Application of boundary-fitted convolutional neural network to simulate non-Newtonian fluid flow behavior in eccentric annulus
Author:
Funder
Universiti Teknologi Petronas
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-022-07092-w.pdf
Reference72 articles.
1. Peng Y, Lv BH, Yuan JL, et al (2014) Application and prospect of the non-Newtonian fluid in industrial field. In: Materials science forum. Trans Tech Publications Ltd, pp 396–401
2. Sun L, Gao H, Pan S, Wang JX (2020) Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data. Comput Methods Appl Mech Eng 361:112732. https://doi.org/10.1016/j.cma.2019.112732
3. Adariani YH (2005) Numerical simulation of laminar flow of non-Newtonian fluids in eccentric annuli. MSc thesis. The University of Tulsa
4. Fang P, Manglik RM, Jog MA (1999) Characteristics of laminar viscous shear-thinning fluid flows in eccentric annular channels. J Nonnewton Fluid Mech 84:1–17. https://doi.org/10.1016/S0377-0257(98)00145-1
5. Raissi M, Perdikaris P, Karniadakis GE (2019) Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J Comput Phys 378:686–707. https://doi.org/10.1016/j.jcp.2018.10.045
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