L23- Fast Time Domain simulation via decomposition Prof olivier de Weck imulation of lti systems LTI systems are very common in engineering applications and simulating them is of great importance small dynamic urge dynamic

Computer technologies have been changing the environment of engineering design-enabling MDO Hardware: Advances in processor speed, memory and storage Software: Powerful disciplinary analysis and simulation programs(e.g. Nastran, Fluent...)

I. Structural Optimization ll. Integrated Structural Optimization lll. Design Space Optimization Massachusetts Institute of Technology-Dr. Yong Kim

Massachusetts Institute of Technology-Prof. de Weck and Prof. Willcox Massachusetts Insfitute of Technology-Prof. de Weck and P Dept. of A Mlesd Why Approximation Methods? 0. Mlesd Approximation Methods

Packaging Geometric measures to check requirements Occupant model Inputs Shoulder room, knee room etc Roominess measure GM RD Trunk∨oume

Why multiobjective optimization? Example-twin peaks optimization History of multiobjective optimization Weighted Sum Approach(Convex Combination) Dominance and Pareto-Optimality Pareto Front Computation -nBl o Massachusetts Institute of Technology-Prof de Weck and Prof Willcox Engineering Systems Division and Dept of Aeronautics and Astronautics

Mlesd Particle Swarm Optimization 5. 9 A pseudo-optimization method (heuristic) inspired by the collective intelligence of swarms of biological populations Flocks of birds Colonies of insects C Rania Hassan 3/2004 Engineering Systems Division -Massachusetts Institute of Technology

Outline Heuristics Background in Statistical Mechanics -Atom Configuration Problem Metropolis Algorithm Simulated Annealing algorithm Sample Problems and Applications · Summary

This 90 minutes lecture to be delivered via telelink addresses a class of MIT undergraduates unrolled in Engineering Design and Rapid Prototyping Course. The students have been introduced to the basic optimization concepts of design variables, design space, objective function

Mesd Heuristic Search Techniques 16888 2S0.77 Main Motivation for Heuristic Techniques: (1)To deal with local optima and not get trapped in them (2)To allow optimization for systems, where the design variables are not only continuous, but discrete