Zobrazeno 1 - 10
of 51
pro vyhledávání: '"Zou, Shufan"'
Publikováno v:
Physics of Fluids 36 (10) 2024
Advances in deep learning have enabled physics-informed neural networks to solve partial differential equations. Numerical differentiation using the finite-difference (FD) method is efficient in physics-constrained designs, even in parameterized sett
Externí odkaz:
http://arxiv.org/abs/2406.10534
In this study, we present a novel computational framework that integrates the finite volume method with graph neural networks to address the challenges in Physics-Informed Neural Networks(PINNs). Our approach leverages the flexibility of graph neural
Externí odkaz:
http://arxiv.org/abs/2405.04466
Publikováno v:
Physics of Fluids 1 April 2024; 36 (4): 043601
The rapid development of deep learning has significant implications for the advancement of Computational Fluid Dynamics (CFD). Currently, most pixel-grid-based deep learning methods for flow field prediction exhibit significantly reduced accuracy in
Externí odkaz:
http://arxiv.org/abs/2309.10050
In this work, based on the moving-least-squares immersed boundary method, we proposed a new technique to improve the calculation of the volume force representing the body boundary. For boundary with simple geometry, we theoretically analyse the error
Externí odkaz:
http://arxiv.org/abs/2110.13612
Autor:
Srinivasan, Navneeth, Shim, Gihun, Tamadate, Tomoya, Zou, Shufan, Li, Li, Hogan, Christopher J., Jr., Yang, Suo
Publikováno v:
In Journal of Aerosol Science June 2024 179
Autor:
Zou, Shufan, Yang, Yantao
Turbulent convection plays a crucial role in many natural environments, ranging from Earth ocean, mantle and outer core, to various astrophysical systems. For such flows with extremely strong thermal driving, an ultimate scaling was proposed for the
Externí odkaz:
http://arxiv.org/abs/2101.06651
Publikováno v:
In Applied Soft Computing March 2024 154
Publikováno v:
Phys. Fluids 33, 036107 (2021)
In a recent paper, Liu et al. [``Lift and drag in three-dimensional steady viscous and compressible flow'', Phys. Fluids 29, 116105 (2017)] obtained a universal theory for the aerodynamic force on a body in three-dimensional steady flow, effective fr
Externí odkaz:
http://arxiv.org/abs/2012.11320
Autor:
Shao, Siyao, Zhou, Dezhi, He, Ruichen, Li, Jiaqi, Zou, Shufan, Mallery, Kevin, Kumar, Santosh, Yang, Suo, Hong, Jiarong
The lack of quantitative risk assessment of airborne transmission of COVID-19 under practical settings leads to large uncertainties and inconsistencies in our preventive measures. Combining in situ measurements and numerical simulations, we quantify
Externí odkaz:
http://arxiv.org/abs/2007.03645
Publikováno v:
In Journal of Computational Science September 2023 72