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
Mlesd Learning Objectives(0) 3 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 arameters and constraints (3)subdivide a complex system into smaller disciplinary models, manage their interfaces and reintegrate them into an overall system model Massachusetts Institute of Technology- Prof de Weck and Prof Willcox Learning Objectives(Il) (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 Massachusetts Institute of Technology.Prof de Weck and Prof willcox
6 11 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 12 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
Mlesd Learning Objectives(u) 3 7) understand the concept of design for value and be familiar with ways to quantitatively assess the expected lifecycle cost of a new system or product (8)sharpen their presentation skills, acquire critical reasoning with respect to the validity and fidelity of their MSDO models and experience the advantages and challenges of teamwork How to achieve these learning objectives Massachusetts Institute of Technology- Prof de Weck and Prof Willcox MSDO Pedagogy e.g. A1- Design of Gue e.g. "NASA LaRC Experiments(DOE) Algorithms ecture AssignmentsLectures A1-A5 Readings e.g"ISIGHT e.g."STSTank MSDO e.g. "Principles of Optimal Design Massachusetts Institute of Technology.Prof de Weck and Prof willcox
7 13 Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Learning Objectives (III) Learning Objectives (III) (7) understand the concept of design for value and be familiar with ways to quantitatively assess the expected lifecycle cost of a new system or product (8) sharpen their presentation skills, acquire critical reasoning with respect to the validity and fidelity of their MSDO models and experience the advantages and challenges of teamwork How to achieve these learning objectives ? 14 Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox MSDO Pedagogy MSDO Pedagogy Guest Lectures Readings Lab Sessions Class Project Assignments A1-A5 e.g. “NASA LaRC” e.g. “iSIGHT Introduction” e.g. “Genetic Algorithms” e.g. “STSTank” e.g. A1 - Design of Experiments (DOE) Lectures e.g. “Principles of Optimal Design” MSDO
Assignments 3 Part(a. Small, simple problems to be solved individually, many just by hand or with a calculator. Goal is to ensure learning of the key ideas regardless of chosen project Application of theory to a project of your choice from either existing class projects or a project related to your research Solution individually or in teams of two or three Assignments A1-A5 scheduled bi-weekly Usually handed out Monday, Tutorial on Friday, due on a Monday two weeks later Massachusetts Institute of Technology- Prof de Weck and Prof Willcox Lectures Lecture schedule in separate document Module 1: Problem Formulation and Setup Module 2: Optimization and search Methods Spring Break Module 3: Multiobjective and Stochastic Challenges Module 4: Implementation Issues and Applications Massachusetts Institute of Technology.Prof de Weck and Prof willcox
8 15 Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Assignments Assignments Part (a) Small, simple problems to be solved individually, many just by hand or with a calculator. Goal is to ensure learning of the key ideas regardless of chosen project Part (b) Application of theory to a project of your choice from either existing class projects or a project related to your research. Solution individually or in teams of two or three. • Assignments A1-A5 scheduled bi-weekly. • Usually handed out Monday, Tutorial on Friday, due on a Monday two weeks later 16 Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Lectures Lectures Lecture schedule in separate document. Module 1: Problem Formulation and Setup Module 2: Optimization and Search Methods --- Spring Break --- Module 3: Multiobjective and Stochastic Challenges Module 4: Implementation Issues and Applications
