6 papers · 1 filter
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…
Field Inversion Symbolic Regression with Embedded Equation Learner for Interpretable Turbulence Model Correction
Li Jiazhe, Wu Chenyu, He Zizhou +1
An interpretable, physics-consistent turbulence model correction framework, termed FISR-Equation Learner (EQL), is proposed by embedding equation learning directly into a Partial D…
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…
Flow Topology Optimization at High Reynolds Numbers Based on Modified Turbulence Models
Chenyu Wu, Yufei Zhang
Flow topology optimization (ToOpt) based on Darcy's source term is widely used in the field of ToOpt. It has a high degree of freedom and requires no initial configuration, making…