A New Approach to Mixed/Boundary Friction Prediction Using the Logistics Curve

There is a strong focus on improving the energy efficiency of machines. Over the last 20-30 years, one way to improve energy efficiency has been to reduce lubricant viscosity. This also has the effect of leading to thinner oil films between the machine’s moving surfaces and is likely to lead to increased mixed/boundary friction. Accurately predicting friction in the mixed/boundary friction regime is therefore becoming of great importance. The work reported here suggests that commonly used asperity friction models significantly underestimate friction in the mixed/boundary friction, and a new model, based on a logistic curve fit, gives a better estimate of mixed/boundary friction, provides good agreement with experimental friction data (from Mini Traction Machine experiments), and is much more straightforward for engineers and tribologists to apply for the estimation of mixed/boundary friction losses.

Effect of Coating and Low Viscosity Oils on Piston Ring Friction under Mixed Regime of Lubrication through Analytical Modelling

This paper presents the impact of coating topography in piston ring-liner conjunction under mixed regime of lubrication using low viscosity oils. The study provides a time efficient analytical model including mixed-hydrodynamics regime of lubrication under different contact conditions. The method modified the expressions of the contact load and area of Greenwood-Tripp model in order to capture the real asperities interaction into contact. The model represents the tribological behavior of a thin top ring at Top Dead Centre, where boundary and mixed conditions are predominant. Electroplated CrN and PVD TiN coated rings were studied to predict the ring friction. The results are compared with an uncoated steel ring. The CrN coating shows slighter coefficient of friction, due to the coating morphology and roughness parameters. The TiN coating presents thicker lubricant films and higher coefficient of friction because the surface topography is quite rough with high peaks. This can be explained because of the major contribution of the roughness parameter and asperity slope in the boundary friction prediction.

Interfacial Friction Prediction in a Vertical Annular Two-Phase Flow Based on Support Vector Regression Machine

Accurate prediction of interfacial friction factor is critical for calculation of pressure drop and investigation of flow mechanism of vertical annular two-phase flows. Theoretical models of interfacial friction factor based on physical insight have been developed; however, these are inconvenient in engineering practice as too many parameters need to be measured. Although many researchers have proposed various empirical correlations to improve computation efficiency, there is no generally accepted simple formula. In this study, an efficient prediction model based on support vector regression machine (SVR) is proposed. Through sensitivity analysis, five factors are determined as the input parameters to train the SVR model, relative liquid film thickness, liquid Reynolds number, gas Reynolds number, liquid Froude number and gas Froude number. The interfacial friction factor is chosen as the output parameter to check the overall performance of the model. With the help of particle swarm algorithm, the optimization process is accelerated considerably, and the optimal model is obtained through iterations. Compared with other correlations, the optimal model shows the lowest average absolute error (AAE of 0.0004), lowest maximum absolute error (MAE of 0.006), lowest root mean square error (RMSE of 0.00076) and highest correlation factor (r of 0.995). The analysis using various data in the literature demonstrates its accuracy and stability in interfacial friction prediction. In summary, the proposed machine learning model is effective and can be applied to a wider range of conditions for vertical annular two-phase flows.

A Real-Time Friction Prediction Model for in Service Drill String Based on Machine Learning Methods Coupling with Mechanical Mechanism Analysis

With highly deviated wells and horizontal wells are widely used in the oil industry. The large slope well sections and long horizontal well sections will lead to a sharp increase of the drill string torque and friction, which may reduce the drilling efficiency, and even lead to accidents. Therefore, real-time and accurate analysis of drill string’s torque and friction is an urgent problem facing by the modern drilling technology. The paper established a real-time friction prediction model that combines machine learning methods with drill string mechanical mechanism analysis model. Based on 84000 sets of field monitoring data obtained on-site, a regular data training set for weight on bit (WOB) and torque prediction was constructed with 23 types of time-series related parameters and 10 types of timing independent parameters. Relationships between time-series related parameters and timing independent parameters with the weight on bit and torque were trained to utilize long and short-term memory (LSTM) neural network and muti-layer back propagation (BP) network respectively. The new developed LSTM-BP neural network achieves high-precision prediction results of WOB and torque with a relative error of less than 14%. Based on derived WOB and torque prediction results, a theoretical mechanical analysis model of the entire drill string was adopted in this paper to develop the quantitative relation between WOB and torque with the friction coefficient of the drill string and oil casing. Suitable friction coefficients along the drill string can be finally obtained by solving the equilibrium function between predicted WOB, torque and measured hook load, rotary-table torque via an iteration algorithm. A case study was performed finally using the proposed intelligent analysis method to calculate the friction coefficients. This proposed methodology can be referenced to decrease the sticking risks and improve the drilling efficiency, which can finally increase the extension limit of horizontal wells in complex strata.

