Faculty Publications
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Item 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.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 A synergistic combination of Asymptotic Computational Fluid Dynamics and ANN for the estimation of unknown heat flux from fin heat transfer(Elsevier B.V., 2018) Kumar, H.; Gnanasekaran, N.This paper deals with conjugate heat transfer from a rectangular fin. The problem consists of mild steel (250 × 150 × 6 mm) fin placed vertically on aluminium base (250 × 150 × 4 mm). The aluminium plate is subjected to an unknown heat flux at the base. The fin set-up is modelled using ANSYS fluent 14.5. The fin geometry is surrounded by extended domain filled with air so as to account for natural convection conjugate heat transfer. Grid independence study is carried out to fix the number of grids. A simple correlation using Asymptotic Computational Fluid Dynamics (ACFD) is developed and the same is used as a forward model to obtain the temperature distribution considering heat flux as the input. The problem is treated as an inverse problem in which a non-iterative method, ANN is used as the inverse model to estimate the unknown heat flux from the information of temperature. The results of the forward model and the ANN predicted values are in close agreement with error less than 1%. Effect of noise on the unknown parameter is also studied extensively. © 2017 Faculty of Engineering, Alexandria UniversityItem Semi-active control of a swing phase dynamic model of transfemoral prosthetic device based on inverse dynamic model(Springer, 2020) Saini, R.S.T.; Kumar, H.; Chandramohan, S.Improving the gait of transfemoral amputees and making it biomimetic and stable has always been a major effort. A dynamic model of the prosthetic device can predict the kinetic and kinematic performances, when incorporated with a musculoskeletal model. In this regard, a dynamic model of a recent trend of variable damping technology will help a great deal in evaluating the performance of the prosthetic device and also in studying the effect of various parameters on the prostheses. The current paper presents the dynamic model of a single axis two segmental prosthetic knee implemented with a magneto-rheological (MR) damper as a variable damping element. The MR damper is modeled mathematically using Bouc–Wen model with model parameters evaluated by minimizing the error norms for time, displacement and velocity between the experimental and the model-generated results using a genetic algorithm. Two different experimental data sets are used, one for mathematical modeling and other to assess the accuracy of the fit model. A Proportional Derivative plus Controlled Torque controller is employed, and the parameters are tuned to minimize the error between the desired and control input torques. Further, an inverse dynamic model using Bouc–Wen model variables is assumed and validated later. This model predicts the current directly and avoids the necessity of solving any quadratic equation, which is required in the case of inverse models based on modified Bouc–Wen. The dynamic model of the prosthesis is analyzed for the swing phase alone, and the results show that the model traces the desired knee angle and also the shank reaches full knee extension at the end of this phase with terminal velocity small enough to be handled by an extension stop. © 2020, The Brazilian Society of Mechanical Sciences and Engineering.