Amesim Hil
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Transcript of Amesim Hil
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1 copyright LMS International - 2007
IMPLEMENTATION
Rapid
controlsprototyping
FUNCTION TEST
(PRE) CALIBRATIONCONTROLENGINE
Model in the Loop
Software in the Loop
DESIGN VALIDATION
FUNCTIONSPECIFICATION
Hardware in the Loop
AMESim and its realtime
solution is used all over the
design cycle in various
subsystems
At the Function Specification stage
Model-in-the-Loop
At the Implementation stage Software-in-the-Loop
At the Function Test stage
Hardware-in-the-Loop
At the (Pre) Calibration stage
AMESim in the Controller validation process
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From Off-line to HIL Simulation
Complete System Simulation
- Simplification Process
- Low Fidelity Modeling
Generated c Code: Software
Block Diagram Model of the
Control Algorithms
Embedded Software on Hardware
SIL
Low Fidelity Plant Model
HIL
High Fidelity Plant Model
MIL
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AMESim : Simulink Interface
Plant models are built in AMESim due to the collection of physical libraries and its fidelity
AMESim model can be exported into Simulink as a s-function and solved using
Co-Simulation (each software uses its own solver)
S-function (purely Simulink solver) In both these cases, an AMESim runtime license is required
Simulink model can be exported into AMESim (through RTW)
AMESim models can be exported into Simulink using the Blackbox export. End users do
not require any AMESim runtime licenses
AMESim
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High frequency model - Time scale: crank angle deg.
Physical model for engine-out emissions (trends)
Engine & exhaust Control development
3D
1D
0D
Scalability of Engine models in AMESim
Map Engine - Time scale: 0.1 s
Look-up tables for engine-out emissions
Generation of the exhaust mass flow with species
3D CFD Model - Time scale: turbulence
Physical models for engine-out emissions
Generation of data for 0D and 1D models
Mean Value Engine Model - Time scale: engine cycle
Look-up tables + physics for engine-out emissions
Engine & exhaust Control development, real-time
Component
System
Functional
Detail Level
4 copyright LMS International - 2010 - Internal Combustion Engine Solution
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5 copyright LMS International - 2007
RT Targets Supported by AMESim are:
dSpace (1005, 1006)
RT-LAB (Opal-RT) xPC target (MathWorks)
LABCAR (ETAS)
LabView RT
ETAS
ADI
AND technologies
AMESim in the Realtime segment
AMESim libraries are real-time compliant
Users can develop AMESim plant models and can test it first hand if they can
be solved using fixed step solvers
Users can directly generate the source codes for various real-time targets
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Work flow for RT simulation
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CHALLENGE: Model
simplification requires
an clear understanding
of the parts of the
system that causes the
model to slow down
Activity Index, Eigen Values, Modal
Shapes and State Count facilities
are used together to simplify the
complex model.
The same user can do detailed analysis
and real time models.
Imagine.Lab
AMESim
Real world to Real time solution
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Debugging an AMESim system
LMS Imagine.Lab comes with some few utilities able to help the user to detect which part of a systemhas been badly modeled or what went wrong during the simulation.
How a modeling error is identified in an AMESim system? It will generally lead to
A code generation problem (very rare)
(sol: pop-up message indicating connection/code generation error)
An integration failure (when Nans or Infs are generated)
(sol: state count facility)
An integration slow down (most of the time)
(sol: state count + LA + activity index)
Spurious discontinuities produced
(sol: discontinuities printout & guilty submodel investigation)
Each modeling problem can hence be detected / analysed with some numerical tools at the cost ofabout an 1hr analysis (depending on the size/complexity of the system and the user knowledge).
Moreover LMS Imagine.Lab allows to compute a system in debug mode, giving access to main theexecutable source within any debugger.
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Debugging tools: State Count facility
This facility prints a counter for each
state variable which is incremented each
time a given variable is the most difficult
to integrate at last converged step.
Double clicking on the concerned
variable allows the user to get the
submodel where this state is computed.
State count facility will allow the user to monitor dynamically which variable slows
down the simulation at a given time. When the integration is slowed down, you will
be able to plot the variable counters dynamically and detect which part of the
system gives problems to the integrator.
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Debugging tools: Linear analysis (LA)
LMS Imagine.Lab comes with LA facilities that allows the user to compute
eigenvalues and natural frequencies present in the system
Selecting the state variables as observer variables in the AMESim model will let
you determine which part of the system is sensitive to a given frequency and whichwill be excited by this frequency (Mode shapes)
Complex eigen values with high natural frequencies always contribute to slow
simulations so special care must be provided to their origins
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Debugging tools: Activity Index
No viscous friction set
Weak springsSpool clearance very small
Low leakage and low viscous friction
Extremely
small
Chamber.
Activity index analysis calculates the percentage of energy that flows through the C, R and I elements over
the total energy in the system.
This normalized value determines which component plays an active role in a model simulation.
It shows the energy-active and energy passive elements in the system thereby allows us to replace the
active ones by a simpler model
Activity index is also a good tool to detect bad input data.