期刊
IEEE ACCESS
卷 9, 期 -, 页码 138876-138902出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3117839
关键词
Conductive-convective-radiative fin; temperature-dependent thermal conductivity; temperature distribution; weighted Legendre neural networks; hybrid soft computing; whale optimization algorithm; Nelder Mead algorithm
资金
- Center of Excellence in Theoretical and Computational Science (TaCS-CoE), KMUTT
A one-dimensional mathematical model for convective-conductive-radiative fins is presented in this paper, along with the development of the LeNN-WOA-NM algorithm to analyze the model. The algorithm's effectiveness and accuracy are validated through experimental data, demonstrating its quality of solutions in temperature distribution analysis.
In this paper, one dimensional mathematical model of convective-conductive-radiative fins is presented with thermal conductivity depending on temperature. The temperature field with insulated tip is determined for a fin in convective, conductive and radiative environments. Moreover, an intelligent soft computing paradigm named as the LeNN-WOA-NM algorithm is designed to analyze the mathematical model for the temperature field of convective-conductive-radiative fins. The proposed algorithm uses function approximating ability of Legendre polynomials based on artificial neural networks (ANN's), global search optimization ability of Whale optimization algorithm (WOA), and local search convergence of Nelder-Mead algorithm. The proposed algorithm is applied to illustrate the effect of variations in coefficients of convection, radiation heat losses, and dimensionless parameter of thermal conductivity on temperature distribution of conductive-convective and radiative fins in convective and radiative environments. The experimental data establishes the effectiveness of the design scheme when compared with techniques in the latest literature. It can be observed that accuracy of approximate temperature increases with lower values of N-c and N-r while decreases with increase in lambda. The quality of solutions obtained by LeNN-WOA-NM algorithm are validated through performance indicators including absolute errors, MAD, TIC, and ENSE.
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