activity
20182026
most citedAnalysis on numerical stability and convergence of RANS turbulence models from the perspective of coupling modes

27 citations · 55 across the 11 of their papers we have counts for

collaborators
Showing physics.flu-dynShow all

7 papers · 1 filter

physics.flu-dyn2025

A novel parallelizable convergence accelerating method: Pointwise Frequency Damping

Zikun Liu, Xukun Wang, Yilang Liu +1

This paper proposes a novel class of data-driven acceleration methods for steady-state flow field solvers. The core innovation lies in predicting and assigning the asymptotic limit…

physics.flu-dyn2025

A data-driven convergence booster for accelerating and stabilizing pseudo time-stepping

Xukun Wang, Yilang Liu, Xiang Yang +1

This paper introduces a novel data-driven convergence booster that not only accelerates convergence but also stabilizes solutions in cases where obtaining a steady-state solution i…

physics.flu-dyn2025

Towards a Generalized SA Model: Symbolic Regression-Based Correction for Separated Flows

Xuxiang Sun, Xianglin Shan, Yilang Liu +1

This study focuses on the numerical simulation of high Reynolds number separated flows and proposes a data-driven approach to improve the predictive capability of the SA turbulence…

physics.flu-dyn2021

High Reynolds number airfoil turbulence modeling method based on machine learning technique

Xuxiang Sun, Wenbo Cao, Yilang Liu +2

In this paper, a turbulence model based on deep neural network is developed for turbulent flow around airfoil at high Reynolds numbers. According to the data got from the Spalart-A…

physics.flu-dyn2021★ 27 cited

Analysis on numerical stability and convergence of RANS turbulence models from the perspective of coupling modes

Yilang Liu, Weiwei Zhang, Zhenhua Xia

Reynolds-averaged Navier-Stokes simulations are still the main method to study complex flows in engineering. However, traditional turbulence models cannot accurately predict flow f…

physics.flu-dyn2020★ 2 cited

A new data assimilation method of recovering turbulent flow field at high-Reynolds numbers for turbulence machine learning

Yilang Liu, Weiwei Zhang

This paper proposes a new data assimilation method for recovering high fidelity turbulent flow field around airfoil at high Reynolds numbers based on experimental data, which is ca…

physics.flu-dyn2018★ 9 cited

Machine learning methods for turbulence modeling in subsonic flows over airfoils

Weiwei Zhang, Linyang Zhu, Yilang Liu +1

Reynolds-Averaged Navier-Stokes(RANS) method will still play a vital role in the following several decade in aerospace engineering. Although RANS models are widely used, empiricism…