Fuel economy benefits in internal combustion engines due to soot restructuring in the particulate filter by water injection

烟灰 柴油颗粒过滤器 微粒 压力降 过滤(数学) 燃烧 滤波器(信号处理) 环境科学 燃料效率 汽车工程 环境工程 工程类 化学 机械 统计 电气工程 物理 有机化学 数学
作者
José Ramón Serrano,Pedro Piqueras,Emanuele Angiolini,Óscar García-Afonso
出处
期刊:International Journal of Engine Research [SAGE]
卷期号:24 (4): 1630-1642 被引量:2
标识
DOI:10.1177/14680874221099898
摘要

Wall-flow particulate filters are key elements to control particulate matter emissions. The stricter emission standards also for non-road mobility machinery makes this device essential to improve the air quality in the short to medium term. However, their high filtration efficiency brings with it an increase in backpressure. This effect becomes more damaging as particles get accumulated in the filter and in hybrid vehicles where exhaust temperature are lower due to more frequent cold starts. Pre-filter water injection is a proven method to reduce the impact of soot load on the pressure drop avoiding the fuel consumption increase. In this paper, the effect of pre-filter water injections is analysed in engine and flow test rig environments. After verifying the impact of consecutive injection events on fuel consumption, the filter was loaded and divided into quarters. These were studied one at a time in flow test rig to separate soot mal-distribution from water drag effects. A wide range of conditions were tested to assess the change in pressure drop generated by a single injection. With this reference, the soot restructuring pattern was analysed employing optical techniques. These provided evidences of the way the soot fragments got released from the particulate layer and moved towards the inlet channels rear end. Additionally, a closer look into the porous wall micro-structure provided insights explaining the lack of effect on filtration efficiency. These results provide a basis for synergistic removal of vehicle condensates for use in fuel consumption reduction.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
coffeecup1完成签到,获得积分10
1秒前
萌萌许完成签到,获得积分10
1秒前
1秒前
斯文鸡完成签到,获得积分10
2秒前
萌萌完成签到,获得积分10
3秒前
sallltyyy完成签到,获得积分10
3秒前
VV完成签到 ,获得积分10
4秒前
4秒前
5秒前
5秒前
coffeecup1发布了新的文献求助10
5秒前
小飞完成签到,获得积分10
6秒前
牛文文完成签到,获得积分10
6秒前
GZX完成签到,获得积分10
6秒前
6秒前
7秒前
8秒前
gaga关注了科研通微信公众号
8秒前
搜集达人应助阿敏采纳,获得10
8秒前
8秒前
复杂瑛发布了新的文献求助10
9秒前
在水一方应助不对也没错采纳,获得10
9秒前
小飞发布了新的文献求助30
10秒前
10秒前
豪哥大大完成签到,获得积分10
11秒前
11秒前
汉堡包应助神勇的曼文采纳,获得10
12秒前
田様应助顾闭月采纳,获得10
12秒前
新的心跳发布了新的文献求助10
12秒前
白石杏发布了新的文献求助10
12秒前
风中寄云发布了新的文献求助10
13秒前
langzi完成签到,获得积分10
15秒前
haifang完成签到,获得积分10
15秒前
大个应助zhui采纳,获得10
15秒前
哎呀完成签到 ,获得积分10
16秒前
17秒前
哈哈哈哈发布了新的文献求助10
17秒前
17秒前
Wang完成签到,获得积分10
17秒前
请叫我风吹麦浪应助kevin采纳,获得20
18秒前
高分求助中
Continuum Thermodynamics and Material Modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Social media impact on athlete mental health: #RealityCheck 1020
Ensartinib (Ensacove) for Non-Small Cell Lung Cancer 1000
Unseen Mendieta: The Unpublished Works of Ana Mendieta 1000
Bacterial collagenases and their clinical applications 800
El viaje de una vida: Memorias de María Lecea 800
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3527884
求助须知:如何正确求助?哪些是违规求助? 3108006
关于积分的说明 9287444
捐赠科研通 2805757
什么是DOI,文献DOI怎么找? 1540033
邀请新用户注册赠送积分活动 716904
科研通“疑难数据库(出版商)”最低求助积分说明 709794