Lubricant additives effects on the hydrodynamic lubrication of misaligned conical–cylindrical bearings

Conical–cylindrical bearings are used in electrohydraulic servo systems to improve the control accuracy, eliminate the static friction and increase the normal load‐carrying capacity. A non‐Newtonian rheological model to investigate theoretically the effects of lubricant additives on the performance of misaligned conical–cylindrical bearings is proposed in this study. In this model, the non‐Newtonian behaviour resulting from blending the lubricant with polymer additives is simulated by Stokes couple stress fluid model. The formed boundary layer at the bearing surface is described through the use of a hypothetical porous medium layer that adheres to the bearing surface. The Brinkman‐extended Darcy equations are utilised to model the flow in the porous region. A stress jump boundary condition is applied at the porous media/fluid film interface. The misalignment of the cylinder rod is also considered. A modified form of the Reynolds equation is derived and solved numerically using a finite difference scheme. The effects of bearing geometry and non‐Newtonian behaviour of the lubricant on the steady‐state performance characteristics such as pressure distribution, load‐carrying capacity and coefficient of friction are presented and discussed. The results showed that lubricant additives significantly increase the load‐carrying capacity and reduce the coefficient of friction as compared to the Newtonian lubricants. Furthermore, the misalignment of the piston rod has significant effects on the performance of conical–cylindrical bearings. Copyright © 2007 John Wiley & Sons, Ltd.     

Automated optimized classification techniques for magnetic resonance brain images

Multimedia Tools and Applications - This paper presents automatic tumor detection and classification approaches for brain magnetic resonance images (MRI). These approaches are based on...     

Inhibition of Ehrlich Ascites Carcinoma by Lactuca serriola in Swiss Albino Mice

To evaluate the antitumor activity of Lactuca serriola against EAC （Ehrlich ascites carcinoma） in Swiss albino mice. The in vivo antitumor activity of the methanol extract of plant Lactuca serriola was evaluated at （100 mg/kg and 200 mg/kg of hole plant and 200 mg/kg, 400 mg/kg of fruit bw） against EAC using mean survival time. After administration of the extracts of Lactuca serriola, viable EAC cell count and body weight in the EAC tumor hosts were observed. The animal was also observed for improvement in the hematological parameters （e.g., hemoglobin content, red and white blood cells count and differential cell count） after treatment of plant extract. Intraperitoneal administration of plant extracts reduced viable EAC cells, increased the survival time and restored altered hematological parameters. Significant efficacy was observed for fruit extract at high concentration 400 mg/kg dose （P 〈 0.05）. It can be concluded that the methyl extract ofLactuca serriola possesses significant antitumor activity.     

A new approach for small-signal modelling of the field effect transistor based on cepstral coefficients and discrete transforms

In this article, a new technique for field effect transistor (FET) small-signal modelling using neural networks is proposed. This technique is based on the combination of the Mel-frequency cepstral coefficients (MFCCs) with different discrete transforms such as the discrete cosine transform (DCT), the discrete sine transform (DST) and the discrete wavelet transform (DWT) of the inputs to the neural networks. The input datasets to traditional neural systems for FET small-signal modelling are the scattering parameters and the corresponding frequencies in a certain band, and the output datasets are the circuit elements. In the proposed approach, these datasets are considered to form random signals. The MFCCs of the random signals are used to generate a small number of features characterising the signals. In addition, other vectors are calculated from the DCT, DST or DWT of the random signals and appended to the MFCC vectors calculated from the signals. The new feature vectors are used to train the neural networks. The objective of using these new vectors is to characterise the random input sequences with more features, to encourage robustness against measurement errors. There are two benefits to these approaches: (1) a reduction in the number of neural network inputs, and hence a faster convergence of the neural training algorithm and (2) robustness against measurement errors in the testing phase. Experimental results show that the techniques based on the discrete transforms are less sensitive to measurement errors than using the traditional and MFCC methods.     

