## About this course

Introduction to the theory and practice of multidisciplinary design optimization of mechanical structures. How can classical design tasks of the engineer be formulated as mathematical optimization tasks and how are they solved using mathematical optimization algorithms? What characterizes an optimal design and how must the modeling of the design task be formulated in order to find this optimum efficiently? What is an admissible design and how can it be ensured that the optimization process returns only physically meaningful valid designs? Fundamentals of mathematical optimization algorithms used to solve such tasks in practice are presented and their interaction with model-based simulation of the structure's behavior is explained. The learning content of the lecture will be implemented on simplified but still practical examples in the computer exercises.

## Learning outcomes

After participating in the Multidisciplinary Design Optimization module, students are able to:

- understand model-based design tasks as optimization problems;
- understand the mathematical principles and optimization algorithms that are essential for use in practice;
- select and apply suitable solution algorithms for a given problem;
- convert practical model-based design tasks into mathematical optimization tasks;
- make a practical implementation of an algorithm and solve a model-based optimization task on the computer;
- recognize current research in the field of multidisciplinary optimization.

## Examination

Papalambros, P. Y., Wilde, D.J.: Principles of Optimal Design: Modeling and Computation, 3rd Edition, Cambridge University Press, 2017

## Course requirements

None (basic studies in mechanical engineering sufficient)

## Activities

The module consists of a lecture and an exercise. In the lecture, the theoretical foundations of Multidisciplinary Design Optimization are taught using lecture, presentation and writing down on tablet PC. Students will be provided with all lecture materials online. In the lecture, the contents are taught, also by means of examples. In the exercises, the contents are deepened and the practical implementation of the theory from the lecture is made comprehensible by means of computer exercises. With this, the students learn to precisely state optimization problems, analytically and computationally solve them and deal with multiple objectives and disciplines.

## Link to more information

- Credits
**ECTS 5** - Contact hours per week
**2** - Instructors
**Akhil Sathuluri, Markus Zimmermann, Nicola Barthelmes, Eduardo Rodrigues Della Noce** - Mode of instruction
**Blended** - Course coordinator