Please use this identifier to cite or link to this item: http://localhost:8081/xmlui/handle/123456789/10491
Title: OPTIMIZATION OF JOURNAL BEARING
Authors: Suresh, Anaskure Avinash
Keywords: MECHANICAL INDUSTRIAL ENGINEERING;JOURNAL BEARING;FEM FORMULATION;MATLAB CODE
Issue Date: 2010
Abstract: Journal bearings have been widely used to support high speed rotating machinery such as turbines and compressors because of their superior durability and load carrying capacity. Therefore, the bearings are important machine elements for enhancing the quality of the rotating machinery. As the performance characteristics of high-speed, hydrodynamic journal bearings operated in both laminar and turbulent flow regimes are governed by a number of bearing parameters, Generally the selection of design variables in bearing design is done by trial & error method using many design charts, however it is not so easy to successfully select optimum design variables by such a method & a considerable time & cost is needed to complete the optimum design of bearings. Earlier the optimization was single objective & done by the classical methods but few years back multi-objective optimization is being done, as it gives better results than single objective optimization. Evolutionary algorithms are increasingly being used for optimization. In this study FEM formulation is used to solve Reynolds's equation and a MATLAB code is developed for computing the nodal pressure distribution and the performance parameters. The variation of performance parameters with eccentricity and for different LID ratios is accurate in comparison with the literature. An optimization problem is formulated to minimize power loss and maximize load carrying capacity. A computational procedure based on Genetic Algorithm is established to find the optimum values of design variables, length to diameter ratio and viscosity. The numerical results are given in graphical form for a wide range of journal rotational speed. For multi-objective optimization methods of weighting functions/scaling parameters as well as pareto optimal fronts are used.
URI: http://hdl.handle.net/123456789/10491
Other Identifiers: M.Tech
Research Supervisor/ Guide: Sharma, Satish C.
metadata.dc.type: M.Tech Dessertation
Appears in Collections:MASTERS' DISSERTATIONS (MIED)

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