MIest 16888 Outline Multidisciplinary System Design Optimization (MSDO) Summarize course content Present some emerging research directions Course Summary Interactive discussion Lecture 25 Fill in paper online course evaluations 12May2004 Prof, olivier de weck Prof. Karen willcox Massachusetts Institute of Technology -Prof de Weck and Prof Wacox Massachusetts institute of Technology. Prof. de Weck and Prof. Willcox MIlesd Learning Objectives(0) MIesd Learning Objectives(U) 5.39 The students will sequential quadratic programming, simulated annealing or genetic algorithms and select the ones most suitable to ( 1)learn how MSDO can support the product development process of complex, multidisciplinary engineered systems the problem at hand (2)learn how to rationalize and quantify a system l evaluation and interpretation of architecture or product design problem by selecting simulation and optimization results, including sensitivity analysis and exploration of performance, cost and risk appropriate obiective functions, design variables tradeoff arameters and constraints (3) subdivide a complex Sys ( 6)be familiar with the basic concepts of multiobjective models, manage their inter and reintegrate them into optimization, including the conditions for optimality and n overall system model the computation of the pareto front Massachusetts Institute of Technology -. de Weck and Prof Wilcox Massachusetts Institute of Techmology.Prof de Weck and Prof willcox1 Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Multidisciplinary System Multidisciplinary System Design Optimization (MSDO) Design Optimization (MSDO) Course Summary Course Summary Lecture 25 12 May 2004 Prof. Olivier de Weck Prof. Karen Willcox 2 Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Outline Outline • Summarize course content • Present some emerging research directions • Interactive discussion • Fill in paper & online course evaluations 3 Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Learning Objectives (I) Learning Objectives (I) The students will (1) learn how MSDO can support the product development process of complex, multidisciplinary engineered systems (2) learn how to rationalize and quantify a system architecture or product design problem by selecting appropriate objective functions, design variables, parameters and constraints (3) subdivide a complex system into smaller disciplinary models, manage their interfaces and reintegrate them into an overall system model 4 Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Learning Objectives (II) Learning Objectives (II) (4) be able to use various optimization techniques such as sequential quadratic programming, simulated annealing or genetic algorithms and select the ones most suitable to the problem at hand (5) perform a critical evaluation and interpretation of simulation and optimization results, including sensitivity analysis and exploration of performance, cost and risk tradeoffs (6) be familiar with the basic concepts of multiobjective optimization, including the conditions for optimality and the computation of the pareto front