2016 Space Systems Analysis B

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Academic unit or major
Graduate major in Mechanical Engineering
Furuya Hiroshi 
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Course description and aims

To design spacecraft systems, it is important to understand mathematical theories of optimization, multi-purpose optimization, multidisciplinary optimization, and the concept of Pareto optimal solutions. This lecture starts with the basic theory of optimization, and covers approaches to Pareto optimization and robust optimization, also covering methods of applying heuristic optimization techniques, etc. to the optimal design of complex systems.

Student learning outcomes

[Objectives] Students will gain an understanding of engineering optimization techniques based on approaches such as the concept of Pareto optimal solutions and robust design needed for designing optimal systems under design requirements given to spacecraft systems. Students will also learn to apply these optimization techniques to design.
[Topics] Focusing on topics such as the mathematical theory of optimization, approximate methods for the optimization of spacecraft systems, heuristic optimization techniques, multipurpose optimization, and multidisciplinary optimization, we will cover application techniques for the optimization of spacecraft system structures, while gaining an understanding of applications for general optimization design.


Structural optimization. Optimal design, Heuristic optimization, Multi-objective optimization, Multi-disciplinary optimization, Algorithms

Competencies that will be developed

Specialist skills Intercultural skills Communication skills Critical thinking skills Practical and/or problem-solving skills

Class flow

Aside from lectures, students will be given exercises and homework (submit as report) as necessary to deepen their understanding.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Basic Concepts of Numerical Optimization for Engineering Design Studying basic concepts of optimization, and treatment of optimization of one variable functions
Class 2 Unconstrained Function Optimization Treatment of unconstrained functions of N-Variables
Class 3 Constrained Function Minimization Techniques Studying constrained functions of N-Variables. Linear Programming
Class 4 Sequential Unconstrained Techniques Studying Sequential Unconstrained Techniques
Class 5 Direct Methods Understanding Direct Methods
Class 6 Approximation Techniques Studying Approximation Techniques
Class 7 Multi-Objective Optimization and Structural Optimization and Multi-disciplinary Optimization Understanding Multi-Objective Optimization and Structural Optimization and Multi-disciplinary Optimization
Class 8 Summary Summary


Numerical Optimization Techniques for Engineering Design, Garret N. Vanderplaats

Reference books, course materials, etc.

Numerical Optimization Techniques for Engineering Design, Garret N. Vanderplaats

Assessment criteria and methods

Students' achievement scores are determined by final examination (about 60%) plus exercise and reports (about 40%).

Related courses

  • MCS.T302 : Mathematical Optimization
  • IEE.A430 : Numerical Optimization
  • MEC.H231 : Design Engineering
  • MEC.K331 : Fundamentals of Computer Aided Engineering
  • MEC.G532 : Taguchi Method
  • MEC.C432 : Structural Integrity Assessment

Prerequisites (i.e., required knowledge, skills, courses, etc.)

Knowledge of analytical mathematics and structural analysis, and experience for computational programming are strongly recommended.

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