Application of neural network in the study of combustion rate of natural gas／diesel dual fuel engine

In order to predict and improve the performance of matural gas/diesel dual fuel engine(DFE),a combustion rate model based on forward meural network was built to study the combustion process of the DFE.The effect of the operating parameters on combustion rate was also studied by means of this model.The study showed that the predicted results were good agreement with the experimental data.It was proved that the de-veloped combustion rate model could be used to successfully predict and optimize the combustion process of dual fuel engine.     

Integrated modeling of nitrogen oxides formation in diesel engines

To account for the effects of both chemistry and flow turbulence, the present study proposes an integrated NO sub-model that combines the extended Zel'dovich mechanism and engine CFD computations to simulate the NO histories in a diesel engine. NOx sub-model parameters and pollutant formation mechanisms can be more easily investigated by solving the NOx sub-model. The new NO formation model incorporating the effects of both chemical kinetics and turbulent mixing was applied to simulate a diesel engine with a quiescent combustion chamber, and one with a re-entrant combustion chamber; the premise of the model being the reaction rate is mainly determined by a kinetic timescale and a turbulent timescale. The results indicate that the predicted NO formulation from the new model agrees well with the measured data. As the utilization of fossil fuels continues to increase, the control of NOx emissions is a worldwide concern; and it is imperative to understand fully the NOx reaction processes in combustion systems. This technology has the potential to enhance the application of various combustion techniques used to reduce NOx emissions from practical combustion systems.     

Adelfa： a futuristic biodiesel fuel for DI diesel engines： a comparative experimental investigation

Biodiesel fuel is found to be a promising alter- native for the petroleum diesel based on the results published by the researchers for a decade. Biodiesel fuel is renewable and non degradable fuel. Many countries use biodiesel fuel for automotives to meet the crisis due to the depletion of the petroleum fuel and to meet the stringent emission norms. Various researches have been carried out with different bio- diesel fuels with vegetable oil as the source and appreciable results were reported. Few biodiesel fuels which have been already tested are Jatropha, Pongamia, Mahua, neem, cotton seed, etc. In this experimental work, Adelfa biodiesel blend is used as the test fuel. The emission and performance charac- teristics were compared with three other different biodiesel fuel blends. Appreciable results imply that Adelfa biodiesel （Nerium oil methyl ester） can be a futuristic biodiesel fuel, which has a good compatibility with the direct injection （DI） diesel engine without any major modification. Moreover, Adelfa can be cultivated in a non agricultural land with fewer sources of water. It is widely spread over all major countries of Asia. Experimental investigations have been carried out on a single cylinder DI diesel engine with standard engine speci- fications. In this experimental work, various Adelfa biodiesel blends is compared with reference fuel （diesel） to choose the best blend which gives a closer performance to diesel. The comparative analysis with other biodiesel fuels has also been done and results have been discussed.     

Study of droplet size and velocity of fuel containing CO2 spray by means of PDA

Injection of fuel containing CO2 has potential to reduce NOx and soot emissions in a diesel engine. This paper presents an experimental study on the spray characteristics of fuel containing CO2 as measured by phase doppler anemometry (PDA). Experiments were performed under atmospheric conditions on diesel hole-type nozzles at constant injection pressure. Effects of CO2 concentration in diesel fuel on the spray pattern, droplet size and velocity were measured. Experimental results show that fuel atomization will improve greatly when the concentration of dissolved CO2 in the fuel exceeds the critical value. The axial and radial velocity of the fuel spray containing CO2 is larger than that of conventional diesel fuel spray near the nozzle exit due to flash boiling phenomena. Downstream of the spray, the radial velocity and droplet size of fuel containing CO2 is much more uniform and smaller than that of pure diesel spray. It is attributed to the greatly enhanced liquid-gas mixing resulting from flash separation of CO2 from the liquid. New insight into the atomization of the fuel containing CO2 was obtained and a possible mechanism to explain the phenomena was proposed. The method may be developed into a new technique for controlling diesel combustion and exhaust emis-sions.     

New controllable premixed combustion for dimethyl ether engine

To meet the requirements of tightening regulations on exhaust emissions, especially NOX and particulate emissions (PM), searching for a method to simultaneously reduce the NOX and PM emissions from diesel engines is a major challenge. The application of alternative fuels in diesel engine is an effective approach in reducing NOX and PM. The latest research showed that DME is an excellent alternative fuel. The experimental studies on diesel engines fueled with DME showed that the engine …     

Machinery Condition Prediction Based on Support Vector Machine Model with Wavelet Transform

Soft failure of mechanical equipment makes its performance drop gradually,which occupies a large proportion and has certain regularity. The performance can be evaluated and predicted through early state monitoring and data analysis. The vibration signal was modeled from the double row bearing,and wavelet transform and support vector machine model（ WT-SVM model） was constructed and trained for bearing degradation process prediction. Besides Hazen plotting position relationships was applied to describing the degradation trend distribution and a 95%confidence level based on t-distribution was given. The single SVM model and neural network（ NN） approach were also investigated as a comparison. Results indicate that the WT-SVM model outperforms the NN and single SVM models,and is feasible and effective in machinery condition prediction.     

