96 citations · 463 across the 18 of their papers we have counts for
19 papers
Reliable and efficient steady CFD from surrogate predictions through Newton-Krylov correction
Mingcheng Lei, Weishao Tang, Yufei Zhang +1
Neural surrogates offer a promising route to accelerating computationally expensive simulations governed by partial differential equations across science and industry. Their practi…
Uncertainty-Aware Data-Based Method for Fast and Reliable Shape Optimization
Yunjia Yang, Runze Li, Yufei Zhang +1
Data-based optimization (DBO) offers a promising approach for efficiently optimizing shape for better aerodynamic performance by leveraging a pretrained surrogate model for offline…
SuperWing: a comprehensive transonic wing dataset for data-driven aerodynamic design
Yunjia Yang, Weishao Tang, Mengxin Liu +3
Machine-learning surrogate models have shown promise in accelerating aerodynamic design, yet progress toward generalizable predictors for three-dimensional wings has been limited b…
Rapid aerodynamic prediction of swept wings via physics-embedded transfer learning
Yunjia Yang, Runze Li, Yufei Zhang +2
Machine learning-based models provide a promising way to rapidly acquire transonic swept wing flow fields but suffer from large computational costs in establishing training dataset…
Machine-learning-based multipoint optimization of fluidic injection parameters for improving nozzle performance
Yunjia Yang, Jiazhe Li, Yufei Zhang +1
Fluidic injection offers a promising solution to improve the performance of the overexpanded single expansion ramp nozzles (SERNs) during vehicle acceleration. However, determining…
Fast buffet onset prediction and optimization method based on a pre-trained flowfield prediction model
Yunjia Yang, Runze Li, Yufei Zhang +1
The transonic buffet is a detrimental phenomenon occurs on supercritical airfoils and limits aircraft's operating envelope. Traditional methods for predicting buffet onset rely on…
Mesh-Agnostic Decoders for Supercritical Airfoil Prediction and Inverse Design
Runze Li, Yufei Zhang, Haixin Chen
Mesh-agnostic models have advantages in terms of processing unstructured spatial data and incorporating partial differential equations. Recently, they have been widely studied for…
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…
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…
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…
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…
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…
Incremental Control System Design and Flight Tests of a Micro-Coaxial Rotor UAV
Z. C. Liu, Y. F. Zhang, Z. D. zhang +1
In this paper, the incremental nonlinear dynamic inversion (INDI) method is applied to the control system design of coaxial rotor UAVs. The aerodynamic uncertainty and anti-disturb…
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…
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…
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…
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…
Learning the aerodynamic design of supercritical airfoils through deep reinforcement learning
Runze Li, Yufei Zhang, Haixin Chen
The aerodynamic design of modern civil aircraft requires a true sense of intelligence since it requires a good understanding of transonic aerodynamics and sufficient experience. Re…
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…