Student Thesis, Master Thesis: Machine Learning for Unsteady Aerodynamics (f/m/x) (Braunschweig)

Job description

The Institute of Aerodynamics and Flow Technology is the German Aerospace Center’s (DLR) central facility for fluid mechanics. Using numerical and experimental methods, we conduct research and development on key technologies to make the transportation systems and wind turbines of the future more climate-friendly and energy-efficient. Join Us The main activity of the C²A²S²E department is the development of numerical methods and processes for the multi-disciplinary simulation and optimization of aircraft - from flight physics design to virtual certification. What to expect The numerical investigation of dynamic responses to atmospheric turbulence as well as structural and flight dynamic excitations is an important task during the aircraft design and certification process. Efficient and high-fidelity methods are desirable because large parameter spaces spanned by, for example, Mach number, flight altitude, load case, and gust shape need to be covered [1, 2]. The state-of-the-art industry approach is to use the linear frequency domain (LFD) method that computes aerodynamic responses using computational fluid dynamics (CFD) by linearising the Reynoldsaveraged Navier–Stokes (RANS) equations around a steady-state solution. While this enables efficient simulations that account for steady aerodynamic nonlinearities such as shocks and boundary layer separation at transonic flight conditions, the method is only valid around the linearisation point, does not account for unsteady nonlinearities, and furthermore is not affordable in large-query scenarios. To account for dynamic nonlinearities, e.g. due to large amplitude gusts, simulations in the time domain are necessary. Solving the unsteady Reynolds-averaged Navier–Stokes (URANS) equations is a possible solution that comes with a computational cost that makes the method unfeasible if multiple parameter combinations are of interest. Hence, a method is thought after that enables fast predictions of the surface flow around an object for the described problem. Your tasks A specific task description can be written after an exchange with the student considering the background, type of thesis (Bachelor Thesis / Master Thesis / Study Thesis), temporal constraints, etc. Some main points are: Literature review regarding the proposed method and other approaches. Familiarisation with the surrogate modelling capabilities within SMARTy [6]. With a focus on Neural ODEs and GNNs. Design of experiments and data production using URANS for a database containing gust simulations. Investigation of the proposed methodology for gust loads. Improvement of the approach based on the findings. Writing of the thesis Your profile Solid Python experience Interest in aerodynamics As this is a student thesis no one is expected fulfil all the requirements. However, the candidate should be confident to be able to quickly familiarise with the following topics: Being a student that needs a thesis topic. Preferably at TU Braunschweig Experience with ML Topics: GNNs, Neural ODEs Skills: PyTorch, PyTorch Geometric Experience with CFD Knowledge about unsteady aerodynamics We look forward to getting to know you! If you have any questions about this position ( Vacancy-ID 5918 ) please contact: Prof. Dr. Stefan Görtz Tel.: +49 531 295 3357