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This paper aims to provide efficient methods to calculate the friction coefficients and film thickness ratios in mixed lubrication (ML) regime for water lubricated bearings. Mathematical models consider influence of micro-asperities contacts which is based on the Gauss random distribution.

Effects of external loads, rotating speeds and radial clearances are obtained. Algorithm shown here is applied to a class of common industrial problems. Calculated Stribeck values are given and evaluated. The calculated and experimental results agree well which proves the correctness of the model.

In Part I, the authors believe that the paper presents the following for the first time: universal methods are developed for the calculation of friction coefficients and film thickness ratios (lambda) in ML regime; effects of different external loads, rotating speeds and radial clearances on friction coefficients and film thickness ratios are presented in detail; comparisons are made between the results predicted by the model and experimental results, and they agree rather well which proves the correctness of the model.

Present work successfully develops universal methods for predicting the friction coefficients and film thickness ratios.

As the companion paper of Part I, this paper aims to get more insight into the essence of lambda and to reveal its nature and role in the transition of lubrication states. Mixed lubrication (ML) model with micro-asperities contacts has been discussed in details in Part I.

Mimetic algorithm is used to get numerical solutions. Relationships between film thickness ratios and lubrication states transition with different external loads, rotating speeds, radial clearances, elastic modulus, surface hardness and roughness parameters are obtained.

The characteristic parameters of transitions from boundary lubrication (BL) to ML and ML to hydrodynamic lubrication (HL) are studied to determine how these parameters change with above factors. Finally, the essence and major influencing factors of lambda are summarized for such bearings.

In Part II, the authors believe that the paper presents for the first time: further insight into the essence of the lambda ratio, and its role in the lubrication states transition are given; the determinations of the characteristic parameters of transition from BL to ML and ML to HL are investigated for the first time; the characteristic parameters of transitions from BL to ML and ML to HL are also studied to determine how parameters (external load, rotating speed, radial clearance, elastic modulus, surface hardness and roughness parameter) change with above factors; a summary of the essence and major influencing factors of lambda for such bearings is given.

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.

This paper presents an investigation into the classical problem using an experimental apparatus and applying an artificial neural network (NN) optimisation approach for determining the pressure distribution in a journal bearing at elasto‐hydrodynamic state of lubrication. The experimental system is employed at different working speeds with different surface roughness of shafts for getting pressure distribution. A NN is employed to predict the real data of the system as an optimisation technique. The NN is a radial basis function back propagation network. The NN has a superior performance to follow the desired results of the system and is employed to analyse such systems parameters in practical applications.

This paper aims to evaluate the vulnerability of typical low-rise reinforced concrete (RC) buildings located in Indonesia subjected to tsunami loading.

The vulnerability of typical three-story RC buildings located in Indonesia subjected to tsunami loading is discussed using fragility curves. Buildings without openings in all stories and buildings with openings in the first story are considered. The fragility curves are obtained by performing tsunami pushover analysis for several load cases, using different tsunami load estimation standards and references. The generalized linear method is used as a curve fitting method to construct the fragility curves.

The fragility curves show that the three-story RC buildings without openings in all stories subjected to tsunami loading have a high probability of collapse. Openings in the first story will reduce the vulnerability of the buildings.

Fragility curves are obtained by carrying out tsunami pushover analysis to evaluate the vulnerability of typical three-story RC buildings located in Indonesia. The results of this study show the need to include tsunami loads in the design code for Indonesian buildings and the benefits of having openings in the first story of the building.

Cavitation in piston-ring lubrication is studied as part of the performance of piston-ring assemblies. Cavitation degrades performance in engineering applications and its effect is that it alters the oil film pressure, generated at the converging-diverging wedge of the interface. Studies tried to shed light to the phenomenon of cavitation and compare it with cavities that have been identified in bearings. The paper aims to discuss this issue.

Lubricant formulations were used for parametric study of oil film thickness (OFT) and friction providing the OFT throughout the stroke and LIF for OFT point measurements. Lubricant formulation affects cavitation appearance and behaviour when fully developed.

Cavitation affects the ring load carrying capacity. Different forms of cavitation were identified and their shape and size (length and width) is dictated from reciprocating speed and viscosity of the lubricant. A clear picture is given from both techniques and friction results give quantifiable data in terms of the effect in wear and cavitation, depending on the lubricant properties.

