gaining experience and deeper understanding in solving structural optimization problems
performing numerical parameter studies via Python scripting
We discuss the theory and application of density-based topology optimization (SIMP), the probably most common structural optimization approach used in industry. The major focus is to gain a deeper understanding of the different aspects of structural optimization (regularization, penalization, mathematical programming) and rating of the results. We also discuss practical impacts (discretization, parametrizing the linear solver) with respect
to the corresponding finite element analysis (linear elasticity).
To this end we use the academic finite element package openCFS, which becomes open source in winter 2020.
It is assumed, that students have a basic background/ understanding in the topics:
finite element analysis (strong and weak form of partial differential equations)
linear algebra (direct and iterative solvers)
basic understanding of gradient based optimization
programming with Python (no advanced skills required)
working on the command line (on your own Linux, Apple or Windows computer)
Characteristic for the lecture is a strong focus on homework in form of numerical excercises, i.e. optimization
problems to be solved with openCFS. The work load might be higher than for other 5 ECTS lectures, especially with insufficient experience in Python. However really doing the homework individually is essential for the lecture as the didactic concept is to develop core principles in structural optimization by numerical studies in the homework.
In the winter semester 2020, the lecture will be online via Zoom, the pdf slides are provided. openCFS will be provided for Linux, macOS and Windows. In the weekly exercises we discuss questions on the homework via screen sharing from students.
As the lecture and exercises are by Zoom only, we can freely shift the schedule.
The lecture is in English with oral exam. All further information on StudOn.
- Gaining experience and deeper understanding in solving structural optimization problems.
Judging complexity of structural optimization problem
Ability to question designs obtained by structural optimization problem
Ability to discuss and compare methods within structural optimization problem
Performing numerical parameter studies via Python scripting.