zone detailed kinetics solveradvection and diffusion (10-100 zones)(~100k cells)
Solutions are mapped back and forth between solvers throughout the cycle
Summary We are developing accurate HCCI and PCCI analysis techniques with greatly improved computational efficiency
bull Direct integration of KIVA and Chemkin
Years of computing time in single processor computer
bull Multi-zone KIVA-Chemkin Days of computing time in single processor computer
T
helliphellip
bull KIVA-Artificial neural networkP τ Ignition
Input Delayφ Time Hours of computing timeVariables
EGR
in single processor computerOutput Input Hidden
Layer Layers Layer
Can we extend our sequential fluid mechanics-chemical kinetics model to model PCCI combustion
High resolution CFD Lower resolution chemical kinetics simulation (105 cells) discretization (10-100 zones)
Fluid mechanics sets the Combustion is very fast temperature distribution and therefore can be where autoignition occurs analyzed without
considering mixing or turbulence
We can try analyzing PCCI by doing a two-directional mapping from KIVA to CHEMKIN and from CHEMKIN back to KIVA
High resolution CFD Chemistry handled by multi-solver handles mixing zone detailed kinetics solveradvection and diffusion (10-100 zones)(~100k cells)
Solutions are mapped back and forth between solvers throughout the cycle
We have applied KIVA-MZ-MPI to analyze partially stratified combustion with a linear fuel distribution at intake valve closing
Head SAE Paper 2006-01-1363
Center line
Liner
Piston
Radial stratification imposed from centerline to liner ldquoUniformrdquo ldquoShallowrdquo and ldquoSteeprdquo
KIVA-MZ-MPI results compare well with
Pres
sure
(bar
)the results from a full integration of KIVA and Chemkin
90 350Phi = 026
Uniform (sim) 80 300Shallow (sim)
70 Steep (sim) 250
Rate of H
60 200
eat R
50 150
40 100
elease (JCA
D)
30 50
20 -20 -15 -10 -5 0 5 10 15
0 20
Crank Angle (Degree) φ=026
KIVA-MZ-MPI gives significant reduction in computational time but still requires 1-2 days in 50-100 processors
8192 CPUs 8 TB Memory 122 Tflopssec
160 Tons 6 MW
We have incorporated a neural network into KIVA3V for fast analysis of HCCI combustion and emissions (KIVA3V-ANN)
hellip
τ T
P
EGR
Ignition Delay Timeφ
Input Variables hellip
Input Hidden Output Layer Layers Layer
tk 1Ignition Condition I ( )t = intt (0) τ
dt = 1
KIVA3V-ANN greatly reduces computational intensity Firing cases take ~10 more time than a motored run
1 3
Is Ignition Criterion
met
No 4
Yes
Turn on global fuel conversion mechanism C8H18 + 85O2 rarr 8CO + 9H2O CO + frac12O2 rarr CO2
2 Calculate Ignition Integral in Each Cell
(0)
1( ) kt
t I t dt
τ = int
pres
sure
KPa
Results agree well with experiments and multi-zone model over a wide range of conditions (geometry φ CR intake pressure)
588 mm 523 mm 901 mm 0305 mm (02314 in) (0206 in) (0355 in) (00120 in)
8500
8000 Experimental Multi-zone
φ=026
7500 Neural network 024
022
7000 020
6500 018
102 mm (402 in) 6000 016
014 5500
φ=010 012 5000
4500
4000 -10 0 10
crank angle degrees Data from Sandia Livermore SAE 2003-01-0752
Scalloped piston iso-octane φ=01-026 CR=181 120 kPa intake
KIVA3V-ANN predicts similar spatial evolution of combustion as KIVA3V-MZ-MPI for ldquonormalrdquo HCCI
KIVA3V-MZ-MPI KIVA3V-ANN
φ=020 φ=020
KIVA3V-ANN predicts similar spatial evolution of CO during combustion as KIVA3V-MZ-MPI for ldquonormalrdquo HCCI
KIVA3V-MZ-MPI KIVA3V-ANN
-4deg
-2deg
TDC
+4deg
+10 φ=020
-4deg
-2deg
TDC
+4deg
+10 φ=020
At φ=010 KIVA3V-ANN predicts high CO emissions because reaction mechanism misses the oxidation of CO in the central core
Kiva3v-MZ-MPI Kiva3v-ANN
φ=010 φ=010
Summary HCCI is dominated by chemical kinetics and can therefore be well characterized by a computationally efficient segregated fluid mechanics-multi-zone chemical kinetics model
Summary Our new fully integrated parallelized fluid mechanics-chemical kinetics code (KIVA3V-MZ-MPI) considers the effect of mixing and therefore applies to partially stratified combustion
