Application of physics-informed neural networks for thin liquid film flows

被引:0
|
作者
Han, Qixun [1 ]
Sun, Xianhu [1 ]
Zhang, Fan [1 ]
Shao, Sujuan [2 ]
Ma, Chicheng [1 ,2 ]
机构
[1] Hebei Univ Technol, Sch Mech Engn, Tianjin 300401, Peoples R China
[2] Shandong Univ Technol, Sch Transportat & Vehicle Engn, Zibo 255000, Peoples R China
关键词
liquid film flow; PINNs; numerical simulation; forward and inverse solutions; FINITE; STABILITY;
D O I
10.1088/1873-7005/adb32e
中图分类号
O3 [力学];
学科分类号
08 ; 0801 ;
摘要
The flow of a film induced by gravity is not only widespread in nature but also has significant applications in various industrial technologies such as coating techniques, nanotechnology, microfluidic chips, and heat exchangers. The film exhibits various nonlinear dynamic phenomena due to the interactions between surface tension, gravity, and other forces, making the study of this type of flow of great importance. However, the theoretical derivation of thin film fluid dynamics is complex, with diverse working conditions, making numerical solutions difficult, time-consuming, and labor-intensive. Therefore, it is of significant importance to seek an approach that differs from theoretical derivation or numerical solutions for the study of thin film fluid dynamics. With the rapid development of deep learning, this paper employs physics-informed neural networks (PINNs) algorithm, in conjunction with the partial differential equation governing the fluid film thickness, to conduct research on forward prediction of the film thickness variation over time and space, and inverse problem solving to determine unknown parameters in the governing equation from data. The study investigates the governing equation for the thickness of a falling film along an inclined plane and applies the PINNs method to solve it. The research predicted the film thickness for three different characteristic waveforms and conducted a comparative analysis with solutions obtained using the commercial software COMSOL. Additionally, the unknown parameters in the governing equation were inversely solved using limited data, and the differences in prediction results with and without noisy data were compared.
引用
收藏
页数:28
相关论文
共 50 条
  • [1] Physics-informed neural networks for periodic flows
    Shah, Smruti
    Anand, N. K.
    PHYSICS OF FLUIDS, 2024, 36 (07)
  • [2] Physics-informed neural networks for inverse problems in supersonic flows
    Jagtap, Ameya D.
    Mao, Zhiping
    Adams, Nikolaus
    Karniadakis, George Em
    JOURNAL OF COMPUTATIONAL PHYSICS, 2022, 466
  • [3] Physics-informed neural networks for learning fluid flows with symmetry
    Kim, Younghyeon
    Kwak, Hyungyeol
    Nam, Jaewook
    KOREAN JOURNAL OF CHEMICAL ENGINEERING, 2023, 40 (09) : 2119 - 2127
  • [4] Physics-informed neural networks for learning fluid flows with symmetry
    Younghyeon Kim
    Hyungyeol Kwak
    Jaewook Nam
    Korean Journal of Chemical Engineering, 2023, 40 : 2119 - 2127
  • [5] Physics-informed neural networks for high-speed flows
    Mao, Zhiping
    Jagtap, Ameya D.
    Karniadakis, George Em
    COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 2020, 360
  • [6] Physics-informed neural networks for incompressible flows with moving boundaries
    Zhu, Yongzheng
    Kong, Weizhen
    Deng, Jian
    Bian, Xin
    PHYSICS OF FLUIDS, 2024, 36 (01)
  • [7] The application of physics-informed neural networks to hydrodynamic voltammetry
    Chen, Haotian
    Kaetelhoen, Enno
    Compton, Richard G.
    ANALYST, 2022, 147 (09) : 1881 - 1891
  • [8] Physics-informed neural network applied to surface-tension-driven liquid film flows
    Nakamura, Yo
    Shiratori, Suguru
    Takagi, Ryota
    Sutoh, Michihiro
    Sugihara, Iori
    Nagano, Hideaki
    Shimano, Kenjiro
    INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS, 2022, 94 (09) : 1359 - 1378
  • [9] Enforcing Dirichlet boundary conditions in physics-informed neural networks and variational physics-informed neural networks
    Berrone, S.
    Canuto, C.
    Pintore, M.
    Sukumar, N.
    HELIYON, 2023, 9 (08)
  • [10] Studying turbulent flows with physics-informed neural networks and sparse data
    Hanrahan, S.
    Kozul, M.
    Sandberg, R. D.
    INTERNATIONAL JOURNAL OF HEAT AND FLUID FLOW, 2023, 104