Multi-Core Simulation of Internal Combustion Engines Us

CO2 maîtrisé | Carburants diversifiés | Véhicules économes | Raffinage propre | Réserves prolongées
Multi-core Simulation of Internal
Combustion Engines using Modelica,
FMI and xMOD
© 2012 IFP Energies nouvelles
Abir Ben Khaled, IFPEN
Mongi Ben Gaid, IFPEN
Daniel Simon, INRIA Rhône Alpes
Nicolas Pernet, IFPEN
© 2012 IFP Energies nouvelles
Outline
2

Introduction, context and motivations

Engine modeling using Modelica and models
interoperability using FMI

Integration and multi-core execution using xMOD

Test cases and demos

Conclusions
Context
From off-line simulation to real-time simulation
Off-line multi-core cosimulation platform
Control algorithms
development (Simulink,...)
Real-time multicore co-simulation
platform
Controllers or actuators validations
© 2012 IFP Energies nouvelles
Real-time multicore co-simulation
platform
3
Physical components
models development
(OpenModelica,
Dymola, AMESim...)
virtual
Virtual integration et
experimentation
laboratory
Integration of
heterogeneous models
Validation of physical
components on test beds
semi-virtual semi-real
Motivations
Processors evolution
© 2012 IFP Energies nouvelles
Computational power
improvement in today
processors is mainly driven by
the augmentation of the
number of cores per processor
Concurrent execution of
threads to exploit Thread Level
Parallelism (TLP)
Efficient exploitation of
resources depends of
4
Splitting applications into
tasks
Distributed scheduling of
tasks
Core
0
Core
1
Core
2
Core
3
Motivations
© 2012 IFP Energies nouvelles
Combining real and virtual
5
Motivations
Enabling new concepts industrialization
© 2012 IFP Energies nouvelles
Developing new engine concepts that reduce pollutants and
consumption requires the ability to simulate, in real-time, the incylinder behaviour (cylinders pressure)
6
Core
0
Core
1
Core
2
Core
3
© 2012 IFP Energies nouvelles
ModEngine library
7
Functional Mock-up interface (FMI)
•
FMI is a set of specifications including
–
© 2012 IFP Energies nouvelles
–
8
FMI for model exchange : describes the software
interface of a hybrid ODE (inputs, outputs,
parameters, variables, API functions that a solver
and an environment needs to call ...)
FMI for co-simulation : provides a software
interface for coupling two or more simulation tools
or components, in a master/slave scheme. A slave
may be a component integrating both the model
and its solver
xMOD Concept

A platform dedicated to models exploitation




To improve :
© 2012 IFP Energies nouvelles

9
Heterogeneous model integration environment
Standalone and optimal model exploitation
Virtual experimentation laboratory
Model exchange : Having the same tools is no longer necessary to
share models  improve the collaboration internally and externally
© 2012 IFP Energies nouvelles
xMOD contributions
10

Proficiency : proficiency in simulation design tools is no longer the "entry
ticket" for exploiting simulation  Help models exploitation by non expert
fields

Tools choice : Modeling language choice has less impact on users 
Congregates the benefits of each tool for each specific application,
without having to leave its own tools

CPU time : xMOD allows a parallel and optimized execution  reduce
the computation time

License cost : model exploitation in xMOD does not require any license
of the original authoring tools if appropriate export features are used 
reduce license cost
xMOD functionalities
© 2012 IFP Energies nouvelles
C
11
C++
A.c
B.cpp
A.xmodel
B.xmodel
A.dll
B.dll
C. mdl
D. ame
E.mo
F.gtm
G.fmu
C.xmodel
D.xmodel
E.xmodel
F.xmodel
G.fmu
C.dll
D.dll
E.dll
F.dll
xMOD Platform
xMOD functionalities
© 2012 IFP Energies nouvelles

