scholarly journals Thermal Flows

Fluids ◽  
2021 ◽  
Vol 6 (6) ◽  
pp. 227
Author(s):  
Marcello Lappa
Keyword(s):  
Heat Transfer ◽  
Industrial Applications ◽  
Thermal Origin ◽  
Transfer Problems ◽  
Thermal Flows

Flows of thermal origin and heat transfer problems are central in a variety of disciplines and industrial applications [...]

Physics-Informed Neural Networks (PINNs) for Heat Transfer Problems

2021 ◽  
Author(s):  
Shengze Cai ◽  
Zhicheng Wang ◽  
Sifan Wang ◽  
Paris Perdikaris ◽  
George Karniadakis
Keyword(s):  
Heat Transfer ◽  
Neural Networks ◽  
Computational Methods ◽  
Automatic Differentiation ◽  
Differential Operators ◽  
Industrial Applications ◽  
Temperature Fields ◽  
Two Phase ◽  
Experimental Heat ◽  
Transfer Problems

Abstract Physics-informed neural networks (PINNs) have gained popularity across different engineering fields due to their effectiveness in solving realistic problems with noisy data and often partially missing physics. In PINNs, automatic differentiation is leveraged to evaluate differential operators without discretization errors, and a multi-task learning problem is defined in order to simultaneously fit observed data while respecting the underlying governing laws of physics. Here, we present applications of PINNs to various prototype heat transfer problems, targeting in particular realistic conditions not readily tackled with traditional computational methods. To this end, we first consider forced and mixed convection with unknown thermal boundary conditions on the heated surfaces and aim to obtain the temperature and velocity fields everywhere in the domain, including the boundaries, given some sparse temperature and velocity measurements. We also consider the prototype Stefan problem for two-phase flow, aiming to infer the moving interface, the velocity and temperature fields everywhere as well as the different conductivities of a solid and a liquid phase, given a few temperature measurements inside the domain. Finally, we present some realistic industrial applications related to power electronics to highlight the practicality of PINNs as well as the effective use of neural networks in solving general heat transfer problems of industrial complexity. Taken together, the results presented herein demonstrate that PINNs not only can solve ill-posed problems, which are beyond the reach of traditional computational methods, but they can also bridge the gap between computational and experimental heat transfer.

THE MODAL IDENTIFICATION METHOD IN NONLINEAR HEAT TRANSFER PROBLEMS

Equipment ◽  
2006 ◽  
Author(s):  
O. Balima ◽  
D. Petit ◽  
J. B. Saulnier ◽  
M. Girault ◽  
Y. Favennec
Keyword(s):  
Heat Transfer ◽  
Modal Identification ◽  
Identification Method ◽  
Nonlinear Heat Transfer ◽  
Transfer Problems

NUMERICAL EFFICIENCY AND VERIFICATION OF AN IMPLICIT SOLVER TO COMPUTE HEAT TRANSFER PROBLEMS

2019 ◽  
Author(s):  
Tobias Luiz Marchioro Toassi ◽  
Francisco Augusto Aparecido Gomes ◽  
Paulo Gemo Conci
Keyword(s):  
Heat Transfer ◽  
Numerical Efficiency ◽  
Implicit Solver ◽  
Transfer Problems
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