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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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.

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Amesim Hil