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 willcox
1 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
Mlesd Learning Objectives(1) 16888 MSDO Pedagogy (7)understand the concept of design for value and be familiar with ways to quantitatively assess the expected e.g. A1-Design of e.g. "Genetic Algorithms e.g. Dr Fenyes Experiments(DOE) lifecycle cost of a new system or product ecturesGM ( 8)sharpen their presentation skills, acquire critical Class reasoning with respect to the validity and fidelity of their A1-A5 Project Sessions MSDO models and experience the advantages and challenges of teamwork Reading e.g. "ISIGHT Have you achieved these learning objectives? e.g." STSTank MSDO e.g."Principles of Optimal Design Massachusetts Institute of Technology-Prof de Weck and Prof Wacox Massachusetts institute of Technology. Prof. de Weck and Prof. Willcox Mlesd Changes from 2002->2004 50 Mlesd Exploration and Optimization MSDO Framework Design Vector Simulation model Objective Vector Enrollment25÷40÷30(nc. listeners) Discipline A Discipline B Moved from Design Studio Eliminated Literature Review Sessions Discipline c iSIGHT- academic version to students Reduced guest speaker involvement Coupling Multiobjective Provided more canned projects Required final report in conference paper format Optimization Algorithms Methods Principles of Optimal Design"-Papalambros textbook Numerical Techniques Sensitivity direct and penalty method Analysis Heuristic Technique Coupling (SA, GA) performance Massachusetts Institute of Technology -. de Weck and Prof Wilcox Massachusetts Institute of Techmology.Prof de Weck and Prof willcox
5 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 Have you achieved these learning objectives ? 6 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. “Dr. Fenyes - GM” 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 7 Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Changes from 2002 Changes from 2002 -> 2004 • Enrollment 25 Æ 40 Æ 30 (incl. listeners) • Moved from Design Studio • Eliminated Literature Review Sessions • iSIGHT - academic version to students • Reduced guest speaker involvement • Provided more canned projects • Required final report in conference paper format • “Principles of Optimal Design” - Papalambros textbook 8 Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Exploration and Optimization Exploration and Optimization MSDO Framework MSDO Framework Discipline A Discipline B Discipline C n I p u t O u t p u t Simulation Model Tradespace Exploration (DOE) Optimization Algorithms Multiobjective Optimization Numerical Techniques (direct and penalty methods) Heuristic Techniques (SA,GA) 1 2 n x x x ª º « » « » « » « » « » ¬ ¼ # Design Vector Coupling 1 2 z J J J ª º « » « » « » « » « » ¬ ¼ # Approximation Methods Coupling Sensitivity Analysis Isoperformance Objective Vector
Mlesd Conceptual Class Schedule 16888 Mlesd Problem Formulation and Setup 50. 9 (NLP) Module 1: Problem Formulation and Setup minD(, p Module 2: Optimization and Search Methods st.g(xp)≤0 Spring Break constraints Module 3: Multiobjective and Stochastic Challenges h(x,P=0 Module 4: Implementation Issues and Applications xLB≤x1≤x1CB bounds wheJ=[J(x)…(x)了 x vector Massachusetts Institute of Technology -Prof de Weck and Prof Wacox Massachusetts institute of Technology. Prof. de Weck and Prof. Willcox Lest Module 1 Module 1: Subsystem Model Problem Formulation Setup Development and Coupling MDo frameworks Design variables distributed analysis vs distributed design Constraints Objective functions ◆CO,csSo,BL|SS Parameters Simulation Development Process Fidelity VS. expense define modules: subsystems or disciplines Breadth Vs Depth design vector, constants vector MDO uses applications N2 diagrams feedback vs feedforward, sorting s Benchmarking test model fidelity against a real system Massachusetts Institute of Technology -. de Weck and Prof Wilcox Massachusetts Institute of Techmology.Prof de Weck and Prof willcox
