Study on the leakage of plunger pair under static condition in high-pressure common rail injector considering deformation effect

Plunger pair is the key component of high pressure common rail injector and its sealing performance is very important. Therefore, it is of great significance to study the leakage mechanism of plunger pair. Under static condition, the high-pressure fuel flow in the gap of the plunger pair caused the deformation of the plunger pair structure and the temperature rise of fuel. For a more comprehensive and accurate study, the effect of deformation, including elastic deformation and thermal expansion, the physical properties of fuel, including density, viscosity and specific heat capacity, as well as the influence of plunger posture in the plunger sleeve, including concentric, eccentric, and inclination condition, are considered in this paper. Firstly, the mathematical models including Reynolds equation, film thickness equation, non-isothermal flow equation, parametric equation of fuel physical property, and section velocity equation are established. The numerical analysis based on finite difference method for the solution of these models is given, which can simultaneously solve for the fuel film pressure distribution, temperature distribution, thickness distribution, distribution of fuel physical properties, and leakage rate. The models are validated by comparing the calculated leakage rates with the measurements. The effects under different posture of plunger are discussed too. Some of the conclusions provided good guidance for the design of high-pressure common rail injector.

Research On Fuel Compensation Control of High-pressure Common-Rail Diesel Engine Based On Crankshaft Segment Signals

This paper studies the high-pressure common-rail diesel engine fuel supply compensation based on crankshaft fragment signals in order to improve the uneven phenomenon of diesel engine fuel supply and realize high efficiency and low pollution combustion. The experiments were conducted on a diesel engine with the model of YN30CR. Based on the characteristics of crankshaft fragment signals, the proportional integral (PI) control algorithm was used to quantify the engine working nonuniformity and extract the missing degree of fuel injection. The quantization method of each cylinder working uniformity and algorithm of fuel compensation control (FOC) based on crankshaft fragment signal were established, and the control strategy of working uniformity at different operating conditions was put forward. According to the principle of FOC control, a FOC control software module for ECU was designed. The FOC software module was simulated on ASCET platform. The results show that: Compared with the traditional quantization method, the oil compensation information extracted from crankshaft fragment signal has stronger anti-interference and more accurate parameters. FOC algorithm can accurately reflect the engine's working nonuniformity, and the control of the nonuniformity is reasonable. The compensation fuel amount calculated by FOC is high consistency with the fuel supply state of each cylinder set by experiment, which meets the requirement of accurate fuel injection control of common-rail diesel engine.

Establishment of a Real-Time Simulation of a Marine High-Pressure Common Rail System

In this paper, the high-pressure common rail system of the marine diesel engine is taken as case study to establish a real-time simulation model of the high-pressure common rail system that can be used as the controlled object of the control system. On the premise of ensuring accuracy, the real-time simulation should also respond quickly to instructions issued by the control system. The development of the real-time simulation is based on the modular modeling method, and the high-pressure common rail system is divided into submodels, including the high-pressure oil pump, common rail tube, injector, and mass conversion. The submodels are built using the “surrogate model” method, which is mainly composed of MAP data and empirical formulas. The data used to establish the real-time simulation are not only from the empirical research into the high-pressure common rail system, but also from simulations of the high-pressure common rail system undertaken in AEMSim. The data obtained from this real-time simulation were compared with the experimental data to verify the model. The error in fuel injection quality is less than 5%, under different pressures and injection durations. In order to carry out dynamic verification, the PID control strategy, the model-based control strategy, and the established real-time simulation are all closed-loop tested. The results show that the developed real-time simulation can simulate the rail pressure wave caused by cyclic injection according to the control signal, and can feedback the control effect of different control strategies. Through verification, it is clear that the real-time simulation of the high-pressure common rail system can depict the rail pressure fluctuation caused by each cycle of fuel injection, while ensuring the accuracy and responsiveness of the simulation, which provides the ideal conditions for the study of a rail pressure control strategy.

Research on a small sample fault diagnosis method for a high-pressure common rail system

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%.