轻型载货汽车车外噪声分析与控制

用声强扫描法对国产某轻型载货汽车车外噪声源进行了识别,确定了其车外主要噪声源。开发了材料声学特性测量系统,并对多种车用吸声、隔声材料进行了测试与分析。根据被试轻型载货汽车车外主要噪声源的特性合理地选择吸声、隔声材料和噪声控制方案,对其进行了降噪处理。通过对降噪前、后该车车外噪声进行测试分析表明,采取降噪措施后,被试车辆车外动态加速噪声由84dB(A)下降到78dB(A),能够满足国家标准GB1495—2002对该类车辆车外动态加速噪声限值的要求。     

底盘测功机加载响应性能研究

对我国在用车工况法尾气排放测试用底盘测功机加载响应性能的测试方法和技术要求进行了研究。指出我国有关文献忽略了影响底盘测功机加载响应性能的重要因素—平均稳定时间所带来的问题,以及美国BAR97测试方法对我国实施的瞬态和简易瞬态工况法存在的不适应问题,根据我国瞬态工况法和简易瞬态工况法的特点,提出新的加载响应性能测试方法,并对某底盘测功机加载响应性能进行了测试和分析。     

柴油机燃用麻疯树油甲酯的氮氧化物排放特性

研究了柴油机燃用麻疯树油甲酯的NOx排放特性。以1台轻型车用直喷式柴油机为试验样机,分别燃用6种不同生物柴油掺混比例的混合燃油,研究总的NOx排放以及NO,NO2,N2O等NOx主要组分的排放特性。结果表明,燃用各种混合燃油的NOx排放曲线形态较为接近,低负荷时差异较小,随负荷增加,排放差异增大。NOx排放以NO和NO2为主,NO排放随着负荷上升而增加,NO在总NOx排放中始终占有最高比例。NO2排放也占有相当比例,在低负荷时较高,随着负荷增大浓度降低,大负荷高温不利于NO2的生成。N2O排放量极低,在中低负荷时有一定生成量,高负荷N2O排放几乎为0,缸内稀燃低温有利于N2O排放的生成。在同一稳定工况下,随生物柴油混合比的提高,NOx,NO,NO2比排放量呈线性增加,N2O比排放量呈线性降低。发动机燃烧生物柴油后,NOx及其组分NO,NO2和N2O的排放量发生改变,而各自的排放变化规律并未发生变化。     

多次喷射对柴油机燃烧与排放影响的试验研究

基于1台高压共轨涡轮增压柴油机,采用不同的预喷正时、预喷油量与后喷正时等,研究了多次喷射对燃烧放热、排放生成与燃油经济性的影响,以实现均质压燃和低温燃烧过程。研究结果表明:随预喷正时提前,缸内峰值压力降低,主燃阶段的滞燃期缩短,NOx和炭烟排放均降低;随预喷油量增加,预喷阶段燃烧的放热率和最大压力升高率增大,NOx和HC排放增大,而PM和CO排放降低;随后喷始点推迟,缸内压力与主放热率峰值差异变小,NOx排放降低,但炭烟排放先增大后逐渐降低。     

机动车PM2．5排放特性

为了获得典型机动车PM2．5排放特性,文章利用符合PMP规程的MEXA一1000SPCS时不同技术类型的机动车进行了PM2．5数量排放研究,并对PM2．5排放较高的两类车型（GDI和国Ⅳ柴油车）进行了冷热试验对比。试验结果表明：不同技术类型机动车PM2．5排放量级差异明显,国V柴油车〈MPI汽油车〈GDI汽油车〈国Ⅳ柴油车;GDI汽油车PM2．5排放受温度影响要大于受负荷的影响;国Ⅳ柴油车型PM2．5排放受车辆负荷的影响要大于受温度的影响;DPF技术能够有效降低PM2．5排放;无论何种车型,瞬态加速工况都会造成PM2．5排放急剧增高。     

