Journal Articles
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884
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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.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 ASMEItem Extensive analysis of PCM-based heat sink with different fin arrangements under varying load conditions and variable aspect ratio(Elsevier Ltd, 2023) Nedumaran, M.S.; Gnanasekaran, N.; Hooman, K.The present study compares a modified variable height fin heat sink with the conventional constant height fin heat sink. The two heat sinks are filled with an equal volume of PCM (n-eicosane) and a fin volume fraction of 8 %. The experiments are performed for constant loads and also different power surge conditions. The pulsed heat loads are applied for two scenarios: 1. Constant load 4 W - power surge and constant load 4 W - power surge - 1800 s no-load condition, and 2. Power surge (50 s, 100 s, and 150 s) - no-load conditions of 1800 s. During experiments, the proposed variable height fin heat sinks possess better thermal performance for all load scenarios. Further, a 3D computational model is developed using ANSYS Fluent 19 to assess not only the effect of fin arrangement for different aspect ratios but also the impact of fin shape. The enclosure aspect ratio employed for the given study ranges from 0.3 to 0.8 for both the heat sinks. Regarding the fin structure in a heat sink, four types of fin shapes are adopted: square, circular, diamond, and triangular. The contour images of temperature and the liquid fraction are shown for the charging process. For the discharging process, the time required for the heat sinks to completely solidify the PCM is discussed. From the outcomes, variable height fin heat sinks provide enhanced melting/solidification for all the aspect ratios and fin shapes considered. As the aspect ratio increases, the time difference between the heat sink for the completion of the discharging cycle is reduced. Moreover, the triangular shaped fin shows a higher enhancement percentage of 2.29 % and 1.43 % during melting and 6.25 % and 12.5 % during solidification for both the heat sinks, respectively. © 2023 The Author(s)