5 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…
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…
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…
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…