System model optimization through functional models execution methodology and application to system level analysis

System Model Optimization through Functional Models Execution:
Methodology and Application to System-level Analysis

Authors: F. Becherini, M. Cencetti, M. Pasquinelli
Template reference : 100181685K-EN

Presenter: M. Pasquinelli

3rd IEEE Track on
Collaborative Modeling & Simulation - CoMetS'12
in WETICE 2012
21st IEEE International Conference on Collaboration Technologies and Infrastructures,
June 25 - 27, 2012, Toulouse (France)

BS_SIT/Eng/COSE Centre
COSE Centre
All rights reserved,
2/26/2010, Thales Alenia Space

List of Contents

Page 2

• Introduction
 Basic concepts
 DEVICE Environment

• Collaborative Modelling Environment
 Web Editor for System model editing
 Integration with CAD and VR

• Functional Model configuration and generation
• Optimization Environment Concept
• Reference case
• Conclusions

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Context

Page 3

• Transition from Document-Based to Model-Based System
Engineering (MBSE)
• Improve computer-aided collaboration between team members and
their tools assuring common semantics
• Conceptual Data Model necessary to structure SE data
 Compatible with current standardization efforts in space: ECSS-E-TM-10-23
(Engineering Database) and ECSS-E-TM-10-25 ("System engineering - Engineering
design model data exchange (CDF)“)
• Model-Driven approach to establish a framework to develop dynamic
architectures (depending on the team needs, size, type of programs,
project phase)
• Define model generation/customization techniques to enable easier
early system simulation use in design (with optimization)
• TAS-I research is oriented towards a Distributed Environment for
Virtual Integrated Collaborative Engineering (DEVICE) framework

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STEPS – WP1D / 2A

STEPS Project Page 4
Project co-financed by EC
Platform: POR FESR – 2007/2013

MBSE ENVIRONMENT

CDF
MBSE ENVIRONMENT

I/F with
System Modeling External
Environments

System
Domain-
Domain-
Specific
Discipline-
Domain-
Manager
Specific System and Discipline Models
Models
Specific
Specific
Models Storage and Management
Models
Models

Product
Portfolio and
Collaborative
System
System
Simulation
Models
Knowledge
Management Cloud
Visualization System

Built from Scratch Improvement

Preliminary Phases
Whole Lifecycle Concept Studies
Feasibility Studies
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Interaction Concept

Page 5

User A Application A

DB
Adapters
Adapters
Adapters
Adapters
User B
Application B

ECSS-E-TM-10-23
DB
Compatible
SYSTEM MODEL:
INSTANCES OF METAMODEL CONCEPTS

Master CDF*
DB
FILES, ANNOTATIONS, ACTIONS ECSS-E-TM-10-25
LINKED TO SYSTEM MODEL ITEMS Compatible
DB
COSE Centre
*CDF=Concurrent Design Facility (with associated Data Model)

MBSE Demonstrator

Page 6

Virtual
Vehicle Simulation Adapter
Adapter Adapter
Environment
D D
DB DB
B B

Virtual Assembly
Virtual Assembly Adapter
Adapter Adapter
Web Editor
Tool
D D
DB DB
B B

Analysis/Simulation Adapter
Adapter Adapter
Web
Tool Collaboration
D DB
DB
B
Consistency Check
Consistency Check

CDF Adapter Master
Master
COSE Centre DB
DB

Web Editor - GUI

Page 7

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Web Editor - Functions

Page 8

• User Management (basic)
 Authentication
 Actions assigned to users
 Discipline-, Role- and Ownership-based authoring and baselining rights
 Discipline-based customized views *on progress*

• System Modeling
 Topology (Elements, Containment, Interfaces, Aspects)
 Functions (Functions, Ports, Variables flow, Function models)
 Finite States (Discrete Models, States, Transitions)
 Values (Properties, Parameters, Constants, Variables, Ranges, Tables)
 Requirements (associated to values or ranges for automatic check)
 Verification (Verification requirements, association to models)
 Operational Scenarios (phases, trajectories, environment, boundary conditions)
 AIT Scenarios (activities, facilities, configurations)

• Interface
 Xml-based generic import/export (by baselines, by data item, or by container with its dependencies)
 Import of properties and models (e.g. to/form DS Catia models)
 Internal File repository (for resources as discipline models) or link to external repos

• System Control
 Properties summaries and budgets
 Modelica –based code composer from functional compositions

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Web Editor from Model

Page 9

generates

Database Migration

generates

Meta-model

generates Rails model

calls
model objects

instructs

Rails View Rails Controller

renders
requests

Interacts
Visualized in
Browser User
HTML/XML/JSON/YAML
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Web Editor with VR

