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20202022
most citedFeature selection and processing of turbulence modeling based on an artificial neural network

96 citations · 455 across the 12 of their papers we have counts for

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Showing physics.flu-dynShow all

10 papers · 1 filter

physics.flu-dyn2022★ 65 cited

Study of transfer learning from 2D supercritical airfoils to 3D transonic swept wings

Runze Li, Yufei Zhang, Haixin Chen

Machine learning has been widely utilized in fluid mechanics studies and aerodynamic optimizations. However, most applications, especially flow field modeling and inverse design, i…

physics.flu-dyn2022★ 38 cited

Data augmented turbulence modeling for three-dimensional separation flows

Chongyang Yan, Yufei Zhang, Haixin Chen

Field inversion and machine learning are implemented in this study to describe three-dimensional separation flow around an axisymmetric hill and augment the Spart-Allmaras model. T…

physics.flu-dyn2022★ 25 cited

Flowfield prediction of airfoil off-design conditions based on a modified variational autoencoder

Yunjia Yang, Runze Li, Yufei Zhang +1

Airfoil aerodynamic optimization based on single-point design may lead to poor off-design behaviors. Multipoint optimization that considers the off-design flow conditions is usuall…

physics.flu-dyn2022★ 31 cited

Physically Interpretable Feature Learning and Inverse Design of Supercritical Airfoils

Runze Li, Yufei Zhang, Haixin Chen

Machine-learning models have demonstrated a great ability to learn complex patterns and make predictions. In high-dimensional nonlinear problems of fluid dynamics, data representat…

physics.flu-dyn2022★ 31 cited

An iterative data-driven turbulence modeling framework based on Reynolds stress representation

Yuhui Yin, Yufei Zhang, Haixin Chen +1

Data-driven turbulence modeling studies have reached such a stage that the fundamental framework is basically settled, but several essential issues remain that strongly affect the…

physics.flu-dyn2021★ 45 cited

Data-driven turbulence modeling in separated flows considering physical mechanism analysis

Chongyang Yan, Haoran Li, Yufei Zhang +1

Accurate simulation of turbulent flow with separation is an important but challenging problem. In this paper, a data-driven Reynolds-averaged turbulence modeling approach, field in…

physics.flu-dyn2021

Aerodynamic Prediction of High-Lift Configuration Using k-(v^2 )-ω Turbulence Model

Shaoguang Zhang, Haoran Li, Yufei Zhang +1

The aerodynamic performance of the high-lift configuration greatly influences the safety and economy of commercial aircraft. Accurately predicting the aerodynamic performance of th…

physics.flu-dyn2020★ 24 cited

Adaptive sampling of pressure distribution features for the airfoil statistical analysis

Runze Li, Yufei Zhang, Haixin Chen

In the area of supercritical wing design, a variety of principles, laws and rules have been summarized by scholars who perform theoretical and experimental analyses. The applicabil…

physics.flu-dyn2020★ 96 cited

Feature selection and processing of turbulence modeling based on an artificial neural network

Yuhui Yin, Pu Yang, Yufei Zhang +2

Data-driven turbulence modeling has been considered an effective method for improving the prediction accuracy of Reynolds-averaged Navier-Stokes equations. Related studies aimed to…

physics.flu-dyn2020★ 27 cited

Numerical Simulation of Iced Wing Using Separating Shear Layer Fixed Turbulence Models

Haoran Li, Yufei Zhang, Haixin Chen

Aerodynamic prediction of glaze ice accretion on airfoils and wing is studied using the Reynolds-averaged Navier-Stokes method. Two separation fixed turbulence models are developed…