Dieses Bild zeigt Hannes Mandler

Hannes Mandler

M.Sc.

Wissenschaftlicher Mitarbeiter
Institut für Thermodynamik der Luft- und Raumfahrt

Kontakt

+49 711 685 62636
 +49 711 685 62317

Pfaffenwaldring 31
70569 Stuttgart
Deutschland
Raum: 1-125

Fachgebiet

Wärmeübertragung :  Entwicklung neuartiger, auf maschinellen Lernmethoden basierter Turbulenzmodelle und Anwendung auf Innenströmungssysteme

  1. Mandler, H., & Weigand, B. (2024). Extrapolation from academic training to industrial test cases: Application of a data-driven closure model to internal cooling channels of gas turbine blades. 1st Workshop on Machine Learning for Fluid Dynamics, Paris, France, 6-8 March 2024.
  2. Mandler, H., & Weigand, B. (2023). Towards interpretable data-driven closure models. 18th European Turbulence Conference, ETC18, Valencia, Spain, 4-6 September 2023.
  3. Mandler, H., & Weigand, B. (2023). Die Entmystifizierung maschinellen Lernens am Beispiel datengetriebener Turbulenzmodellierung. Jahrestreffen der DECHEMA-Fachgruppen Computational Fluid Dynamics und Wärme- und Stoffübertragung, Frankfurt, Germany, 6-8 March 2023.
  4. Mandler, H., & Weigand, B. (2023). Embedding explicit smoothness constraints in data-driven turbulence models. Proceedings of the 14th international ERCOFTAC symposium on engineering turbulence modelling and measurements, ETMM14, Castelldefels, Spain, 6-8 September 2023. https://www.researchgate.net/publication/374778444_Embedding_explicit_smoothness_constraints_in_data-driven_turbulence_models
  5. Mandler, H., & Weigand, B. (2023). Feature importance in neural networks as a means of interpretation for data-driven turbulence models. Computers & Fluids, 265, 105993. https://doi.org/10.1016/j.compfluid.2023.105993
  6. Mandler, H., & Weigand, B. (2022). A realizable and scale-consistent data-driven non-linear eddy viscosity modeling framework for arbitrary regression algorithms. International Journal of Heat and Fluid Flow, 97, 109018. https://doi.org/10.1016/j.ijheatfluidflow.2022.109018
  7. Mandler, H., & Weigand, B. (2022). On frozen-RANS approaches in data-driven turbulence modeling: Practical relevance of turbulent scale consistency during closure inference and application. International Journal of Heat and Fluid Flow, 97, 109017. https://doi.org/10.1016/j.ijheatfluidflow.2022.109017
  8. Gerber, V., Baab, S., Förster, F. J., Mandler, H., Weigand, B., & Lamanna, G. (2021). Fluid injection with supercritical reservoir conditions: Overview on morphology and mixing. The Journal of Supercritical Fluids, 169, 105097. https://doi.org/10.1016/j.supflu.2020.105097

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