3 Multidisciplinary System Design Optimization(MSDO) Introduction Lecture 1 4 February 2004 Prof. olivier de weck Prof. Karen willcox Massachusetts Institute of Technology- Prof de Weck and Prof Willcox Introductions Olivier de Weck. Ph. D- Lecturer Assistant Professor, deweck(@mit. edu Karen Willcox Ph D. -Lecturer Assistant Professor, willcox@mit. edu lI Yong Kim, Ph D.-Assistant Lecturer Postdoctoral Fellow, kiy(@mit. edu Jackie Dilley- Course Assistant dilley(@mit. edu Massachusetts Institute of Technology- Prof de Weck and Prof willcox
1 1 Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Multidisciplinary System Multidisciplinary System Design Optimization (MSDO) Design Optimization (MSDO) Introduction Introduction Lecture 1 4 February 2004 Prof. Olivier de Weck Prof. Karen Willcox 2 Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Introductions Introductions Olivier de Weck, Ph.D. – Lecturer Assistant Professor , deweck@mit.edu Karen Willcox, Ph.D. – Lecturer Assistant Professor , kwillcox@mit.edu Il Yong Kim, Ph.D. – Assistant Lecturer Postdoctoral Fellow , kiy@mit.edu Jackie Dilley – Course Assistant jdilley@mit.edu
Today's Topics 3 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 Course rationale Computational Design and Concurrent Engineering(CE) are becoming an increasingly important part of the Product Development Process(PDP)in Industry MIT offerings strong in linear programming and constrained convex optimization(single objective) However, there is a perceived gap at MIT mostly management, not design focus multiobjective opt MDO vibrant research field but no course to represent it This is noT a traditional optimization course: M-S-D-0 Massachusetts Institute of Technology- Prof de Weck and Prof willcox
2 3 Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Today’s Topics Today’s Topics • 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 4 Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Course Rationale Course Rationale • Computational Design and Concurrent Engineering (CE) are becoming an increasingly important part of the Product Development Process (PDP) in Industry • MIT offerings strong in linear programming and constrained convex optimization (single objective) • However, there is a perceived gap at MIT: - mostly management, not design focus - multiobjective optimization - MDO vibrant research field but no course to represent it • This is NOT a traditional optimization course: M-S-D-O
Mlesd role of MSDo in Engineering Systems 45.33 Goal: Create advanced and complex engineering systems that must be competitive not only in terms of performance, but also in terms of manufacturability serviceability and overall life-cycle cost effectiveness Need: A rigorous, quantitative multidisciplinary design methodology that can work hand-in-hand with the intuitive non-quantitative and creative side of the design process This class presents the current state-of-the-art in concurrent, multidisciplinary design optimization(MDO) Massachusetts Institute of Technology- Prof de Weck and Prof Willcox Mlesd Product Development Process 550: 3 creativity modeling simulation rch experiments The Enterprise design technique Lifecycle optimization(MDO) Manufacturing 下 "process information" Design assembly SRR The Syst Requirement Constraints 是是 Architect System Engineer The Environment: technological, economic, political, social, nature Massachusetts Institute of Technology- Prof de Weck and Prof Willcox
3 5 Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Role of MSDO in Engineering Systems Goal: Create advanced and complex engineering systems that must be competitive not only in terms of performance, but also in terms of manufacturability, serviceability and overall life-cycle cost effectiveness. Need: A rigorous, quantitative multidisciplinary design methodology that can work hand-in-hand with the intuitive non-quantitative and creative side of the design process. This class presents the current state-of-the-art in concurrent, multidisciplinary design optimization (MDO) 6 Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Product Development Process Product Development Process 1 Beginning of Lifecycle - Mission - Requirements - Constraints Customer Stakeholder User Architect Designer System Engineer Conceive Design Implement “process information” “turn information to matter” SRR PDR CDR iterate iterate The Environment The Environment: technological, economic, political, social, nature The Enterprise The System creativity architecting trade studies modeling simulation experiments design techniques optimization (MDO) virtual real Manufacturing assembly integration choose create
Nexus Spacecraft Example NASA Nexus Spacecraft Concept Centroid Jitter on Focal Plane [RSS Los] 三>8 Sunshield Module Requirement: Jz2=5 um android x um Goal: Find a"balanced"system design, where the flexible structure, the optics and the control systems work together to achieve a desired pointing performance, given various constraints Massachusetts Institute of Technology- Prof de Weck and Prof Willcox
4 7 Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Nexus Spacecraft Example Nexus Spacecraft Example OTA 012 meters Instrument Module Sunshield -60 -40 -20 0 20 40 60 -60 -40 -20 0 20 40 60 Centroid X [µm] Centroid Y [µm] Centroid Jitter on Focal Plane [RSS LOS] T=5 sec 14.97 µm 1 pixel Requirement: Jz,2=5 µm Goal: Find a “balanced” system design, where the flexible structure, the optics and the control systems work together to achieve a desired pointing performance, given various constraints NASA Nexus Spacecraft Concept
Automotive Example 3 Ferrari 360 Spider Goal: High end vehicle shape optimization while improving car afety for fixed performance level and given geometric constraints Reference: G. Lombardi. A. Vicere. H urodynamic Design for High Performance Cars", AlAA-98-4789, MAO Conference, SI Louis. 1998 Massachusetts Institute of Technology- Prof de Weck and Prof Willcox Course Objectives The course will fill an existing gap in MIT's offerings in the area of simulation and optimization of multidisciplinary systems during the conceive and design phases develop and codify a prescriptive approach to multidisciplinary modeling and quantitative assessment of new or existing system/product designs engage junior faculty and graduate students in the emerging research field of MDo, while anchoring the CDIO principles in the graduate curriculum Massachusetts Institute of Technology.Prof de Weck and Prof willcox
5 9 Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Automotive Example Automotive Example Goal: High end vehicle shape optimization while improving car safety for fixed performance level and given geometric constraints Reference: G. Lombardi, A. Vicere, H. Paap, G. Manacorda, “Optimized Aerodynamic Design for High Performance Cars”, AIAA-98-4789, MAO Conference, St. Louis, 1998 Ferrari 360 Spider 10 Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Course Objectives Course Objectives The course will • fill an existing gap in MIT’s offerings in the area of simulation and optimization of multidisciplinary systems during the conceive and design phases • develop and codify a prescriptive approach to multidisciplinary modeling and quantitative assessment of new or existing system/product designs • engage junior faculty and graduate students in the emerging research field of MDO, while anchoring the CDIO principles in the graduate curriculum