HC-PM COUPLING MODEL FOR PARTICULATE MATTER EMISSION OF DIESEL ENGINES

A rapid, phenomenological model that predicts paniculate matter (PM) emissionof diesel engines is developed and formulated. The model is a chemical equilibrium compositionmodel, and is based on the formation mechanisms of PM and unburned hydrocarbon (HC) emissions ofdiesel engines. It can evaluate the emission concentration of PM via the emission concentration ofHC. To validate the model, experiments are carried out in two research diesel engines. Comparisonsof the model results with the experimental data show good agreement. The model can be used toevaluate the concentration of PM emission of diesel engines under lack of PM measuring instruments.In addition, the model is useful for computer simulations of diesel engines, as well as electroniccontrol unit (ECU) designs for electronically controlled diesel engines.

Effects of diesel oil on the polychaete Annelida in model benthic ecosystem

An experiment about the effect of diesel oil pollution on the model benthic ecosystem was conducted inthe land-based tank at the 3rd Institute of Oceanography, State Oceanic Administration, in Xiamen, added with No.0 diesel oil at concentrations of 5, 25, 125, 625 mg/dm3 in water of series sub-tanks for 16 h. The changes in polychaete community were studied in the period of two weeks with results that though all concentrations did not basically altered the population structure and composition, the total biomass decreased significantly and individualstended to be smaller in size. The average weight of individual decreased with the increase of diesel pollutant stress.

Simulation of a DI Diesel Engine Performance Fuelled on Biodiesel Using a Semi-Empirical 0D Model

Diesel engines have proven over the years important in terms of efficiency and fuel consumption to power generation ratio. Many research works show the potential of biodiesel as a substitute for conventional gasoil. Mainly, previous and recent researches have focused on experimental investigation of diesel engine performance fuelled by biodiesel. Researches on the mathematical description of diesel engine process running on biodiesel are scarce, and mostly about chemical and thermodynamic description of the combustion process of biodiesel rather than performance studies. This work describes a numerical investigation on the performance analysis of a diesel engine fuelled by palm oil biodiesel. The numerical investigation was made using a semi empirical 0D model based on Wiebe’s and Watson’s model which was implemented via the open access numerical calculation software Scilab. The model was validated first by comparing with experimental pressure and performance data of a one cylinder engine at rated speed and secondly by comparing with a six cylinders engine performance data at various crankshaft rotational speeds. Simulations were then made to analyze the engine performance when running on biodiesel. The calculations were made at constant combustion duration and constant coefficient of excess air. Results showed that the model matches the overall experimental data, such as the power output and peak cylinder pressure. The ignition delay was somehow underestimated by the model for the first experiment, which caused a slight gap on in cylinder pressure curve, whereas it predicted the average ignition delay fairly well for the second set of validation. The simulations of engine performance when running on biodiesel confirmed results obtained in previous experimental researches on biodiesel. The model will be further investigated for engine control when shifting to biodiesel fuel.

Cooling of a Diesel Reformate Fuelled Solid Oxide Fuel Cell by Internal Reforming of Methane： A Modelling Study

In this paper a system combining a diesel reformer using catalytic partial oxidation (CPOX) with the Solid Oxide Fuel Cell (SOFC) for Auxiliary Power Unit (APU) applications is modeled with respect to the cooling effect provided by internal reforming of methane in anode gas channel. A model mixture consisting of 80% n-hexadecane and 20% 1-methylnaphthalin is used to simulate the commercial diesel. The modelling consists of several steps. First, equilibrium gas composition at the exit of CPOX reformer is modelled in terms oxygen to carbon (O/C) ratio, fuel utilization ratio and anode gas recirculation. Second, product composition, especially methane content, is determined for the methanation process at the operating temperatures ranging from 500 ℃ to 520 ℃. Finally, the cooling power provided by internal reforming of methane in SOFC fuel channel is calculated for two concepts to increase the methane content of the diesel reformate. The results show that the first concept, operating the diesel reformer at low O/C ratio and/or recirculation ratio, is not realizable due to high probability of coke formation, whereas the second concept, combining a methanation process with CPOX, can provide a significant cooling effect in addition to the conventional cooling concept which needs higher levels of excess air.

