电控单体泵系统喷射特性仿真研究

对电控单体泵的喷射特性进行了建模仿真。研究了燃油系统结构参数对喷射压力和喷油速率的影响以及转速对喷射延迟的影响。结果表明:喷射延迟时间随着转速的增加而增加;喷射压力与柱塞—喷孔直径比成正比,而喷油速率与柱塞—喷孔直径比成反比,柱塞—喷孔的直径比决定了燃油喷射系统可能达到的最大喷油压力和最大喷油速率;最大喷射压力和最大喷射速率都随着长度比的升高而降低,最大喷射压力受长度比的影响比最大喷射速率更加敏感。     

大跨桥梁油罐车燃烧火灾模型计算方法研究

桥梁上油罐车燃烧可分为油罐车火灾和燃油泄漏油池火灾2种,为了建立2种定量分析的火灾模型,基于火灾学原理,采用理论分析与FDS数值模拟相结合的方法,提出了考虑危化品种类、桥面风、油罐车尺寸等因素的油罐车火灾最大热释放速率定量计算方法;建立了燃烧油池最大直径、扩散时间以及直径扩大速度的求解方程,提出了可表征不同泄漏孔径下油池扩散、燃烧动态过程的数学模型,并通过前人的试验结果对模型的正确性进行了验证。通过对依托工程的分析,结果表明:油罐车火灾时,最大热释放速率与桥面风速正相关,但增长幅度逐渐减小,风速从4.96 m·s^-1增至10.84 m·s^-1时,最大热释放速率的变化范围为62.89~113.54 MW,随风速增加至10.84 m·s^-1,燃烧时间逐渐变短,缩短至原来的57%,火焰高度逐渐降低,趋近于9.5 m(含油罐车高度);火焰核心区域随风速增大而增大,且向下风向倾斜。泄漏油池燃烧时,泄漏孔径的变化对热释放速率和油池扩散时间影响较小;泄漏速率比接近于泄漏孔半径的平方比,油池最大直径比、扩大速度比与泄漏孔半径比相当,燃烧时间随泄漏孔半径的增大而减小,减小速度变缓;随着燃烧油池直径增大,火焰高度增加,火焰核心区域增大;当扩散至最大直径时,其火焰的水平影响区域比油罐车燃烧更广,但燃烧时间更短。     

传统燃油车的电动化改制方案研究

文章以前置后驱的传统燃油车改制为前置前驱的纯电动汽车案例,展开整车设计分析,在维持原型车的外形、长宽高、轴距和轮距等基本参数的基础上,提出了整车布置结构变化的改制设计方案,设定了电动车整车主要性能指标。在电动化设计中取消了传统燃油动力总成及其附属的燃油、进排气等系统,依据整车性能目标要求,结合车辆空间及整车结构原理,对驱动电机动力总成、电池PACK、VCU、PDC三合一等高压系统零部件进行选型及布置设计。     

PEM燃料电池组动态单电压的稳定性分析

在动态工况下,PEM燃料电池单电压的稳定性是燃料电池动力系统持续稳定运行的保障,而质子交换膜的水合状态、阴极和阳极的排水状态、氢氧燃料传质效应都会显著影响单电压的稳定性。本文基于PEM燃料电池组动态工况试验,引入相对平均电压、相对差异度等指标以分析各燃料电池组单电压的稳定性,探究燃料电池组各电堆单元单电压随工况的变化规律及影响因素,以评价燃料电池电堆性能,指导系统控制策略的制定。     

中小型LNG运输船推进系统选型分析

为了促进LNG运输船主动力推进装置方面的研究,推动LNG运输船推进系统的转型,通过对LNG运输船几种推进方式特点的对比分析,为中小型LNG运输船主推进系统的选型提供参考。     

霍尔油位传感器的应用设计

目前,油位传感器普遍采用铍青铜电刷加厚膜电阻形式,作为一类技术成熟的油位传感器,有着无可比拟的价格和技术优势,虽在行业中占据主导地位,但这种传感器也有难以克服的缺陷,在正常通电状态下一般很难达到预期的设计寿命。随着油价上涨,劣质汽油流入市场,汽油中硫磷等腐蚀性物质对厚膜电阻腐蚀严重;耐乙醇汽油的丁晴橡胶浮子在生产过程中残留有硫元素,溶解在汽油中会腐蚀厚膜电阻,这两方面是导致油位传感器使用寿命降低的关键因素。     

喷气燃料静电消除技术研究

详细分析了喷气燃料中产生静电的原因,静电引起火灾的条件,讨论了避免静电产生的措施和消除静电荷的办法,研究了静电消除器的设计方法、材料选择、性能特点和应用注意事项.多年的应用及测试数据证明,自感应静电消除器能高效地消除喷气燃料输送过程中产生的静电荷,能确保喷气燃料使用中的静电安全.     