9 Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Conceptual Class Schedule Conceptual Class Schedule 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 10 Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Problem Formulation and Setup Problem Formulation and Setup > @ , , 1 1 min , s.t. , 0 , =0 where i LB i i UB T z T i n x xx J J xxx d d d ª º ¬ ¼ J xp g(x p) h(x p) Jx x x " " " (NLP) objective constraints design vector bounds 11 Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Module 1: Module 1: Problem Formulation & Setup Problem Formulation & Setup • Design variables • Constraints • Objective functions • Parameters • Fidelity vs. expense • Breadth vs. Depth • MDO uses & applications 12 Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Module 1: Subsystem Model Module 1: Subsystem Model Development and Coupling Development and Coupling ¾ MDO frameworks distributed analysis vs. distributed design CO, CSSO, BLISS ¾ Simulation Development Process define modules: subsystems or disciplines design vector, constants vector ¾ N2 diagrams feedback vs feedforward, sorting ¾ Benchmarking test model fidelity against a real system
MIest Module 2: Exploration and 16888 Module 3: Multi-Objective Optimization Algorithms Optimization Isoperform i DOE ( full factorial, orthogonal arrays, one-at-a Iso: find set of performance-invariant solutions time) GP: minimize deviation from target point GA, SA& Gradient-based techniques Domination basic understanding of algorithms weak vs strong, domination matrix, ranking how to choose an algorithm s Pareto Front Computation reasons for algorithm failure concave versus convex, jumps, multiple optimality criteria dimensions implementation of several algorithms s MO Algorithms Sensitivity Analysis weighted-sum-approach, NBI Jacobian and Hessian, scaling compromise programming physical programming finite difference approximation multiobjective heuristics: SA, GA utility theory Massachusetts Institute of Technology-Prof de Weck and Prof Wacox sachusetts institute of Technology. Prof. de Weck and Prof. willcox MIEsa Module 4: Implementation Issues, 503 Mlet Class Projects-Applications(2004)50 Applications Aircraft Systems Design Silent Aircraft Design(air ation methods Environmental Design Space Shuttle Fuel Tank Space(Barter, Jonker, Kit Hsieh, Hynes, Lawson, response surtace methodology Axial Compressor Desig i Design for Value cost models elicopter Platform(Freuler, Mark, Toupet) MSDO Siddiqi) SPHERES(lshutkina, Nolet) market/revenue models Orbit Transfe Space Station e Robust Design robustness Ground Infrastructure eliability probabilistic methods Applications hip Design(Boaz, Structural Optimization(Nadir Visualization Dickmann, Woll) Radio Telescope Array s Computational Strategies Hydrofoil Ship Design (Chatzakis) Massachusetts Institute of Technology -. de Weck and Prof Wilcox Massachusetts Institute of Techmology.Prof de Weck and Prof Willcox
13 Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Module 2: Exploration and Module 2: Exploration and Optimization Algorithms Optimization Algorithms ¾ DOE (full factorial, orthogonal arrays, one-at-atime) ¾ GA, SA & Gradient-based techniques: • basic understanding of algorithms • how to choose an algorithm • reasons for algorithm failure • optimality criteria • implementation of several algorithms ¾ Sensitivity Analysis Jacobian and Hessian, scaling finite difference approximation 14 Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Module 3: Multi Module 3: Multi-Objective Objective Optimization Optimization ¾ Isoperformance and Goal Programming • Iso: find set of performance-invariant solutions • GP: minimize deviation from target point ¾ Domination weak vs strong, domination matrix, ranking ¾ Pareto Front Computation concave versus convex, jumps, multiple dimensions ¾ MO Algorithms weighted-sum-approach, NBI compromise programming, physical programming multiobjective heuristics: SA , GA utility theory 15 Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Module 4: Implementation Issues, Module 4: Implementation Issues, Applications