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Our recent effort on data-driven turbulence modeling for compressible separated flows has been published. This study introduces novel input features that incorporate compressible and rotational effects in a data-augmented framework. The proposed features are based on physics- and knowledge-driven model corrections, following best practices in turbulence modeling. The trained model with proposed input features shows improved predictive performance for separated compressible flows.
Link (authors' link) : https://authors.elsevier.com/sd/article/S1270-9638(25)00640-6
Seoyeon Heo, Yusu Kim, Yeji Yun, Solkeun Jee. (2025). Data-Augmented Turbulence Modeling for Separated Compressible Flow around Axisymmetric Bodies. Aerospace Science and Technology, vol. 166, 110569. https://doi.org/10.1016/j.ast.2025.110569.
