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NDIA Conference Tutorial Jacksonville 6 Mar 06 [Read-Only]

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40 pages
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 ...
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Air Academy Associates
Copyright 2006
How to Improve the Effectiveness, Efficiency, and Integration of Test & Evaluation (T&E) and Modeling and Simulation (M&S)
Dr. Mark J. Kiemele Air Academy Associates
NDIA Conference on T&E and M&S Jacksonville, FL March 6, 2006
Some Basic Definitions
Integrating T&E with M&S
The Heart and Soul of Multivariate Testing
Agenda
Summary of Modeling the Simulator
1
Copyright 2006
Air Academy Associates
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Some 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
2
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
3
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)2λ λ + −
The equation for the impedance (Z) through this circuit is defined by: Z=R1R2 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
r Where
Engineering Relationships - V = IR - F = ma R1
R2
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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
Examples of High Fidelity Simulation Models
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Examples of High Fidelity Simulation Models
Postprocessing: Finite Element Analysis and Computational Fluid Dynamics results visualization Altair® HyperMesh® I-deas MSC.Patran FEMB EnSight FIELDVIEW ICEM CFD Visual3 2.0 (PVS) COVISE
Preprocessing: Finite Element Analysis and Computational Fluid Dynamics mesh generation ICEM-CFD Gridgen Altair® HyperMesh® I-deas® MSC.Patran TrueGrid® GridPro FEMB ANSA
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Electronics
Simulation of stress and vibrations of turbine assembly for use in nuclear power generation
Simulation of underhood thermal cooling for decrease in engine space and increase in cabin space and comfort
Evaluation of dual bird-strike on aircraft engine nacelle for turbine blade containment studies
Automotive
Power
Applications of Modeling and Simulation
Aerospace
Evaluation of cooling air flow behavior inside a computer system chassis
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Functional Compatibility: - to determine if various components or subassemblies work together
Screening: - to separate the critical parameters from those that are not critical with regard to functionality or performance capability
Modeling: - to build prediction capability of the performance measures and perform sensitivity and interaction analyses on the critical parameters
Reasons for Test & Evaluation