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Erosion is one of the most common forms of material failure and equipment damage in gas transmission pipelines. Shale gas fields use hydraulic fracturing whereby solid particles are often carried in the gas flow, and the pipeline is in a high-pressure state, which is more likely to cause erosion. The prediction of particle erosion regulation in gas-solid two-phase flow is an effective means to ensure the safe operation of shale gas fields. In this paper, an integrated CFD-DPM model is established to investigate the erosion of 90° elbow in a shale gas field under gas-solid two-phase flow, employing the realizable k-ε turbulence model, discrete phase model, and erosion rate prediction model. The reliability of the proposed numerical models is verified by comparing the predicted data with the experimental data. Moreover, the effects of six important factors on maximum erosion rate are analyzed, including gas velocity, mass flow rate of sand particles, particle diameter, shape coefficient of sand particles, pipeline diameter, elbow radius of curvature. Specifically, the results indicate that the gas velocity, mass flow rate and shape coefficient of sand particles are positively correlated with the maximum erosion rate, while the pipe diameter and the elbow radius of curvature are negatively correlated with the maximum erosion rate. A new correlation was developed, which included four dimensionless groups, namely Reynolds number, diameter ratio, density ratio and particle number. The correlation can be used to predict maximum corrosion rate of elbows. This work can provide data reference and theoretical basis for mitigating the erosion rate of pipelines and managing the integrity of gas pipelines.

Reliable operating conditions of hydrostatic turntables are prerequisite to ensuring machine tool performance. The hydrostatic turntable is affected by multiple working conditions, therefore, methods for evaluating turntable load-carrying capacity has become research hotspot. In this paper, by analyzing bearing capacity, parameters closely related to the support performance of turntables are selected as recognition features and an artificial neural network (ANN) training method is proposed. The ANN method is based on numerical solutions of over-determined nonlinear equations (ODNE) to intelligently evaluate turntable performance. In this study, ANN and ODNE training are applied to evaluate the performance of hydrostatic turntables. Finally, to verify the feasibility of the method, an intelligent monitoring system is established to collect data on machine tools.

Purpose This paper aims to study the nonlinear supporting performance of hydrostatic ram under the impact of cutting force and search for an optimal solution to improve its stiffness. Design/methodology/approach The Reynolds equation was applied to resolve the carrying capability of a single oil pad numerically, and an iteration method was used to analyze the nonlinear supporting force and stiffness of a pair of oil pads placed face-to-face. The total offset of ram could be obtained after the displacement of aspectant oil pads was solved by the bisection method. From the comparison of the offset values of ram evaluated under different support conditions, the optimal solution was determined. Findings In this study, an optimized oil supply allocation, concluded as 1.16:0.84, is proposed to improve the performance of hydrostatic ram supporting structure. Originality/value The supporting performance of hydrostatic ram could be improved by appropriate allocation of oil supply without extra energy consumption.

Analyzing of nonlinear carrying performance of hydrostatic ram is among key research questions to improve heavy machine tools because the machining precision will be directly influenced by it. A dynamic model of nonlinear supporting characters for hydrostatic ram is developed in this work. Modified Reynolds equation is resolved by finite difference method numerically to determine the carrying capability. Dynamic equilibrium equation of hydrostatic ram under cutting force impact is solved by Runge–Kutta method to obtain the variation of tool tip position and evaluate the machining accuracy. An optimal oil supply rate allocation and oil pad size is proposed to improve the static and dynamic performance simultaneously based on method of bisection.

The accuracy maintenance and operation reliability of the hydrostatic turntable are associated with many factors whose influence on bearing load carrying capacity is the key question of researching the hydrostatic turntable. In this study, an intelligent hydrostatic turntable monitoring system is proposed based on multi-source data acquisition. Firstly, the timely main parameter variations are acquired through different kinds of sensors installed on the turntable to form an input data aggregation. Then, by analyzing the static, dynamic, and thermal characteristics of the turntable using Finite Difference Method (FDM) and Runge–Kutta method (RKM), a set of equations for the bearing performance evaluation is established. Computational results are converted to the motor signal for the pump for controlling oil supply rate. Finally, the load-carry supporting performance of each oil pad is controlled precisely and individually by a pump with multiple outputs driven by servo motors. An intelligent input, processing, and output (IPO) real-time system of the hydrostatic turntable is established to monitor its service performance. Finally, the accuracy compensation by data statistics and reliability evaluation by abnormal data detection are carried out to improve the working performance of hydrostatic turntable.

Hydrostatic systems are considered as essential supporting structures in heavy machine tools. The calculations and analyses for hydrostatic bearings are always laborious because of the involvement of several disciplines such as elastic mechanics, hydromechanics, thermodynamics, and other factors in the design. It is well known that large data and cloud technology are capable of processing and transmission of hydrostatic system studies. In this work, a cloud manufacture model is presented to provide big data storage, transmission, and processing platform for designers, manufacturers, and users of hydrostatic bearings. All participants involved in the manufacturing were linked together by timely information communication in order to ensure that the customized products met with the actual conditions in the cloud server. Based on the actual requirements of the user, the carrying capacity of hydrostatic system was analyzed by a designer using finite difference method and the results were sent to the manufacturer for machining of components. The monitored data from the consumer could be fed back to the designer for performance evaluation through the cloud server. It was found that customized manufacturing tasks could be finished more reliably and efficiently if all participants exchanged the data through cloud platform in real time.

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 engineering practice. Articles about hydrostatic bearings since 1990 are collected in this review. Researching status is evaluated in two aspects: basic theory and typical application. This article presents a review of research articles related to introducing developments in hydrostatic bearings. Basic theory contains equations and analysis methods which include analytic, numerical, and experimental methods. Typical applications are based on rectangular oil pad, circular oil pad, and journal bearings. Moreover, this article focuses on the analysis of the relevant model, solution, and optimization and summarizes the hotspots and development directions.