Engine results are limited due to manufacturing difficulties of visualisation windows and oil starvation. Therefore, full stroke length sized windows were not an option and motoring tests were implemented due to materials limitations (adhesive and quartz windows). Lubricant manufacturer has to give data regarding the chemistry of the lubricants.

The contribution of cavitation in piston-ring lubrication OFT, friction measurements and lubricant parameters that try to shed light to the different forms of cavitation. A link between viscosity, cavitation, shear thinning properties, OFT and friction is given.

The purpose of this paper is to improve hydrodynamic load-carrying capacity of a water-lubricated journal bearing by a new bush structure. Water-lubricated bearing is becoming more and more popular since it is environmentally friendly and saves energy. However, contrary to oil and grease-lubricated bearings, water-lubricated bearing is limited in many situations due to its low hydrodynamic load-carrying capacity.

The present article proposes a new bearing bush, with a transition-arc structure, which is favorable for increasing hydrodynamic load-carrying capacity. Hydrodynamic load-carrying capacity was calculated by means of three-dimensional computational fluid dynamics (3-D CFD) analysis. Several variants of a journal bearing with a transition-arc structure of different dimensions are analyzed, while the radial clearance of the bearing, eccentricity ratio and the velocity of the journal remain unchanged.

The results show that obvious changes are found in hydrodynamic load-carrying capacity of a water-lubricated journal bearing. For different width over diameter (L/D) bearing ratios, the relationship between hydrodynamic load-carrying capacity and the magnitude of the transition-arc structure dimension is researched.

The research presented here leads to a design reference guideline that could be used by the designer engineer to design smart journal bearings for improving the hydrodynamic load-carrying capacity.

This paper presents an investigation for analysing the load carrying capacity of journal bearing in a variety of conditions using a proposed neural network (NN). The NN structure is very suitable for this kind of system. The network is capable of predicting the pressures of the experimental system. The network has parallel structure and fast learning capacity. It can be outlined from the results for both approaches, NN could be used to model journal bearing systems in real time applications.

The purpose of this paper is to study the influence of structural parameters of oil groove (such as central angle number, depth and so on) on pressure, flow, load capacity and transmitted torque between friction pairs of hydro-viscous clutch.

According to the working process of friction pairs of hydro-viscous clutch, mathematical models of hydrodynamic load capacity and torque transmitted by the oil film were built based on viscosity-temperature property. Then analytical solutions of pressure, flow, load capacity and transmitted torque were obtained; effects of central angle of oil groove zone and friction contact zone, oil film thickness, number of oil grooves on pressure, flow, load capacity and torque were studied theoretically.

The research found that the central angle of oil groove zone, number of oil grooves and oil groove depth have similar effects on flow, which means that with the increase of central angle, number or depth of oil grooves, the flow also increases; pressure in friction contact zone and oil groove zone drops along radial direction, whereas its value in oil groove zone is higher. With the increase of the central angle of oil groove zone, pressure in friction contact zone and friction contact zone rises, and the load capacity increases, whereas the transmitted torque drops. Number of oil grooves has little effect on load capacity. When the oil film thickness increases, its flow increases accordingly, whereas the pressure, load capacity and transmitted torque drops. Meanwhile, the transmitted torque decreases with the increase of number of oil grooves, whereas the oil groove depth nearly has no effects on transmitted torque.

In this paper, mathematical models of hydrodynamic load capacity and torque transmitted by oil film were built based on viscosity-temperature property in the working process of hydro-viscous clutch, and their analytical solutions were obtained; effects of structural parameters of oil groove on transmission characteristics of hydro-viscous clutch based on viscosity-temperature property were revealed. The research results are of great value to the theory development of hydro-viscous drive technology, the design of high-power hydro-viscous clutch and relative control strategy.

This paper investigates the pressure variations on the steel shafts on the journal bearing system with low temperature and variable speed. This paper mainly consist of two parts, experimental and simulation. In the experimental work, journal bearing system is tested with different shafts speed and temperature conditions. The temperature of the system's working conditions was under minus. The collected experimental data such as pressure variations are employed as training and testing data for an artificial neural network. The neural network is a feed forward three layered network. Quick propagation algorithm is used to update the weight of the network during the training. Finally, neural network predictor has superior performance for modelling journal bearing systems with load disturbances.