Faculty Publications
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Item 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.Item Determination of interfacial heat transfer coefficient for the flow assisted mixed convection through brass wire mesh(Elsevier Masson SAS 62 rue Camille Desmoulins Issy les Moulineaux Cedex 92442, 2019) Kotresha, B.; Gnanasekaran, N.In this work, a numerical investigation of Darcy?Forchheimer mixed convection from a heated vertical flat plate embedded in a brass wire mesh porous medium is carried out to determine the coupled effects of flow and thermal diffusion. The numerical model consists of a two dimensional computational domain in which conjugate heat transfer analysis is performed to predict the hydrodynamic and thermal performance of the brass wire mesh in a vertical channel using Local Thermal Non-Equillibrium (LTNE) model. The novelty of the present study is to acquire the interfacial heat transfer coefficient, an as yet another challenging task, of the wire mesh porous medium so as to provide a quick and feasible solution to modeling of fluid flow and heat transfer through brass wire mesh porous media. The results of heat transfer through brass wire mesh are reported in terms of Colburn j factor, performance factor and are compared with other porous mediums available in literature. The present study not only opens up new vistas for more parametric studies but also provides practical and cost effective assessment to design new porous materials. © 2018 Elsevier Masson SASItem 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 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.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 Performance score based multi-objective optimization for thermal design of partially filled high porosity metal foam pipes under forced convection(Elsevier Ltd, 2022) Jadhav, P.H.; Trilok, G.; Gnanasekaran, N.; Mobedi, M.Optimization study in flow through metal foams for heat exchanging applications is very much essential as it involves variety of fluid flow and structural properties. Moreover, the identification of best combinations of structural parameters of metal foams for simultaneous improvement of heat transfer and pressure drop is a pressing situation. In this work, six different metal foam configurations are considered for the enhancement of heat transfer in a circular conduit. The foam is aluminum with PPI varying from 10 to 45 and almost the same porosity (0.90-0.95). The aluminum foams are chosen from the available literature and they are partially filled in the conduit to reduce the pressure drop. For a constant heat flux condition, the goal is to find out the efficient metal foam and configurations when air is considered as a working fluid. A special attention is paid to the preference between pressure drop and heat transfer enhancements. That is why TOPSIS optimization techniques with five different criteria (contains the combination of the weightage/priority of heat transfer and pressure drop) is used. Based on the numerical results of heat and fluid flow in conduit, it is found that when an equal importance is given to both heat transfer and friction effect, 30 PPI aluminum foam with 80% filling on the inner lateral of the pipe provides the best score as 0.8197. The best configuration and PPI for different preferences between friction and heat transfer enhancements is discussed in details. The Reynolds number varies from 4500 to 16500. © 2021 Elsevier Ltd