High resolution CFD Chemistry handled by multi-solver handles mixing zone detailed kinetics solveradvection and diffusion (10-100 zones)(~100k cells)
Solutions are mapped back and forth between solvers throughout the cycle
Summary We are developing accurate HCCI and PCCI analysis techniques with greatly improved computational efficiency
bull Direct integration of KIVA and Chemkin
Years of computing time in single processor computer
bull Multi-zone KIVA-Chemkin Days of computing time in single processor computer
T
helliphellip
bull KIVA-Artificial neural networkP τ Ignition
Input Delayφ Time Hours of computing timeVariables
EGR
in single processor computerOutput Input Hidden
Layer Layers Layer
We can try analyzing PCCI by doing a two-directional mapping from KIVA to CHEMKIN and from CHEMKIN back to KIVA
High resolution CFD Chemistry handled by multi-solver handles mixing zone detailed kinetics solveradvection and diffusion (10-100 zones)(~100k cells)
Solutions are mapped back and forth between solvers throughout the cycle
We have applied KIVA-MZ-MPI to analyze partially stratified combustion with a linear fuel distribution at intake valve closing
Head SAE Paper 2006-01-1363
Center line
Liner
Piston
Radial stratification imposed from centerline to liner ldquoUniformrdquo ldquoShallowrdquo and ldquoSteeprdquo
KIVA-MZ-MPI results compare well with
Pres
sure
(bar
)the results from a full integration of KIVA and Chemkin
90 350Phi = 026
Uniform (sim) 80 300Shallow (sim)
70 Steep (sim) 250
Rate of H
60 200
eat R
50 150
40 100
elease (JCA
D)
30 50
20 -20 -15 -10 -5 0 5 10 15
0 20
Crank Angle (Degree) φ=026
KIVA-MZ-MPI gives significant reduction in computational time but still requires 1-2 days in 50-100 processors
8192 CPUs 8 TB Memory 122 Tflopssec
160 Tons 6 MW
We have incorporated a neural network into KIVA3V for fast analysis of HCCI combustion and emissions (KIVA3V-ANN)
hellip
τ T
P
EGR
Ignition Delay Timeφ
Input Variables hellip
Input Hidden Output Layer Layers Layer
tk 1Ignition Condition I ( )t = intt (0) τ
dt = 1
KIVA3V-ANN greatly reduces computational intensity Firing cases take ~10 more time than a motored run
1 3
Is Ignition Criterion
met
No 4
Yes
Turn on global fuel conversion mechanism C8H18 + 85O2 rarr 8CO + 9H2O CO + frac12O2 rarr CO2
2 Calculate Ignition Integral in Each Cell
(0)
1( ) kt
t I t dt
τ = int
pres
sure
KPa
Results agree well with experiments and multi-zone model over a wide range of conditions (geometry φ CR intake pressure)
588 mm 523 mm 901 mm 0305 mm (02314 in) (0206 in) (0355 in) (00120 in)
8500
8000 Experimental Multi-zone
φ=026
7500 Neural network 024
022
7000 020
6500 018
102 mm (402 in) 6000 016
014 5500
φ=010 012 5000
4500
4000 -10 0 10
crank angle degrees Data from Sandia Livermore SAE 2003-01-0752
Scalloped piston iso-octane φ=01-026 CR=181 120 kPa intake
KIVA3V-ANN predicts similar spatial evolution of combustion as KIVA3V-MZ-MPI for ldquonormalrdquo HCCI
KIVA3V-MZ-MPI KIVA3V-ANN
φ=020 φ=020
KIVA3V-ANN predicts similar spatial evolution of CO during combustion as KIVA3V-MZ-MPI for ldquonormalrdquo HCCI
KIVA3V-MZ-MPI KIVA3V-ANN
-4deg
-2deg
TDC
+4deg
+10 φ=020
-4deg
-2deg
TDC
+4deg
+10 φ=020
At φ=010 KIVA3V-ANN predicts high CO emissions because reaction mechanism misses the oxidation of CO in the central core
Kiva3v-MZ-MPI Kiva3v-ANN
φ=010 φ=010
Summary HCCI is dominated by chemical kinetics and can therefore be well characterized by a computationally efficient segregated fluid mechanics-multi-zone chemical kinetics model
Summary Our new fully integrated parallelized fluid mechanics-chemical kinetics code (KIVA3V-MZ-MPI) considers the effect of mixing and therefore applies to partially stratified combustion
High resolution CFD Chemistry handled by multi-solver handles mixing zone detailed kinetics solveradvection and diffusion (10-100 zones)(~100k cells)
