an integrated approach for simulating diesel ... - siemens

STAR Global Conference 2012

DARS Workshop

March 20, 2012

Amsterdam - NETHERLANDS

An Integrated Approach for Simulating Diesel

Engine Performance using Direct Injection

Stochastic Reactor Model (DI-SRM)

Michal Pasternak, Andrea Matrisciano

Division of Thermodynamics

Brandenburg University of Technology

DARS Workshop 2012

Amsterdam, Netherlands

M.

Paste

rnak, F

. M

auss,

A. M

atr

iscia

no

, B

randenburg

Univ

ers

ity o

f T

echnolo

gy

2

Applications

Evaluation of engine performance parameters such as IMEP, fuel consumption, etc.

An interaction between engine components and influence on engine performance

Engine optimisation towards high performance and low emissions

Engine cylinder models for cycle simulations – challenges, demands

In-cylinder performance and emissions formation at different operating points

Low CPU demand, fast calibration and validation procedure

Inputs for engine aftertreatment processes: catalytic converter, DPF, EGR

Detailed information about exhaust emissions – chemical kinetics

HC, CO, NOx, soot

Introduction: 0D Modelling of IC Engines

Integrated simulation methods are needed to take concurrently

into consideration engine performance and kinetic of emission

formation, and to ensure CPU acceptable for cycle simulations

Background

Modelling and Method

Model Performance

Exemplary Applications

Summary

DARS Workshop 2012


M.

Paste

rnak, F

. M

auss,

A. M

atr

iscia

no

, B

randenburg

Univ

ers

ity o

f T

echnolo

gy

3

Specific Objectives & Presentation Outline

0D based simulation method for DI Diesel engine performance

parameters study

Concept of the integrated simulation method

DI-SRM for DI Diesel engines

Chemical sub-model

Optimizer

Exemplary application

1. Model development and calibration

In-cylinder parameters and exhaust emissions validation

2. Simulation of EGR effects on NOx and soot emissions

Outlook and summary

Background


Model Performance


Summary

DARS Workshop 2012


M.

Paste

rnak, F

. M

auss,

A. M

atr

iscia

no

, B

randenburg

Univ

ers

ity o

f T

echnolo

gy

4

Integrated approach concept

Integrated Approach for 0D based Engine Simulations

Emissions prediction

Fuels for Diesel engines

Reaction mechanisms

Speed-up of models development

calibration, training and control

Optimization of engine output

parameters

Optimizer

Engine in-cylinder processes

with detailed chemistry

0D, PDF based approach with

low CPU (up to < 5sec per cycle)

DI-SRM DI-SRM 0D Engine

Direct Injection

Stochastic

Reactor

Model

Optimization Algorithms

Processes control and automation

Fuel Model

Complex Reaction

Mechanisms

Background


Model Performance


Summary

DARS Workshop 2012


M.

Paste

rnak, F

. M

auss,

A. M

atr

iscia

no

, B

randenburg

Univ

ers

ity o

f T

echnolo

gy

5

Overall engine modelling concept with DI-SRM

Direct Injection Stochastic Reactor Model (DI-SRM)

Temperature and mass fractions – random variables

Chemical kinetics and in-cylinder turbulence modelling

Transport equation for Mass Density Function (F())

0D model of engine in-

cylinder processes

In-cylinder content as

ensemble of particles

Background


Model Performance


Summary

DARS Workshop 2012


M.

Paste

rnak, F

. M

auss,

A. M

atr

iscia

no

, B

randenburg

Univ

ers

ity o

f T

echnolo

gy

6

Time evolution of the MDF defined by the transport equation

DI-SRM

Qi (II) chemical reaction, volume changes, heat transfer

fuel injection: particles creation, ensemble update

R.h.s (III) mixing in composition space – modelling

Mixing time model affecting turbulence intensity

Key model parameter during validation


1( ; ) ( ( ) ( ; )) ( ; )

i

j

i

i i j

I IIIII

JF t Q F t F t

t x

,

1

RN

ii i j j

j

MQ

1 ,

1 1

1 1( ) ( )

S RN Ngi

S i i j j w

i jv i v v

h AMRT dVQ h p T T

c M mc dt mc

,

finj

i i f i S

mQ Y Y i 1,...,N

m , ,

1

1 Sfinj

S 1 i f i f i

ip

mQ Y h h

c m

Background


Model Performance


Summary

DARS Workshop 2012


M.

