79 citations · 150 across the 12 of their papers we have counts for
13 papers
Fast sparse flow field prediction around airfoils via multi-head perceptron based deep learning architecture
Kuijun Zuo, Shuhui Bu, Weiwei Zhang +3
In order to obtain the information about flow field, traditional computational fluid dynamics methods need to solve the Navier-Stokes equations on the mesh with boundary conditions…
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…
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…
Deep Learning for Multi-Fidelity Aerodynamic Distribution Modeling from Experimental and Simulation Data
Kai Li, Jiaqing Kou, Weiwei Zhang
The wind-tunnel experiment plays a critical role in the design and development phases of modern aircraft, which is limited by prohibitive cost. In contrast, numerical simulation, a…
Experimental study on Vortex-Induced Vibration of Cylinder at Subcritical Reynolds Number
Zhen Lyu, Weiwei Zhang, Jiaqing Kou
Numerical simulation results in recent years show that vortex-induced vibration (VIV) can occur at a subcritical Reynolds number. And the VIV has been observed numerically at Reyno…
Machine Learning of Partial Differential Equations from Noise Data
Wenbo Cao, Weiwei Zhang
Machine learning of partial differential equations from data is a potential breakthrough to solve the lack of physical equations in complex dynamic systems, but because numerical d…
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…
Transonic aeroelasticity: a new perspective from the fluid mode
Chuanqiang Gao, Weiwei Zhang
Within the transonic regime, the aeroelastic problems exhibit many unique characteristics compared with subsonic and supersonic cases. Although a lot of research has been carried o…
Comparisons of Two Reduced-Order Models for Linearized Unsteady Aerodynamic Identification
Jiaqing Kou, Weiwei Zhang
This paper compares the performance of two unsteady aerodynamic reduced-order models (ROMs), namely linear Volterra series and the autoregressive with exogenous input (ARX) model,…
Provably size-guaranteed mesh generation with superconvergence
Xiangrong Li, Nan Qi, Yufeng Nie +1
The properties and applications of superconvergence on size-guaranteed Delaunay triangulation generated by bubble placement method (BPM), are studied in this paper. First, we deriv…
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…
A novel unsteady aerodynamic Reduced-Order Modeling method for transonic aeroelastic optimization
Ziyi Wang, Weiwei Zhang, Xiaojing Wu +1
In aircraft design, structural optimization and uncertainty quantification concerning transonic aeroelastic issues are computationally impractical, because the iterative process re…
Mode Multigrid - A novel convergence acceleration method
Yilang Liu, Weiwei Zhang, Jiaqing Kou
This paper proposes a mode multigrid (MMG) method, and applies it to accelerate the convergence of the steady state flow on unstructured grids. The dynamic mode decomposition (DMD)…