Solutions are mapped back and forth between solvers throughout the cycle
Summary We are developing accurate HCCI and PCCI analysis techniques with greatly improved computational efficiency
bull Direct integration of KIVA and Chemkin
Years of computing time in single processor computer
bull Multi-zone KIVA-Chemkin Days of computing time in single processor computer
T
helliphellip
bull KIVA-Artificial neural networkP τ Ignition
Input Delayφ Time Hours of computing timeVariables
EGR
in single processor computerOutput Input Hidden
Layer Layers Layer
We have applied KIVA-MZ-MPI to analyze partially stratified combustion with a linear fuel distribution at intake valve closing
Head SAE Paper 2006-01-1363
Center line
Liner
Piston
Radial stratification imposed from centerline to liner ldquoUniformrdquo ldquoShallowrdquo and ldquoSteeprdquo
KIVA-MZ-MPI results compare well with
Pres
sure
(bar
)the results from a full integration of KIVA and Chemkin
90 350Phi = 026
Uniform (sim) 80 300Shallow (sim)
70 Steep (sim) 250
Rate of H
60 200
eat R
50 150
40 100
elease (JCA
D)
30 50
20 -20 -15 -10 -5 0 5 10 15
0 20
Crank Angle (Degree) φ=026
KIVA-MZ-MPI gives significant reduction in computational time but still requires 1-2 days in 50-100 processors
8192 CPUs 8 TB Memory 122 Tflopssec
160 Tons 6 MW
We have incorporated a neural network into KIVA3V for fast analysis of HCCI combustion and emissions (KIVA3V-ANN)
hellip
τ T
P
EGR
Ignition Delay Timeφ
Input Variables hellip
Input Hidden Output Layer Layers Layer
tk 1Ignition Condition I ( )t = intt (0) τ
dt = 1
KIVA3V-ANN greatly reduces computational intensity Firing cases take ~10 more time than a motored run
1 3
Is Ignition Criterion
met
No 4
Yes
Turn on global fuel conversion mechanism C8H18 + 85O2 rarr 8CO + 9H2O CO + frac12O2 rarr CO2
2 Calculate Ignition Integral in Each Cell
(0)
1( ) kt
t I t dt
τ = int
pres
sure
KPa
Results agree well with experiments and multi-zone model over a wide range of conditions (geometry φ CR intake pressure)
588 mm 523 mm 901 mm 0305 mm (02314 in) (0206 in) (0355 in) (00120 in)
8500
8000 Experimental Multi-zone
φ=026
7500 Neural network 024
022
7000 020
6500 018
102 mm (402 in) 6000 016
014 5500
φ=010 012 5000
4500
4000 -10 0 10
crank angle degrees Data from Sandia Livermore SAE 2003-01-0752
Scalloped piston iso-octane φ=01-026 CR=181 120 kPa intake
KIVA3V-ANN predicts similar spatial evolution of combustion as KIVA3V-MZ-MPI for ldquonormalrdquo HCCI
KIVA3V-MZ-MPI KIVA3V-ANN
φ=020 φ=020
KIVA3V-ANN predicts similar spatial evolution of CO during combustion as KIVA3V-MZ-MPI for ldquonormalrdquo HCCI
KIVA3V-MZ-MPI KIVA3V-ANN
-4deg
-2deg
TDC
+4deg
+10 φ=020
-4deg
-2deg
TDC
+4deg
+10 φ=020
At φ=010 KIVA3V-ANN predicts high CO emissions because reaction mechanism misses the oxidation of CO in the central core
Kiva3v-MZ-MPI Kiva3v-ANN
φ=010 φ=010
Summary HCCI is dominated by chemical kinetics and can therefore be well characterized by a computationally efficient segregated fluid mechanics-multi-zone chemical kinetics model
Summary Our new fully integrated parallelized fluid mechanics-chemical kinetics code (KIVA3V-MZ-MPI) considers the effect of mixing and therefore applies to partially stratified combustion
High resolution CFD Chemistry handled by multi-solver handles mixing zone detailed kinetics solveradvection and diffusion (10-100 zones)(~100k cells)
Solutions are mapped back and forth between solvers throughout the cycle
Summary We are developing accurate HCCI and PCCI analysis techniques with greatly improved computational efficiency
bull Direct integration of KIVA and Chemkin
Years of computing time in single processor computer
bull Multi-zone KIVA-Chemkin Days of computing time in single processor computer
T
helliphellip
bull KIVA-Artificial neural networkP τ Ignition
Input Delayφ Time Hours of computing timeVariables
EGR
in single processor computerOutput Input Hidden
