How to Improve the Effectiveness, Efficiency, and Integration of Test & Evaluation (T&E) and Modeling and Simulation (M&S)Dr. Mark J. KiemeleAir Academy AssociatesNDIA Conference on T&E and M&SJacksonville, FLMarch 6, 2006Air AcademyAssociatesCopyright2006Agenda• Some Basic Definitions• The Heart and Soul of Multivariate Testing• Integrating T&E with M&S • Examples of Iterative Use of Modeling and Simulation • Summary of “Modeling the Simulator” Air AcademyAssociatesCopyright20061Some Basic Definitions• System: a collection of entities which act and interact together to achieve some goal• Model: a simplified representation of a system developed for the purpose of studying a system• Simulation: the manipulation of a model in such a way that it allows the investigation of the performance of a system. • Modeling and Simulation: a discipline for developing a level of understanding of the interaction of the parts of Air Academya system, and of the system as a wholeAssociatesCopyright20062About ModelsAll models are simplifications of reality.There is always a tradeoff as to what level of detail should be included in the model:If too little detail, there is a risk of missing relevant interactions and the resultant model does not promote understandingIf too much detail, there is a risk of overly complicating the model and actually preclude the development of understandingThe goodness of a model depends on the extent to ...
System:a collection of entities which act and interact together to achieve some goal
Model:a simplified representation of a system developed for the purpose of studying a system
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Simulation:the manipulation of a model in such a way that it allows the investigation of the performance of a system.
Modeling and Simulation:a discipline for developing a level of understanding of the interaction of the parts of a system, and of the system as a whole
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About Models
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All models are simplifications of reality.
The goodness of a model depends on the extent to which it promotes understanding
If too much detail, there is a risk of overly complicating the model and actually preclude the development of understanding
If too little detail, there is a risk of missing relevant interactions and the resultant model does not promote understanding
There is always a tradeoff as to what level of detail should be included in the model:
Air Academy Associates
Copyright 2006
Types of Models
High-Fidelity Models: - many variables and many interactions - highly detailed and complex - needed for visualization - difficult to manipulate
Low-Fidelity Models: - much fewer number of variables - can be manipulated more easily - provides higher-level view of system - presents a more aggregate view of the system
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Discrete Event Simulation: - studies a sequence of countable events - assumption is that nothing of importance takes place between events
Types of Simulation
Monte Carlo Simulation: - provides for variability in the inputs - y = f(x + variation), where the variation is modeled as some probability distribution
Deterministic Simulation: - for each combination of inputs parameters, there is one and only one output value - y = f(x)
The equation for magnetic force at a distance X from the center of a solenoid is: λ − H=N2Ιr2.+5.(5+x+x)2+r2(.5λ.5λxx)2 λ λ + −
The equation for the impedance (Z) through this circuit is defined by: Z=R1⋅R2 R1+R2
λ
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N : total number of turns of wire in the solenoid Ι in the wire, in amperes: current r : radius of helix (solenoid), in cm λ: length of the helix (solenoid), in cm x : distance from center of helix (solenoid), in cm H : magnetizing force, in amperes per centimeter
Explicit Finite Element Analysis :Impact simulation, metal forming LS-DYNA RADIOSS PAM-CRASH®, PAM-STAMP
Implicit Finite Element Analysis:Linear and nonlinear statics, dynamic response MSC.Nastran, MSC.Marc ANSYS® Pro MECHANICA ABAQUS® Standard and Explicit ADINA
Mechanical motion:Multibody kinetics and dynamics ADAMS® DADS