Faculty Publications

Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736

Publications by NITK Faculty

Browse

Filters

2019 - 20204

Settings

Author: search.filters.author.Arun, M.Subject: search.filters.subject.Solidification

Search Results

Now showing 1 - 4 of 4
  • No Thumbnail Available
    Item
    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.
  • No Thumbnail Available
    Item
    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.
  • No Thumbnail Available
    Item
    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
    (Springer, 2019) Vishweshwara, P.S.; Gnanasekaran, N.; Arun, M.
    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 modelling error. Gaussian noise is then added to the temperature distribution obtained using the forward approach to represent real-time experiments. The hallmark of the present work is to reduce the computational time of both the forward and the inverse methods and to simultaneously estimate the unknown parameters using a-priori engineering knowledge. The results of the present methodology prove that the simultaneous estimation of unknown parameters can be effectively obtained only with the use of Bayesian framework. © 2019, Indian Academy of Sciences.
  • No Thumbnail Available
    Item
    Inverse approach using bio-inspired algorithm within Bayesian framework for the estimation of heat transfer coefficients during solidification of casting
    (American Society of Mechanical Engineers (ASME), 2020) Vishweshwara, P.S.; Gnanasekaran, N.; Arun, M.
    In any parameter estimation problem, it is desirable to obtain more information in one single experiment. However, it is difficult to achieve multiple objectives in one single experiment. The work presented in this paper is the simultaneous estimation of heat transfer coefficient parameters, latent heat, and modeling error during the solidification of Al-4.5 wt %Cu alloy with the aid of Bayesian framework as an objective function that harmoniously matches the mathematical model and measurements. A 1D transient solidification problem is considered to be the mathematical model/forward model and numerically solved to obtain temperature distribution for the known boundary and initial conditions. Genetic algorithm (GA) and particle swarm optimization (PSO) are used as an inverse approach and the estimation of unknown parameters is accomplished for both pure and noisy temperature data. The use of Bayesian framework for the estimation of unknown parameters not only provides the information about the uncertainties associated with the estimates but also there is an inherent regularization term in which the inverse problem boils down to well-posed problem thereby plethora of information is extracted with less number of measurements. Finally, the results of this work open up new prospects for the solidification problem so as to obtain a feasible solution with the present approach. © © 2020 by ASME