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Piston ring dynamic problem plays an important role in the lubricant characteristics of a reciprocating engine, which lead to engine wear and the increased consumption of lubricating oil. A cavitation analysis of the piston ring lubrication with two-dimensional Reynolds equation has rarely been reported owing to the complex working condition. The purpose of this study is to establish a precise model that can provide guidance for the design of the piston ring.

In this paper, a cavitation model and its effect on the piston ring lubrication was studied in a simulation program based on the mass-conserving theory which is solved by means of the Newton–Raphson method. In this study, some models such as mixed lubrication, asperity contact, blow-by/blow-back flow and cavitation have been coupled with the lubrication model.

The established model has been compared with the traditional model that deals with cavitation by using the Reynolds boundary condition algorithm. The cavitation zone, pressure distribution and density distribution between the piston ring and the cylinder have also been predicted. Studies of the changing trend for the pressure distribution and the cavitation zone at few typical crank angles have been listed to illustrate the cavitation changing rule. The analysis of the results indicates that the developed simulation model can adequately illustrate the lubrication problem of the piston ring system. All the analyses will provide guidance for the oil film rupture and the reformation process.

A two-dimensional cavitation model based on the mass-conserving theory has been built. The cavitation-forming and -developing process for the piston ring–liner lubrication has been studied. Non-cavitation occurs in the vicinity of top dead center and bottom dead center. The non-cavitation period will be longer in the vicinity of 360° of crank angle. The density distribution in the cavitation zone can be obtained.

The purpose of this paper is to assess the performance of competing methods and model selection, which are non‐trivial issues given the financial implications. Researchers have adopted various methods including statistical models and machine learning methods such as neural networks to assist decision making in direct marketing. However, due to the different performance criteria and validation techniques currently in practice, comparing different methods is often not straightforward.

This study compares the performance of neural networks with that of classification and regression tree, latent class models and logistic regression using three criteria – simple error rate, area under the receiver operating characteristic curve (AUROC), and cumulative lift – and two validation methods, i.e. bootstrap and stratified k‐fold cross‐validation. Systematic experiments are conducted to compare their performance.

The results suggest that these methods vary in performance across different criteria and validation methods. Overall, neural networks outperform the others in AUROC value and cumulative lifts, and the stratified ten‐fold cross‐validation produces more accurate results than bootstrap validation.

To select predictive models to support direct marketing decisions, researchers need to adopt appropriate performance criteria and validation procedures.

The study addresses the key issues in model selection, i.e. performance criteria and validation methods, and conducts systematic analyses to generate the findings and practical implications.

The purpose of this paper is to improve the environment and save energy, friction reduction, lower oil consumption and emissions demand that are the chief objectives of the automotive industry. The piston system is the largest frictional loss source, which accounts for about 40 per cent of the total frictional loss in engine. In this paper, the reciprocating tribometer, which is updated, was used to evaluate the friction and wear performances.

An alternate method is introduced to investigate the effect of reciprocating speed, normal load, oil pump speed and ring sample and oil temperature on friction coefficient with the ring/liner of a typical inline diesel engine. The orthogonal experiment is designed to identify the factors that dominate wear behavior. To understand the correlations between friction coefficients and wear well, different friction coefficient results were compared and explained by oil film build-up and asperity contact theory, such as the friction coefficient over a long period and averaged the friction coefficient over one revolution.

The friction coefficient changes little but fluctuates with a small amplitude in the stable stage. The sudden change of frequency, load and stroke will lead to the oil film rupture. The identification for the factors that dominates the wear loss is ranged as F (ring sample) > , E (oil sample) > , B (stroke) > , D (temperature) > , A (load) > , G (liner) > and C (frequency).

This paper develops and verifies a methodology capable of mimicking the real engine behavior at boundary and mixed lubrication regimes which can minimize frictional losses, wear, reduce much work for the experiment and reduce the cost. The originality of the work is well qualified, as very few papers on a similar analysis have been published, such as: The friction coefficient values fluctuating in the whole stage may be caused by the vibration of the system; suddenly, boundary alternation may help the oil film to form the lubrication; and weight loss mainly comes from the contribution of the friction coefficient value fluctuation. The paper also found that the statistics can gain more information from less experiment time based on a design of experiment.

The paper aims to develop an intelligent anti-corrosion expert system based on browser/server (B/S) architecture to realize an intelligent corrosion management system.

The system is based on Java EE technology platform and model view controller (MVC) three-tier architecture development model. The authors used an extended three-dimensional interpolation model to predict corrosion rate, and the model is verified by cross-validation method. Additionally, MySQL is used to realize comprehensive data management.

The proposed anti-corrosion system thoroughly considers a full use of corrosion data, relevant corrosion prediction and efficient corrosion management in one system. Therefore, this system can achieve an accurate prediction of corrosion rate, risk evaluation, risk alert and expert suggestion for equipment in petrochemical plants.

Collectively, this present study has important ramifications for the more efficient and scientific management of corrosion data in enterprises and experts’ guidance in controlling corrosion status. At the same time, the digital management of corrosion data can provide a data support for related theoretical researches in corrosion field, and the intelligent system also offers examples in other fields to improve system by adding intelligence means.