Page 10

Properties
API
file

CATIA Model

V 3D Models (VRMLs)
E Web Editor
R
I
T
A
CATIA Tree
S System Model To EPS
Mapping
A
d.
User

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VERITAS=Virtual Environment Research in Thales Alenia Space
EPS=Explicit Product Structure All rights reserved,

VR, Modelica, Functions and Elements

Page 11
Geometrical Models Virtual Reality

Property
Product Elements
(Defined Elements and Related Interfaces)
Parameter

Allocation

Constants/Params
Functions
(Defined Functions & related Ports)
Variables

Map

Parameters

Function Models Variables Map
(Modelled Functions for Simulation)

Equation

Modelica Results

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Functional Simulation Concept

ObjectPage 12
Function Topology

+ =

• The physical behavior is described by means of equations as relations
between quantities.
 Acausal modeling through differential algebraic equations (DAEs).
 Models can be directly generated (i.e., writing equations) or through composition of
previously defined models (reuse).
• Modelica is the language chosen for its object-oriented features.
 It has multi-domain capabilities because it is not domain-dependent, well suited for
system design.
 Note that in Modelica there is no distinction between function and topology for a
simulated object since they are mixed.
• Modelica can integrate simulation models written in other languages
and/or compiled
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Simulation Model Composition

Page 13

FunctionModel

Binary Code Modelica Code
Model Association
Source Code model Electric DC Motor
Ports contain Real i,w,W,T;
variables
v1 v2 parameter Real k=10;
i1 T parameter Real L=0.025;
«FunctionDefinition» parameter Real R=0.14;
GenerateTorquefromEMPower parameter Real V0=20;
equation
Variables Constants der(i)*L= v0-R*i-k*w;
W
i2 der(w)*J=T;
k,v0,L,R:Constant:Real Mapping T=k*i;
Mapping Mapping W=R*i^2;
«ElementDefinition» v0=p.v-n.v;
p.i+n.i=0;
Motor i=p.i;
end Electric DC Motor;
Parameters Properties
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Optimization Concept

Page 14

• Simulation management
 Simulation environment interfaced through Python scripting.
• Semi-automatic model generation
 Exploiting the Modelica construct replaceable+redeclare.

(From DAKOTA User Manual)
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Optimization Framework

Page 15

• Process definition file (.in)
• Simulation scripting input file
(.mos in the Modelica
interface)
• Simulation environment
(Dymola™ for Modelica)
• Results postprocessing
• Dakota cycling

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Reference Case

Page 16

model EPS
"Complete scenario consisting of environment, power generation
and load (motorwheel)"
inner function solarirradiance=sun;
inner function vocfunc=voclinear;
parameter Modelica.SIunits.Temperature TFIXED=310;
inner World.Environment environment;
BatteryPack batteryPack(Ns=5,Np=25,
redeclare Battery_xp batt[batteryPack.Ns*batteryPack.Np]);
Load_MW. Motorwheelunplugged shuntMotorwheelunplugged;
equation
connect(batteryPack.pinpack2, environment.G);
Replaceable element
connect(Motorwheel.p, batteryPack.pinpack1);
connect(Motorwheel.n, environment.G);
connect(Motorwheel.flange_b1, environment.flange_a1);
end EPS;

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Reference Case

Page 17

• Comparison between Web
editor-generated and
Dymola™-created models
showed no difference.
• Different types of batteries are
instantiated and related
simulation outputs were
plotted and compared.
• End to end evaluation (from
model definition, to model
composition and execution) of
the different models during
optimization cycles (increasing
complexity of battery models)

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Conclusions

Page 18

• Research prototypes and related results demonstrate the
feasibility of the overall approach
• Collaboration between users is eased by web-based
technologies and model-driven approach
• The use of the Modelica language showed consistency with the
model-based approach used for system modeling
• Collaboration between tools is eased by configurable interfaces
based on semantically meaningful system model transformed in
specific formats or accessed by specific services
• The framework enables the possibility to use optimization
techniques, regenerating the simulation model used in the
different iterations

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Next Steps

Page 19

• DAKOTA set-up: the optimization cycle definition requires the
execution of command line instructions, due to the characteristics of the
used framework. Process management may be improved through the
usage of a dedicated web-based GUI.

• Web Editor user interface and collaborative process for the
simulation management is to be improved for actual use in a pilot
project (next months).

• More complex optimization scenarios are in definition for the next
step.

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Thank you

Page 20

Thank you for your attention!
Questions?

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System model optimization through functional models execution methodology and application to system level analysis

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