1 Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Multidisciplinary System Design Optimization (MSDO) 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 ? 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) 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) (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 5 Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox 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 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 -> 2004 Enrollment 25 ? 40 ? 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 MSDO Framework Discipline A Discipline B Discipline C I n 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 ao ?? ?? ?? ?? ?? ?? # Design Vector Coupling 1 2 z J J J ao ?? ?? ?? ?? ?? ?? # Approximation Methods Coupling Sensitivity Analysis Isoperformance Objective Vector 9 Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox 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 >@ ,, 1 1 min , s.t. , 0 , =0 where iLB i iUB T z T in xxx JJ xxx d dd ao ?? Jxp 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: 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 Development and Coupling ? MDO frameworks ? distributed analysis vs. distributed design ? CO, CSSO, BLISS ? Simulation Development Process ? define modules: subsystems or disciplines ? design vector, constants vector ? N 2 diagrams ? feedback vs feedforward, sorting ? Benchmarking ? test model fidelity against a real system 13 Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Module 2: Exploration and Optimization Algorithms ? DOE (full factorial, orthogonal arrays, one-at-a- time) ? GA, SA & Gradient-based techniques: basic understanding of algorithms how to choose an algorithm reasons for algorithm failure optimality criteria implementation of several algorithms ? Sensitivity Analysis ? Jacobian and Hessian, scaling ? finite difference approximation 14 Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Module 3: Multi-Objective Optimization ? Isoperformance and Goal Programming Iso: find set of performance-invariant solutions GP: minimize deviation from target point ? Domination ? weak vs strong, domination matrix, ranking ? Pareto Front Computation ? concave versus convex, jumps, multiple dimensions ? 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, Applications ? Approximation Methods reduced-basis methods response surface methodology ? Design for Value cost models market/revenue models ? Robust Design robustness reliability probabilistic methods ? Visualization ? Computational Strategies 16 Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Class Projects – 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) 17 Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox Class Projects – 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 21 Massachusetts Institute of Technology - Prof. de Weck and Prof. Willcox 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 A6 (Final Papers) Grading - 20% Paper Evaluations (ESD)