4 papers
Data-driven Augmentation of a Turbulence Model in Three dimensional Separated Flows
Chenyu Wu, Shaoguang Zhang, Yufei Zhang
Classic turbulence models often struggle to accurately predict complex flows. Although data-driven techniques have addressed these shortcomings, most existing research has concentr…
Numerical Simulation of Three-dimensional High-Lift Configurations Using Data-Driven Turbulence Model
Shaoguang Zhang, Chenyu Wu, Yufei Zhang
Traditional Reynolds-averaged Navier-Stokes (RANS) equations often struggle to predict separated flows accurately. Recent studies have employed data-driven methods to enhance predi…
Data-driven Turbulence Modeling for Separated Flows Considering Non-Local Effect
Chenyu Wu, Shaoguang Zhang, Changxin Guo +1
This study aims to enhance the generalizability of Reynolds-averaged Navier-Stokes (RANS) turbulence models, which are crucial for engineering applications. Classic RANS turbulence…
Development of a Generalizable Data-driven Turbulence Model: Conditioned Field Inversion and Symbolic Regression
Chenyu Wu, Shaoguang Zhang, Yufei Zhang
This paper addresses the issue of predicting separated flows with Reynolds-averaged Navier-Stokes (RANS) turbulence models, which are essential for many engineering tasks. Traditio…