Layer Layers Layer
KIVA-MZ-MPI results compare well with
Pres
sure
(bar
)the results from a full integration of KIVA and Chemkin
90 350Phi = 026
Uniform (sim) 80 300Shallow (sim)
70 Steep (sim) 250
Rate of H
60 200
eat R
50 150
40 100
elease (JCA
D)
30 50
20 -20 -15 -10 -5 0 5 10 15
0 20
Crank Angle (Degree) φ=026
KIVA-MZ-MPI gives significant reduction in computational time but still requires 1-2 days in 50-100 processors
8192 CPUs 8 TB Memory 122 Tflopssec
160 Tons 6 MW
We have incorporated a neural network into KIVA3V for fast analysis of HCCI combustion and emissions (KIVA3V-ANN)
hellip
τ T
P
EGR
Ignition Delay Timeφ
Input Variables hellip
Input Hidden Output Layer Layers Layer
tk 1Ignition Condition I ( )t = intt (0) τ
dt = 1
KIVA3V-ANN greatly reduces computational intensity Firing cases take ~10 more time than a motored run
1 3
Is Ignition Criterion
met
No 4
Yes
Turn on global fuel conversion mechanism C8H18 + 85O2 rarr 8CO + 9H2O CO + frac12O2 rarr CO2
2 Calculate Ignition Integral in Each Cell
(0)
1( ) kt
t I t dt
τ = int
pres
sure
KPa
Results agree well with experiments and multi-zone model over a wide range of conditions (geometry φ CR intake pressure)
588 mm 523 mm 901 mm 0305 mm (02314 in) (0206 in) (0355 in) (00120 in)
8500
8000 Experimental Multi-zone
φ=026
7500 Neural network 024
022
7000 020
6500 018
102 mm (402 in) 6000 016
014 5500
φ=010 012 5000
4500
4000 -10 0 10
crank angle degrees Data from Sandia Livermore SAE 2003-01-0752
Scalloped piston iso-octane φ=01-026 CR=181 120 kPa intake
KIVA3V-ANN predicts similar spatial evolution of combustion as KIVA3V-MZ-MPI for ldquonormalrdquo HCCI
KIVA3V-MZ-MPI KIVA3V-ANN
φ=020 φ=020
KIVA3V-ANN predicts similar spatial evolution of CO during combustion as KIVA3V-MZ-MPI for ldquonormalrdquo HCCI
KIVA3V-MZ-MPI KIVA3V-ANN
-4deg
-2deg
TDC
+4deg
+10 φ=020
-4deg
-2deg
TDC
+4deg
+10 φ=020
At φ=010 KIVA3V-ANN predicts high CO emissions because reaction mechanism misses the oxidation of CO in the central core
Kiva3v-MZ-MPI Kiva3v-ANN
φ=010 φ=010
Summary HCCI is dominated by chemical kinetics and can therefore be well characterized by a computationally efficient segregated fluid mechanics-multi-zone chemical kinetics model
Summary Our new fully integrated parallelized fluid mechanics-chemical kinetics code (KIVA3V-MZ-MPI) considers the effect of mixing and therefore applies to partially stratified combustion
High resolution CFD Chemistry handled by multi-solver handles mixing zone detailed kinetics solveradvection and diffusion (10-100 zones)(~100k cells)
Solutions are mapped back and forth between solvers throughout the cycle
Summary We are developing accurate HCCI and PCCI analysis techniques with greatly improved computational efficiency
bull Direct integration of KIVA and Chemkin
Years of computing time in single processor computer
bull Multi-zone KIVA-Chemkin Days of computing time in single processor computer
T
helliphellip
bull KIVA-Artificial neural networkP τ Ignition
Input Delayφ Time Hours of computing timeVariables
EGR
in single processor computerOutput Input Hidden
Layer Layers Layer
KIVA-MZ-MPI gives significant reduction in computational time but still requires 1-2 days in 50-100 processors
8192 CPUs 8 TB Memory 122 Tflopssec
160 Tons 6 MW
We have incorporated a neural network into KIVA3V for fast analysis of HCCI combustion and emissions (KIVA3V-ANN)
hellip
τ T
P
EGR
Ignition Delay Timeφ
Input Variables hellip
Input Hidden Output Layer Layers Layer
tk 1Ignition Condition I ( )t = intt (0) τ
dt = 1
KIVA3V-ANN greatly reduces computational intensity Firing cases take ~10 more time than a motored run
1 3
Is Ignition Criterion
met
No 4
Yes
Turn on global fuel conversion mechanism C8H18 + 85O2 rarr 8CO + 9H2O CO + frac12O2 rarr CO2
2 Calculate Ignition Integral in Each Cell
(0)
1( ) kt
t I t dt
τ = int
pres
sure
KPa
Results agree well with experiments and multi-zone model over a wide range of conditions (geometry φ CR intake pressure)
