System-of-Systems Engineering - Liophant Simulation

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Physical vs. Computational ExperimentsPhysical Experiments• Often a limited number offactors• Data collection must often bedone in “one shot” (forexample, one growing season)• Types of Error– Human Error: Experimentermakes a mistake– Systemic Error: Flaw inphilosophy of the experimentadds a consistent bias to result– Random Error: Measurementinaccuracies due to theinstruments being usedComputational Experiments•Often have a larger number offactors than real worldexperiments•Data collection is sequential innature•Types of Error– Human Error: Bugs in the code,incorrectly entered boundaryconditions, etc– Systemic Error: Consistenterrors due to approximations inthe code– Random Error: Does not exist incomputational experimentsDeveloping Methods and Techniques for System of Systems EngineeringDr. Dimitri Mavris, dimitri.mavris@ae.gatech.edu30
Design of Computer Simulation• Computer simulation is a numerical technique forconducting experiments on certain types ofmathematical and logical models describing thebehavior of a system (or some componentthereof) on a digital computer over extendedperiods of real time. (Burdick & Naylor, 1966)• Design of Computer Simulation (DoCS) is gearedtoward developing sound experimental designpractices foe experiments performed oncomputational simulationsDeveloping Methods and Techniques for System of Systems EngineeringDr. Dimitri Mavris, dimitri.mavris@ae.gatech.edu31
Page 1 and 2: Developing Methods and Techniques f
Page 3 and 4: The Aerospace Systems Design Labora
Page 5 and 6: Motivation- Emphasis on “Early-Ph
Page 7 and 8: What is a system?“A set of functi
Page 9 and 10: System of Systems• “A set or ar
Page 11 and 12: What is SoSE?• System of Systems
Page 13 and 14: The Evolution of New Ideas“All tr
Page 15 and 16: What is needed for this New Paradig
Page 17 and 18: An Architecture-based Approach to S
Page 19 and 20: The System Alternative Space• The
Page 21 and 22: The Architecture-based Technology E
Page 23 and 24: Extension to Systems-of-Systems Ana
Page 25 and 26: Fundamental Properties of a Complex
Page 27 and 28: Enablers for Complex SoS• Design
Page 29: Design of Experiments• Design of
Page 33 and 34: Agent-Based Behavioral Modeling•
Page 35 and 36: System Dynamics Modeling• System
Page 37 and 38: Discrete Event Simulations• Discr
Page 41 and 42: Surrogate ModelsWhy do we use surro
Page 43 and 44: Challenges to Surrogate Modeling fo
Page 45 and 46: Fitting Modal ResponsesDeveloping M
Page 51 and 52: Pareto Optimal Solutions in Many Di
Page 53 and 54: Interactive Electronic Design Revie
Page 55 and 56: Collaboration and Integration in So
Page 57 and 58: Concluding Remarks• System of Sys
Page 59 and 60: Selecting a DesignDeveloping Method
Page 61 and 62: A New Paradigm Shift: Simulation Ba
Page 63 and 64: Which are simple? Which arecomplica
Page 65 and 66: What is a Markov Chain• Markov Ch
Page 67 and 68: Markov Chains• The transition pro

System-of-Systems Engineering - Liophant Simulation

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