Influence of the heavy fuel spill from the Prestige tanker wreckage in the overlying seawater column levels of copper, nickel and vanadium (NE Atlantic ocean)

The water column above the Prestige wreckage was sampled during two consecutive campaigns: Prestinaut (December 2002) two weeks after the tanker sunk and HidroPrestige0303 (March 2003) one month after the sealing of the main fuel leaks. Samples of the original cargo fuel and the emulsified fuel in the surface of the ocean were also collected. Analysis of the fuel indicated the release of 135 kg of Cu, 1700 kg of Ni and 5300 kg of V from the original fuel to the water column, remaining 35 kg of Cu, 3100 kg of Ni and 13,800 kg of V in the emulsified fuel. The metal partitioning between the water column and the emulsioned floating fuel, Cu > Ni ~ V, are in accordance with the stability index for the metal–nitrogen bond in metalloporphyrins. This release had an impact on dissolved trace metal concentrations in the water column. An increase on dissolved copper (2.8–4.7 nM) and nickel (2.2–8.0 nM) with respect to natural values (1–3 nM for Cu and 1.6–5 nM for Ni) was observed. Values for vanadium (28–35 nM) were in the range of pristine North Atlantic waters (30–36 nM). This contamination was especially observed in the upper water column (0–50 m), associated with the mixing of seawater with the fuel moving upwards, and in deep waters, where the residence time of fuel is higher. Future research in this field should focus on the environmental variables and the processes that control the release of contaminants from fuels for a better assessment of the contamination in oil-spill events.     

Driving cycles that reproduce driving patterns,energy consumptions and tailpipe emissions

This study presents the Energy Based Micro-trip (EBMT) method, which is a new method to construct driving cycles that represent local driving patterns and reproduce the real energy consumption and tailpipe emissions from vehicles in a given region. It uses data of specific energy consumption, speed, and percentage of idling time as criteria of acceptable representativeness. To study the performance of the EBMT, we used a database of speed, fuel consumption, and tailpipe emissions (CO₂, CO, and NO_x), which was obtained monitoring at 1 Hz, the operation of 15 heavy-duty vehicles when they operated within different traffic conditions, during eight months. The speed vs. time data contained in this database defined the local driving pattern, which was described by 19 characteristic parameters (CPs). Using this database, we ran the EBMT and described the resulting driving cycle by 19 characteristics parameters (CPs*). The relative differences between CPs and CPs* quantified how close the obtained driving cycle represented the driving pattern. To observe tendencies of our results, we repeated the process 1000 times and reported the average relative difference (ARD) and the interquartile range (IQR) of those differences for each CP.. We repeated the process for the case of a traditional Micro-trip method and compared to previous results. The driving cycles constructed by the EBMT method showed the lowest values of ARDs and IQRs, meaning that it produces driving cycles with the highest representativeness of the driving patterns, and the best reproduction of energy consumption, and tailpipe emissions.     

The role of alternative fuel vehicles: Using behavioral and sensor data to model hierarchies in travel

Greater adoption and use of alternative fuel vehicles (AFVs) can be environmentally beneficial and reduce dependence on gasoline. The use of AFVs vis-à-vis conventional gasoline vehicles is not well understood, especially when it comes to travel choices and short-term driving decisions. Using data that contains a sufficiently large number of early AFV adopters (who have overcome obstacles to adoption), this study explores differences in use of AFVs and conventional gasoline vehicles (and hybrid vehicles). The study analyzes large-scale behavioral data integrated with sensor data from global positioning system devices, representing advances in large-scale data analytics. Specifically, it makes sense of data containing 54,043,889 s of speed observations, and 65,652 trips made by 2908 drivers in 5 regions of California. The study answers important research questions about AFV use patterns (e.g., trip frequency and daily vehicle miles traveled) and driving practices. Driving volatility, as one measure of driving practice, is used as a key metric in this study to capture acceleration, and vehicular jerk decisions that exceed certain thresholds during a trip. The results show that AFVs cannot be viewed as monolithic; there are important differences within AFV use, i.e., between plug-in hybrids, battery electric, or compressed natural gas vehicles. Multi-level models are particularly appropriate for analysis, given that the data are nested, i.e., multiple trips are made by different drivers who reside in various regions. Using such models, the study also found that driving volatility varies significantly between trips, driver groups, and regions in California. Some alternative fuel vehicles are associated with calmer driving compared with conventional vehicles. The implications of the results for safety, informed consumer choices and large-scale data analytics are discussed.