Paste

rnak, F

. M

auss,

A. M

atr

iscia

no

, B

randenburg

Univ

ers

ity o

f T

echnolo

gy

7

Turbulence treatment – mixing time modelling for engine conditions

Regimes with different mixing intensity

Turbulent time scale (t) must be

given at each time of the cycle (!)

Curl’s mixing model

Mixing in composition space

Mixing frequency

Variable mixing time model

Mixing time based on the mean

conditional scalar dissipation rate history


1t

C

1

tmixNp N

( ) ( ) ( ) ( )1( ) ( ) ( ( ) ( ))

2

n m n mt t t t t t

Ilu

1

stτ χ

Background


Model Performance


Summary

B

z1

A0

soi’

eoi

’

’inj

z2

0 1 2( ) ( , , , , , , , )injt f A B soi eoi z z

DARS Workshop 2012


M.

Paste

rnak, F

. M

auss,

A. M

atr

iscia

no

, B

randenburg

Univ

ers

ity o

f T

echnolo

gy

8

Chemical Kinetics Sub-Model

Reaction mechanisms – exemplary validation

n-Heptane (A)

Basis model for Diesel engine

simulations – skeletal model (121species)

n-Heptane (B)

Small size model (28species)

for CPU oriented applications

Based o

n:

Tsuru

shim

a, T

., P

roc. C

om

b. In

st.

2009.

32:

p.

2835

-2841.

0.01

0.1

1

10

100

0.7 0.9 1.1 1.3 1.5

1000/T (K-1)

Ignitio

n D

ela

y (

ms)

Exp phi 0.5Exp phi 1.0Exp phi 2.0Sim phi 0.5Sim phi 1.0Sim phi 2.0

n-heptane

p=40bar

0.01

0.1

1

10

100

0.8 0.9 1 1.1 1.2 1.3 1.4 1.5

1000/T (K-1)

Ignitio

n d

ela

y (

ms)

Exp.

Calc.

Phi=1.0

p=40bar

Ze

uch,

T.,

More

ac,

G.,

Ahm

ed, S

.S.,

and M

auss,

F.,

Com

bust.

Fla

me 6

51

–674 (

2008).

Background


Model Performance


Summary

DARS Workshop 2012


M.

Paste

rnak, F

. M

auss,

A. M

atr

iscia

no

, B

randenburg

Univ

ers

ity o

f T

echnolo

gy

9

Genetic Algorithms (GA) based modelling

Natural evolution based concept

“Stronger properties are inherited to the next

generation”

Remarks, features, issues

Solution accuracy depends on the evaluation

function

Accuracy of the solution increases with time

Main benefits

Parallelisation – easy for computations

Suitable for multi-objectives problems

Suitable for problems with many local

minimum and maximum, i.e. with “noisy character”

Optimizer

Kusia

k e

t al.(2

009)

Background


Model Performance


Summary

10

Applications – Simulation process and

model performance

DARS Workshop 2012


M.

Paste

rnak, F

. M

auss,

A. M

atr

iscia

no

, B

randenburg

Univ

ers

ity o

f T

echnolo

gy

11

Overall simulation method

Model calibration based on one model parameter – mixing time

Applications: simulation of performance and exhaust emissions formation

Simulation Process

Background


Model Performance


Summary

DARS Workshop 2012


M.

