Author
D.K. Bharuka
Bio: D.K. Bharuka is an academic researcher from Indian Institute of Technology Roorkee. The author has contributed to research in topics: Bearing (mechanical) & Thrust bearing. The author has an hindex of 1, co-authored 1 publications receiving 94 citations.
Topics: Bearing (mechanical), Thrust bearing, Geometric shape, Thrust
Papers
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TL;DR: In this article, a theoretical study concerning the static and dynamic performance of a circular thrust pad hydrostatic bearing having recesses of different geometric shapes has been performed using the Finite Element Method.
104 citations
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TL;DR: In this article, a theoretical study concerning the performance of an externally pressurized multirecess hydrostatic/hybrid flexible journal bearing system by varying the geometric shape of recess and using the membrane flow valve restrictor as a compensating element is presented.
68 citations
TL;DR: In this article, the influence of the dimple geometry on hybrid thrust pad bearing and non-Newtonian lubricant was investigated using Finite Element Method (FEEM) and the results indicated that the values of load carrying capacity, frictional power loss, maximum pressure and fluid film stiffness coefficient are significantly affected by the behavior of lubricant.
55 citations
TL;DR: In this article, the influence of the tilt and recess shape on the static and dynamic performance characteristics of the hydrostatic thrust pad bearing system having Rabinowitsch fluid model lubricant was numerically analyzed.
35 citations
TL;DR: A key part that provides precision and long life to machine tools is the hydrostatic bearing as discussed by the authors, which is one of the embodiments of tribology, mechanics, optimization method, and structural design in engine design.
Abstract: Hydrostatic bearing is a key part that provides precision and long life to machine tools. It is one of the embodiments of tribology, mechanics, optimization method, and structural design in enginee...
34 citations
TL;DR: It is concluded that the vibration analysis based on the analysis of the dynamic behaviour of oil pressure subjected to disturbances such as changes in oil operating conditions including viscosity, is successfully feasible.
Abstract: Neural network based functional approximation techniques associated with rule based techniques are applied on the condition monitoring task of rotating machines equipped with hydrostatic self levitating bearings. Based on fluid online measured characteristic data, including pressures and temperature, the inherent hydraulic pumping system and the self levitating shaft is monitored and diagnosed applying vibration analysis carried out using virtual measurements. Required signals are achieved by conversion of measured data (fluid temperatures and pressures) into virtual data (vibration magnitudes) by means of neural network functional approximation techniques. Previous to the condition monitoring task (vibration analysis), a supervision task of the system behaviour is carried out in order to validate the information being processed. It is concluded that the vibration analysis based on the analysis of the dynamic behaviour of oil pressure (non accelerometer based signals) subjected to disturbances such as changes in oil operating conditions including viscosity, is successfully feasible.
30 citations