Faculty Publications
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Item Rheodynamic lubrication of a squeeze film bearing under sinusoidal squeeze motion(Springer Science and Business Media, LLC, 2007) Kandasamy, A.; Vishwanath, K.P.Lubricants with variable viscosity are assuming importance for their applications in polymer industry, thermal reactors and in biomechanics. With the bearing operations in machines being subjected to high speeds, loads, increasing mechanical shearing forces and continually increasing pressures, there has been an increasing interest to use non-Newtonian fluids characterized by an yield value. The most elementary constitutive equation in common use that describes a material which yields is that of Bingham fluid. In the present work, the problem of a circular squeeze film bearing lubricated with Bingham fluid under the sinusoidal squeeze motion has been analyzed. The shape and extent of the core for the case of sinusoidal squeeze motion has been determined numerically for various values of the Bingham number. Numerical solutions have been obtained for the bearing performances such as pressure distribution and load capacity for different values of Bingham number, Reynolds number and for various amplitudes of squeeze motion. The effects of fluid inertia, non-Newtonian characteristics, and the amplitudes of squeeze motion on the bearing performances have been discussed. Copyright © 2007 SBMAC.Item Inertia effects in circular squeeze film bearing using Herschel-Bulkley lubricants(2010) Vishwanath, K.P.; Kandasamy, A.Recent engineering trends in lubrication emphasize that in order to analyze the performance of bearings adequately, it is necessary to take into account the combined effects of fluid inertia forces and non-Newtonian characteristics of lubricants. In the present work, the effects of fluid inertia forces in the circular squeeze film bearing lubricated with Herschel-Bulkley fluids with constant squeeze motion have been investigated. Herschel-Bulkley fluids are characterized by an yield value which leads to the formation of a rigid core in the flow region. The shape and extent of the core formation along the radial direction is determined numerically for various values of Herschel-Bulkley number and power-law index. The bearing performances such as pressure distribution and load capacity for different values of Herschel-Bulkley number, Reynolds number, power-law index have been computed. The effects of fluid inertia and non-Newtonian characteristics on the bearing performances have been discussed. © 2009 Elsevier Inc. All rights reserved.Item Application of differential transform method for estimating thermal cycle developed in GTA welding of high carbon steel joints(Trans Tech Publications Ltd ttp@transtec.ch, 2015) Dutta, J.; Narendranath, S.; Zhilin, Z.This article reveals a detailed study of temperature cycle formed during Gas Tungsten Arc welding of high carbon steel (AISI 1090) butt joints. Experimental work has been carried out to estimate the temperature distribution along fusion boundary to longitudinal direction of the weldment by mounting thermocouples on the plate along with Data Acquisition System. Heat flux distribution due to moving point heat source has been demonstrated by implementing Gaussian surface heat flux and Angular Torch model. Cooling rate has predicted by application of Adams cooling rate equation. Conduction-convection phenomena plays dominant role for evaluating heat loss from the weld joint and Differential Transform Method (DTM) has been applied to judge non-dimensional temperature distribution. Analytical studies has shown well agreement with experimental temperature distribution. © (2015) Trans Tech Publications.Item Machine Learning for Vortex Flowmeter Design(Institute of Electrical and Electronics Engineers Inc., 2022) Thummar, D.; Reddy, Y.J.; Venugopal, V.Vortex flowmeters are one of the broadly used flow measurement devices in various industrial applications. The shape of the bluff body is the most critical parameter in the design of vortex flowmeter. The conventional approach of bluff body design relies on parametric shape optimization of a bluff body using experimentation and computational fluid dynamics simulations, which are expensive and time-consuming. In this study, we propose a novel machine learning (ML)-based approach to design bluff body shapes. Two ML models are developed using supervised ML using an artificial neural network (ANN). The first model predicts new optimum bluff body shapes for a given input flow characteristic. The second model predicts the deviation in Strouhal number for a given bluff body to determine its optimality. Data from the literature on the geometry of bluff bodies and fluid flow properties such as blockage ratio, Reynolds number, and Strouhal number are used for training ML models. The obtained ML results are in close agreement (±3.0%) compared with the computational fluid dynamics simulation results. This approach may find broad applicability for designing other fluid flowmeters. © 1963-2012 IEEE.