Simulation of quasi-dimensional model for diesel engine working process

In order to satisfy the demand of validity and real time operating performance of diesel engine model used in hardware-in-the-loop simulation system,a simplified quasi-dimensional model for diesel engine working process was proposed,which was based on the phase-divided spray mixing model.The software MATLAB/Simulink was utilized to simulate diesel engine performance parameters.The comparisons between calculated results and experimental data show that the relative error of power and brake specific fuel consumption is less than 2.8%,and the relative error of nitric oxide and soot emissions is less than 9.1%.At the same time,the average computational time for simulation of one working process with the new model is 36 s,which presents good real time operating performance of the model.The simulation results also indicate that the nozzle flow coefficient has great influence on the prediction precision of performance parameters in diesel engine simulation model.

柴油机Wiebe模型参数优化及燃烧性能预测

基于一台单缸柴油机进行了发动机性能实验,通过结合单、双Wiebe燃烧模型和机器学习算法,提出了一种可预测的Wiebe燃烧模型,开展了不同边界条件下的燃烧参数和规律预测研究.首先,使用代数化Wiebe方程的线性拟合,根据线性拟合精度选取单、双Wiebe模型.然后,使用列文伯格-马夸尔特(Levenberg-Marquardt,LM)算法拟合Wiebe方程得到相应的6个Wiebe参数,实现放热率Wiebe参数化.最后,基于该Wiebe燃烧参数,应用误差反向传播神经网络(back propagation neural network,BP-NN)和随机森林(random forest,RF)算法,开发了实用性更广泛的两种Wiebe燃烧预测模型,研究了不同边界条件下的燃烧规律.结果显示:代数Wiebe方程的线性拟合精度小于等于0.99000时放热率曲线更复杂,此时选用双Wiebe方程可得到高精度的Wiebe燃烧参数,反之选用单Wiebe方程即可;在1200 r/min和2200 r/min时选择双Wiebe方程对放热率进行拟合,拟合精度R^(2)均大于0.99000,误差平方和均小于0.01,通过Wiebe参数重新构建的放热率和实验放热率基本一致.基于LM算法的放热率拟合算法,可以很好地反映柴油机不同工况下的燃烧特征.对比两种不同的燃烧预测模型BP-NN和RF发现:BP-NN模型对一Wiebe形状因子m1和一Wiebe燃烧初始相位φ_(01)的预测精度更高,而RF算法对一Wiebe燃烧比例α和燃烧结束相位φ_(end)的预测精度更高,因此,针对不同燃烧参数选择不同预测模型可以有效提高Wiebe燃烧预测模型的精度.

船用柴油机均值模型建模及仿真研究

柴油机模型的研究是探究柴油机性能、实现虚拟仿真的基础,可缩短仿真计算时间、有效节约柴油机开发成本。本文以RK270柴油机为研究对象,使用GT-POWER软件建立稳态性能仿真模型,在此基础上,采用仿真精度高、计算速度快的平均值法,以C++语言建立柴油机平均值模型,并归纳通用的平均值模型建模标定校核流程;提出二元响应拟合的方法,解决了涡轮增压器特性参数处理困难的问题,并借助数学工具,获得了压气机主要工作区域内拟合精度较高的模型。完成模型开发后,分析了8组工况下的仿真结果,通过误差分析,验证了模型的准确性,丰富完善了柴油机建模理论和方法,可应用于实时控制模型的动态仿真或者实现与船舶动力系统的交互。

基于GT-Power的压电晶体喷油器性能仿真分析

为优化柴油机压电晶体喷油器性能,采用GT-Power建立高压共轨压电晶体喷油器仿真模型,改变模型中压电晶体的材料属性,设置不同电压条件,仿真分析压电晶体执行器的弹性模量、晶体驱动电压对执行器位移、喷油针阀压强、喷油器温度及每循环喷油量的影响。仿真结果表明:压电晶体喷油器弹性模量大于75 GPa,执行器位移随驱动电压的增大而增大;喷油器弹性模量大于80 GPa,喷油针阀处的压强增大,温度升高;相比弹性模量为55 GPa,弹性模量为80 GPa时,压电晶体执行器位移增大15.6%,喷油器每循环喷油量增加2.3%。压电晶体喷油器弹性模量为75~80 GPa,可以提高喷油速率,缩短喷油持续期,有利于提高柴油机热效率。