12
Specific interfaces creation

Better simulation workspace
customized for each
engineering field

Easy interactivity with the
model in real-time

Easy to use exchange vector
of models

Protect models know how
xMOD functionalities
• Compatibility to new Functional Mock-up Interface
(FMI) standard standard from MODELISAR project
– FMI-for-model exchange 1.0
• Available solvers in xMOD : Euler, Runge-Kutta, DASSL,
DASKR, CVODE, LSODAR
xMOD
© 2012 IFP Energies nouvelles
Solver
13
– FMI-for-cosimulation 1.0
xMOD
FMU
Model
FMU
Model
Solver
Multi-core co-simulation : approach
B
A
D
C
F
E
A
D
B
A
© 2012 IFP Energies nouvelles
C
14
E
F
D
B
C
E
F
E
F
D
B
A
C
E
F
E
F
E
F
Multi-core co-simulation : workflow




© 2012 IFP Energies nouvelles

15
Decompose the global system into subsystems (preferably with weak
coupling)
Export each subsystem to an FMU
Assemble generated FMUs in xMOD
Each FMU is executed into a separate thread
– with a dedicated solver (fixed step or variable step)
– with a dedicated integration step-size
– with a dedicated communication step-size
A global master ensures proper data transfer during communication
points according to a well defined model of computation
Engine model splitting

Partitioning engine
model from a
physical point of
view

© 2012 IFP Energies nouvelles

16
16

Minimize the
number of
discontinuities per
subsystem
 Minimize
integration interrupts
 Minimize
interaction dynamics
Engine Simulator
AirPath
Combustion
chamber 1
ECU
Combustion
chamber 2
Combustion
chamber 3
Combustion
chamber 4
Data exchange overview
V a ria ble integ ra tion s tep
OR
E ng ine s im ula to r
C o m m unic a tio n s tep
E ng ine s im ula to r
Fix ed integ ra tion s tep
C om m unic a tio n s tep
© 2012 IFP Energies nouvelles
I nitia liza tio n
17
17
17
E x c ha ng e 1
E x c ha ng e 2
E C U C o ntro ller
C o m m unic a tio n
s tep
F4RT Wiebe test case
Test case description







© 2012 IFP Energies nouvelles

18
Gasoline engine
Modeled in Modelica/Dymola using IFPEN library ModEngine
Split into 5 subsystems : AirPath + 4 cylinders
Combustion chambers models are based on a Wiebe combustion
model
500 µs communication step
LSODAR used for integrating all split sub-models, except controls
LSODAR tolerance = 10-4
Global simulator has 87 state variables and 420 event indicators
F4RT Wiebe test case
Benefits of model splitting and co-simulation
Events only here
© 2012 IFP Energies nouvelles
Almost no events: Forward
integration could performed
19
19
On single core,
X2 speed-up
F4RT Wiebe test case
Benefits of model splitting and co-simulation
© 2012 IFP Energies nouvelles
step size = max
20
20
Complete Diesel Hybrid vehicle demonstrator
Test case description






© 2012 IFP Energies nouvelles

21
Diesel engine
12 gas model
Modeled in AMESim using IFP-Engine library
Combustion chambers models are based on Barba phenomenological
combustion model
Vehicle dynamics modeled in AMESim
Complete controls
Around 300 state variables
Complete Diesel Hybrid vehicle demonstrator
© 2012 IFP Energies nouvelles
Comparative results : Simulink & xMOD
22
Using Simulink
Requires applying a unique solver and step-size
Euler solver with step-size of 25 µs applied to the global system
Uses only one core of the machine
Using xMOD
Engine model was split into 5 parts : air path + 4 cylinders
Air path is integrated using Euler solver with 100 µs
Cylinders are integrated using Euler solver with 25 µs
Vehicle model is integrated with RK4 solver with 500 µs
Remaining models and controls are run with appropriate solvers and
step-sizes (from 500 µs to 100 ms)
Results
A speedup of X25 was observed on a 6-core machine using xMOD wrt
Simulink
Simulation in xMOD is 20% faster than real-time
Conclusion



© 2012 IFP Energies nouvelles

23
Approach for complex systems simulation
Similar speed-up results observed with other engine models
This approach was successfully to production models from
automotive OEM
OpenProd remaining objectives
– Integrating OpenModelica to this toolchain
– Linux & Xenomaï compatibility