Quantification of emission variability for off-road equipment in China based on real-world measurements

• Emissions from 53 in-use diesel-fueled off-road equipment were measured. • There exists a large off-road equipment variability in emissions. • Engine operations have significant impacts on real-world tailpipe emissions. • Emission inventory development should take into account job duties and operations. The objective of this paper is to quantify the variability in emissions of off-road equipment using a portable emission measurement system. A total of 53 commonly used equipment for agriculture, base construction, paving construction, and material handling were selected. Time-based and fuel-based emissions were quantified by different duty and engine modes. Three duty modes (idling, moving, and working) were used. Ten engine modes were defined based on normalized engine revolutions-per-minute and manifold absolute pressure, respectively. Composite emission factors taking into account both duty modes and its corresponding time percentage during a typical duty cycle were estimated. Results showed that there existed a large off-road equipment variability in emissions. Depending on duty and engine modes, time-based NO emissions ranged from 3.1 to 237.9, 29.1‒1475.6, 83.2‒681.6, and 3.2‒385.2 g/h for agriculture, base construction, paving construction and material handling equipment, respectively while for fuel-based NO emissions these ranges were 5.3‒52.0, 11.7‒69.0, 4.8‒30.8, and 11.0‒54.6 g/kg, respectively. Furthermore, emission factors derived from this study exhibited a much larger variability compared to those used in NONROAD by US EPA and National Guideline for Off-road Equipment of China. This implied that localized measurements of emissions are needed for improvement of accuracy of emission inventory. Furthermore, both equipment types and operations should be considered for development of emission inventory and control strategy.     

基于多尺度排列熵和极限学习机的风机叶片覆冰故障检测方法*

针对风机叶片结冰故障检测中状态数据维度高和检测率低的问题,提出1种使用功率数据驱动的多尺度排列熵（multiscale permutation entropy,MPE）和极限学习机（extreme learning machine,ELM）的风机叶片结冰故障检测方法。首先,使用多尺度排列熵提取功率数据的多重尺度特征,得到特征向量;随后,采用极限学习机,结合环境温度,对结冰故障进行检测;最后,通过使用某风电场的数据采集与监视控制系统（supervisory control and data acquisition,SCADA）对数据进行仿真。研究结果表明:所提方法的故障检测率达到100％,同时虚警率仅有0.14％,表明所提方法在风机叶片的覆冰故障检测中的有效性。研究结果可为风机叶片覆冰故障检测提供1种有效方法。     

车辆冷却系统高原热平衡性能研究

目的 分析海拔高度(大气压力)、环境温度、冷却液温度以及冷却风量等对车辆冷却系统高原散热性能的影响.方法 采用模拟试验与仿真相结合的方法 .结果 海拔升高1000 m,发动机水套散热量平均增加约5.3％;冷却液温度升高10℃,发动机水套散热量平均降低约14.5％;海拔升高1000 m,若保持发动机热平衡状态不变(出口冷...