588 mm 523 mm 901 mm 0305 mm (02314 in) (0206 in) (0355 in) (00120 in)
8500
8000 Experimental Multi-zone
φ=026
7500 Neural network 024
022
7000 020
6500 018
102 mm (402 in) 6000 016
014 5500
φ=010 012 5000
4500
4000 -10 0 10
crank angle degrees Data from Sandia Livermore SAE 2003-01-0752
Scalloped piston iso-octane φ=01-026 CR=181 120 kPa intake
KIVA3V-ANN predicts similar spatial evolution of combustion as KIVA3V-MZ-MPI for ldquonormalrdquo HCCI
KIVA3V-MZ-MPI KIVA3V-ANN
φ=020 φ=020
KIVA3V-ANN predicts similar spatial evolution of CO during combustion as KIVA3V-MZ-MPI for ldquonormalrdquo HCCI
KIVA3V-MZ-MPI KIVA3V-ANN
-4deg
-2deg
TDC
+4deg
+10 φ=020
-4deg
-2deg
TDC
+4deg
+10 φ=020
At φ=010 KIVA3V-ANN predicts high CO emissions because reaction mechanism misses the oxidation of CO in the central core
Kiva3v-MZ-MPI Kiva3v-ANN
φ=010 φ=010
Summary HCCI is dominated by chemical kinetics and can therefore be well characterized by a computationally efficient segregated fluid mechanics-multi-zone chemical kinetics model
Summary Our new fully integrated parallelized fluid mechanics-chemical kinetics code (KIVA3V-MZ-MPI) considers the effect of mixing and therefore applies to partially stratified combustion
High resolution CFD Chemistry handled by multi-solver handles mixing zone detailed kinetics solveradvection and diffusion (10-100 zones)(~100k cells)
Solutions are mapped back and forth between solvers throughout the cycle
Summary We are developing accurate HCCI and PCCI analysis techniques with greatly improved computational efficiency
bull Direct integration of KIVA and Chemkin
Years of computing time in single processor computer
bull Multi-zone KIVA-Chemkin Days of computing time in single processor computer
T
helliphellip
bull KIVA-Artificial neural networkP τ Ignition
Input Delayφ Time Hours of computing timeVariables
EGR
in single processor computerOutput Input Hidden
Layer Layers Layer
We have incorporated a neural network into KIVA3V for fast analysis of HCCI combustion and emissions (KIVA3V-ANN)
hellip
τ T
P
EGR
Ignition Delay Timeφ
Input Variables hellip
Input Hidden Output Layer Layers Layer
tk 1Ignition Condition I ( )t = intt (0) τ
dt = 1
KIVA3V-ANN greatly reduces computational intensity Firing cases take ~10 more time than a motored run
1 3
Is Ignition Criterion
met
No 4
Yes
Turn on global fuel conversion mechanism C8H18 + 85O2 rarr 8CO + 9H2O CO + frac12O2 rarr CO2
2 Calculate Ignition Integral in Each Cell
(0)
1( ) kt
t I t dt
τ = int
pres
sure
KPa
Results agree well with experiments and multi-zone model over a wide range of conditions (geometry φ CR intake pressure)
588 mm 523 mm 901 mm 0305 mm (02314 in) (0206 in) (0355 in) (00120 in)
8500
8000 Experimental Multi-zone
φ=026
7500 Neural network 024
022
7000 020
6500 018
102 mm (402 in) 6000 016
014 5500
φ=010 012 5000
4500
4000 -10 0 10
crank angle degrees Data from Sandia Livermore SAE 2003-01-0752
Scalloped piston iso-octane φ=01-026 CR=181 120 kPa intake
KIVA3V-ANN predicts similar spatial evolution of combustion as KIVA3V-MZ-MPI for ldquonormalrdquo HCCI
KIVA3V-MZ-MPI KIVA3V-ANN
φ=020 φ=020
KIVA3V-ANN predicts similar spatial evolution of CO during combustion as KIVA3V-MZ-MPI for ldquonormalrdquo HCCI
KIVA3V-MZ-MPI KIVA3V-ANN
-4deg
-2deg
TDC
+4deg
+10 φ=020
-4deg
-2deg
TDC
+4deg
+10 φ=020
At φ=010 KIVA3V-ANN predicts high CO emissions because reaction mechanism misses the oxidation of CO in the central core
Kiva3v-MZ-MPI Kiva3v-ANN
φ=010 φ=010
Summary HCCI is dominated by chemical kinetics and can therefore be well characterized by a computationally efficient segregated fluid mechanics-multi-zone chemical kinetics model
Summary Our new fully integrated parallelized fluid mechanics-chemical kinetics code (KIVA3V-MZ-MPI) considers the effect of mixing and therefore applies to partially stratified combustion
