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The purpose of this paper is to study the flow field characteristics of the micro-scale textured bearing surfaces using the lattice Boltzmann method based on the microscopic dynamics of the fluid.

Considering the inertia effects and the micro-scale effects, the models of a single micro-scale texture unit cell with different shapes and different film thickness ratios are established. The influence of pressure difference between inlet and outlet of the unit cell on flow characteristics is studied.

The surface pressure distribution, flow patterns and pressure contours in the flow field are obtained. The results reveal that the pressure difference has a significant influence on the characteristics of the micro-textured flow field.

The results have certain guiding significance for further step investigation on microscopic lubrication mechanism of the water-lubricated polymer bearings.

The purpose of this paper is to investigate pressure distribution of the journal bearings with aluminium shafts with varying surface porosity in varying revolutions using experimental and neural network approach.

The collected experimental data such as pressure variations is employed as training and testing data for an artificial neural network (ANN). Back propagation algorithm is used to update the weight of the network during the training.

Neural network predictor has superior performance for modelling journal bearing systems with shafts of different surface porosities.

Back propagation algorithm is used training algorithm for proposed neural networks. Various training algorithms can be used to train proposed network. The spectrum of the journal surface porosity can be enlarged.

From the experimental and simulation results, neural network exactly follows the experimental results. Because of that, this kind of neural network predictors can be applied on journal bearing systems in practice applications.

This paper discusses a new modelling scheme known as ANNs. A neural network predictor has been employed to analyze of the effects of shaft surface porosity in hydrodynamic lubrication of journal bearing.

To discuss the effects of metal matrix composite (MMC) journal structure on the pressure distribution and, consequently, on the load‐carrying capacity of the bearing are predicted using feed forward architecture of neurons.

The inputs to the networks are the collection of experimental data. These data are used to train the network using the Batch Back‐prop, Online Back‐prop and Quickprop algorithms.

The neural network (NN) model outperforms the available experimental model in predicting the pressure as well as the load‐carrying capacity.

The experiment specimens used in this study have been made of MMC with aluminum based reinforced with SiC ceramic particles, using the stir casting technique. Various composite journal structures can be investigated.

The simulation results suggest that the neural predictor would be used as a predictor for possible experimental applications on modelling journal bearing system.

This paper discusses a new modelling scheme known as artificial NNs. An experimental and a NN approach have been employed for analysing MMC journal structure for hydrodynamic journal bearings and their effects on the system performance.

The purpose of this paper is to study the effects of shaft surface profiles on the load carriage capacity of journal bearings using an experimental and neural network approach. The paper aims to inspect the performance characteristics of journal bearing systems; the presence of transverse and longitudinal roughness on journal‐shaft surfaces is studied using the proposed neural network.

The collected experimental data such as pressure variations are employed as training and testing data for an artificial neural network (ANN). Quick propagation algorithm is used to update the weight of the network during the training.

As a result, a shaft with a transverse profile displays a favorable performance as far as load carriage capacity is concerned. Moreover, the proposed neural network structure outperforms the available experimental model in predicting the pressure as well as the load carriage capacity.

The paper discusses a new modelling scheme known as ANN. A neural network predictor has been employed to analyze the effects of shaft surface profiles in the hydrodynamic lubrication of journal bearings.

The purpose of this study is to understand the functional properties of ball valve in a compressible flow and simulation of experimental data collection of ball valve, was completely simulated.

Equations are solved according to finite volume and simplified algorithms. By measuring the flow parameters, including pressure and temperature at different points in the simulation circuit, flow coefficients and localized drop in the valve were determined in different openness cases of test valve and compared with experimental results. Determining a graph for flow coefficient variations in terms of the percentage of openness of the valve is very effective on the flow control as well as on optimizing its cross-section.

In the supersonic flow, flow coefficients and local drops of the valve are dependent on several parameters, including fluid flow rate. Flow coefficient graphs at different angles of the test valve show that by increasing the valve opening angle, the flow coefficient increases so that it reaches from 1.72 m³/h at a 30° angle to 46.29 m³/h at a 80° angle. It should be noted that these values in the experimental test were obtained 1.53 m³/h and 49.68 m³/h, respectively, and the percentage difference of these values by simulation was obtained for the angle of 30 degrees 11.7% and for the angle of 80°, about 7% per hour at an angle of 80°. Also, the coefficients of localized loss at different angles of test valve show that by increasing the angle of opening of the valve, the amount of localized loss decreases, so that the average value of 1515.2 in the angle of 30° reaches 1.9 at an angle of 80°. The percentage difference of these values by simulation, for the angle of 30° and 3.5% for the angle of 80°, was about 11.1%.

Determining a graph for flow coefficient variations versus the percentage of openness of the valve is very effective on the flow control as well as on optimizing its cross-section. In the supersonic flow, flow coefficients and local drop coefficients of the valve are dependent on several parameters, including fluid flow rate.

This paper investigates the load carrying capacity of the journal bearings with steel shafts with varying surface texture in varying revolutions using experimental and neural network (NN) approach.

In this study, we used a shaft with smooth surface with the same material properties compare their load carrying capacities of the shafts with three different pitches and two different profiles. The experimental data, such as pressure and oil temperature, are employed as training and testing data for NN. Quick Prop algorithm is used to update the weight of the network during the training.

The designated NN has superior performance for modelling of the system. Therefore, the proposed neural predictor would be used as a predictor for possible experimental applications on modelling bearing system.

Mobil 0W‐40 lubricant was used and kept at temperature of 18°C. The surface of the shafts has been in two types: smooth, that is without and with profiles, that is trapezoidal and saw.

Owing to the parallel structure and fast learning of NN, this kind of algorithm will be utilized to model other types of bearing systems.

