Journal Articles

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884

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Author: search.filters.author.Gnanasekaran, N.Subject: search.filters.subject.Cylinders (shapes)

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    A neural network based method for estimation of heat generation from a teflon cylinder
    (Global Digital Central, 2016) Kumar, S.; Kumar, H.; Gnanasekaran, N.
    The paper reports the estimation of volumetric heat generation (qv) from a Teflon cylinder. An aluminum heater, which acts as a heat source, is placed at the center of the Teflon cylinder. The problem under consideration is modeled as a three dimensional steady state conjugate heat transfer from the Teflon cylinder. The model is created and simulations are performed using ANSYS FLUENT to obtain temperature data for the known heat generation qv. The numerical model developed using ANSYS acts as a forward model. The inverse model used in this work is Artificial Neural Network (ANN). Estimation of heat generation is carried out by minimizing the error between the simulated temperature and the experimental/surrogated temperature. The efficacy of the ANN method is explored for the estimation of unknown heat generation as both forward model and inverse model. The concept of Asymptotic Computational Fluid Dynamics (ACFD) is introduced as a fast forward model which is obtained by performing CFD simulations. The unknown heat generation is estimated for the surrogated data using ANN. In order to mimic experiments, noise is added to the surrogated data and estimation of heat generation is also carried out for the perturbed/noise added temperature data. © 2016, Global Digital Central. All rights reserved.
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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.