Integrated modeling of nitrogen oxides formation in diesel engines

To account for the effects of both chemistry and flow turbulence, the present study proposes an integrated NO sub-model that combines the extended Zel’dovich mechanism and engine CFD computations to simulate the NO histories in a diesel engine. NOx sub-model parameters and pollutant formation mechanisms can be more easily investigated by solving the NOx sub-model. The new NO formation model incorporating the effects of both chemical kinetics and turbulent mixing was applied to simulate a diesel engine with a quiescent combustion chamber, and one with a re-entrant combustion chamber; the premise of the model being the reaction rate is mainly determined by a kinetic timescale and a turbulent timescale. The results indicate that the predicted NO formulation from the new model agrees well with the measured data. As the utilization of fossil fuels continues to increase, the control of NOx emissions is a worldwide concern; and it is imperative to understand fully the NOx reaction processes in combustion systems. This technology has the potential to enhance the application of various combustion techniques used to reduce NOx emissions from practical combustion systems.     

General analytical mass transfer model for VOC emissions from multi-layer dry building materials with internal chemical reactions

A general mass transfer based model was developed for analyzing volatile organic compound (VOC) emissions from dry multi-layer building materials with two emission surfaces.This model adds to an earlier multi-layer model by considering chemical reactions within the materials.Consequently,it can be used to analyze the effect of these chemical reactions on removing VOCs,and for characterizing secondary VOC emissions from the building material.The model was validated with literature data and our experimental results.Some typical secondary emissions were analyzed using this model,and obviously differed from the primary emissions.The model is a useful tool for predicting,analyzing and "designing" the VOC emission characteristics,including secondary emissions,of building materials.     

A menthod for predicting in—cylinder compound combustion emissions

This paper presents method using a large steady-state engine operation data matrix to provide necessary information for successfully training a predictive network,while at the same time eliminating errors produced by the dispersive effects of the emissions measurement system.The steady-state training conditions of compund fuel allow for the correlation of time-averaged in -cylinder combustion variables to the engine-out Nox and HC emissions.The error back-propagation neural network(EBP)is then capable of learming the relationships between these variables and the measured gaseous emissions,and then interpolating between steadystate points in the matrix.This method for NOx and HC has been proved highly successful.     

Yarn Quality Prediction for Small Samples Based on AdaBoost Algorithm

In order to solve the problems of weak prediction stability and generalization ability of a neural network algorithm model in the yarn quality prediction research for small samples, a prediction model based on an AdaBoost algorithm(AdaBoost model) was established. A prediction model based on a linear regression algorithm(LR model) and a prediction model based on a multi-layer perceptron neural network algorithm(MLP model) were established for comparison. The prediction experiments of the yarn ev...     

基于RBF神经网络的船用柴油机NOx排放的预测

依据4190ZLC-2型船用四冲程增压柴油机实际试验测得的数据,利用MATLAB中的神经网络工具箱,建立了基于径向基函数神经网络（RBF）的柴油机氮氧化物（NOx）排放浓度的预测模型。在预测模型建立过程中选取柴油机油耗率（SFOC）、功率、转速等参数数值作为输入矩阵,柴油机的氮氧化物排放浓度作为输出矩阵。仿真结果表明:该方法预测精度高,可为控制氮氧化物的排放提供依据。     

蔗糖水溶液乳化柴油对发动机性能的影响研究*

在一台双缸直喷式柴油机上,对燃烧蔗糖水溶液乳化柴油时发动机的燃油经济性和排放性进行试验。分析结果表明:在柴油机参数不做改变的情况下,与纯柴油相比,蔗糖水溶液乳化柴油的动力性有所下降,当量燃油消耗率和碳烟以及CO排放量在中高负荷工况下都有明显降低,NOx排放在各种工况下都显著降低;但HC的排放量增加。与乳化柴油相比,蔗糖水溶液乳化柴油的动力性升高,当量燃油消耗率和碳烟以及CO排放量在中低负荷时有所增加,大负荷时明显降低;NOx的排放量在中小负荷时明显减少,大负荷时略有增加,HC排放在各种工况下都显著降低。     

