Journal Articles
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884
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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 An approach for characterizing twin-tube shear-mode magnetorheological damper through coupled FE and CFD analysis(Springer Verlag service@springer.de, 2018) Gurubasavaraju, T.M.; Kumar, H.; Mahalingam, A.The most promising technology in the field of semi-active suspension systems is the use of magnetorheological property of MR fluid, whose material behavior can be controlled through external magnetic field. Devices developed based on this principle are adaptive and controllable as desired for a specific application. It is important to understand the damping characteristics of these devices before employing them, using experimental or computational approaches. In the present work, both experimental and computational methods have been adopted for characterizing a twin-tube MR damper with an intention to develop a computational approach as an alternative to experimental test in the preliminary design stage. Initially, experimental characterization of MR damper was carried out at 1.5 and 2 Hz frequencies for damper stroke length of ± 5 mm under different DC currents ranging from 0.1 to 0.4 A. Later, coupled finite-element and computational fluid dynamic analysis has been carried out to estimate the damping force under same conditions as used in the experiment. The results of computation are in good agreement with experimental ones. Furthermore, using this computational approach, the damping force at different frequencies of 1.5, 2, 3, and 4 Hz has been estimated and its time histories are also plotted. The influence of fluid flow gap on the damping force has been determined and results revealed that damping force behaves inversely with fluid flow gap. © 2018, The Brazilian Society of Mechanical Sciences and Engineering.Item A Bayesian inference approach: estimation of heat flux from fin for perturbed temperature data(Springer India, 2018) Kumar, H.; Gnanasekaran, N.This paper reports the estimation of the unknown boundary heat flux from a fin using the Bayesian inference method. The setup consists of a rectangular mild steel fin of dimensions 250×150×6 mm3 and an aluminium base plate of dimensions 250×150×8 mm3. The fin is subjected to constant heat flux at the base and the fin setup is modelled using ANSYS14.5. The problem considered is a conjugate heat transfer from the fin, and the Navier–Stokes equation is solved to obtain the flow parameters. Grid independence study is carried out to fix the number of grids for the study considered. To reduce the computational cost, computational fluid dynamics (CFD) is replaced with artificial neural network (ANN) as the forward model. The Markov Chain Monte Carlo (MCMC) powered by Metropolis–Hastings sampling algorithm along with the Bayesian framework is used to explore the estimation space. The sensitivity analysis of the estimated temperature with respect to the unknown parameter is discussed to know the dependency of the temperature with the parameter. This paper signifies the effect of a prior model on the execution of the inverse algorithm at different noise levels. The unknown heat flux is estimated for the surrogated temperature and the estimates are reported as mean, Maximum a Posteriori (MAP) and standard deviation. The effect of a-priori information on the estimated parameter is also addressed. The standard deviation in the estimation process is referred to as the uncertainty associated with the estimated parameters. © 2018, Indian Academy of Sciences.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 Performance analysis of a semi-active suspension system using coupled CFD-FEA based non-parametric modeling of low capacity shear mode monotube MR damper(SAGE Publications Ltd, 2019) Gurubasavaraju, G.; Kumar, H.; Mahalingam, A.In this work, an approach for formulation of a non-parametric-based polynomial representative model of magnetorheological damper through coupled computational fluid dynamics and finite element analysis is presented. Using this, the performance of a quarter car suspension subjected to random road excitation is estimated. Initially, prepared MR fluid is characterized to obtain a relationship between the field-dependent shear stress and magnetic flux density. The amount of magnetic flux induced in the shear gap of magnetorheological damper is computed using finite element analysis. The computed magnetic field is used in the computational fluid dynamic analysis to calculate the maximum force induced under specified frequency, displacement and applied current using ANSYS CFX software. Experiments have been conducted to verify the credibility of the results obtained from computational analysis, and a comparative study has been made. From the comparison, it was found that a good agreement exists between experimental and computed results. Furthermore, the influence of fluid flow gap length and frequency on the induced force of the damper is investigated using the computational methods (finite element analysis and computational fluid dynamic) for various values. This proposed approach would serve in the preliminary design for estimation of magnetorheological damper dynamic performance in semi-active suspensions computationally prior to experimental analysis. © IMechE 2018.