Thesis -Application of PINNs on 3D CFD Data (Dept.: DAC)

Master Thesis

This thesis investigates the potential of Physics-Informed Neural Networks (PINNs) in the field of fluid dynamics. The focus is set on turbulence modelling allowing applications relevant to industrial contexts. The thesis should cover a comprehensive literature review, highlighting two promising use cases: (a) up-sampling and (b) acceleration of simulations. Up-sampling refers to enhancing the resolution of simulation outputs - such as velocity fields - from coarse to fine grids (also known as super-resolution). Acceleration encompasses methods aimed at reducing computational time for solving fluid dynamics problems. Based on insights gained from the literature, one promising approach will be applied in a case study using a fluid dynamic dataset provided by AVL. The implementation will be carried out in Python, leveraging deep learning frameworks such as TensorFlow and PyTorch to incorporate state-of-the-art functionalities.

YOUR RESPONSIBILITIES:

• Literature Review on PINNs
• Application of PINNs on 3D fluid dynamics data

YOUR PROFILE:

• Ongoing studies in the fields of Mechanical Engineering, Data Science, Informatics or similar
• Experience in python coding
• Experience in Data Science beneficial
• Experience in Fluid Dynamics beneficial
  
  

• You can write your thesis independently and receive professional guidance and support from our experienced employees.
• You will have the opportunity to exchange ideas with experts in the company and benefit from their expertise.
• Take the opportunity to immerse yourself in the world of AVL and embed your theoretical knowledge in a practical environment.

The successful completion of the thesis is remunerated with a one-time fee of EUR €3,500.00 before tax.

You don't want to write your final thesis just for the books, then explore the mobility of the future together with us! Maybe you will be a part of it soon!

At AVL, we foster and celebrate diversity: We recognize that diverse ways of thinking are required to achieve our vision of a greener, safer, and better world of mobility. Different backgrounds, attitudes, interests, and experiences make us successful. As Equal Opportunity Employer we consider all qualified applicants without regard to ethnicity, religion, gender, sexual orientation or disability status.

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