In order to fully exploit the potential of modern gas turbines, further design steps are necessary to optimize the operational performance. In this context, the possibility of alternative fuels such as hydrogen also places new demands on gas turbines. In our research, we use machine learning methods to create mathematical models of various components of the combustion system that can be used for analysis and optimization. With the participating students, we will pursue the new approach of Cluster-Based network modeling to analyze and evaluate experimental flow data from a combustion chamber. For this purpose, we will give an introduction to the method as well as to combustion technology in general. In the subsequent project-oriented part of the course, participants will then apply the method to more complex data in small groups.

Ideally, participants should have a solid basic knowledge of mathematics. In addition, basic knowledge of programming (ideally Python) is highly recommended. Knowledge of fluid dynamics, combustion, or machine learning may be beneficial, but is not a mandatory requirement. The module can be credited with 6 ECTS in the free elective area. You can register for the module by sending us an email (xstudent(at)fd.tu-berlin.de). If you have any questions, we will be happy to answer them by mail.

We are looking forward to meet you !