2019
DOI: 10.1007/s12046-019-1076-2
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Simultaneous estimation of unknown parameters using a-priori knowledge for the estimation of interfacial heat transfer coefficient during solidification of Sn–5wt%Pb alloy—an ANN-driven Bayesian approach

Abstract: The present methodology focuses on model reduction in which the prevalent one-dimensional transient heat conduction equation for a horizontal solidification of Sn-5wt%Pb alloy is replaced with Artificial Neural Network (ANN) in order to estimate the unknown constants present in the interfacial heat transfer coefficient correlation. As a novel approach, ANN-driven forward model is synergistically combined with Bayesian framework and Genetic algorithm to simultaneously estimate the unknown parameters and modelli… Show more

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Cited by 5 publications
(1 citation statement)
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“…They have incorporated various assumptions regarding heat transfer mode [4], dependence on process parameters [5], dependence of thermophysical properties on temperature [6], formation of the shrinkage gap between casting and mold during solidification [7], assignment of different HTCs to different regions of the casting [8] etc. Iterative inverse approaches have also been reported starting with an HTC guess and improving it gradually according to deterministic optimization algorithms [9] or numerical schemes [10], genetic algorithm [11], neural networks [12] or even according to experience. This is most often based on minimization of the temperature evolution difference between experimental and simulation results [13].…”
Section: Introductionmentioning
confidence: 99%
“…They have incorporated various assumptions regarding heat transfer mode [4], dependence on process parameters [5], dependence of thermophysical properties on temperature [6], formation of the shrinkage gap between casting and mold during solidification [7], assignment of different HTCs to different regions of the casting [8] etc. Iterative inverse approaches have also been reported starting with an HTC guess and improving it gradually according to deterministic optimization algorithms [9] or numerical schemes [10], genetic algorithm [11], neural networks [12] or even according to experience. This is most often based on minimization of the temperature evolution difference between experimental and simulation results [13].…”
Section: Introductionmentioning
confidence: 99%