26 citations · 34 across the 7 of their papers we have counts for
8 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…
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)…