Master Thesis “Active flow control using machine learning approaches”

Virtual Vehicle Research GmbH, Graz

VIRTUAL VEHICLE is a leading international R&D center for the automotive and rail industries. The center focuses on advanced virtualization of vehicle development. This linking of numerical simulations and hardware testing leads to a powerful HW-SW system design.

TASKS

Literature study for active flow control using machine learning approaches
Definition of the reduced thermal comfort use case
Specification of the machine learning approach
CFD simulation setup in OpenFOAM for thermal comfort use case
Machine learning framework in Keras for thermal comfort use case and interface communication
Derived active flow control strategies for predefined scenarios using the machine learning framework

PROFILE

Studies Mathematics, Technical Mathematics, Physics, Mechanical Engineering
Basic knowledge in Linear Algebra, Numerical Mathematics, Probability Theory
Fluid Dynamics and Heat Transfer
Basic programming skills: Python and C/C++ on Linux or Windows
Knowledge in machine learning frameworks (Tensorflow, Keras) and/or OpenFOAM is beneficial

OFFER

Collaboration and contribution in an engaged, dynamic team
Interesting work in an international research center
Paid Thesis
Mentoring programme
Professional and personal development opporturnities
Diverse sports and health activities
Corporate events

For technical questions, please contact Alexander Kospach, alexander.kosapch@v2c2.at, +43-(0)316-873-9055.

Supervision by

Univ.-Ass. Mag.rer.nat. Dr.rer.nat. Manfred Liebmann University of Graz / Institute for Mathematics and Scientific Computing manfred.liebmann@uni-graz.at

APPLY NOW and JOIN OUR TEAM!