Abstract
Heat transfer has a vital role in material selection, machinery efficacy, and energy consumption. The notion of heat transfer is essential in understanding many phenomena related to several engineering fields. Particularly, Mechanical, civil and chemical engineering. The presentation of the heat transfer model in this manuscript is a dedication to the heat transfer characteristics such as conduction, convection, and radiation. The heat energy consumption mainly depends on these characteristics. A better conductive and convective paradigm is required for miniaturization of heat loss or transfer. The phenomenon is mathematically assumed with the required parameters. A new mathematical strategy is also designed and implemented in the manuscript to evaluate the dynamics of heat transfer model. The mathematical approach is the hybrid structure of the Sine-Cosine algorithm and Interior point algorithm. The validation of new technique is evaluated by mean absolute deviation, root mean square errors, and error in Nash–Sutcliffe efficiency. For better illustration, an extensive data set executed by the proposed mathematical strategy is also drawn graphically with convergence plots.
Highlights
W ITH the increase in energy consumption, the miniaturization of heat loss and heat transfer on surfaces and in devices surge the interest of researchers
Analysis of temperature profiles in longitudinal fin is presented in [27], heat and entropy generation in flow of nonNewtonian fluid is analysed by Artificial neural networking (ANN) in [28], temperature distribution in convective straight fins [29], Multi order Fractional Differential Equations [30], Restoring Moment and Damping Effects Using Neuro-evolutionary Technique [31], non-linear MHD Jeffery–Hamel blood flow model, Optimal power flow solution in power systems [32], [33], Falkner–Skan flow problem is solved by Sine-Cosine algorithm (SCA)-SQP in [34], unipolar pump flow is evaluated by ANN-SCA-SQP by [35]
EMPIRICAL RESULTS AND DISCUSSION This section discusses the outcome of dynamics of heat transfer, given in equation (5)
Summary
W ITH the increase in energy consumption, the miniaturization of heat loss and heat transfer on surfaces and in devices surge the interest of researchers. The researchers work in many engineering applications like thermal energy storage, heat transfer exchangers, Solar collectors, thermal control in electronic devices, etc. Heat transfer has many application such as PCM pipe bank thermal storage for space heating [18], Thermal performance of self-rewetting gold nanofluids [19], heat pipe cooled device with thermo-electric generator for nuclear power application [20], etc For such problems researcher introduce many numerical techniques or solved by existent techniques like Entropy generation and heat transfer analysis by Finite difference method [21]. Analysis of temperature profiles in longitudinal fin is presented in [27], heat and entropy generation in flow of nonNewtonian fluid is analysed by Artificial neural networking (ANN) in [28], temperature distribution in convective straight fins [29], Multi order Fractional Differential Equations [30], Restoring Moment and Damping Effects Using Neuro-evolutionary Technique [31], non-linear MHD Jeffery–Hamel blood flow model, Optimal power flow solution in power systems [32], [33], Falkner–Skan flow problem is solved by SCA-SQP in [34], unipolar pump flow is evaluated by ANN-SCA-SQP by [35].
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