Faculty Publications
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Publications by NITK Faculty
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Item 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.Item MCMC and approximation error model for the simultaneous estimation of heat flux and heat transfer coefficient using heat transfer experiments(Begell House Inc., 2018) Gnanasekaran, N.; Kumar, M.K.; Balaji, C.This work deals with the simultaneous estimation of the heat flux and the heat transfer coefficient from a mild steel fin losing heat to the ambient by natural convection. Steady state heat transfer experiments are performed on a mild steel fin of dimension 150x250x6 (all dimensions are in mm) placed on to an aluminum base plate of dimension 150x250x8 (all dimensions are in mm). The experimental set up is placed inside a large enclosure to avoid natural disturbances. Nine calibrated K-type thermocouples are used to measure the temperatures of the fin and the base plate. The forward solution of a three dimensional conjugate heat transfer fin model is solved using commercially available ANSYS software in order to obtain the temperature distribution of the fin. An inverse problem is proposed for the estimation of unknown parameters within the Bayesian framework of statistics. Furthermore, a model reduction in the form of Approximation Error Model (AEM) is considered for the inverse conjugate natural convection heat transfer problem. Such an approach not only mitigates the complexity of the inverse problem but also compensates the model reduction with all necessary statistical parameters. Additionally, the sample space within the Bayesian framework is explored with the help of Markov Chain Monte Carlo Method (MCMC) along with the Metropolis-Hastings algorithm. The results of the inverse estimation using Approximation Error Model based on the experimental temperature prove to be a promising alternative in inverse conjugate heat transfer problems. © 2018 International Heat Transfer Conference. All rights reserved.Item Computation of error model for the inverse bioheat transfer problem(Dalian University of Technology, 2018) Gnanasekaran, N.; Vishweshwara, P.S.An inverse estimation of size and location of tumor is proposed in this paper using Bayesian framework. The forward model comprises of the Pennes equation and solved using commercial software. The forward solution of the problem is validated against the available literature and the results are found to be promising. Estimation of the size and location of the tumor is attempted based on Bayesian framework along with the Markov chain Monte Carlo method. This paper also demonstrates 2D and 3D modelling of the cancerous tissue and exploits the advantage of 2D model in the computation of MCMC method. An Approximation Error Model (AEM) is proposed in order to statistically account the model error during the estimation of the unknown parameters. The results of the AEM provide a new trend in the parametric study of cancerous tissue. © 2018 by the authors of the abstracts.