Faculty Publications

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Publications by NITK Faculty

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Author: search.filters.author.Gnanasekaran, N.Subject: search.filters.subject.Computational fluid dynamics simulations

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    A Markov Chain Monte Carlo-Metropolis Hastings Approach for the Simultaneous Estimation of Heat Generation and Heat Transfer Coefficient from a Teflon Cylinder
    (Taylor and Francis Ltd. michael.wagreich@univie.ac.at, 2018) Kumar, H.; Kumar, S.; Gnanasekaran, N.; Balaji, C.
    This paper reports the use of Markov Chain Monte Carlo (MCMC) and Metropolis Hastings (MH) approach, to solve an inverse heat transfer problem. Three-dimensional, steady state, conjugate heat transfer from a Teflon cylinder of dimensions 100 mm diameter and 100 mm length with uniform volumetric internal heat generation is considered. The goal is to estimate volumetric heat generation and heat transfer coefficient, given the temperature data at certain fixed location on the surface of the cylinder. The internal volumetric heat generation is specified as input and the temperature and heat transfer coefficient values are obtained by a numerical solution to the governing equation. The temperature values also depend on heat transfer coefficient which is obtained by solving Navier–Stokes equation to obtain flow information. In order to reduce the computational cost, a neural network is trained from the computational fluid dynamics simulations. This is posed as an inverse problem wherein volumetric heat generation and heat transfer coefficient are unknown but the temperature data is known by conducting experiments. The novelty of the paper is the simultaneous determination of volumetric heat generation and heat transfer coefficient for the experimentally measured steady-state temperatures from a Teflon cylinder using MCMC-MH as an inverse model in a Bayesian framework and finally, the estimates are reported in terms of mean, maximum a posteriori, and the standard deviation which is the uncertainty associated with the estimated parameters. © 2018 Taylor & Francis Group, LLC.
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    Investigation of Mixed Convection Heat Transfer Through Metal Foams Partially Filled in a Vertical Channel by Using Computational Fluid Dynamics
    (American Society of Mechanical Engineers (ASME) infocentral@asme.org, 2018) Kotresha, B.; Gnanasekaran, N.
    Two-dimensional computational fluid dynamics simulations of mixed convection heat transfer through aluminum metal foams partially filled in a vertical channel are carried out numerically. The objective of the present study is to quantify the effect of metal foam thickness on the fluid flow characteristics and the thermal performance in a partially filled vertical channel with metal foams for a fluid velocity range of 0.05-3 m/s. The numerical computations are performed for metal foam filled with 40%, 70%, and 100% by volume in the vertical channel for four different pores per inch (PPIs) of 10, 20, 30, and 45 with porosity values varying from 0.90 to 0.95. To envisage the characteristics of fluid flow and heat transfer, two different models, namely, Darcy Extended Forchheirmer (DEF) and Local thermal non-equilibrium, have been incorporated for the metal foam region. The numerical results are compared with experimental and analytical results available in the literature for the purpose of validation. The results of the parametric studies on vertical channel show that the Nusselt number increases with the increase of partial filling of metal foams. The thermal performance of the metal foams is reported in terms of Colburn j and performance factors. © Copyright 2018 by ASME.
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    3D coupled conduction-convection problem using in-house heat transfer experiments in conjunction with hybrid inverse approach
    (Emerald Group Holdings Ltd., 2019) Vishweshwara, P.S.; Kumar, M.K.; Gnanasekaran, N.; Mahalingam, A.
    Purpose: Many a times, the information about the boundary heat flux is obtained only through inverse approach by locating the thermocouple or temperature sensor in accessible boundary. Most of the work reported in literature for the estimation of unknown parameters is based on heat conduction model. Inverse approach using conjugate heat transfer is found inadequate in literature. Therefore, the purpose of the paper is to develop a 3D conjugate heat transfer model without model reduction for the estimation of heat flux and heat transfer coefficient from the measured temperatures. Design/methodology/approach: A 3 D conjugate fin heat transfer model is solved using commercial software for the known boundary conditions. Navier–Stokes equation is solved to obtain the necessary temperature distribution of the fin. Later, the complete model is replaced with neural network to expedite the computations of the forward problem. For the inverse approach, genetic algorithm (GA) and particle swarm optimization (PSO) are applied to estimate the unknown parameters. Eventually, a hybrid algorithm is proposed by combining PSO with Broyden–Fletcher–Goldfarb–Shanno (BFGS) method that outperforms GA and PSO. Findings: The authors demonstrate that the evolutionary algorithms can be used to obtain accurate results from simulated measurements. Efficacy of the hybrid algorithm is established using real time measurements. The hybrid algorithm (PSO-BFGS) is more efficient in the estimation of unknown parameters for experimentally measured temperature data compared to GA and PSO algorithms. Originality/value: Surrogate model using ANN based on computational fluid dynamics simulations and in-house steady state fin experiments to estimate the heat flux and heat transfer coefficient separately using GA, PSO and PSO-BFGS. © 2019, Emerald Publishing Limited.