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

Sensitivity Analysis effect of changing design variables effect of changing parameters effect of changing constraints Gradient calculation methods Analytical and Symbolic Finite difference Adjoint methods

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

How to know whether optimization is needed

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

Design of Experiments Overview Full Factorial Design Parameter stud One at a time Latin Hypercubes Orthogonal arrays Effects DoE Paper Airplane Experiment

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

Last time discussed standard approach Sequential modular analysis lecture 3) Modules are executed sequentially with or without feedback loops · MDO frameworks Other Approaches

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

Course rationale Role of msdo in Engineering Systems Learning objectives Pedagogy and Course Administration a historical perspective on Mdo MSDO Framework introduction The“ dairy farm” sample problem Massachusetts Institute of Technology- Prof de Weck and Prof Willcox