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