基于Modelica语言建模的柴油机稳动态性能仿真与试验

针对传统柴油机建模效率不高、所建模型抽象不易理解、软件平台开放程度差、模型通用性低的问题,采用多领域统一物理建模规范Modelica语言和面向对象的建模思想建立了柴油机的仿真模型。以上柴D4114B型发电用柴油机为例,在解决上述问题的同时模拟了负荷特性下柴油机的稳态过程,最高压力、排温等稳态数据点的实测值与仿真值的最大相对误差为8.9%,证明了所建立的柴油机模型具有较好的稳态性能。在此基础上进行了动态仿真,模拟了转速、单缸循环油量等参数的动态变化规律,并将部分参数的仿真值与实测值进行比较,两者吻合得比较好,证明了所建立的柴油机模型能较好地预测其动态性能,可为后续的基于Modelica语言的柴油机建模提供参考。

基于模型预测控制的双机组混合动力船舶能量管理研究

[目的]船舶柴电混合动力系统合理分配柴油机和电机输出功率,可大幅降低油耗和排放。针对传统混合动力能量管理策略的性能最优与运算实时的矛盾,提出采用模型预测控制(MPC)进行能量管理瞬时优化。[方法]首先,利用反向建模法搭建由双柴油发电机组、储能系统、岸电组成的客渡船混合动力系统能量流模型;然后,提出以油耗和电能消耗的总温室气体(GHG)排放为目标函数,在系统约束条件下可在线滚动优化求解的MPC能量管理算法,最终进一步进行了不同预测时域长度的灵敏度分析。[结果]仿真结果表明,MPC较传统规则控制方法可分别降低4.85%的燃油消耗量和3.54%的温室气体总排放。[结论]相比于传统的规则控制策略,MPC油耗低,温室气体排放少,且计算负荷较小,具有良好的实船应用潜力。

基于变分模态分解的多缸柴油机缸套振动差异分析

多缸柴油机缸套振动噪声响应的差异导致缸套润滑、磨损及穴蚀现象不同,引起缸套可靠性差异并缩短整机使用寿命,研究差异的产生原因有助于柴油机的维护与设计优化。内燃机振动噪声的主要来源是燃烧冲击与活塞二阶运动引起的缸套敲击,而多缸柴油机缸套振动噪声的差异主要来源于活塞敲缸。以某重型六缸柴油机为研究对象,基于变分模态分解技术对多缸振动响应差异开展了溯源分析。建立了活塞敲缸动力学计算模型并通过试验验证了参数设置的合理性,通过变分模态分解从振动响应中提取了主要频率成分与相关模态。结果表明:多缸柴油机各缸振动幅值分布趋势一致,缸套中上部的振动最剧烈;各缸振动差异与机体模态特性有关,缸套间振动差异与机体第7阶模态振型趋势一致,体现为第6缸振动最强,第2缸振动最弱。

船舶柴油动力系统的多工况数学建模研究

本文研究船舶柴油机动力系统,重点分析船舶柴油机的动力装置,给出船舶柴油发电机转速和输出功率之间的关系曲线,并构建出船舶柴油机推进负载,分析船舶螺旋桨转矩和转速之间的曲线关系;构建船舶柴油机动力系统多工况数学模型;对模型进行仿真,并对柴油机中的燃烧率、压强以及扭矩进行分析;构建的船舶柴油机动力系统多工况数学模型,对我国船舶柴油动力系统的快速发展具有借鉴作用。

基于近红外光谱分析的土壤污染柴油含量预测方法研究

为了快速定量测量柴油污染土壤中的柴油含量,扩大近红外光谱技术在污染土壤监测中的应用,制备了含柴油的高岭土样本集F1和含柴油的实际土壤样本集F2,对近红外光谱检测数据建立了BP神经网络、随机森林和支持向量机回归3种预测模型。结果表明,预测模型对样本集F1的预测精度良好,但无法准确预测样本集F2中的柴油含量。通过加标和重复加标处理,改善了实际土壤中柴油含量的预测效果。与直接建模相比,由柴油高岭土样本加入少量实际土壤样本后建立的近红外光谱模型,能更准确地预测柴油含量。