Applications ¾ Approximation Methods • reduced-basis methods • response surface methodology ¾ Design for Value • cost models • market/revenue models ¾ Robust Design • robustness • reliability • probabilistic methods ¾ Visualization ¾ Computational Strategies 16 Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Class Projects Class Projects – Applications (2004) Applications (2004) Aircraft Systems Design Spacecraft and Constellation Design Marine Applications Ground Infrastructure and Vehicles (air) (water) (land) (space) MSDO - Space Shuttle Fuel Tank (Hsieh, Hynes, Lawson, Posner, Usan) - Communication Satellite Constellations (Mellein, Siddiqi) - SPHERES (Ishutkina, Nolet) - Orbit Transfer (Taylor) - Space Station Utilization (Saenz Otero) - Structural Optimization (Nadir) - Radio Telescope Array (Bounova) - Silent Aircraft Design (Diedrich, Tan) - Environmental Design Space (Barter, Jonker, King) - Supersonic Jet (Robinson) - Axial Compressor Design (Castiella) - Helicopter Platform (Freuler, Mark, Toupet) - Ship Design (Boaz, Dickmann, Wolf) - Hydrofoil Ship Design (Chatzakis)
MIeS Class ss Projects -Al pl pplications g88 Tools Silent Aircraft Design(air Constellation Design Generic Technical Computing Environments General Aviation Conceptual Interferometers(Howell) Matlab, Mathematica, Maple, Excel Design(Vincent, Chan) Hybrid Constellations(Chan Performance and Financial Shah, Samuels, Underwood) ptimization of Aircraft(Peoples Constellation Reconfiguration C, C++, Fortran, (Java), Visual Basic Schuman) (Scialom, Verani) UAV sensor MSDO Artificial Gravity Fluent NASTRAN, Solidworks ProE placement(Jourdan) Satellite(Kuwata ary CAe code Compressor design Ground Infrastructure (land) Connectivity Data Exchange Codes Marine Corporate Facilities(Kalligeros DOME(MIT)/CO(Oculus), ICEmaker, ISIGHT(Fiper) pplications(water) Sensor Network for structural A Optimization Health Monitoring ( Yu) Sail Boat Optimization (Willis) ISIGHT, CPLEX, Excel Plug-Ins, Matlab Toolboxes, AMPL Vehicle Suspension Optimization Offshore Wind Turbine support (Gray, Wronski) ptimization (Withee) Radio Telescope Array(Cohanim Massachusetts Institute of Technology -Prof de Weck and Prof Wacox Massachusetts institute of Technology.Prof. de Weck and Prof. willcox AlAA MDO TC View Deal with design models of realistic size and fidelity. that will not lead to erroneous conclusions Analysis and Formulations and Organization and Cultur Reduce the tedium of coupling variables and results Software from disciplinary models, such that engineers don't MD Training spend 50-80% of their time doing data transfer Data and software MD process de-/ re- composition Allow for creativity, intuition and"beauty, while lity resu leveraging rigorous, quantitative tools in the design process. Hand-shaking: qualitative vs quantitative Data visualization in multiple dimension Human interface Incorporation of higher-level upstream and MD analysis and eds at 1998 downstream system architecture aspects in early design: staged deployment, safety and security, environmental sustainability, platform design etc Massachusetts Institute of Technology -. de Weck and Prof Wilcox Massachusetts Institute of Techmology.Prof de Weck and Prof Willcox
17 Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Class Projects Class Projects – Applications (2003) Applications (2003) Aircraft Systems Design Spacecraft and Constellation Design Marine Applications Ground Infrastructure and Vehicles (air) (water) (land) (space) MSDO - Interferometers (Howell) - Hybrid Constellations (Chan, Shah, Samuels, Underwood) - Constellation Reconfiguration (Scialom, Verani) - Artificial Gravity Satellite (Kuwata) - Corporate Facilities (Kalligeros) - Sensor Network for Structural Health Monitoring (Yu) - Vehicle Suspension Optimization (Gray, Wronski) - Radio Telescope Array (Cohanim) - Silent Aircraft Design (Benveniste, Lei, Manneville) - General Aviation Conceptual Design (Vincent, Chan) - Performance and Financial Optimization of Aircraft (Peoples, Schuman) - UAV sensor