High resolution CFD Chemistry handled by multi-solver handles mixing zone detailed kinetics solveradvection and diffusion (10-100 zones)(~100k cells)
Solutions are mapped back and forth between solvers throughout the cycle
Summary We are developing accurate HCCI and PCCI analysis techniques with greatly improved computational efficiency
bull Direct integration of KIVA and Chemkin
Years of computing time in single processor computer
bull Multi-zone KIVA-Chemkin Days of computing time in single processor computer
T
helliphellip
bull KIVA-Artificial neural networkP τ Ignition
Input Delayφ Time Hours of computing timeVariables
EGR
in single processor computerOutput Input Hidden
Layer Layers Layer
KIVA3V-ANN greatly reduces computational intensity Firing cases take ~10 more time than a motored run
1 3
Is Ignition Criterion
met
No 4
Yes
Turn on global fuel conversion mechanism C8H18 + 85O2 rarr 8CO + 9H2O CO + frac12O2 rarr CO2
2 Calculate Ignition Integral in Each Cell
(0)
1( ) kt
t I t dt
τ = int
pres
sure
KPa
Results agree well with experiments and multi-zone model over a wide range of conditions (geometry φ CR intake pressure)
588 mm 523 mm 901 mm 0305 mm (02314 in) (0206 in) (0355 in) (00120 in)
8500
8000 Experimental Multi-zone
φ=026
7500 Neural network 024
022
7000 020
6500 018
102 mm (402 in) 6000 016
014 5500
φ=010 012 5000
4500
4000 -10 0 10
crank angle degrees Data from Sandia Livermore SAE 2003-01-0752
Scalloped piston iso-octane φ=01-026 CR=181 120 kPa intake
KIVA3V-ANN predicts similar spatial evolution of combustion as KIVA3V-MZ-MPI for ldquonormalrdquo HCCI
KIVA3V-MZ-MPI KIVA3V-ANN
φ=020 φ=020
KIVA3V-ANN predicts similar spatial evolution of CO during combustion as KIVA3V-MZ-MPI for ldquonormalrdquo HCCI
KIVA3V-MZ-MPI KIVA3V-ANN
-4deg
-2deg
TDC
+4deg
+10 φ=020
-4deg
-2deg
TDC
+4deg
+10 φ=020
At φ=010 KIVA3V-ANN predicts high CO emissions because reaction mechanism misses the oxidation of CO in the central core
Kiva3v-MZ-MPI Kiva3v-ANN
φ=010 φ=010
Summary HCCI is dominated by chemical kinetics and can therefore be well characterized by a computationally efficient segregated fluid mechanics-multi-zone chemical kinetics model
Summary Our new fully integrated parallelized fluid mechanics-chemical kinetics code (KIVA3V-MZ-MPI) considers the effect of mixing and therefore applies to partially stratified combustion
High resolution CFD Chemistry handled by multi-solver handles mixing zone detailed kinetics solveradvection and diffusion (10-100 zones)(~100k cells)
Solutions are mapped back and forth between solvers throughout the cycle
Summary We are developing accurate HCCI and PCCI analysis techniques with greatly improved computational efficiency
bull Direct integration of KIVA and Chemkin
Years of computing time in single processor computer
bull Multi-zone KIVA-Chemkin Days of computing time in single processor computer
T
helliphellip
bull KIVA-Artificial neural networkP τ Ignition
Input Delayφ Time Hours of computing timeVariables
EGR
in single processor computerOutput Input Hidden
Layer Layers Layer
pres
sure
KPa
Results agree well with experiments and multi-zone model over a wide range of conditions (geometry φ CR intake pressure)
588 mm 523 mm 901 mm 0305 mm (02314 in) (0206 in) (0355 in) (00120 in)
8500
8000 Experimental Multi-zone
φ=026
7500 Neural network 024
022
7000 020
6500 018
102 mm (402 in) 6000 016
014 5500
φ=010 012 5000
4500
4000 -10 0 10
crank angle degrees Data from Sandia Livermore SAE 2003-01-0752
Scalloped piston iso-octane φ=01-026 CR=181 120 kPa intake
KIVA3V-ANN predicts similar spatial evolution of combustion as KIVA3V-MZ-MPI for ldquonormalrdquo HCCI
KIVA3V-MZ-MPI KIVA3V-ANN
φ=020 φ=020
KIVA3V-ANN predicts similar spatial evolution of CO during combustion as KIVA3V-MZ-MPI for ldquonormalrdquo HCCI
KIVA3V-MZ-MPI KIVA3V-ANN
-4deg
-2deg
TDC
+4deg
+10 φ=020
-4deg
-2deg
TDC
+4deg
+10 φ=020
At φ=010 KIVA3V-ANN predicts high CO emissions because reaction mechanism misses the oxidation of CO in the central core
