Element Differential Method for Non-Fourier Heat Conduction in the Convective-Radiative Fin with Mixed Boundary Conditions

Ma, Jun; Sun, Yasong; Li, Sida

doi:10.3390/coatings12121862

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…Ma et al. [37] probed the efficiency of convective‐radiative extended surface in the presence of periodic boundary conditions. Machine learning (ML), a branch of artificial intelligence, has advanced rapidly nowadays.…”

Section: Introductionmentioning

confidence: 99%

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A physics‐informed machine learning prediction for thermal analysis in a convective‐radiative concave fin with periodic boundary conditions

Kumar,

Srilatha,

Karthik

et al. 2024

Z Angew Math Mech

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The present research is focused on the inspection of unsteady heat dissipation through a radiative‐convective concave profiled fin along with the periodic boundary conditions. Additionally, the long‐short‐term memory machine learning (LSTM‐ML) approach is used in this study to examine the periodic fluctuation in the temperature of the fin. The current research is devoted to solving the highly non‐linear equation using a physics‐informed neural network (PINN) approach. Using the proper dimensionless terms, the associated fin problem is transformed into a non‐dimensional system, and the resulting partial differential equation (PDE) is then numerically solved using the finite difference method (FDM). Using the data‐driven LSTM‐ML technique, the time‐dependent periodic heat transmission in the concave fin is also examined. The impact of various factors on the temperature profile of the concave extended surface is explained, and the results are visually displayed. The temperature distribution in the concave fin diminishes as the convection‐conduction parameter and radiation‐conduction parameter rise. As the amplitude and thermal conductivity parameters improve, so does the temperature of the concave fin. Furthermore, it is demonstrated that although LSTM‐ML and PINN closely matched the FDM findings during the training domain, only PINN with designed characteristics has the potential to predict accurately beyond the trained region by capturing the physics of the problem.

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Section: Introductionmentioning

confidence: 99%

“…The thermal attributes of an extended surface with periodic heat transfer were analyzed by Souayeh and Abro [36]. Ma et al [37] probed the efficiency of convective-radiative extended surface in the presence of periodic boundary conditions. Machine learning (ML), a branch of artificial intelli-gence, has advanced rapidly nowadays.…”

mentioning

confidence: 99%

A physics‐informed machine learning prediction for thermal analysis in a convective‐radiative concave fin with periodic boundary conditions

Kumar,

Srilatha,

Karthik

et al. 2024

Z Angew Math Mech

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“…The structure of a heat exchanger affects its heat transfer performance. Studies have shown that heat exchanger optimization can be achieved by optimizing and improving the heat exchanger structure [6,7]. Mario et al [8] obtained locally optimized fin patterns by considering the topology optimization of plate-fin heat exchangers.…”

Section: Introductionmentioning

confidence: 99%

Simulation of Turbulent Flow Structure and Particle Deposition in a Three-Dimensional Heat Transfer Duct with Convex Dimples

Han

et al. 2023

Coatings

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In engineering applications, dust deposition on the heat transfer channel greatly reduces the efficiency of heat transfer. Therefore, it is very significant to study the characteristics of particle deposition for thermal energy engineering applications. In this study, the Reynolds stress model (RSM) and the discrete phrase model (DPM) were used to simulate particle deposition in a 3D convex-dimpled rough channel. A discrete random walk model (DRW) was used for the turbulent diffusion of particles, and user-defined functions were developed for collisions between particles and walls. An improved deposition model of rebound between particles was developed. The flow structure, secondary flow, temperature distribution, Q criterion, and particle deposition distribution in the convex-dimpled rough channel were analyzed after a study of the grid independence and a numerical validation. The results showed that these mechanisms affected the flow structure in the flow field. For tiny particles (dp ≤ 10 μm), the presence of convex dimples promoted their deposition. The rates of particle deposition in the presence of convex dimples were 535, 768, 269, and 2 times higher than in smooth channels (particle sizes of 1, 3, 5, and 10 μm, respectively). However, for large particles (dp > 10 μm), although the presence of convex dimples had a certain effect on the location distribution of particle deposition, it had little effect on the deposition rates of large particles, which were 0.99, 0.98, 0.97 and 0.96 times those in the smooth channel, respectively.

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Analysis of dynamic coupled thermoelasticity problems based on element differential method

Tan,

Xu,

Zheng

et al. 2024

International Journal of Heat and Mass Transfer

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Element Differential Method for Non-Fourier Heat Conduction in the Convective-Radiative Fin with Mixed Boundary Conditions

Cited by 3 publications

References 21 publications

A physics‐informed machine learning prediction for thermal analysis in a convective‐radiative concave fin with periodic boundary conditions

A physics‐informed machine learning prediction for thermal analysis in a convective‐radiative concave fin with periodic boundary conditions

Simulation of Turbulent Flow Structure and Particle Deposition in a Three-Dimensional Heat Transfer Duct with Convex Dimples

Analysis of dynamic coupled thermoelasticity problems based on element differential method

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