5 papers
Data-driven detached-eddy simulations based on explicit algebraic stress expressions for turbulent flows
Hao-Chen Liu, Zifei Yin, Xin-Lei Zhang +1
This work proposes a data-driven explicit algebraic stress-based detached-eddy simulation (DES) method. Despite the widespread use of data-driven methods in model development for b…
Shape optimization for trailing-edge noise reduction using large-eddy simulation and ensemble-based method
Qingyong Luo, Xin-Lei Zhang, Guowei He
In this work, the trailing-edge shape of an airfoil is optimized to reduce the acoustic noise based on large-eddy simulation (LES). It is achieved by the ensemble Kalman method, wh…
A framework for learning symbolic turbulence models from indirect observation data via neural networks and feature importance analysis
Chutian Wu, Xin-Lei Zhang, Duo Xu +1
Learning symbolic turbulence models from indirect observation data is of significant interest as it not only improves the accuracy of posterior prediction but also provides explici…
Optimizing flow control with ensemble Kalman method for mitigating flow-induced vibration
Liu Yi, Wang Shizhao, Zhang Xin-Lei +1
The ensemble Kalman method is introduced for optimizing flow control strategies in order to mitigate the flow-induced vibration of structures. Different types of control strategies…
A wall model for separated flows: embedded learning to improve a posteriori performance
Zhideng Zhou, Xin-lei Zhang, Guo-wei He +1
The development of a wall model using machine learning methods for the large-eddy simulation (LES) of separated flows is still an unsolved problem. Our approach is to leverage the…