Pavement Friction Prediction Using 3D Texture Parameters

This article deals with the possibility of predicting skid resistance based on non-contact scanning of the road surface. The study is based on comparing pavement texture parameters with coefficients of friction measured on a wide variety of road surfaces, while other test conditions were the same and constant. The measurements of the coefficient of friction were performed using a pendulum tester. The pavement texture was measured using a static road scanner, and 85 different 3D texture parameters were calculated. The study shows that the determination of the friction using only single texture parameters is not sufficient. Based on this statement, the next part of the research analyzed the influence of the mutual combination of surface texture parameters. A linear regression model was chosen to determine the friction coefficient prediction formula based on the combination of texture parameters. Statistically, the most significant parameters in the prediction model proved to be the valley material portion, characterizing the microtexture, and the arithmetic mean curvature, characterizing the pavement macrotexture. The obtained regression model proved to be statistically significant with R2 = 0.81 for Pendulum Test Value prediction.

A Digital Twin for Friction Prediction in Dynamic Rubber Applications with Surface Textures

Surface texturing is an effective method to reduce friction without the need to change materials. In this study, surface textures were transferred to rubber samples in the form of dimples, using a novel laser surface texturing (LST)—based texturing during moulding (TDM) production process, developed within the European Project MouldTex. The rubber samples were used to experimentally determine texture-induced friction variations, although, due to the complexity of manufacturing, only a limited amount was available. The tribological friction measurements were hence combined with an artificial intelligence (AI) technique, i.e., Reduced Order Modelling (ROM). ROM allows obtaining a virtual representation of reality through a set of numerical strategies for problem simplification. The ROM model was created to predict the friction outcome under different operating conditions and to find optimised dimple parameters, i.e., depth, diameter and distance, for friction reduction. Moreover, the ROM model was used to evaluate the impact on friction when manufacturing deviations on dimple dimensions were observed. These results enable industrial producers to improve the quality of their products by finding optimised textures and controlling nominal surface texture tolerances prior to the rubber components production.

An efficient procedure to predict the dynamic loads for piston liner systems in marine engines

The piston ring-cylinder liner (PRCL) is one of the most important parts of marine diesel engines and contributes 25% to 50% of total friction loss. The lubrication simulation analysis of the PRCL system is a challenging task. Complete understanding and precise prediction of lubrication loads is a key to understanding the friction behavior of PRCL systems as the accuracy of the friction prediction depends upon precise prediction of lubrication loads. Therefore, this paper focuses on the gas pressure calculation which is the primary source of lubrication loads. The procedure presented combines the advantages of two mainstream methods to predict loads in the PRCL system. The result is a significant reduction in the computation time without compromising on accuracy. Firstly, a comparison of both approaches is presented which suggests that each technique has its limitations (one is time-bound, and one is accuracy-bound). Then, the results from both calculation methods are verified against literature and a parametric study is performed to identify the key structural parameters of PRCL system that affect the calculation efficiency. Finally, a correlation coefficient is introduced into the analysis to combine the two approaches which then identifies the conditions under which the use of the faster method becomes invalid and replaces it with the more accurate approach. This ensures optimum performance of the calculation procedure by switching between the fast and the accurate method depending upon the accuracy requirement under given conditions, thereby, simplifying the dynamic and lubrication model of PRCL systems. The study has direct implications for the tribological design of the PRCL interface.

State of the art of friction modelling at interfaces subjected to elastohydrodynamic lubrication (EHL)

Elastohydrodynamic lubrication (EHL) is a type of fluid-film lubrication where hydrodynamic behaviors at contact surfaces are affected by both elastic deformation of surfaces and lubricant viscosity. Modelling of contact interfaces under EHL is challenging due to high nonlinearity, complexity, and the multi-disciplinary nature. This paper aims to understand the state of the art of computational modelling of EHL by (1) examining the literature on modeling of contact surfaces under boundary and mixed lubricated conditions, (2) emphasizing the methods on the friction prediction occurring to contact surfaces, and (3) exploring the feasibility of using commercially available software tools (especially, Simulia/Abaqus) to predict the friction and wear at contact surfaces of objects with relative reciprocating motions.