No abstract

In this study, rough elliptic bore journal bearing performance is predicted using an artificial neural network (ANN) technique. The effects of non-circularity and roughness are quantified to elliptic and isotropic in macro and micro scale, respectively. The numerically estimated performance parameters like load, friction, and flow-in at different eccentricities [0.3 (low), 0.5 (medium), and 0.8 (high)], non-circularities [0.5 (low), 1.0 (medium), and 2.0 (high)], and roughness factors [0.1 (low), 0.2 (medium), 0.3 (medium), and 0.4 (high)] are used to train and build the ANN model. The training continued until the maximum mean square error is achieved, and the best-fitting plot is generated. With a confidence level of 99.75% or an R-value of 0.99757, the results predicted are found to be satisfactory.

Rough elliptic bore misaligned journal bearing performance involves many geometric and operational parameters, which directly or indirectly affect the thermohydrodynamic performance output. Improper design and manufacturing of journal bearings lead to enhanced friction, reduced operational life, and poor serviceability. A rule of thumb is to understand the operational efficiency of the bearing through modelling and simulation and to implement the knowledge of bearing critical parameters in manufacturing and operation. Therefore, decision-making in bearing parameter selection is a crucial process, for which several optimization tools and techniques have been developed from time to time. Moreover, these techniques have their own merits and demerits. This paper proposes a grey-based fuzzy approach to optimize the thermohydrodynamic performance of journal bearings with roughness, bore non-circularity, and shaft misalignment. Based on the results, the optimal level of factors is ε1 (0.3)-β1 (0.5)-G3 (2)-y1 (0.1), while at this condition, the optimal solutions for responses, such as Wis, Wth, Fis, Fth, Qis, and Qth are 3.684, 2.84, 165.2, 178.3, 5.67, and 6.32, respectively.

The current research is focused on investigating the influence of bearing irregularities like eccentricities (ε), non-circularities (G), L/D ratio (β), surface roughness coefficient (Y) on hydrodynamic performance of journal bearing such as load bearing capacity, friction force, lubricating oil flow.The performance is evaluated through finite difference method technique implementing a Newton-Raphson method of error convergence. The simulation outputs were than designed using an optimization tool entitled as Design of Experiment (DoE) on basis of response surface methodology (RSM). The outputs of RSM models were meant to forecast the response parameters like, load bearing capacity, friction force, flow of lubricant and further required to identify the noticeable interdependencies within the input parameters while evaluating the lubrication performance. The optimization of the bearing irregularities is done considering desirability approach of the response surface methodology in identifying proper combination of parameters.

Coal washery rejected coals and coal slurries have better options in froth flotation as a separation process. In this study, coal slurry received from Sudamdih coal washery, Jharkhand, India, examined for cleaning through fixed cell flotation. The Petrography study of the feed sample showed the presence of vitrinite, semi vitrinite and liptinite and inertinite as major minerals. General full factorial statistical design package (Minitab V17) was used to develop the regression models for the responses like froth height, froth density, recovery and ash content of clean coal. Results showed that experimental responses like froth height, froth density, recovery and ash content were found to be more sensitive to the frother dosage. The coefficient of correlation (R2) values between the experimental and the predicted values of the flotation responses was found to be >0.98 for all the models. Further, flotation tests were conducted for varying pulp density and its effect on recovery, froth height, ash content, tests with five levels of pulp density (8, 9, 10, 11, 12 and 13%solids by weight). It found that the coal slurry sample from the study area could be cleaned with sufficient efficiency. The cleaned coal is suitable for powder coal consuming industries.