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Abstract:In this paper, an opposed-piston two-stroke (OP2S) gasoline direct injection (GDI) engine is introduced and its working principles and scavenging process were analyzed. An optimization function was established to optimize the scavenging system parameters, include intake port height, exhaust port height, intake port circumference ratio, the exhaust port circumference ratio and opposed-piston motion phase difference. The effect of the port height on the effective compression ratio and effective expansion ratio were considered, and indicated mean effective pressure (IMEP) was employed as the optimization objective instead of scavenging efficiency. Orthogonal experiments were employed to reduce the calculation work. The effect of the scavenging parameters on delivery ratio, trapping ratio, scavenging efficiency and indicated thermal efficiency were calculated, and the best parameters were also obtained by the optimization function. The results show that IMEP can be used as the optimization objective in the uniflow scavenging system; intake port height is the main factor to the delivery ratio, while exhaust port height is the main to engine trapping ratio, scavenging efficiency and indicated thermal efficiency; exhaust port height is the most important factor to effect the gas exchange process of OP2S-GDI engine.

In the fault diagnosis of high-pressure common rail diesel engines, it is often necessary to face the problem of insufficient diagnostic training samples due to the high cost of obtaining fault samples or the difficulty of obtaining fault samples, resulting in the inability to diagnose the fault state. To solve the above problem, this paper proposes a small-sample fault diagnosis method for a high-pressure common rail system using a small-sample learning method based on data augmentation and a fault diagnosis method based on a GA_BP neural network. The data synthesis of the training set using Least Squares Generative Adversarial Networks (LSGANs) improves the quality and diversity of the synthesized data. The correct diagnosis rate can reach 100% for the small sample set, and the iteration speed increases by 109% compared with the original BP neural network by initializing the BP neural network with an improved genetic algorithm. The experimental results show that the present fault diagnosis method generates higher quality and more diverse synthetic data, as well as a higher correct rate and faster iteration speed for the fault diagnosis model when solving small sample fault diagnosis problems. Additionally, the overall fault diagnosis correct rate can reach 98.3%.

Abstract:In opposed-piston, opposed-cylinder (OPOC) two-stroke diesel engines, the relative movement rules of opposed-pistons, combustion chamber components and injector position are different from those of conventional diesel engines. In this study, the combustion and emission characteristics of the OPOC which is equipped with a common-rail injection system are investigated by experimental and numerical simulation. Different split injection strategies involving different pilot injection/fuel mass ratios and injection intervals were compared with a single injection strategy. The numerical simulation was applied to calculate and analyze the effect of split injection strategies on the combustion and emission after validation with the same experimental result (single injection strategy). Results showed that using split injection had a significant beneficial effect on the combustion process, because of the acceleration effect that enhances the air-fuel mixture. Additionally, the temperature of the split injection strategies was higher than that of single strategy, leading to the nitrogen oxides (NO x ) increasing and soot decreasing. In addition, it has been found that the split injection condition with a smaller pilot injection/fuel mass ratio and a medium injection interval performed better than the single injection condition in terms of the thermo-atmosphere utilization and space utilization.

An investigation was carried out in order to study the fretting fatigue behavior of an AlSi9Cu2Mg aluminum alloy. The fretting fatigue tests of AlSi9Cu2Mg were performed using a specially designed testing machine. The failure mechanism of fretting fatigue was explored by studying the fracture surfaces, fretting scars, fretting debris, and micro-hardness of fretting fatigue specimens using scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), and micro Vickers hardness test techniques. The experimental results show that the fretting fatigue limit (42 MPa) is significantly reduced to approximately 47% of the plain fatigue limit (89 MPa) under 62.5 MPa contact pressure. Furthermore, the fretting fatigue life decreases with increasing alternating stress and increasing contact pressure. The examination results suggest that the stress concentrates induced by oxidation-assisted wear on the contact interface led to the earlier initiation and propagation of crack under the fretting condition.