Mes 16888 ESO.77 Multidisciplinary System Design Optimization( MSDO) Decomposition and Coupling Lecture 4 17 February 2004 Olivier de weck o Massachusetts Institute of Technology -Prof de Weck and Prof Willcox
Multidisciplinary System Multidisciplinary System Design Optimization (MSDO) Design Optimization (MSDO) Decomposition and Coupling Lecture 4 17 February 2004 Olivier de Weck 1 © Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox
M Today's Topics 6888 ESO.77 ast time discussed standard approach Sequential modular analysis(Lecture 3) Modules are executed sequentially with or without feedback loops · MDO frameworks Other Approaches Distributed analysis Distributed design o Massachusetts Institute of Technology -Prof de Weck and Prof Willcox
Today’s Topics Today’s Topics Last time discussed standard approach: Sequential modular analysis (Lecture 3). Modules are executed sequentially with or without feedback loops. • MDO frameworks Other Approaches: – Distributed analysis – Distributed design © Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox 2
M esd Fundamentally different approaches in MDO 16883 ESO.77 Distributed Analysis disciplinary models provide analysis all optimization done at system level non-hierarchical decomposition hierarchical decomposition Distributed Design provide disciplinary models with design tasks optimization at subsystem and system levels CO BLISS o Massachusetts Institute of Technology -Prof de Weck and Prof Willcox
Fundamentally different approaches in MDO Fundamentally different approaches in MDO Distributed Analysis -disciplinary models provide analysis -all optimization done at system level non-hierarchical decomposition hierarchical decomposition Distributed Design -provide disciplinary models with design tasks CSSO -optimization at subsystem and system levels CO BLISS © Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox 3
Mlesd Standard Optimization Problem 50. 9 Given x x∈R J:R”→>R Optimization Engine g:R”→>R J(x) Solve the problem x g(x min(x) st,g(x)≥0 Function Evaluator That is, findx S t J()sf(x), vxe dom()ndom(g) o Massachusetts Institute of Technology -Prof de Weck and Prof Willcox
Standard Optimization Problem Standard Optimization Problem Given * x x 0 J ( ) x x gx ( ) Optimization Engine Function Evaluator ∈ n x ! n J : ! → → ! n m g : ! ! Solve the problem min J x( ) s.t. g x( ) ≥ 0 * * That is, find x s.t. J( x ) ≤ f x ( ), ∀ ∈ x dom( J ) ∩ dom( ) g © Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox 4
Mlesd Distributed Analysis 16888 ESO.77 Disciplinary models provide analysis Optimization is controlled by some overseeing code or database e.g. Genie database system(Stanford) ISight(Optimizer) iSight Gene optimizer design variables NPSoL Shared data X subsystem analyses Structures Aero Local data ocal data o Massachusetts Institute of Technology -Prof de Weck and Prof Willcox
Distributed Analysis Distributed Analysis • Disciplinary models provide analysis • Optimization is controlled by some overseeing code or database e.g. GenIE database system (Stanford) ISight (Optimizer) iSight GenIE NPSol Shared data Local data Structures Local data Aero Optimizer design variables constraints x J(x),g(x),h(x) subsystem analyses © Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox 5