Instead of traditional methods, NN has fast learning and parallel processing structure. Moreover, NN can be used to process multiple‐input/multiple‐output data unlike other empirical modelling tools which can map one dependent variable at a time. Therefore, this method is able to predict the load carrying capacity with steel shafts with varying surface texture in varying revolutions satisfactorily where common techniques have failed.

The purpose of this paper is to investigate and discuss the influence of the pattern, size and orientation of textures on journal bearing load carriage capacity. An important development in load carriage capacity of journal bearings can be obtained by forming regular surface structure in the form of threaded on their shaft surfaces. This is performed both theoretically and experimentally using shafts with textured (threaded) and untextured surfaces. Each screw thread can serve either as a micro‐hydrodynamic bearing in cases of full or mixed lubrication or as a micro reservoir for lubricant in cases of starved lubrication conditions.

The pressure distribution and the load‐carrying capacity are predicted using feed forward architecture of neurons. The inputs to the neurons are a collection of experimental data. These data are used to train the network using the delta‐bar‐delta, batch‐backprop, backprop, and backprop‐rand algorithms. The proposed neural model outperforms the available experimental system in predicting the pressure as well as load‐carrying capacity.

Theoretical models are developed using a neural network approach, and tests are performed, to investigate the potential of threaded textured surfaces in tribological components like mechanical seals, piston rings and journal bearings. In these tests, load carriage capacity is significantly increased with threaded textured shaft surfaces to the shafts with non‐textured surfaces.

The paper discusses a new modelling scheme known as artificial neural networks. A neural network predictor has been employed to analyze the effects of shaft surface profiles in hydrodynamic lubrication of journal bearing.

The purpose of this paper is to empirically investigate the pressures and drivers that have been experienced by Chinese manufacturing small and medium enterprises (SMEs) in terms of green supply chain management (GSCM).

The research framework and hypotheses are examined by a questionnaire survey through e-mails conducted in China in 2011. The empirical analysis is based on the data from 202 SME manufacturers in China. Validity and reliability of the items employed in the research is assessed through Cronbach’s α test. Hypotheses for the identification of GSCM pressures and drivers to SMEs as well as the differences that exist among different industrial sectors are tested by adopting descriptive statistics analysis and analysis of variance test.

This study finds that Chinese manufacturing SMEs have been under pressures from a variety of sources, including regulations, customers, suppliers and public awareness in terms of GSCM. Besides, internal drivers are also an important encouragement for SMEs to consider GSCM. Moreover, Chinese manufacturing SMEs from different industrial sectors show some differences in experiencing pressures or being motivated by drivers.

The main limitations to this paper are the relatively small sample of SMEs and the potentially overlooked variables.

Chinese manufacturing SMEs and their larger customers, as well as governments, are likely to obtain some implications from this study if they are willing to consider any GSCM initiatives throughout the supply chain.

The paper clearly explores the GSCM pressures and drivers faced by the Chinese manufacturing SMEs where the results may differ from the findings through the studies on large enterprises or SMEs in other national context.

This study aims to minimize the pressure drop across wavy microchannels using secondary branches without compromising its capacity to transfer the heat. The impact of secondary flows on the pressure drop and heat transfer capabilities at different Reynolds numbers are investigated numerically for different wavy microchannels. Finally, different channels are evaluated using performance evaluation criteria to determine their effectiveness.

To investigate the flow and heat transfer capabilities in wavy microchannels having secondary branches, a 3D conjugate heat transfer model based on finite volume method is used. In conventional wavy microchannel, secondary branches are introduced at crest and trough locations. For the numerical simulation, a single symmetrical channel is used to minimize computational time and resources and the flow within the channels remains single-phase and laminar.

The findings indicate that the suggested secondary channels notably improve heat transfer and decrease pressure drop within the channels. At lower flow rates, the secondary channels demonstrate superior performance in terms of heat transfer. However, the performance declines as the flow rate increased. With the same amplitude and wavelength, the introduction of secondary channels reduces the pressure drop compared with conventional wavy channels. Due to the presence of secondary channels, the flow splits from the main channel, and part of the core flow gets diverted into the secondary channel as the flow takes the path of minimum resistance. Due to this flow split, the core velocity is reduced. An increase in flow area helps in reducing pressure drop.

Many complex and intricate microchannels are proposed by the researchers to augment heat dissipation. There are challenges in the fabrication of microchannels, such as surface finish and achieving the required dimensions. However, due to the recent developments in metal additive manufacturing and microfabrication techniques, the complex shapes proposed in this paper are feasible to fabricate.

Wavy channels are widely used in heat transfer and micro-fluidics applications. The proposed wavy microchannels with secondary channels are different when compared to conventional wavy channels and can be used practically to solve thermal challenges. They help achieve a lower pressure drop in wavy microchannels without compromising heat transfer performance.

Oil film thickness measurements made in the front main bearing of an operating 3.8 L, V‐6 engine were compared with rheological measurements made on a series of commercial and experimental oil blends. High‐temperature, high‐shear‐rate viscosity measurements correlated with the film thickness of all single‐grade and many multigrade oils. However, the film thickness provided by some multigrade oils were larger than could be accounted for by their high‐temperature, high‐shear‐rate viscosities alone. Although the pressure/viscosity coefficients of some of the oils were significantly different from those of the majority of oils tested, they were not oils which produced unusual film thicknesses. As a consequence, correcting oil viscosities for the esimated pressures acting within the bearing was unsuccessful in improving the correlations. The correlations were improved, however, by accounting for the elastic properties of the multigrade oils. Measurements of oil relaxation times at high temperatures and shear rates showed large differences in elastic properties among the test oils. A good correlation (R2 = 0.73) was obtained from a multiple linear regression of film thickness as a function of both high‐temperature, high‐shear‐rate viscosities and relaxation times.