system model simulation - monte carlo,
TRANSCRIPT
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Dr. Saulius Pavalkis, Chief MBSE Solutions Architect
System Model Simulation - Monte Carlo,
Probabilities, and more…
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Classifier executionSimulation
The purpose of a simulation is to gain systemunderstanding without manipulating the realsystem, either because it is not yet defined oravailable, or because it cannot be exerciseddirectly due to cost, time, resources or riskconstraints.
Simulation is typically performed on a model ofthe system.
© 2013 No Magic, Inc. Exclusively for No Magic Use2
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System engineering process (V process)
Credits: Pawel Chadzynski & Michael Pfenning - MBSE and the Business of Engineering
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Cameo Simulation Toolkit
• Model execution framework for MagicDraw Model debugging and animation environment
Pluggable engines, languages and evaluators
User Interface prototyping
Co-simulation orchestration
• The standard based model execution of: Activities (OMG fUML standard)
Composite structures (OMG PSCS)
Statemachines (W3C SCXML and OMG PSSM standards)
Actions/scripts (OMG ALF, JSR223 scripting)
Parametrics (OMG SysML standard)
Sequence diagrams (OMG UML Testing Profile)
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Engineering analysis
• Automated Requirements Verification
• Trade studies / trade-off analysis
• Mass/cost/power rollups
• Timing and duration analysis
• Monte Carlo analysis
• Model-based testing
• Co-simulation environment
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Simulation and Analysis
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User Interface + animation
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GUI examples
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Monte Carlo Analysis
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Monte Carlo - Uncertainty Propagation
The Monte Carlo method is a
method for analyzing uncertainty
propagation, where the goal is to
determine how random variation,
lack of knowledge, or error affects
the sensitivity, performance, or
reliability of the system that is
being modeled.
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Monte Carlo simulation
Distributed Property Normal distribution
– Mean +- Standard deviation
Uniform distribution
– Min – max
Number of runs
Recording CSV
Histograms
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Demo!
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Questions?
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Lumped element model
The lumped element model (also called lumped parameter
model, or lumped component model) simplifies the description of
the behaviour of spatially distributed physical systems into
a topology consisting of discrete entities that approximate the
behaviour of the distributed system under certain assumptions. It is
useful in electrical systems (including electronics),
mechanical multibody systems, heat transfer, acoustics, etc.
Mathematically speaking, the simplification reduces the state
space of the system to a finite dimension, and the partial differential
equations (PDEs) of the continuous (infinite-dimensional) time and
space model of the physical system into ordinary differential
equations (ODEs) with a finite number of parameters.