TY  - CONF
AU  - Sarma, Rakesh
AU  - Hübenthal, Fabian
AU  - Orland, Fabian
AU  - Terboven, Christian
AU  - Lintermann, Andreas
TI  - Predicting Turbulent Boundary Layer Flows Using Transformers Coupled to the Multi-Physics Simulation Tool m-AIA
VL  - 69
CY  - Jülich
PB  - Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag
M1  - FZJ-2025-02459
T2  - Schriften des Forschungszentrums Jülich IAS Series
SP  - 76 - 79
PY  - 2025
AB  - Time-marching of turbulent flow fields is computationally expensive with traditional numerical solvers. In this regard, transformer neural network, which has been largely successful in many other technical and scientific domains, can potentially predict complex flow fields faster compared to physics-based solvers. In this study, a transformer model is trained for a turbulent boundary layer problem, which is then coupled to the multi-physics solver m-AIA to make predictions of velocity fields. The method can potentially contribute to significant reduction in computational effort while maintaining high accuracy.
T2  - 35th Parallel CFD International Conference 2024
CY  - 2 Sep 2024 - 4 Sep 2024, Bonn (Germany)
Y2  - 2 Sep 2024 - 4 Sep 2024
M2  - Bonn, Germany
LB  - PUB:(DE-HGF)8 ; PUB:(DE-HGF)7
DO  - DOI:10.34734/FZJ-2025-02459
UR  - https://juser.fz-juelich.de/record/1041827
ER  -