Paste

rnak, F

. M

auss,

A. M

atr

iscia

no

, B

randenburg

Univ

ers

ity o

f T

echnolo

gy

12

Self-calibration method for Diesel engines simulations

Fast and simple

procedure for model

validation

Model Calibration

Mixing time

Background


Model Performance


Summary

DARS Workshop 2012


M.

Paste

rnak, F

. M

auss,

A. M

atr

iscia

no

, B

randenburg

Univ

ers

ity o

f T

echnolo

gy

13

DI Diesel engine

In-cylinder pressure

results

n-heptane (A)

Settings

100 cycles

200 particles

1CAD time step

Model Performance: In-Cylinder Parameters

Pro

ceedin

gs o

f IC

DE

RS

2011

Engine Type

Diesel Engine

Bore (mm) 81

Stroke (mm) 95.5

Compression ratio (–) 16.3

Fuel n-heptane

Background


Model Performance


Summary

DARS Workshop 2012


M.

Paste

rnak, F

. M

auss,

A. M

atr

iscia

no

, B

randenburg

Univ

ers

ity o

f T

echnolo

gy

14

Rate of heat release and mean in-cylinder temperature

Model Performance: In-Cylinder Parameters

Background


Model Performance


Summary

Pro

ceedin

gs o

f IC

DE

RS

2011

DARS Workshop 2012


M.

Paste

rnak, F

. M

auss,

A. M

atr

iscia

no

, B

randenburg

Univ

ers

ity o

f T

echnolo

gy

15

Engine-out emissions

Integrated results at EVO

CAD based history of emissions formation

High accuracy in predicting NOx and unburned HC

Model Performance: Emissions

Pro

ceedin

gs o

f IC

DE

RS

2011

Background


Model Performance


Summary

16

Applications – Simulating NOx and soot

formation

DARS Workshop 2012


M.

Paste

rnak, F

. M

auss,

A. M

atr

iscia

no

, B

randenburg

Univ

ers

ity o

f T

echnolo

gy

17

Diesel engine


results

n-Heptane

Mixing time

calibration

Baseline Model Validation

Refe

rence d

ata

: N

akov,

G.,

Mauss,

F.,

Wenzel, C

., K

rüger,

C. ”A

pplic

atio

n o

f a S

tatio

nary

Fla

mele

t

Lib

rary

Based C

FD

Soot

Model fo

r Low

-NO

x D

iesel C

om

bustio

n”,

TH

IES

EL 2

010.

Engine DI Diesel

Bore (mm) 83

Stroke (mm) 99

Load (bar) 14

Compression ratio (–) 16.2

Background


Model Performance

Exemplary Application

Summary

DARS Workshop 2012


M.

Paste

rnak, F

. M

auss,

A. M

atr

iscia

no

, B

randenburg

Univ

ers

ity o

f T

echnolo

gy

18

Model Application to Simulate EGR Effects

Refe

rence e

xperim

enta

l data

: N

akov,

G.

et

al.,T

HIE

SE

L 2

010.

Experiment: solid line

Simulation: dash-doted line

•Effects of EGR variation Constant SOI The same mixing time scales


•The same fuel mass and injection rate AFR variation The same load

Very similar/same

conditions for

air fuel mixing

Background


Model Performance


Summary

DARS Workshop 2012


M.

Paste

rnak, F

. M

auss,

A. M

atr

iscia

no

, B

randenburg

Univ

ers

ity o

f T

echnolo

gy

19

Mean in-cylinder temperature and soot formation – oxidation

Detailed kinetic soot model: Mauss (1997) & n-Heptane from Zeuch et al. (2008)


Refe

rence e

xperim

enta

l data

: N

akov,

G.

et

al.,T

HIE

SE

L 2

010.