基于AM-CNN-LSTM模型的柴油机NO_(x)排放预测

为精确控制选择性催化还原(selective catalytic reduction,SCR)系统的尿素喷射,提出一种基于注意力机制(attention mechanism,AM)的卷积神经网络(convolutional neural networks,CNN)-长短时记忆网络(long shortterm memory,LSTM)模型预测柴油机NO_(x)排放的方法,根据柴油机NO_(x)生成机理和车辆实际道路测试采集的数据选取相关变量;使用AM-CNN模型提取特征,利用LSTM模型对提取的特征进行分析预测NO_(x)排放。结果表明:该混合模型对NO_(x)排放的预测精度较高,计算时间较少,平均绝对误差为5.307×10^(-6),决定系数为0.932。根据预测模型中输入参数权重分析影响NO_(x)生成的关键因素,可以为优化柴油机燃烧过程提供参考。

基于MBSE的柴油机系统设计建模方法

随着国防武器装备对动力要求越来越高,柴油机系统复杂度急剧提升,传统的以文档为核心的系统工程方法已经无法有效满足柴油机高效、准确、可靠的研制需求,为此,在柴油机研发过程中引入基于模型的系统工程(MBSE)方法。提出了基于RFLP的MBSE的柴油机系统建模流程,及支撑该流程运行的柴油机模型体系,在此基础上,以柴油机某研制任务为例,以模型为核心,给出了基于MBSE系统建模过程及模型实例,对柴油机需求分析建模、柴油机功能建模、柴油机系统架构分析与综合进行了阐述,体现了基于模型的设计方法在柴油机研制过程需求早期验证、功能快速仿真、数据可追溯方面的优势,从而有效提高研发设计效率。

柴油机活塞环润滑油膜厚度超声波测量方法研究

活塞组件-缸套摩擦副位置处良好润滑状态能够保障柴油机的稳定运行,润滑油膜厚度能够对其润滑状态进行判断。因此本文以润滑油膜厚度为测量目标,基于等效弹簧模型超声波测量原理对活塞环位置处润滑油膜厚度开展实验研究。搭建了润滑油膜厚度超声测量实验平台,通过实验证实了超声方法的测量能力。构建了柴油机活塞环润滑油膜厚度测量方案,设计并搭建了活塞环润滑油膜厚度动态测量系统,对不同转速下的润滑油膜厚度进行了实验测量。通过对实验数据进行处理,在活塞环处测得了2.2~3.9μm的润滑油膜厚度,并且随转速增加,油膜厚度呈现增大趋势,证实了该方法的可行性。

基于回归分析的发动机HC排放预测模型

将回归分析方法用在一台经过改造的汽油/柴油双燃料发动机HC排放研究上。以1700r/min@60 N·m工况为例描述了试验设计、模型阶次选择、异常点去除、正态分布假设判断整个过程,获得了精度较高的HC排放模型,通过将试验结果和仿真分析结果进行对比验证了所建模型的正确性。

基于GWO-LSTM的柴油机NO_(x)排放预测

柴油机NO_(x)是机动车主要的有害排放物质,精确测量NO_(x)排放有利于SCR尿素喷射的控制从而减少排放,而现有的氮氧传感器和通过标定获得的排放MAP均难以实现瞬态条件下NO_(x)的实时测量。使用主成分分析法(PCA)对全球统一瞬态试验循环(WHTC)的柴油机工况参数进行降维处理,基于长短期记忆神经网络(LSTM)搭建柴油机NO_(x)实时预测模型,并利用灰狼优化算法(GWO)对LSTM模型进行参数优化。结果显示:GWO-LSTM预测模型在未训练的数据集上的平均相对误差(MAPE)为3.23%,证明该模型能够精准实现柴油机NO_(x)排放的实时预测,并具有良好的泛化能力和可靠性,为以软件替代硬件实现柴油排放控制提供了参考。

基于机器学习的柴油机标准油耗模型

针对卡车油耗影响因素多,无法简单进行对比的问题,设计一种基于机器学习的油耗模型。通过前期大量数据的训练和模拟,该模型可以利用车辆行驶参数(如发动机转速、转矩、车速)以及环境因素(如环境温度、环境压力)来计算实时油耗,并得到累计油耗,最终可将偏差控制在3%以内。该方法可为柴油机的油耗分析提供新的思路。