Bachelor / Master Thesis: AI-assisted sensitivity analysis of the influence of aerodynamic input parameters on the structure-borne sound of a 6-MW wind turbine

The Chair for Wind Power Drives (CWD) researches the behavior of drive systems in modern multi-megawatt wind turbines (WTs). Research objectives include increasing the availability, robustness, and energy efficiency of WTs, as well as reducing the levelized cost of electricity. To achieve this, state-of-the-art engineering software and system test benches are utilized.
For the acoustic evaluation of wind turbines, it is important to understand how the wind excites the structure into vibration. This thesis investigates how aerodynamic input variables influence the so-called surface velocities of the rotor blade and tower, taking into account a high-fidelity drivetrain model. These surface velocities form the physical boundary conditions for the subsequent sound radiation calculations.
The objective of this thesis is to investigate the quantitative relationship between varying wind field parameters and operating points and surface velocities. Based on the dataset obtained from the simulations, artificial intelligence (AI) is used to model these complex relationships and efficiently predict the vibration behavior.

Tasks:

• Research on the state of the art and familiarization with the existing simulation model
• Conducting parameter studies with different wind field excitations
• Evaluating the generated data set to identify key aerodynamic parameters
• Training an AI model to efficiently predict vibration behavior

Requirements:

• Motivation to work independently and on one’s own responsibility, ability to communicate and work in a team
• Interest in wind energy and data analysis
  ▪
  Basic programming experience in Python for data analysis is desirable but not required
• Prior knowledge of multibody simulation or artificial intelligence is a plus

We offer:

• Scientific work in a highly motivated, interdisciplinary team
• Intensive supervision
• Working on a topic with high industrial relevance
• Opportunity to contribute to a scientific publication
• Immediate start possible

We look forward to your application by email:

Wenjian He, M. Sc. RWTH
Chair for Wind Power Drives

Campus-Boulevard 61, 52074 Aachen
wenjian.he@cwd.rwth-aachen.de