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From Siloed Products to Connected Ecosystem: Building a Sustainable and Scala...
System model optimization through functional models execution methodology and application to system level analysis
1. Page 1
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
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2. 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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3. 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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4. 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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5. 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)
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6. 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
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7. Web Editor - GUI
Page 7
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8. 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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9. 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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10. 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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11. 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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12. 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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13. 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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14. 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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15. 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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16. 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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17. 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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18. 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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19. 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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20. Thank you
Page 20
Thank you for your attention!
Questions?
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