Zobrazeno 1 - 10
of 412
pro vyhledávání: '"Chen, Haixin"'
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 datasets. Here, we propose a physics-embedded transfer learning framework to
Externí odkaz:
http://arxiv.org/abs/2409.12711
Fluidic injection provides a promising solution to improve the performance of overexpanded single expansion ramp nozzle (SERN) during vehicle acceleration. However, determining the injection parameters for the best overall performance under multiple
Externí odkaz:
http://arxiv.org/abs/2409.12707
Publikováno v:
AIAA Journal 2024
Mesh-agnostic models have advantages in terms of processing unstructured spatial data and incorporating partial differential equations. Recently, they have been widely studied for constructing physics-informed neural networks, but they need to be tra
Externí odkaz:
http://arxiv.org/abs/2402.17299
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 multiple computational fluid dynamics simulations to assess a series o
Externí odkaz:
http://arxiv.org/abs/2402.17939
Publikováno v:
Chinese Journal of Aeronautics 2023
Machine learning has been widely utilized in fluid mechanics studies and aerodynamic optimizations. However, most applications, especially flow field modeling and inverse design, involve two-dimensional flows and geometries. The dimensionality of thr
Externí odkaz:
http://arxiv.org/abs/2206.02625
Publikováno v:
Physics of Fluids 2022
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. The discrete adjoint method is used to solve the field inversion proble
Externí odkaz:
http://arxiv.org/abs/2204.13566
Publikováno v:
AIAA Journal 2022
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 usually applied to improve the robustness and expand the flight envelope. Ma
Externí odkaz:
http://arxiv.org/abs/2204.07934
Publikováno v:
AIAA Journal 2022
Machine-learning models have demonstrated a great ability to learn complex patterns and make predictions. In high-dimensional nonlinear problems of fluid dynamics, data representation often greatly affects the performance and interpretability of mach
Externí odkaz:
http://arxiv.org/abs/2204.07815
Publikováno v:
Theoretical and Applied Mechanics Letters 2022
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 performance, including accuracy, smoothness, and generalization capaci
Externí odkaz:
http://arxiv.org/abs/2204.07810
Publikováno v:
In International Review of Economics and Finance November 2024 96 Part B