Kiva3v-MZ-MPI Kiva3v-ANN
φ=010 φ=010
Summary HCCI is dominated by chemical kinetics and can therefore be well characterized by a computationally efficient segregated fluid mechanics-multi-zone chemical kinetics model
Summary Our new fully integrated parallelized fluid mechanics-chemical kinetics code (KIVA3V-MZ-MPI) considers the effect of mixing and therefore applies to partially stratified combustion
High resolution CFD Chemistry handled by multi-solver handles mixing zone detailed kinetics solveradvection and diffusion (10-100 zones)(~100k cells)
Solutions are mapped back and forth between solvers throughout the cycle
Summary We are developing accurate HCCI and PCCI analysis techniques with greatly improved computational efficiency
bull Direct integration of KIVA and Chemkin
Years of computing time in single processor computer
bull Multi-zone KIVA-Chemkin Days of computing time in single processor computer
T
helliphellip
bull KIVA-Artificial neural networkP τ Ignition
Input Delayφ Time Hours of computing timeVariables
EGR
in single processor computerOutput Input Hidden
Layer Layers Layer
KIVA3V-ANN predicts similar spatial evolution of combustion as KIVA3V-MZ-MPI for ldquonormalrdquo HCCI
KIVA3V-MZ-MPI KIVA3V-ANN
φ=020 φ=020
KIVA3V-ANN predicts similar spatial evolution of CO during combustion as KIVA3V-MZ-MPI for ldquonormalrdquo HCCI
KIVA3V-MZ-MPI KIVA3V-ANN
-4deg
-2deg
TDC
+4deg
+10 φ=020
-4deg
-2deg
TDC
+4deg
+10 φ=020
At φ=010 KIVA3V-ANN predicts high CO emissions because reaction mechanism misses the oxidation of CO in the central core
Kiva3v-MZ-MPI Kiva3v-ANN
φ=010 φ=010
Summary HCCI is dominated by chemical kinetics and can therefore be well characterized by a computationally efficient segregated fluid mechanics-multi-zone chemical kinetics model
Summary Our new fully integrated parallelized fluid mechanics-chemical kinetics code (KIVA3V-MZ-MPI) considers the effect of mixing and therefore applies to partially stratified combustion
High resolution CFD Chemistry handled by multi-solver handles mixing zone detailed kinetics solveradvection and diffusion (10-100 zones)(~100k cells)
Solutions are mapped back and forth between solvers throughout the cycle
Summary We are developing accurate HCCI and PCCI analysis techniques with greatly improved computational efficiency
bull Direct integration of KIVA and Chemkin
Years of computing time in single processor computer
bull Multi-zone KIVA-Chemkin Days of computing time in single processor computer
T
helliphellip
bull KIVA-Artificial neural networkP τ Ignition
Input Delayφ Time Hours of computing timeVariables
EGR
in single processor computerOutput Input Hidden
Layer Layers Layer
KIVA3V-ANN predicts similar spatial evolution of CO during combustion as KIVA3V-MZ-MPI for ldquonormalrdquo HCCI
KIVA3V-MZ-MPI KIVA3V-ANN
-4deg
-2deg
TDC
+4deg
+10 φ=020
-4deg
-2deg
TDC
+4deg
+10 φ=020
At φ=010 KIVA3V-ANN predicts high CO emissions because reaction mechanism misses the oxidation of CO in the central core
Kiva3v-MZ-MPI Kiva3v-ANN
φ=010 φ=010
Summary HCCI is dominated by chemical kinetics and can therefore be well characterized by a computationally efficient segregated fluid mechanics-multi-zone chemical kinetics model
Summary Our new fully integrated parallelized fluid mechanics-chemical kinetics code (KIVA3V-MZ-MPI) considers the effect of mixing and therefore applies to partially stratified combustion
High resolution CFD Chemistry handled by multi-solver handles mixing zone detailed kinetics solveradvection and diffusion (10-100 zones)(~100k cells)
Solutions are mapped back and forth between solvers throughout the cycle
Summary We are developing accurate HCCI and PCCI analysis techniques with greatly improved computational efficiency
bull Direct integration of KIVA and Chemkin
Years of computing time in single processor computer
