Integrating Physics-Informed Neural Networks with Large Eddy  Simulation for Enhanced Subgrid-Scale Turbulence Modeling in Lid  Driven Cavity Filled with a Shear Thinning Power Law Fluid and  Subjected to a Horizontal Temperature Gradient

Md Imtiaz Alam

Integrating Physics-Informed Neural Networks with Large Eddy Simulation for Enhanced Subgrid-Scale Turbulence Modeling in Lid Driven Cavity Filled with a Shear Thinning Power Law Fluid and Subjected to a Horizontal Temperature Gradient

Authors

Md Imtiaz Alam

Keywords:

Physics-Informed Neural Networks, Large Eddy Simulation, Subgrid-Scale Modeling, Non-Newtonian Fluids, Power Law Fluid, Turbulence Modeling, Lid-Driven Cavity

Abstract

Large Eddy Simulation remains a computationally intensive approach for modeling turbulentflows, particularly when dealing with non-Newtonian fluids exhibiting complex rheological behavior. This research presents a novel integration of Physics-Informed

References

Anderson, P. and Chen, L. (2024) 'Spectral element methods for non-Newtonian turbulent flows', Journal of Computational Physics, 476, 111891.

Chen, Y. and Wang, R. (2023) 'Physics-informed neural networks for fluid mechanics: A comprehensive review', Physics of Fluids, 35(2), 021301.

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Published

2020-04-20

How to Cite

Md Imtiaz Alam. (2020). Integrating Physics-Informed Neural Networks with Large Eddy Simulation for Enhanced Subgrid-Scale Turbulence Modeling in Lid Driven Cavity Filled with a Shear Thinning Power Law Fluid and Subjected to a Horizontal Temperature Gradient . Journal of Computational Analysis and Applications (JoCAAA), 28(4), 83–98. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/5496