Open AccessComputational fluid dynamics iteration driven by data
Author(s) -
Zhijun Zhou,
Qi Zhang,
Xichuan Cai,
Kun Li,
J. W. Zhao
Publication year - 2022
Publication title -
thermal science/thermal science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.339
H-Index - 43
eISSN - 2334-7163
pISSN - 0354-9836
DOI - 10.2298/tsci210313227z
Subject(s) - computational fluid dynamics , computer science , mathematics , fluid dynamics , burgers' equation , iterative method , space (punctuation) , mathematical optimization , partial differential equation , algorithm , mechanics , mathematical analysis , physics , operating system
Data-driven approaches have achieved remarkable success in different applications, however, their use in solving PDE has only recently emerged. Herein, we present the potential fluid method, which uses existing data to nest physical meanings into mathematical iterative processes. Potential fluid method is suitable for PDE, such as CFD problems, including Burgers? equation. Potential fluid method can iteratively determine the steady-state space distribution of PDE. For mathematical reasons, we compare the potential fluid method with the finite difference method and give a detailed explanation.