bull Multi-zone KIVA-Chemkin Days of computing time in single processor computer
T
helliphellip
bull KIVA-Artificial neural networkP τ Ignition
Input Delayφ Time Hours of computing timeVariables
EGR
in single processor computerOutput Input Hidden
Layer Layers Layer
At φ=010 KIVA3V-ANN predicts high CO emissions because reaction mechanism misses the oxidation of CO in the central core
Kiva3v-MZ-MPI Kiva3v-ANN
φ=010 φ=010
Summary HCCI is dominated by chemical kinetics and can therefore be well characterized by a computationally efficient segregated fluid mechanics-multi-zone chemical kinetics model
Summary Our new fully integrated parallelized fluid mechanics-chemical kinetics code (KIVA3V-MZ-MPI) considers the effect of mixing and therefore applies to partially stratified combustion
High resolution CFD Chemistry handled by multi-solver handles mixing zone detailed kinetics solveradvection and diffusion (10-100 zones)(~100k cells)
Solutions are mapped back and forth between solvers throughout the cycle
Summary We are developing accurate HCCI and PCCI analysis techniques with greatly improved computational efficiency
bull Direct integration of KIVA and Chemkin
Years of computing time in single processor computer
bull Multi-zone KIVA-Chemkin Days of computing time in single processor computer
T
helliphellip
bull KIVA-Artificial neural networkP τ Ignition
Input Delayφ Time Hours of computing timeVariables
EGR
in single processor computerOutput Input Hidden
Layer Layers Layer
Summary HCCI is dominated by chemical kinetics and can therefore be well characterized by a computationally efficient segregated fluid mechanics-multi-zone chemical kinetics model
Summary Our new fully integrated parallelized fluid mechanics-chemical kinetics code (KIVA3V-MZ-MPI) considers the effect of mixing and therefore applies to partially stratified combustion
High resolution CFD Chemistry handled by multi-solver handles mixing zone detailed kinetics solveradvection and diffusion (10-100 zones)(~100k cells)
Solutions are mapped back and forth between solvers throughout the cycle
Summary We are developing accurate HCCI and PCCI analysis techniques with greatly improved computational efficiency
bull Direct integration of KIVA and Chemkin
Years of computing time in single processor computer
bull Multi-zone KIVA-Chemkin Days of computing time in single processor computer
T
helliphellip
bull KIVA-Artificial neural networkP τ Ignition
Input Delayφ Time Hours of computing timeVariables
EGR
in single processor computerOutput Input Hidden
Layer Layers Layer
Summary Our new fully integrated parallelized fluid mechanics-chemical kinetics code (KIVA3V-MZ-MPI) considers the effect of mixing and therefore applies to partially stratified combustion
High resolution CFD Chemistry handled by multi-solver handles mixing zone detailed kinetics solveradvection and diffusion (10-100 zones)(~100k cells)
Solutions are mapped back and forth between solvers throughout the cycle
Summary We are developing accurate HCCI and PCCI analysis techniques with greatly improved computational efficiency
bull Direct integration of KIVA and Chemkin
Years of computing time in single processor computer
bull Multi-zone KIVA-Chemkin Days of computing time in single processor computer
T
helliphellip
bull KIVA-Artificial neural networkP τ Ignition
Input Delayφ Time Hours of computing timeVariables
EGR
in single processor computerOutput Input Hidden
Layer Layers Layer
Summary We are developing accurate HCCI and PCCI analysis techniques with greatly improved computational efficiency
bull Direct integration of KIVA and Chemkin
Years of computing time in single processor computer
bull Multi-zone KIVA-Chemkin Days of computing time in single processor computer
T
helliphellip
bull KIVA-Artificial neural networkP τ Ignition
Input Delayφ Time Hours of computing timeVariables
EGR
in single processor computerOutput Input Hidden
Layer Layers Layer
Recommended