Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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20192

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Author: search.filters.author.Vishweshwara, P.S.Subject: search.filters.subject.Solidification

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    Estimation of interfacial heat transfer coefficient for horizontal directional solidification of Sn-5wt%pb alloy using genetic algorithm as inverse method
    (Springer Verlag, 2019) Vishweshwara, P.S.; Gnanasekaran, N.; Arun, M.
    In the present work, a one-dimensional transient solidification heat transfer problem is solved to determine the unknown interfacial heat transfer coefficient (IHTC) at the mold–metal interface using genetic algorithm (GA), an evolutionary and widely known algorithm, as an inverse method. The forward model is numerically solved to obtain the exact temperatures by incorporating the appropriate correlation for the IHTC that varies with time. In order to mimic experiments, the exact temperatures are then perturbed with the standard deviations of 0.01, 0.02, and 0.03. In the inverse estimation, genetic algorithm is used to minimize the objective function, thereby reducing the error between the measured and the simulated temperatures. The study on the performance parameters of the algorithm is also discussed in detail. © Springer Nature Singapore Pte Ltd. 2019.
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    A novel framework for the estimation of interfacial heat transfer coefficient using Bat algorithm during solidification of metal casting
    (Toronto Metropolitan University, 2019) Vishweshwara, P.S.; Gnanasekaran, N.; Arun, M.
    In the present work, the interfacial heat transfer coefficient (IHTC) at the mold metal interface is estimated during solidification of Al-4.5wt%Cu alloy using ANN-Bat-Bayesian framework. The forward model comprises of a one dimensional transient governing equation for the solidification of metal casting and is solved using explicit finite difference scheme with the available IHTC correlation from the literature. Within the range of values of constants of IHTC correlation, a set of numerical simulation is performed and corresponding temperature output is trained using Artificial Neural Network (ANN). The network created acts a fast forward model replacing the FDM scheme during IHTC estimation thus reducing computational time. Bat algorithm is used as inverse method along with the Bayesian framework, that drives towards the accurate retrieval of unknown parameters. © 2019, Toronto Metropolitan University. All rights reserved.