MIest Class Project 3 Option A- Use a pre-existing project These are prepared simulation codes that you can use as your class project for solving part (b) of the assignments in lieu of a personal research-related proble AAERO-ASTRO\6.888\AIRCRAFT(C-Code) MAERO-ASTRO\6.888\COMSATS (MATLAB) AAERO-ASTRO\.888\SHUTTLETANK(Excel) MAERO-ASTRO\16.888\SUPERSONIC (Excel) Option B- Formulate your own project .This is an opportunity to push your research forward -Form teams of 1-3 students Must be a design problem, must be multidisciplinai Write 1 page project proposal in A1 Massachusetts Institute of Technology- Prof de Weck and Prof Willcox Lab sessions This room is the" Design Studio”……. NoT just another computer cluster. There is a lot of thought behind the facade Complex Systems Development and Operations Laboratory Result of the most recent strategic plan of the Dept. of Aeronautics and Astronautics CDIO System Architecture and Systems Engineering Aerospace Information Engineering Massachusetts Institute of Technology.Prof de Weck and Prof willcox
9 17 Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Class Project Class Project Option A – Use a pre-existing project These are prepared simulation codes that you can use as your class project for solving part (b) of the assignments in lieu of a personal research-related problem: \\AERO-ASTRO\16.888\AIRCRAFT (C-Code) \\AERO-ASTRO\16.888\COMSATS (MATLAB) \\AERO-ASTRO\16.888\SHUTTLETANK (Excel) \\AERO-ASTRO\16.888\SUPERSONIC (Excel) Option B – Formulate your own project -This is an opportunity to push your research forward -Form teams of 1-3 students -Must be a design problem, must be multidisciplinary -Write 1 page project proposal in A1 18 Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Lab Sessions Lab Sessions This room is the “Design Studio” …. NOT just another computer cluster. There is a lot of thought behind the façade. “Complex Systems Development and Operations Laboratory” Result of the most recent strategic plan of the Dept. of Aeronautics and Astronautics at MIT. New Focus: - CDIO - System Architecture and Systems Engineering - Aerospace Information Engineering
Tools and Infrastructure 3 Physical Infrastructure: Design Studio 33-218 Computational Infrastructure: Class folder: MAERO-ASTRO\6.888 Located on AA-desiGN PC network Will setup individual usernames and passwords Software Infrastructure Matlab, Excel, C-compiler ISIGHT-donated by Engineous Software Inc. Participate in iSIGHT academic bEta test program CO-donated by Oculus Technologies Corp Massachusetts Institute of Technology- Prof de Weck and Prof Willcox Readings 15 Panos Y Papalambros and Douglass J. Wilde, " Principles of Optimal Design-Modeling and Computation", 2nd edition, ISBN 0 521 62727 3, (paperback), Cambridge University Press, 2000-Recommended Garret N. Vanderplaats, "Numerical Optimization Techniques for Engineering Design, ISBN 0-944956-01-7, Third Edition, Vanderplaats Research Development Inc, 2001-Recommended(out of print R. E Steuer. Multiple Criteria Optimization: Theory, Computation and Application". Wiley, New York, 1986.-Reserve David E. Goldberg, Genetic Algorithms-in Search, Optimization ning", Addison-Wesley, ISBN 0 201 15767-5, 1989 will assign at the end of each lecture Massachusetts Institute of Technology.Prof de Weck and Prof willcox
10 19 Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Tools and Infrastructure Tools and Infrastructure • Physical Infrastructure: Design Studio 33-218 • Computational Infrastructure: - Class folder: \\AERO-ASTRO\16.888 - Located on AA-DESIGN PC network - Will setup individual usernames and passwords • Software Infrastructure: - Matlab, Excel, C-compiler - iSIGHT - donated by Engineous Software Inc. (Participate in iSIGHT academic BETA test program) - CO - donated by Oculus Technologies Corp. 20 Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Readings Readings will assign at the end of each lecture Panos Y. Papalambros and Douglass J. Wilde, “Principles of Optimal Design – Modeling and Computation”, 2nd edition, ISBN 0 521 62727 3, (paperback), Cambridge University Press, 2000 - Recommended Garret N. Vanderplaats, “Numerical Optimization Techniques for Engineering Design”, ISBN 0-944956-01-7, Third Edition, Vanderplaats Research & Development Inc., 2001- Recommended (out of print?) R. E. Steuer.” Multiple Criteria Optimization: Theory, Computation and Application”. Wiley, New York, 1986. - Reserve David E. Goldberg, “Genetic Algorithms – in Search, Optimization & Machine Learning”, Addison –Wesley, ISBN 0 201 15767-5, 1989 - Reserve