Multidisiplinary Design Optimization (EE 518)

2021 Spring
Faculty of Engineering and Natural Sciences
Electronics Engineering(EE)
3
10.00
Güllü Kızıltaş Şendur gkiziltas@sabanciuniv.edu,
Click here to view.
English
Doctoral, Master
--
Lecture,Project,
Click here to view.

CONTENT

This course addresses the design of complex multidisciplinary systems using optimization. This is not a traditional optimization course. Rather, focus is on how optimization can be used in the design of multidisciplinary systems. Each of the three concepts will be emphasized: multidisciplinary systems, design and optimization.

OBJECTIVE

The goal of this course is to address the design of complex multidisciplinary systems using optimization. This is not a traditional optimization course. Rather, focus is on how optimization can be used in the design of multidisciplinary systems. Each of the three concepts will be emphasized: multidisciplinary systems, design and optimization.
The course content will be applicable to system architecture and design of a broad range of mechatronic engineering systems.

LEARNING OUTCOME

At the end of this course students will
* learn how MDO can support the product development process of complex, multidisciplinary engineered systems
* learn how to rationalize and quantify a system architecture or product design problem by selecting appropriate objective functions, design parameters and constraints
* subdivide a complex system into smaller disciplinary models, manage their interfaces and reintegrate them into an overall system model
* be able to use gradient-based numerical optimization algorithms, e.g. sequential quadratic programming (SQP) and various modern heuristic optimization techniques such as simulated annealing (SA) or genetic algorithms (GA) and select the ones most suitable to the problem at hand
* perform a critical evaluation and interpretation of analysis and optimization results, including sensitivity analysis and exploration of performance, cost and risk tradeoffs
* sharpen their presentation skills, acquire critical reasoning with respect to the validity and fidelity of their MDO models and experience the advantages and challenges of teamwork

RECOMENDED or REQUIRED READINGS

Readings

Papalambros, Panos Y., and Douglass J. Wilde. Principles of Optimal Design - Modeling and Computation. 2nd ed. Cambridge, UK: Cambridge University Press, 2000. ISBN: 0521627273. (Paperback)
Vanderplaats, Garret N. Numerical Optimization Techniques for Engineering Design. 3rd ed. Colorado Springs: Vanderplaats Research & Development Inc., 2001. ISBN: 0944956017.
Haftka R. T., and Gurdal, Z. Elements of Structural Optimization 3rd ed., Kluwer Academic Publishers, 1992
Steuer, R. E. Multiple Criteria Optimization: Theory, Computation and Application. New York: Wiley, 1986. ISBN: 047188846X.
Alexandrov, N. M., and M. Y. Hussaini, eds. Multidisciplinary Design Optimization: State of the Art. Proceedings in Applied Mathematics Series, No. 80. Soc for Industrial & Applied Math, 1997. ISBN: 0898713595.
Statnikov, Roman B., and Joseph B. Matusov. Multicriteria Optimization and Engineering. New York: Chapman and Hall, 1995. ISBN: 0412992310.