placement (Jourdan) - Compressor design (Dorca, Perrot) - Sail Boat Optimization (Willis) - Offshore Wind Turbine support optimization (Withee) 18 Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Tools • Generic Technical Computing Environments - Matlab, Mathematica, Maple, Excel • Programming Languages for Simulation - C, C++, Fortran, (Java), Visual Basic • Special Purpose CAD/CAE - Fluent, NASTRAN, Solidworks, ProE,... • Multidisciplinary CAE codes - FEMLAB • “Connectivity” Data Exchange Codes - DOME (MIT)/CO (Oculus), ICEmaker, iSIGHT(Fiper) • Optimization - iSIGHT, CPLEX, Excel Plug-Ins, Matlab Toolboxes, AMPL 19 Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Challenges of MSDO • Deal with design models of realistic size and fidelity that will not lead to erroneous conclusions • Reduce the tedium of coupling variables and results from disciplinary models, such that engineers don’t spend 50-80% of their time doing data transfer • Allow for creativity, intuition and “beauty”, while leveraging rigorous, quantitative tools in the design process. Hand-shaking: qualitative vs. quantitative • Data visualization in multiple dimensions • Incorporation of higher-level upstream and downstream system architecture aspects in early design: staged deployment, safety and security, environmental sustainability, platform design etc... 20 Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox AIAA MDO TC View Analysis and Approximations Analysis and Approximations Organization and Culture Organization and Culture Design Problem Formulations and Solutions Design Problem Formulations and Solutions Information Processing and Management Information Processing and Management Cost-fidelity trade-off Cost-fidelity trade-off MD analysis and sensitivity analysis MD analysis and sensitivity analysis SD analysis and sensitivity analysis SD analysis and sensitivity analysis Parametric product data models Parametric product data models High-fidelity results inclusion High-fidelity results inclusion Approximations Approximations Design problem formulation Design problem formulation MD optimization MD optimization SD optimization SD optimization Optimization procedures Optimization procedures Optimization algorithms Optimization algorithms Problem de- /re- composition Problem de- /re- composition Software engineering Software engineering MD computing MD computing Human interface Human interface MD environment MD environment Data visualization, storage, management Data visualization, storage, management Data and software standards Data and software standards MD Training MD Training MD in existing organization MD in existing organization MD in integrated product teams MD in integrated product teams MD process insertion MD process insertion * Based on Special Session on Industry Needs at 1998 AIAA/MA&O
Mlesd Interesting Research Directions 50 Mlesd Last items (1) Design of Families of Systems/Products (2) Design of Reconfigurable Systems A6 (Final Papers) Grading-20% (3) Massively Parallel Computing(Grid Computing), model Paper Evaluations(ESD) (4)Design Opti (5) Further Refinement of Search Algorith (6)Visualization and Data/Process Coupling (7) High-fidelity Multidisciplinary Optimization ( 8)Optimization under Uncertainty Massachusetts Institute of Technology-Prof de Weck and Prof Wacox Massachusetts institute of Technology. Prof. de Weck and Prof. Willcox
21 Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Interesting Research Directions Interesting Research Directions (1) Design of Families of Systems/Products (2) Design of Reconfigurable Systems (3) Massively Parallel Computing (Grid Computing), model reduction - data compression techniques (4) Design Optimization - Financial Engineering (5) Further Refinement of Search Algorithms (6) Visualization and Data/Process Coupling (7) High-fidelity Multidisciplinary Optimization (8) Optimization under Uncertainty 22 Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Last Items Last Items • A6 (Final Papers) Grading - 20% • Paper Evaluations (ESD)
