Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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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.Item A novel framework for the estimation of interfacial heat transfer coefficient using Bat algorithm during solidification of metal casting(Toronto Metropolitan University, 2019) Vishweshwara, P.S.; Gnanasekaran, N.; Arun, M.In the present work, the interfacial heat transfer coefficient (IHTC) at the mold metal interface is estimated during solidification of Al-4.5wt%Cu alloy using ANN-Bat-Bayesian framework. The forward model comprises of a one dimensional transient governing equation for the solidification of metal casting and is solved using explicit finite difference scheme with the available IHTC correlation from the literature. Within the range of values of constants of IHTC correlation, a set of numerical simulation is performed and corresponding temperature output is trained using Artificial Neural Network (ANN). The network created acts a fast forward model replacing the FDM scheme during IHTC estimation thus reducing computational time. Bat algorithm is used as inverse method along with the Bayesian framework, that drives towards the accurate retrieval of unknown parameters. © 2019, Toronto Metropolitan University. All rights reserved.Item A Surrogate Forward Model Using Artificial Neural Networks in Conjunction with Bayesian Computations for 3D Conduction-Convection Heat Transfer Problem(Springer, 2020) Kumar, M.K.; Vishweshwara, P.S.; Gnanasekaran, N.The present work describes the determination of heat flux at the boundary for a conjugate heat transfer problem based on a coupled three-dimensional conduction-convection fin numerical model, also referred to as complete model. The model is developed using commercially available software and solved along with Navier–Stokes equation in order to acquire the required temperature distribution. An inverse analysis is proposed by treating the boundary heat flux as unknown while the temperatures of the fin are known. The inverse analysis is greatly accomplished with the help of Bayesian framework that combines the solution of the forward model and the simulated measurements. Markov chain Monte Carlo (MCMC) is applied to explore the sample space that drives samples to proper convergence and the selection or acceptance of the new samples is performed using Metropolis–Hastings algorithm. Thus, the novelty of the present work is the use of artificial neural network (ANN) as surrogate model, that not only retains the full nature of the complete model but also acts as a fast forward model in the inverse analysis, within the Bayesian framework that quantifies the uncertainty of heat flux. The results of the present work emphasize that even for noise-added temperature data the final estimates are very close to the actual values and the uncertainty of the unknown heat flux is reported in terms of standard deviation accompanied by mean and maximum a posteriori (MAP). © 2020, Springer Nature Singapore Pte Ltd.