On the hydrodynamic lubrication analysis of piston rings

A theoretical hydrodynamic lubrication analysis of piston rings is presented. A numerical scheme is developed to solve the Reynolds' equation and the load equilibrium equation simultaneously. The hydrodynamic effects are examined in detail by considering solutions to the problem of lubricating the top compression ring in a two-stroke diesel engine. The minimum film thickness is computed throughout a complete cycle. The pressure profiles and film shapes at different crankshaft angles are presented. In the method proposed, the camtation boundary condition within the lubricated conjunction is included in the analysis.     

An inverse solution for finite journal bearings lubricated with couple stress fluids

This paper presents an inverse solution for finite journal bearings lubricated with couple stress fluids to estimate the eccentricity ratio and the couple stress parameter for a given experimentally measured pressure distribution. The least-squares optimization technique is used to solve the inverse problem. An efficient numerical scheme is developed to solve the direct lubrication problem, which consists of the modified Reynolds equation, the film thickness equation, and the boundary conditions for the pressure field. The flexibility of the bearing liner is considered in the film thickness equation by a simple elastic model. The proposed inverse algorithm was tested using numerically simulated pressure distribution. The results showed that as the percentage of random error added to the numerically calculated pressure data points increases, the number of iterations required for convergence increases slightly, and the accuracy of the predictions decreases especially in the case of elastic liner.     

On the hydrodynamic liquid lubrication analysis of slider/disk interface

A numerical solution for ultrathin hydrodynamic liquid lubrication of slider/disk interface is introduced. Both surface roughness effects and non-Newtonian behavior of the liquid lubricant are incorporated into the hydrodynamic lubrication analysis. A non-Newtonian liquid is used as the lubricant, and its behavior is described by a power-law rheological model. The contact pressure is calculated for a Gaussian surface roughness. The hydrodynamic load capacity is calculated by using an averaged form for the Reynolds equation. The finite difference scheme, with Gauss–Seidel iterative-relaxation method, is applied to solve the average Reynolds equation. The effects of surface roughness parameter, surface pattern parameter, and the power-law exponent on hydrodynamic pressure distribution, hydrodynamic load capacity are studied and discussed.     

Predicting Volumetric and Transport Properties of Sour Gases and Gas Condensates Using EOSs, Corresponding State Models, and Empirical Correlations

Compressibility, density, and viscosity of natural gases are necessary in most petroleum engineering calculations. Some of these calculations are gas metering, gas compression, design of processing units, and design of pipeline and surface facilities. Properties of natural gases are also important in calculation of gas flow rate through reservoir rock, material balance calculations, and evaluation of gas reserves. Usually the gas properties are measured in laboratory. Occasionally, experimental data become unavailable and these properties are estimated from equations of state, corresponding state models or empirical correlations. This article presents the results of using various equations of state, corresponding state methods, and correlations to predict the volumetric and transport properties of sour gases and gas condensates. Capabilities of PR-EOS, SRK-EOS, and PT-EOS to predict gas compressibility and density of 2100 gas samples under various schemes of binary interaction number are thoroughly investigated. This study also reports a comparison between recently developed EOS-based viscosity models and other methods to estimate the viscosity of highly sour gases and rich gas condensates.     

Characterization of Asphaltenes and Resins Separated from Water-in-Oil Emulsions

Knowledge of the properties and behavior of asphaltenes and resins is indispensable for the design of preventive and curative measure for emulsion problems created by the presence of asphaltene, resins, and other organic and inorganic solids. In order to understand the phenomena of water-oil emulsions formed in Kuwaiti oil fields and determine the factors involved in the stabilization of these emulsions, the role of asphaltenes, resins and wax separated from various samples of oil field emulsions formed in Burgan oil field have been evaluated. Physicochemical properties of asphaltenes, resins, wax, and de-asphalted de-resined (DADR) oil samples have been studied via FT-IR, ¹H, and ¹³C NMR, elemental analysis, and differential scanning calorimetry (DSC). These emulsion samples contain different amounts of water ranges from 24 to 35%, asphaltene content ranges from 0.9 to 1.7%, and resin content from 3.7 to 4.6%. IR-FT spectra were performed to identify the various functional groups which have an effect on the stability of water-oil emulsions. The freezing behavior of an emulsion was characterized by differential scanning calorimetry to determine whether the water in the emulsion is free water or emulsified water.