静电雾化技术在汽车节能降排中的应用

本文分析了燃油液滴的荷电方法，确定了使用浸润电极的强制荷电方法，设计了适当的荷电装置，在发动机稳流气流气道试验台上用SQ－Ⅱ型运动粒子瞬态激光全息测试仪对应用静电雾化技术改善雾化前后的雾场进行了对比分析，统计结果证明应用静电雾化技术可降低粒径，改善雾化均匀程度；CA6102发动机台架试验结果表明，应用静电雾化技术可以大幅度地降低发动机排放中CO、HC的含量，并可使比油耗有一定程度的降低。     

目标和环境特性对被动声呐浮标探测范围的影响

被动声呐浮标的探测性能不仅和自身设备性能有关,还和目标运动特征及环境特性有着密切的关系。通过建立潜艇目标运动、海洋环境的数学模型,利用浅海平滑平均声场理论推导了潜艇深度变化后被动声呐浮标的探测范围,讨论了潜艇采用不同航速、航深以及海洋环境对被动声呐浮标探测范围的影响,并进行了仿真。结果表明,目标和环境特性对被动声呐浮标的探测范围有重要影响,这对航空反潜作战训练具有一定的指导意义。     

超声降解废水机理及空化气泡运动过程数值模拟

阐述了超声降解废水的机理,并根据以体积作为动力学参数的气泡动力学模型进行数值模拟,结果表明,低频下的空化强度更大,更有利于降解过程,而驱动声压和初始气泡大小则存在一个最佳值,同时与传统的R-P方程作了比较,两个模型模拟结果基本一致,表明本文所用模型也适用于对气泡运动过程的模拟.     

Optimization of Wiedemann and Fritzsche car-following models for emission estimation

Recent studies have provided that the vehicle trajectories generated by car-following models may not represent the real driving characteristics, thus leading to significant emission estimation errors. In this paper, two of the most widely used car-following models, Wiedemann and Fritzsche models, were selected and analyzed based on the massive field car-following trajectories in Beijing. A numerical simulation method was designed to generate the following car’s trajectories by using the field trajectories as the input. By comparing the simulated and the filed data, the representativeness of the simulated regime fractions and VSP distributions were evaluated. Then, the mechanism of car-following models was investigated from the aspects of regime determination and the acceleration rule in each regime. Further, the regime threshold parameters and acceleration model were optimized for emission estimations. This study found that the “Following” regime threshold of SDX and the maximum acceleration in “Free Driving” regime are critical parameters for Wiedemann model. The differences between the Wiedemann simulated VSP distribution and the field one can be reduced separately by applying the optimized SDX and maximum acceleration model individually. However, a much sharper reduction was observed by optimizing both parameters simultaneously, and the emission estimation errors were further reduced, which were less than 4% in the case studies. Fritzsche model generated more realistic VSP distributions and emissions, while the maximum accelerations could be further optimized for high speed conditions.     

Quantifying the environmental impacts of increasing high occupancy vehicle lanes in the United States

To what extent will increasing High Occupancy Vehicle (HOV) lane-kilometer incentivize carpooling and reduce emissions of air pollutants and greenhouse gases? To answer these questions, we develop a multiple regression model relating HOV lanes and other socioeconomic factors to carpooling propensity in all 50 U.S. states and the District of Columbia, then calculate the extent to which increasing HOV lane-kilometers would lead to reductions in carbon dioxide equivalent (CO₂e) and major air pollutants across the U.S., by state. Increasing HOV lane extent has the greatest potential to reduce annual CO₂e in the District of Columbia, followed by Hawaii and New York. The smallest potential is found in states with the lowest population density, led by North Dakota. We then explore the extent to which recommendations made at one level of data aggregation (that of individual states) may be valid for another level, such as individual counties. The only state with sufficient data available to disaggregate the model to the county level is California, where we found a lower potential for state-wide CO₂e emission reductions under the county-level model as compared to the state-level model (0.69% as compared to 1.08%, under the same hypothetical scenario), albeit with significant differences in emission reduction potential between counties with higher vs. lower population densities. This analysis demonstrates the potential to generate generalizable insight into the magnitude of vehicle emission reductions that might be achieved through expanding HOV lanes, and highlights the importance of data disaggregation in identifying the optimal locations for potential reductions.