PINN-FFHT: A physics-informed neural network for solving fluid flow and heat transfer problems without simulation data

Qingyang Zhang (), Xiaowei Guo (), Xinhai Chen (), Chuanfu Xu () and Jie Liu
Additional contact information
Qingyang Zhang: Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, Changsha, 410073 Hunan, P. R. China
Xiaowei Guo: Institute for Quantum Information & State Key Laboratory of High Performance Computing, National University of Defense Technology, Changsha, 410073 Hunan, P. R. China
Xinhai Chen: Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, Changsha, 410073 Hunan, P. R. China
Chuanfu Xu: Institute for Quantum Information & State Key Laboratory of High Performance Computing, National University of Defense Technology, Changsha, 410073 Hunan, P. R. China
Jie Liu: Laboratory of Software Engineering for Complex Systems, National University of Defense Technology, Changsha, 410073 Hunan, P. R. China

International Journal of Modern Physics C (IJMPC), 2022, vol. 33, issue 12, 1-21

Abstract: In recent years, physics-informed neural networks (PINNs) have come to the foreground in many disciplines as a new way to solve partial differential equations. Compared with traditional discrete methods and data-driven surrogate models, PINNs can learn the solutions of partial differential equations without relying on tedious mesh generation and simulation data. In this paper, an original neural network structure PINN-FFHT based on PINNs is devised to solve the fluid flow and heat transfer problems. PINN-FFHT can simultaneously predict the flow field and take into consideration the influence of flow on the temperature field to solve the energy equation. A flexible and friendly boundary condition (BC) enforcement method and a dynamic strategy that can adaptively balance the loss term of velocity and that of temperature are proposed for training PINN-FFHT, serving to accelerate the convergence and improve the accuracy of predicted results. Three cases are predicted to validate the capabilities of the network, including the laminar flow with the Dirichlet BCs in heat transfer, respectively, under the Cartesian and the cylindrical coordinate systems, and the thermally fully developed flow with the Neumann BCs in heat transfer. Results show that PINN-FFHT is faster in convergence speed and higher in accuracy than traditional PINN methods.

Keywords: Physics-informed neural networks (PINNs); partial differential equations; fluid flow; heat transfer; boundary conditions (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0129183122501662
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:ijmpcx:v:33:y:2022:i:12:n:s0129183122501662

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0129183122501662

Access Statistics for this article

International Journal of Modern Physics C (IJMPC) is currently edited by H. J. Herrmann

More articles in International Journal of Modern Physics C (IJMPC) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().