'Design variables' diaporamas de présentation

Developments in Evolutionary Algorithms and Multi Disciplinary Optimisation

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The University of Sydney L. F. Gonzalez E. J. Whitney K . Srinivas

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Response Surface Methodology

Response Surface Methodology. Introduction. Main Part I (General Concept). Main Part II (Advanced RSM). Ending. Contents. Basic Concept, Definition, & History of RSM Introduction ; Motivation. DOE (Design Of Experiments) Experiments (Numerical) & Databases

10.19k views • 50 slides

Multidisciplinary Design of Complex Engineering Systems With Implications for Manufacturing

Multidisciplinary Design of Complex Engineering Systems With Implications for Manufacturing. Juan J. Alonso Department of Aeronautics & Astronautics Stanford University jjalonso@stanford.edu AIM Meeting April 6, 2004. Outline. Introduction The design process Design goals and challenges

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Mathematics-in-Action Engine Design Workshop

Mathematics-in-Action Engine Design Workshop. Ian Halliwell Senior Research Scientist Avetec Inc, Springfield, Ohio July 21, 2008. Contents. 1. The Role of Mathematics in the Design of Gas Turbine Engines Mathematics, Science & Engineering The Preliminary Design Process Detailed Design

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M. Kokkolaras and P.Y Papalambros University of Michigan

Optimal Multilevel System Design under Uncertainty NSF Workshop on Reliable Engineering Computing Savannah, Georgia, 16 September 2004. M. Kokkolaras and P.Y Papalambros University of Michigan. Z. Mourelatos Oakland University. Outline. Design by Decomposition

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Simulation-Supported Decision Making

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Optimization of a heterogeneous unmanned mission

Optimization of a heterogeneous unmanned mission. Yves Boussemart Anna Massie Brian Mekdeci. Motivation. Unmanned Vehicles Wide variety of uses: Surveillance Search & rescue Mining Dull, Dirty, Dangerous Problem: Given a mission Optimal # of UVs? Optimal operator strategies.

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Genetic Algorithms

Genetic Algorithms. Genetic algorithms imitate natural optimization process, natural selection in evolution. D eveloped by John Holland at the University of Michigan for machine learning in 1975. Similar algorithms developed in Europe in the 1970s under the name evolutionary strategies

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PART II Design Optimization

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FULLY STRESSED DESIGN in MSC.Nastran

FULLY STRESSED DESIGN in MSC.Nastran. Presented by Erwin H. Johnson Project Manager MSC.Software 3rd MSC.Software Worldwide Aerospace Users Conference Toulouse, FRANCE April 8-10, 2002. AGENDA. Introduction Theory Requirements Implementation Examples Concluding Remarks.

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Simulation-Based Design Study Modeling a Wind Turbine

Simulation-Based Design Study Modeling a Wind Turbine. Chris Gloss, Andrew Lincoln, Matthew Mustard, John Semmens , & Shereef Shehab. Introduction. Scope. Goals Develop a fully functional model of a 1.5 MW wind turbine based on industry standards

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Mechanical Seal Analysis

Mechanical Seal Analysis. Nicholas McDermott. Overview. Background Balanced vs. Unbalanced Design Variables Pressure Distribution Factor Spring Pressure Outer Pressure Shaft Speed Conclusions. Background.

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Part IV: Blade Geometry Optimization

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Optimization with surrogates

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7. Reliability based design

7. Reliability based design. Objectives Learn formulation of reliability design problem. Understand difference between reliability-based design and deterministic design Learn how to quickly approximate reliability of component using mean value, first order, second moment method

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Choice design variables

Choice design variables . The design variables we have considered for optimization define dimensions or angles or in more general terms the magnitude of a variable or the number of items.

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Car Optimization Example

Car Optimization Example. David Rosen Georgia Tech. Optimization Example. Optimization of car body for: noise-vibration-harshness (NVH) and crash test (roof crush during roll-over) 1999-2000 SGI Origin 2000 supercomputer with 256 processors 3 days of computer time.

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A Design Optimization Method using Evidence Theory

Power Set (All sets). Element. (X). Model (Transfer Function). Evidence Theory. ( Possibility Theory). Output. Input. What is possible may not be probable What is impossible is also improbable. B. A. Evidence Theory. Evidence Theory. Probability Theory. Possibility Theory.

311 views • 1 slides