2021 Faculty Courses School of Engineering Department of Mechanical Engineering Graduate major in Mechanical Engineering
Space Systems Analysis B
- Academic unit or major
- Graduate major in Mechanical Engineering
- Instructor(s)
- Hiroshi Furuya
- Class Format
- Lecture
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 1-2 Thu
- Class
- -
- Course Code
- MEC.M531
- Number of credits
- 100
- Course offered
- 2021
- Offered quarter
- 3Q
- Syllabus updated
- Jul 10, 2025
- Language
- English
Syllabus
Course overview and goals
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.
Course description and aims
[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.
Keywords
Structural optimization. Optimal design, Heuristic optimization, Multi-objective optimization, Multi-disciplinary optimization, Algorithms
Competencies
- 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/Objectives
| Course schedule | Objectives | |
|---|---|---|
| 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 |
Study advice (preparation and review)
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.
Textbook(s)
Numerical Optimization Techniques for Engineering Design, Garret N. Vanderplaats
Reference books, course materials, etc.
Numerical Optimization Techniques for Engineering Design, Garret N. Vanderplaats
Evaluation methods and criteria
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
Knowledge of analytical mathematics and structural analysis, and experience for computational programming are strongly recommended.
Contact information (e-mail and phone) Notice : Please replace from ”[at]” to ”@”(half-width character).
furuya.h.ab[at]m.titech.ac.jp
Office hours
Contact by email for appointment.