73 citations · 252 across the 10 of their papers we have counts for
10 papers
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…
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…
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…
Reconstruction and fast prediction of a 3D flow field based on a variational autoencoder
Gongyan Liu, Runze Li, Xiaozhou Zhou +2
Reconstruction and fast prediction of flow fields are important for the improvement of data center operations and energy savings. In this study, an artificial neural network (ANN)…
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…
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…
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…
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…