Jan Hauke Harmening

wiss. Mitarb.

Zur Person

Fachbereich Maschinenbau Bocholt (FB 6)

Informationen zur Person

Forschungsgruppe Physics-Informed Neural Networks

Forschungsgebiete

  • Deep learning
  • Physics-informed neural networks
  • Computational fluid dynamics
  • Flow control

Lehre

Forschung

Harmening, J. H., Pioch, F., Fuhrig, L., Peitzmann, F. J., Schramm, D. & el Moctar, O. (2024). Data-assisted training of a physics-informed neural network to predict the separated Reynolds-averaged turbulent flow field around an airfoil under variable angles of attack. Neural Comput & Applic.

Harmening, J. H., Peitzmann, F. J. & el Moctar, O. (2024). Effect of Network Architecture on Physics-Informed Deep Learning of the Reynolds-Averaged Turbulent Flow Field around Cylinders without Training Data. Frontiers in Physics, 12.

Pioch, F., Harmening, J. H., Müller, A. M., Peitzmann, F. J., Schramm, D., & el Moctar, O. (2023). Turbulence Modeling for Physics-Informed Neural Networks: Comparison of Different RANS Models for the Backward-Facing Step Flow. Fluids8(2), 43.

Harmening, J. H., Pioch, F., & Schramm, D. (2022). Physics Informed Neural Networks as Multidimensional Surrogate Models of CFD Simulations. Proceedings of the Machine Learning und Artificial Intelligence in Strömungsmechanik und Strukturanalyse, Wiesbaden, Germany, 16.-17.05.2022.

Harmening, J. H., Devananthan, H., Peitzmann, F. J., & el Moctar, B. O. (2022). Aerodynamic Effects of Knitted Wire Meshes—CFD Simulations of the Flow Field and Influence on the Flow Separation of a Backward-Facing Ramp. Fluids7(12), 370.

Bach, D., Harmening, J. H., Höfer, M., Masselter, T., & Speck, T. (2018). Separation of Entrained Air Bubbles from Oil in the Intake Socket of a Pump Using Oleophilic and Oleophobic Woven and Nonwoven Fabrics. Journal of Fluids Engineering140(3), 031301.