ArticleStructural and multidisciplinary optimization : journal of the International Society for Structural and Multidisciplinary Optimization2022
Analysis of heat transmission in convective, radiative and moving rod with thermal conductivity using meta-heuristic-driven soft computing technique.
Article in Structural and multidisciplinary optimization : journal of the International Society for Structural and Multidisciplinary Optimization, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- An improved water strider algorithm for solving the inverse Burgers Huxley equation.Scientific reports · 2024Article
- Machine learning-based prediction of heat transfer performance in annular fins with functionally graded materials.Scientific reports · 2024Article
- A fractional order nonlinear model of the love story of Layla and Majnun.Scientific reports · 2023Article
- Quantitative Study of Non-Linear Convection Diffusion Equations for a Rotating-Disc Electrode.Entropy (Basel, Switzerland) · 2023Article
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3 authors.
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Abstract
Abstract: The present study analyzes the thermal attribute of conductive, convective, and radiative moving fin with thermal conductivity and constant velocity. The basic Darcy's model is utilized to formulate the governing equation for the problem, which is further nondimensionalized using certain variables. Moreover, an effective soft computing paradigm based on the approximating ability of the feedforword artificial neural networks (FANN's) and meta-heuristic approach of global and local search optimization techniques is developed to quantify the effect of variations in significant parameters such as ambient temperature, radiation-conduction number, Peclet number, nonconstant thermal conductivity, and initial temperature parameter on the temperature gradient of the rod. The results by the proposed FANN-AOA-SQP algorithm are compared with radial basis function approximation, Runge-Kutta-Fehlberg method and machine-learning algorithms. An extensive graphical and statistical analysis based on solution curves and errors such as absolute errors, mean square error, standard deviations in Nash-Sutcliffe efficiency, mean absolute deviations, and Theil's inequality coefficient are performed to show the accuracy, ease of implementation, and robustness of the design scheme. Graphical Abstract:
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