PreprintArticleVersion 1This version is not peer-reviewed
Critical Investigation of Surrogate Modeling Based on Simultaneous Physics-Informed Deep Learning of the High Reynolds Number Flow around Airfoils under Variable Angles of Attack
Version 1
: Received: 10 July 2024 / Approved: 11 July 2024 / Online: 11 July 2024 (12:28:57 CEST)
How to cite:
Harmening, J. H.; Peitzmann, F.-J.; el Moctar, O. Critical Investigation of Surrogate Modeling Based on Simultaneous Physics-Informed Deep Learning of the High Reynolds Number Flow around Airfoils under Variable Angles of Attack. Preprints2024, 2024070959. https://doi.org/10.20944/preprints202407.0959.v1
Harmening, J. H.; Peitzmann, F.-J.; el Moctar, O. Critical Investigation of Surrogate Modeling Based on Simultaneous Physics-Informed Deep Learning of the High Reynolds Number Flow around Airfoils under Variable Angles of Attack. Preprints 2024, 2024070959. https://doi.org/10.20944/preprints202407.0959.v1
Harmening, J. H.; Peitzmann, F.-J.; el Moctar, O. Critical Investigation of Surrogate Modeling Based on Simultaneous Physics-Informed Deep Learning of the High Reynolds Number Flow around Airfoils under Variable Angles of Attack. Preprints2024, 2024070959. https://doi.org/10.20944/preprints202407.0959.v1
APA Style
Harmening, J. H., Peitzmann, F. J., & el Moctar, O. (2024). Critical Investigation of Surrogate Modeling Based on Simultaneous Physics-Informed Deep Learning of the High Reynolds Number Flow around Airfoils under Variable Angles of Attack. Preprints. https://doi.org/10.20944/preprints202407.0959.v1
Chicago/Turabian Style
Harmening, J. H., Franz-Josef Peitzmann and Ould el Moctar. 2024 "Critical Investigation of Surrogate Modeling Based on Simultaneous Physics-Informed Deep Learning of the High Reynolds Number Flow around Airfoils under Variable Angles of Attack" Preprints. https://doi.org/10.20944/preprints202407.0959.v1
Abstract
Physics-informed neural networks can be trained to serve as surrogate models by learning how spatial physical fields develop with change of parameters. This is an advantage over traditional numerical methods that can only be used to calculate a single solution field for a single discrete setting of a parameter. While many studies report accurate results of physics-informed deep learning of laminar flows, evaluations of surrogate modeling of high Reynolds number flows remain sparse. To contribute to this question, we here explore the capabilities and limits of physics-informed deep learning to solve the Reynolds-averaged Navier-Stokes equations for the flow around two airfoils under variable angles of attack. No labeled training data were provided and a single network was trained to simultaneously learn several solution fields in a single training run. The network captured essential flow features such as boundary layers and high and low pressure regions. Moreover, the qualitative correlations of lift and drag with the angle of attack and the higher drag of the thicker airfoil were captured. However, comparisons with reference simulations revealed an underestimation of the shear layer gradients as well as deviations of the fields' extrema. Thus, we also debate potential future improvements to increase the accuracy of the predicted flow. This work offers a prospect on the capabilities and current limitations of physics-informed neural networks for surrogate modeling of high Reynolds number flows. We argue that the method is promising and this work is aimed to encourage further research and development of the method when applied to Reynolds-averaged flows at high Reynolds numbers.
Keywords
Physics-informed deep learning; unsupervised learning; Reynolds-averaged Navier-Stokes equations; high Reynolds number flow; variable geometry; parameterized surrogate modeling
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
