Payload, Range and Speed: Where Will We Go and Why?

Definitions of Modeling and simulation physics-based modeling empirical modelIng Model/simulation Development Process module identification module ordering: DSM,'s and N2 diagrams module coding fidelity and benchmarking model execution simulation Computational Issues

A methodology for the design of complex engineering systems and subsystems that coherently exploits the synergism of mutually interacting phenomena Optimal design of complex engineering systems which

Existence Uniqueness of an optimum Solution Kuhn-Tucker Conditions Convex Spaces Unconstrained problems Linear Programming o Massachusetts Institute of

Sequential Linear Programming Penalty and Barrier Methods Sequential Quadratic Programming Mixed Integer Programming C Massachusetts Institute of Technology - Prof de Weck and Prof Willcox Engineering Systems Division and Dept of Aeronautics and Astronautics

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

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

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

Alternatives to Weighted Sum(WS)Approach Multiobjective Heuristic Programming Utility Function Optimization Physical Programming(Prof. Messac) Application to Space System Optimization Lab Preview(Friday 4-9-2003-Section 1) Massachusetts Institute of Technology-prof. de Weck and Prof. Willcox Engineering Systems Division and Dept of Aeronautics and Astronautics

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