水平对置活塞发动机高空热平衡特性研究

目的研究不同海拔大气压力特别是高空条件对水平对置活塞汽油机热平衡性能的影响。方法利用内燃机高空模拟试验台进行不同海拔高度(0~7000 m)下水平对置活塞汽油机的热平衡试验,测得不同海拔下排温、缸体表面温度等特征参数,计算热流量分配特性,并对比分析热流量分配特性随海拔高度的变化规律。结果随海拔升高,汽油机排温和缸体表面温度逐渐下降,且海拔愈高,缸体表面温度下降幅度愈大。汽油机有效功率随海拔升高逐渐下降,余项损失百分比逐渐上升,且在低转速下,变化幅度增大。在6000 m模拟海拔、3000 r/min转速下,汽油机有效热效率不到5%,而余项损失达到了30%以上,此时部分汽油甚至并未燃烧。结论高空环境对水平对置汽油机热平衡性能造成严重影响,成为制约其高海拔性能恢复的关键因素。     

柴油机增压系统变海拔自适应技术研究现状与发展趋势

分析了高原环境条件下柴油机功率下降、油耗增加、增压器效率下降、涡轮超温超速等问题,考虑采用自适应增压系统改进柴油机和增压器高原运行存在的问题。介绍了可变截面涡轮增压系统(VGT)、普通二级可调增压系统(TST)、复合增压系统(C2T)、基于VGT二级可调增压系统(R~2T)几种典型的可调增压系统的结构原理和高原应用现状。最后,结合柴油机变海拔和变工况的运行特点,从增压系统结构布置、智能优化算法、控制理论、多系统协同控制和增压器内部流场优化控制五个方面提出了增压系统高海拔自适应技术的发展趋势。     

一种基于状态向量推理的SDG-HAZOP分析方法

HAZOP是分析系统可靠性的一种有效方法,在石油、化工工业中得到了广泛应用。但人工HAZOP分析存在费时、费力、成本高等缺点,而计算机辅助SDG-HAZOP分析的提出有效地改善了人工分析的弊端,然而传统的计算机辅助SDG-HAZOP是纯定性的分析与推理,分析的结果往往过于冗繁。本文介绍了一种改进的基于状态向量推理的计算机辅助SDG-HAZOP分析方法,该方法基于SDG定性模型,构造了一个计算机自动推理引擎,并引入了状态向量的概念,通过正向反向推理,快速高效地发掘出偏差产生的后果和原因。通过应用实践表明,与人工HAZOP分析法和现有SDG-HAZOP分析方法相比,该方法具有完备性好、评价结果系统性和条理性强等优点。     

DOC/CCRT老化对柴油公交车气态物排放特性的影响

基于重型底盘测功机,研究新鲜及老化氧化型催化器(DOC)、氧化型催化器耦合催化型颗粒捕集器(DOC+CDPF,CCRT)对在用国Ⅲ柴油公交车进行中国典型城市公交车循环CCBC时一氧化碳(CO)、总碳氢化合物(THC)、一氧化氮(NO)、二氧化氮(NO_2)和氮氧化物(NO_x)、二氧化碳(CO_2)等气态物排放特性的影响.结果表明:采用新鲜及老化DOC/CCRT,均可以降低CO、THC、NO排放,增加NO_2排放,NO_x及CO_2排放基本不变;在怠速、加速、减速及匀速这4种工况下,新鲜DOC比老化DOC对CO、THC氧化效率好,新鲜CCRT比老化CCRT对THC氧化效率好,但老化CCRT对CO氧化效率更好,新鲜DOC/CCRT比老化DOC/CCRT对NO减少幅度高,对NO_2增加幅度高,但对NO_x排放总量基本无影响.     

内燃机曲轴系振动响应的多体系统动力学分析方法

介绍了运用多体系统动力学进行内燃机曲轴系振动响应分析的方法.通过综合运用CAD建模、有限元分析以及机械系统仿真等技术,可以建立包括曲轴柔性体以及随曲轴一起转动的零件刚性体在内的整个曲轴系的模型,计算零件间的相互作用,分析曲轴的动态变形.建立了某车用内燃机曲轴系的多体系统动力学模型,并对其振动响应进行了计算.     

居室放射性

本文介绍了放射性的来源,讨论了室内γ射线和氡及其子体,计算了相关的年剂量当量     

Investigation the effect of adding graphene oxide into diesel/higher alcohols blends on a diesel engine performance

ABSTRACT

This article aims to study the influence of the addition of graphene oxide nanoparticles (GO) to diesel/higher alcohols blends on the combustion, emission, and exergy parameters of a CI engine under various engine loads. The higher alcohols mainly n-butanol, n-heptanol, and n-octanol are blended with diesel at a volume fraction of 50%. Then, the 25 and 50 mg/L concentrations of GO are dispersed into diesel/higher alcohols blends using an ultrasonicator. The GO structures are examined using TEM, TGA, XRD and FTIR. The findings show that there is a reduction in p_max. and HRR when adding higher alcohols with diesel fuel. Regarding engine emission, there is a significant improvement in emissions formation with adding higher alcohols. The addition of GO into diesel/higher alcohols blends improves the brake thermal efficiency by 15%. Moreover, the p_max. and HRR are both enhanced by 4%. The CO, UHC and smoke formation are reduced considerably by 40%, 50 and 20%, respectively, while NO_x level is increased by 30% with adding GO. Finally, adding high percentages of n-butanol, n-heptanol, and n-octanol with diesel fuel with the presence of GO has the potential to achieve ultra-low CO, UHC, and smoke formation meanwhile keeping high thermal efficiency level.     

NaOH溶液吸收CO2的试验研究

为了满足国际海事组织对船舶CO2排放的要求,建立了一种针对船舶尾气CO2的循环吸收系统,利用NaOH溶液吸收CO2.NaOH溶液完成第一步吸收反应后在第二步反应中被还原,从而可以循环利用.分析了初始反应温度、NaOH浓度及溶液中的Na2CO3对CO2吸收率的影响,并计算了循环反应中NaOH的再生率和CaO的过量系数.结果表明,CaO过量系数为1.2时对CO2固化效果最佳,此时NaOH溶液再生率达79.31％.研究表明,NaOH溶液吸收船舶尾气中CO2的循环系统效率高、成本低.     

Modelling and Prediction of Diesel Engine Performance using Relevance Vector Machine

Diesel engines are being increasingly adopted by many car manufacturers today, yet no exact mathematical diesel engine model exists due to its highly nonlinear nature. In the current literature, black-box identification has been widely used for diesel engine modelling and many artificial neural network (ANN) based models have been developed. However, ANN has many drawbacks such as multiple local minima, user burden on selection of optimal network structure, large training data size, and over-fitting risk. To overcome these drawbacks, this article proposes to apply an emerging machine learning technique, relevance vector machine (RVM), to model and predict the diesel engine performance. The property of global optimal solution of RVM allows the model to be trained using only a few experimental data sets. In this study, the inputs of the model are engine speed, load, and cooling water temperature, while the output parameters are the brake-specific fuel consumption and the amount of exhaust emissions like nitrogen oxides and carbon dioxide. Experimental results show that the model accuracy is satisfactory even the training data is scarce. Moreover, the model accuracy is compared with that using typical ANN. Evaluation results also show that RVM is superior to typical ANN approach.