柴油特性对车用柴油机排放影响的模糊评估

论述了控制发动机有害物质排放的前处理方法,建立了柴油的组分和性质与车用柴油机的排放之间关系的模糊评估模型。利用该模型对10种柴油的排放性能进行了评估,分析了柴油的组分和性质与柴油机排放之间的关系。结果表明：发动机使用硫和芳香族化合物含量低、密度较小、馏程较低、十六烷值较大的燃料时,可获得较好的排放性能。     

基于神经网络的车用汽油机稳态排放特性建模研究

车用汽油机稳态工况下废气排放与汽油机转速、负荷、空燃比和点火正时等影响因素之间是非线性关系 ,通过对汽油机试验排放数据的学习 ,建立起描述车用汽油机废气 HC,CO,NOx 排放与上述因素之间关系的 BP神经网络模型 ,该模型可用于排放预测和实时控制     

基于MATLAB的地沟油生物柴油掺烧比优化分析

通过10种不同掺烧比的地沟油生物柴油在双缸直喷式柴油发动机上的发动机台架试验,研究了地沟油生物柴油的动力性、经济性以及排放性随掺烧比变化规律。并根据实验结果,在柴油机应用较多的中等负荷情况下利用遗传算法通过MATLAB仿真了最优掺烧比。优化结果显示:中等负荷情况下,地沟油生物柴油与柴油的最佳掺烧比为21∶78。此时,发动机功率降低不多,油耗率增加4.43%,NOX排放减少5.01%,碳烟排放减少20.76%,其综合性能达到最优。     

基于深度极限学习机的柴油机尾气排放预测

准确预测拖拉机等柴油机械实际工况污染物排放在排放清单建立和区域污染物排放控制方面具有重要意义。基于拖拉机不同运行状态下发动机转速、油耗、燃烧比、CO、HC、NOX和PM等实测数据作为数据源,建立深度极限学习机(Deep Extreme Learning Machine,DELM）的预测模型,并对拖拉机怠速、行走和旋耕等基本工况下的污染物排放进行预测。为进一步评估DELM预测模型的适应性,将其与支持向量机(support vector machine, SVM)和前馈神经网络(Back propagation neural network, BPNN)模型进行对比分析。结果表明,1）DELM模型在预测排放时间序列方面具有一定优势,其预测拖拉机在怠速、行走和旋耕3种状态下的NOX、HC、CO和PM排放均方根误差均值分别为5.269×10-5、5.195×10-5、5.135×10-5和2.795×10-5。2）DELM模型与SVM和BP对比发现,DELM模型在鲁棒性以及适应性方面的优势显著。3）DELM方法的较高的准确度和泛化性,为基于发动机状态数据预测移动源尾气排放提供思路和方法。     

基于析因试验的连杆衬套磨损分析

柴油机连杆衬套-活塞销摩擦副的耐磨性能直接影响柴油机的使用寿命。基于完全析因试验,以主轴转速、配合间隙、加载载荷作为试验因素,磨损量作为评价指标,获得试验因素主效应及交互效应的主次顺序。结果表明:试验因素之间的交互效应是存在的,且超过某些单个因素的主效应。另外在单个因素中,主轴转速的主效应最强。利用RBF神经网络对高转速下的连杆衬套磨损量进行的预测,将预测值与实测值进行对比,发现网络具有较强的预报性能,可以有效提高参数的设计效率及降低实际实验所需的成本。     

直喷式柴油机燃用生物柴油的性能与排放

为了研究生物柴油在柴油机上的应用,在一台四缸涡轮增压直喷式柴油机上进行不同掺混比生物柴油与柴油混合燃料的性能对比试验,分析了在不同转速和负荷下柴油机燃用不同掺混比混合燃料的动力性、经济性和排放特性。研究结果表明:生物柴油与柴油相比,在2 200 r/min负荷特性下,有效能耗率减少,NOx排放增加较多,中小负荷碳烟排放基本相同,大负荷碳烟排放明显降低,中小负荷HC排放明显降低,大负荷HC排放基本一致,CO排放基本不变;在外特性下,功率略有增加,HC排放和碳烟排放均有所降低,CO排放和NOx排放增加。     

大功率船用柴油机智能诊断系统的研究与实现

通过对船舶柴油机故障诊断仿真系统的模型,结构原理与功能的研究,着重介绍了基于ＲＢＦ神经网络故障智能诊断方法,并以实例对涡轮增压系统和气缸组件与燃料系统故障进行分析判断,指出开发实船柴油故障诊断的计算机辅助分析系统具有指导意义。