Predictive modeling through physics-informed neural networks for analyzing the thermal distribution in the partially wetted wavy fin

DOI10.1002/zamm.202400180MaRDI QIDQ6650851

Unnamed Author, Varun Kumar Ravikumar Shashikala, Taseer Muhammad, Harjot Singh Gill, Unnamed Author, Unnamed Author, Siddesh Alur Shivaprakash, Kalachar Karthik

Publication date: 9 December 2024

Published in: ZAMM. Zeitschrift für Angewandte Mathematik und Mechanik (Search for Journal in Brave)

Mathematics Subject Classification ID

Thermodynamics and heat transfer (80Axx) Diffusion and convection (76Rxx) Foundations, constitutive equations, rheology, hydrodynamical models of non-fluid phenomena (76Axx)

Cites Work

• Partially wet fin efficiency for the longitudinal fins of rectangular, triangular, concave parabolic, and convex parabolic profiles
• Finite circular fin method for heat and mass transfer characteristics for plain fin-and-tube heat exchangers under fully and partially wet surface conditions
• Analysis of a convective-radiative continuously moving fin with temperature-dependent thermal conductivity
• Data-driven deep learning of partial differential equations in modal space
• Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• Thermal performance of six different types of wavy-fins
• Analysis of heat transfer using Local thermal non‐equilibrium conditions for a non‐Newtonian fluid flow containing Ti₆Al₄V and AA7075 nanoparticles in a porous media
• The use of physics-informed neural network approach to image restoration via nonlinear PDE tools
• Exploration of Arrhenius activation energy on hybrid nanofluid flow over a curved stretchable surface
• Performance analysis of a longitudinal fin under the influence of magnetic field using differential transform method with Pade approximant

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