Zhenpeng He

Piston dynamic characteristics analyses based on FEM method Part I: Effected by piston skirt parameters

The dynamic and lubrication characteristics are all very complex problem in piston-liner analysis, and they have great effect on the power output, vibration, noise emission. In this paper, the numerical model which concludes lubrication part and dynamic motion is established, the lubrication is solved by the finite element method, and dynamic equation is solved by Runge-Kutta. The effect of piston skirt parameters on dynamic characteristics are compared based on a typical inline six-cylinder engine, such as: clearance, offset of piston pin, length of piston skirt, position of bump, curvature parameter and ellipticity of the piston, all the result mainly focus on the slap noise of the engine. All the analyses are very useful to design of piston-liner at the development of the engine, and it can provide the guidance for the design of the low noise engine.

Tribilogical performances of connecting rod and by using orthogonal experiment, regression method and response surface methodology

Different from tradition analysis method, the statistics method with suitable design of experiment is used to gain more information.The identification of the factors dominating the bearing behaviors is obtained.The new regression models without insignificant components are established through the stepwise regression.The SVM model and POS-SVM model are established to identify the asperity contact. Dynamic lubrication analysis of connecting rod is a very complex problem. Some factors have great effect on lubrication, such as clearance, oil viscosity, oil supplying hole, bearing elastic modulus, surface roughness, oil supplying pressure and engine speed and bearing width. In this paper, ten indexes are used as the input parameters to evaluate the bearing performances: minimum oil film thickness (MOFT), friction loss, the maximum oil film pressure (MOFP) and average of the oil leakages (OLK). Two orthogonal experiments are combined to identify the factors dominating the bearing behavior. The stepwise regression is used to establish the regression model without insignificant variables, and two most important variables are used as the input to carry out the surface response analysis for each model. At last, the support vector machine (SVM) is used to identify the asperity contact. Compared with SVM model, the particle swarm optimization-support vector machines (PSO-SVM) can predict the asperity contact more precise, especially to the samples near dividing line. In future work, more soft computing methods with statistical characteristic are used to the tribology analyses.

A Concurrent Reynolds BC Algorithm for Piston Ring Cavitation Lubrication Problems with Surface Roughness

Piston ring dynamics play an important role in the lubricant characteristics of reciprocating engines that lead to engine wear and high consumption of lubricating oil. Due to the complexity of realistic test and working conditions, a study of cavitation with surface roughness and its effect on piston ring lubrication was conducted in a simulation program based on mass-conserving theory that is solved with the Newton-Raphson method. Lubrication models such as mixed lubrication, asperity contact, blow-by/blow-back flow, and cavitation were used in this study. The simulation algorithm consists of four processes: establishment of the three different lubrication models, dealing with the finite difference method, numerical stability treatment for the cavitation zone, and the addition of surface roughness and the shear factor to the model. Results calculated with this model were compared with two other models, and an analysis of the results indicates that the developed simulation program can illustrate problems of piston ring lubrication in accordance with the state of art of lubrication theory.

The multi-objective optimization of the damaged aircraft trailer based on a dynamic model

A damaged aircraft trailer is an essential piece of airport emergency rescue equipment which is made up of frames and multiple suspensions. As a load–force transferring mechanism, the suspension bears heavy loads which can cause fatigue damage. Therefore, reducing the maximum stress of the suspension is necessary to improve the vehicle performance. Besides, lightweight design should be considered to reduce energy consumption. Thus, lighter suspension which can bear more pressure is the optimization objective of this research. A multi-objective optimization method was carried out to analyze the suspension arm of a damaged aircraft trailer. Firstly, to investigate the dynamic characteristics and the reliability of the damaged aircraft trailer, a detailed three combined damaged aircraft trailers model was built. Based on the flexible-rigid coupled method, dynamic simulation of the damaged aircraft trailer was conducted in MSC.ADAMS. Then a suspension model was established, and the stress under different loads was measured to verify the accuracy of the finite element suspension arm model by experiments. Based on the design of experiment method, the effect of suspension arm parameters were obtained to build the approximate models. Besides, the influences of some effect parameters on optimal objectives were analyzed based on the surface response method. During the optimization process, a non-dominated sorting genetic algorithm II was adopted to optimize the mass and stress of the suspension arm. The results show that the mass of the suspension arm is reduced from 146.81 kg to 126.69 kg, which is a reduction of 14%. The maximum von Mises stress is changed from 325 MPa to 297 MPa, which is a decrease of 8.6%. This optimal method can be extended to the overall vehicle, which has an important significance in the whole damaged aircraft trailer characteristics improvement design.

The lubrication analysis of the piston skirt-liner system based on design of experiment and particle swarm optimization-support vector regression

Good design for the piston skirt can improve efficiency without harming emissions, while it simultaneously reduces wear and improves the reliability of the engine. In this paper, the finite element method is applied to solve the Reynolds equation to analyze the piston skirt-liner system lubrication, and the side motion of piston skirt solved with Runge–Kutta is coupled with lubrication. Some factors that have an effect on the lubrication and dynamic characteristics are selected as variables, such as the clearance, offset distance of the piston pin, bump position, curvature, the length of piston skirt, and the ellipticity of the piston skirt. The orthogonal experimental design which includes six factors with five levels is used to analyze the dominance of these structure factors, and the effects of all the responses for the structural parameters to the friction loss are also identified, and the orthogonal samples simulated by the experiment are conducted to do the regression for the piston skirt. Some regression models are introduced to predict the friction loss, the precise comparison of all the models are compared, and the larger error appears in these prediction models, and then the particle swarm optimization-support vector regression is also used to predict the friction loss, and the results agree well with each other. All the analyses are very useful to provide guidance for the design of the piston skirt-liner during the development of the engine.

Analysis of Oil Consumption in Cylinder of Diesel Engine for Optimization of Piston Rings

The performance and particulate emission of a diesel engine are affected by the consumption of lubricating oil. Most studies on oil consumption mechanism of the cylinder have been done by using the experimental method, however they are very costly. Therefore, it is very necessary to study oil consumption mechanism of the cylinder and obtain the accurate results by the calculation method. Firstly, four main modes of lubricating oil consumption in cylinder are analyzed and then the oil consumption rate under common working conditions are calculated for the four modes based on an engine. Then, the factors that affect the lubricating oil consumption such as working conditions, the second ring closed gap, the elastic force of the piston rings are also investigated for the four modes. The calculation results show that most of the lubricating oil is consumed by evaporation on the liner surface. Besides, there are three other findings: (1) The oil evaporation from the liner is determined by the working condition of an engine; (2) The increase of the ring closed gap reduces the oil blow through the top ring end gap but increases blow-by; (3) With the increase of the elastic force of the ring, both the left oil film thickness and the oil throw-off at the top ring decrease. The oil scraping of the piston top edge is consequently reduced while the friction loss between the rings and the liner increases. A neural network prediction model of the lubricating oil consumption in cylinder is established based on the BP neural network theory, and then the model is trained and validated. The main piston rings parameters which affect the oil consumption are optimized by using the BP neural network prediction model and the prediction accuracy of this BP neural network is within 8%, which is acceptable for normal engineering applications. The oil consumption is also measured experimentally. The relative errors of the calculated and experimental values are less than 10%, verifying the validity of the simulation results. Applying the established simulation model and the validated BP network model is able to generate numerical results with sufficient accuracy, which significantly reduces experimental work and provides guidance for the optimal design of the piston rings diesel engines.