5 citations · 5 across the 1 of their papers we have counts for
5 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…
Aerodynamic Prediction of a CRM High-lift Configuration using a modified three equation turbulence mode
Shaoguang Zhang, Haoran Li, Yufei Zhang
Aerodynamic simulations were carried out in the study presented in this paper focusing on the stall performance of the High-Lift Common Research Model obtained from the fourth AIAA…
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…