The same air-fuel mixing conditions for each case

Soot formation and oxidation affected mainly by the local

air fuel ratio – chemical effects simulation 500

700

900

1100

1300

1500

1700

1900

-50 -30 -10 10 30 50 70

Crank angle (deg ATDC)

Mean

tem

pera

ture

(K

)

Sim, Egr18%

Sim, Egr23%

Sim, Egr27%

0.00

0.20

0.40

0.60

0.80

1.00

1.20

-10 10 30 50 70 90 110


So

ot

ma

ss

(-)

DI-SRM

Exp

0.00

0.20

0.40

0.60

0.80

1.00

1.20

-10 10 30 50 70 90 110


So

ot

ma

ss

(-)

DI-SRM

Exp

0.00

0.20

0.40

0.60

0.80

1.00

1.20

-10 10 30 50 70 90 110


So

ot

ma

ss

(-)

DI-SRM

Exp

500

700

900

1100

1300

1500

1700

1900

-50 -30 -10 10 30 50 70


Me

an

te

mp

era

ture

(K

)

Sim, Egr18%

Sim, Egr23%

Sim, Egr27%

EGR=18% EGR=23% EGR=27%

AFR=1.51 AFR=1.42 AFR=1.33

Background


Model Performance


Summary

DARS Workshop 2012


M.

Paste

rnak, F

. M

auss,

A. M

atr

iscia

no

, B

randenburg

Univ

ers

ity o

f T

echnolo

gy

20

Engine-out NOx and Soot


Refe

rence e

xperim

enta

l data

: N

akov,

G.

et

al.,T

HIE

SE

L 2

010.

0.00

0.20

0.40

0.60

0.80

1.00

1.20

EGR 18% EGR 23% EGR 27%

No

rmalized

co

ncen

trati

on

(-)

EXP

SIM

NOx

0.00

0.20

0.40

0.60

0.80

1.00

1.20

EGR 18% EGR 23% EGR 27%

No

rma

lize

d c

on

ce

ntr

ati

on

(-)

EXP

SIM

Soot

•Correct prediction of NOx concentration at EVO for different EGR rates

•Correct trend in predicting mass of soot as a function of EGR changes

•Influence of the baseline model validation accuracy

Background


Model Performance


Summary

DARS Workshop 2012


M.

Paste

rnak, F

. M

auss,

A. M

atr

iscia

no

, B

randenburg

Univ

ers

ity o

f T

echnolo

gy

21

Single run cost

Solution settings

Particles clustering

100 cycles, 200 particles

1.0 CAD global time step

Overall simulation procedure using the optimizer – validation process

Genetic algorithm, 10 individuals calculated at once

DI-SRM parallelized on 32 CPUs,~9 populations needed

10 individuals in each population

Possible benefits

Reaction mechanism

More complex reaction mechanisms, multi-component fuels

Optimization

Engine performance parameters, engine-fuel matching

Computations

Fuel 2: ~1.5 hour for

complete validation

(p, ROHR, emissions)

Background


Model Performance


Summary

Fuel Hardware CPU (s)

1) n-heptane – A (121 species)

33s/1cycle

2) n-heptane – B (28 species)

- Parallel on 32 CPUs

- 2.9 GHz

- Parallel on 32 CPUs 5s/1cycle

DARS Workshop 2012


M.

Paste

rnak, F

. M

auss,

A. M

atr

iscia

no

, B

randenburg

Univ

ers

ity o

f T

echnolo

gy

22

High level of automation of engine simulation processes thanks to an integration

with general purpose engine optimizer

Speed up of the overall simulation process (validation, optimization, application)

Applicable for simulating engine – fuels interaction (optimization, fuels database)

Capability of the DI-SRM to accurately simulate Diesel engine in-cylinder

performance such as pressure, heat release or mean temperature

Correct treatment of the turbulent mixing

High accuracy in simulating NOx

Promising results of simulating soot formation and oxidation

Correct trend in predicting mass of soot at EVO

Predictive behaviour of the model in terms of simulating chemical effects

in soot formation/oxidation and NOx formation (sensitivity on EGR and AFR)

Summary

Background


Model Performance


Summary

an integrated approach for simulating diesel ... - siemens

Documents