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Author: search.filters.author.Gnanasekaran, N.Has files: No

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    Inverse Estimation of Interfacial Heat Transfer Coefficient During the Solidification of Sn-5wt%Pb Alloy Using Evolutionary Algorithm
    (Pleiades journals, 2019) Vishweshwara, P.S.; Gnanasekaran, N.; Mahalingam, M.
    The study of the interfacial heat transfer coefficient (IHTC) is one of the major concerns during solidification of casting. In order to find out the IHTC at the metal–mold interface, a one dimensional transient heat conduction model is numerically investigated during horizontal directional solidification of Sn–5wt%Pb alloy. The forward model is solved using explicit finite difference method to obtain the exact temperatures for the known boundary conditions. The estimation of the unknown IHTC is attempted using Particle Swarm Optimization (PSO) as an inverse approach along with Bayesian framework. In order to prove the robustness of the proposed methodology, the estimation is accomplished for the simulated measurements. The simulated measurements are then added with noise to replicate the experimental data. The present approach not only minimizes the difference between simulated and measured temperatures but also takes in to account “a priori” information about the unknown parameters. © 2019, Springer Nature Singapore Pte Ltd.
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    Accelerating MCMC using model reduction for the estimation of boundary properties within Bayesian framework
    (Pleiades journals, 2019) Gnanasekaran, N.; Kumar, M.K.
    In this work, Artificial Neural Network (ANN) and Approximation Error Model (AEM) are proposed as model reduction methods for the simultaneous estimation of the convective heat transfer coefficient and the heat flux from a mild steel fin subject to natural convection heat transfer. The complete model comprises of a three-dimensional conjugate heat transfer from fin whereas the reduced model is simplified to a pure conduction model. On the other hand, the complete model is then replaced with ANN model that acts as a fast forward model. The modeling error that arises due to reduced model is statistically compensated using Approximation Error Model. The estimation of the unknown parameters is then accomplished using the Bayesian framework with Gaussian prior. The sampling space for both the parameters is successfully explored based on Markov chain Monte Carlo method. In addition, the convergence of the Markov chain is ensured using Metropolis–Hastings algorithm. Simulated measurements are used to demonstrate the proposed concept for proving the robustness; finally, the measured temperatures based on in-house experimental setup are then used in the inverse estimation of the heat flux and the heat transfer coefficient for the purpose of validation. © Springer Nature Singapore Pte Ltd. 2019.
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    Analysis of forced convection heat transfer through graded PPI metal foams
    (Pleiades journals, 2019) Kotresha, B.; Gnanasekaran, N.
    A forced convection heat transfer through high porosity graded Pores per inch (PPI) metal foam heat exchanger is numerically solved in this paper. The physical domain of the problem consists of a heat exchanger system attached to the bottom of a horizontal channel to absorb heat from the exhaust gas leaving the system. Two different pore densities of the metal foam 20 and 40 along with two different metal foam materials are considered for the enhancement of heat transfer in the present numerical investigation. The metal foam heat exchanger is considered as a homogeneous porous medium and is modeled using Darcy Extended Forchheirmer model. The heat transfer through the metal foam porous media is solved by using local thermal equilibrium (LTE) model. The effect of graded pore density and graded thermal conductivity is investigated and compared with the nongraded PPI metal foam. The heat exchanger system is simulated over a velocity range of 6–30 m/s. The pressure drop decreases for the graded pore density metal foams compared to the higher PPI metal foam and also increases with increase in the fluid inlet velocity. The results of temperature and velocity distribution for the graded and nongraded metal foams are compared and discussed elaborately. © Springer Nature Singapore Pte Ltd. 2019.