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.

A novel fractional grey forecasting model with variable weighted buffer operator and its application in forecasting China's crude oil consumption

Oil is an important strategic material and civil energy.Accurate prediction of oil consumption can provide basis for relevant departments to reasonably arrange crude oil production,oil import and export,and optimize the allocation of social resources.Therefore,a new grey model FENBGM(1,1)is proposed to predict oil consumption in China.Firstly,the grey effect of the traditional GM(1,1)model was transformed into a quadratic equation.Four different parameters were introduced to improve the accuracy of the model,and the new initial conditions were designed by optimizing the initial values by weighted buffer operator.Combined with the reprocessing of the original data,the scheme eliminates the random disturbance effect,improves the stability of the system sequence,and can effectively extract the potential pattern of future development.Secondly,the cumulative order of the new model was optimized by fractional cumulative generation operation.At the same time,the smoothness rate quasi-smoothness condition was introduced to verify the stability of the model,and the particle swarm optimization algorithm(PSO)was used to search the optimal parameters of the model to enhance the adaptability of the model.Based on the above improvements,the new combination prediction model overcomes the limitation of the traditional grey model and obtains more accurate and robust prediction results.Then,taking the petroleum consumption of China's manufacturing industry and transportation,storage and postal industry as an example,this paper verifies the validity of FENBGM(1,1)model,analyzes and forecasts China's crude oil consumption with several commonly used forecasting models,and uses FENBGM(1,1)model to forecast China's oil consumption in the next four years.The results show that FENBGM(1,1)model performs best in all cases.Finally,based on the prediction results of FENBGM(1,1)model,some reasonable suggestions are put forward for China's oil consumption planning.

Using Less Oil Despite the unquenchable thirst for oil to fuel nina's fast growing economy,the oil consumption growth rate is dropping

At first glance, the official figures of China's oil consumption in 2005 seem a bit confusing. With a robust economic growth and rising annual oil imports, China made the surprise announcement that its oil consumption growth rate was dropping sharply, from 15.3 percent in 2004 to 2.1 percent in 2005. Earlier, China's Ministry

Forecast and analysis of the "roof effect" of world net oil-exporting capacity

Oil is extremely crucial to the development of the modern economy. It is important to forecast the oil supply capacity due to its scarcity and non-renewability. This paper attempts to forecast and analyze thirty-five current and potential net oil-exporting countries. Integrating both qualitative and quantitative methods, the oil production and consumption are predicted based on historical data, so that the world net oil-exporting capacity can be obtained. The results show that the ＂roof effect＂ of the world net oil-exporting capacity may appear before 2030. Unconventional oil will play an important role in the future world oil market. The competition and cooperation relationships between OPEC and non-OPEC will last for a long time.

Characterization of Particle Emissions of Turbocharged Direct Injection Gasoline Engine in Transients and Hot Start Conditions

Reducing pollutant emissions,particularly soot particles emitted by internal combustion engines,is a major challenge for car manufacturers.In this paper,the experimental setup is a turbocharged three-cylinders gasoline direct injection engine installed on a HORIBA dynamic test driven by a HORIBA STARS computer.The particle-measuring device is a Pegasor Particles Sensor that measures the current carried by previously electrically charged particles.The hot engine stabilized tests,with lambda parameter lower or equal to one,have very low emission levels,unlike dynamic tests.As a consequence,the present paper deals with experiments in transient conditions.Unlike diesel engine,cycle tests show that particulate emissions vary widely.To understand the phenomenon,a simple transient was created and reproduced a hundred times in order to obtain enough data to analyze and compare these different tests.This transient starts from idle to reach the speed of 2000 r/min and 60 N·m in 5 s.To reach this point,it is necessary to stay in full load for about 3 s.The maximum deviations of particles reaches 85%with the standard deviationσ=18%.The cylinder pressure sensor shows significant variations at the very beginning of each transient,i.e.,during the first 500 ms.This kind of result was observed for Worldwide harmonized Light vehicles Test Cycles(WLTC)with a maximum deviations of particles reaching 75%withσ=30%,on Real Drive Emissions Cycle(RDE)with a maximum deviations of particles reaching 45%withσ=22%and for a 300 s Mini-Cycle with a maximum deviations of particles reaching 70%withσ=17%.The Mini-cycle is made up of the five largest accelerations of the WLTP cycle.A complete analysis highlights the importance of filling the first engine cycles.This depends on the opening speed of the throttle,the position of the crankshaft at the beginning of the transient,and the acceleration of the first cycles.But,the NO_(x) sensor shows very slight variations between each test.As a consequence,it appears that the variation of particles emissions is not only related to variation of equivalence ratio but with another setting,which may be the oil consumption.Finally,from these results,it is possible to determine a particle characterization function.It consists of two functions.The first one is the average of the emitted particles level which depends on the engine speed,engine acceleration,engine torque and torque acceleration.The second function,which corresponds to dynamic variations in emissions,mainly depends on oil consumption in the cylinder and on the combustion quality of the first transient engine cycles.