Heat transfer and thermal analysis in a semi-spherical fin with temperature-variant thermal properties: an application of Probabilists’ Hermite collocation method

Kumar, R. S. Varun; Jagadeesha, K. C.; Prasannakumara, B. C.

doi:10.1080/17455030.2023.2198031

Cited by 4 publications

(1 citation statement)

References 27 publications

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“…Wang and Shi 36 probed the heat transmission in the convective extended surface with variable thermal conductivity. Utilizing the collocation approach, Kumar et al 37 deliberated the heat transport analysis in the semi-spherical extended surface with variable thermal conductivity.…”

Section: Introductionmentioning

confidence: 99%

Predicting the thermal distribution in a convective wavy fin using a novel training physics-informed neural network method

Chandan,

Saadeh,

Qazza

et al. 2024

Sci Rep

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Fins are widely used in many industrial applications, including heat exchangers. They benefit from a relatively economical design cost, are lightweight, and are quite miniature. Thus, this study investigates the influence of a wavy fin structure subjected to convective effects with internal heat generation. The thermal distribution, considered a steady condition in one dimension, is described by a unique implementation of a physics-informed neural network (PINN) as part of machine-learning intelligent strategies for analyzing heat transfer in a convective wavy fin. This novel research explores the use of PINNs to examine the effect of the nonlinearity of temperature equation and boundary conditions by altering the hyperparameters of the architecture. The non-linear ordinary differential equation (ODE) involved with heat transfer is reduced into a dimensionless form utilizing the non-dimensional variables to simplify the problem. Furthermore, Runge–Kutta Fehlberg’s fourth–fifth order (RKF-45) approach is implemented to evaluate the simplified equations numerically. To predict the wavy fin's heat transfer properties, an advanced neural network model is created without using a traditional data-driven approach, the ability to solve ODEs explicitly by incorporating a mean squared error-based loss function. The obtained results divulge that an increase in the thermal conductivity variable upsurges the thermal distribution. In contrast, a decrease in temperature profile is caused due to the augmentation in the convective-conductive variable values.

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Section: Introductionmentioning

confidence: 99%

Predicting the thermal distribution in a convective wavy fin using a novel training physics-informed neural network method

Chandan,

Saadeh,

Qazza

et al. 2024

Sci Rep

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Solving an electrically conducting nanofluid over an impermeable stretching cylinder problem with a spectral reproducing kernel method

Foroutan,

Hashemi,

Rezapour

et al. 2024

J Therm Anal Calorim

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In this paper, a nonlinear mechanical system of ordinary differential equations (ODEs) with multi-point boundary conditions is considered by a novel type of reproducing kernel Hilbert space method (RKHSM). To begin, we define the unknown variables in terms of the reproducing kernel function. The roots of the Shifted Chebyshev polynomials (SCPs) are then utilized to collocate the resulting system. Finally, Newton’s iterative method is employed to find the unknown expansion coefficients. The solutions of this system of equations, which arise from the flow of an electrically conducting nanofluid over an impermeable stretching cylinder, are numerically analyzed, and convergence analysis is discussed to demonstrate the reliability of the presented method (PM). Tables and figures are provided to further discuss the solutions and assess the effectiveness of the method in comparison to other techniques in the literature.

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Entropy optimized flow subject to variable fluid characteristics and convective conditions

Khan,

Hayat,

Razaq

et al. 2024

Alexandria Engineering Journal

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Heat transfer and thermal analysis in a semi-spherical fin with temperature-variant thermal properties: an application of Probabilists’ Hermite collocation method

Cited by 4 publications

References 27 publications

Predicting the thermal distribution in a convective wavy fin using a novel training physics-informed neural network method

Predicting the thermal distribution in a convective wavy fin using a novel training physics-informed neural network method

Solving an electrically conducting nanofluid over an impermeable stretching cylinder problem with a spectral reproducing kernel method

Entropy optimized flow subject to variable fluid characteristics and convective conditions

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