Mest 16888 ES077 Multidisciplinary System Design Optimization(MSDO) Structural Optimization Design Space Optimization Lecture 18 Apr7,2004 Yong Kim C Massachusetts Institute of Technology-Dr ll Yong Kim
1 © Massachusetts Institute of Technology – Dr. Il Yong Kim Multidisciplinary System Design Optimization (MSDO) Structural Optimization & Design Space Optimization Lecture 18 April 7, 2004 Il Yong Kim
16888 ESD.J7 I. Structural Optimization Integrated Structural optimization I. Design Space Optimization o Massachusetts Institute of Technology-Dr Il Yong Kim
2 © Massachusetts Institute of Technology – Dr. Il Yong Kim I. Structural Optimization II. Integrated Structural Optimization III. Design Space Optimization
Mlesd Structural optimization 16888 E77 Definition An automated synthesis of a mechanical component based on structural properties. A method that automatically generates a mechanical component design that exhibits optimal structural performance o Massachusetts Institute of Technology-Dr Il Yong Kim
3 © Massachusetts Institute of Technology – Dr. Il Yong Kim Structural Optimization * Definition - An automated synthesis of a mechanical component based on structural properties. - A method that automatically generates a mechanical component design that exhibits optimal structural performance
Mles 16888 od Structural Optimization ESD.J7 minimize f(x) subject to g(x)≤0 X X∈S Typically, FEM is used BC's are given Loads are given <Q> How to represent the structure? or Which ty pe of design variables to use? min compliance <A>(1)Size Optimization s.t. m smc (2 Shape Optimization 3)Topology Optimization o Massachusetts Institute of Technology-Dr Il Yong Kim
4 © Massachusetts Institute of Technology – Dr. Il Yong Kim Structural Optimization minimize ( ) subject to ( ) 0 () 0 f g h S d x x x x BC’s are given Loads are given How to represent the structure? or Which type of design variables to use? <Q> Typically, FEM is used. (1) Size Optimization (2) Shape Optimization (3) Topology Optimization <A> min compliance s.t. m dmC ?
Mlesd Size optimization example 16888 E77 Beams (2-Dim minimize f(x) subject g(x)≤0 h(x)=0 X∈ Design variables(x) fx): compliance x: thickness of each beam g(x): mass h(x): state equation Number of design variables(ndv) ndv= 5 o Massachusetts Institute of Technology-Dr Il Yong Kim
5 © Massachusetts Institute of Technology – Dr. Il Yong Kim Size Optimization Example f(x) : compliance g(x) : mass h(x) : state equation • Design variables (x) x : thickness of each beam • Number of design variables (ndv) ndv = 5 Beams (2-Dim) minimize ( ) subject to ( ) 0 () 0 f g h S d x x x x