The thermodynamic characteristics of high efficiency, internal-combustion engines

热效率 燃烧 压缩比 工作(物理) 热的 热力学 工作产出 化学 热力循环 材料科学 内燃机 核工程 物理 工程类 有机化学
作者
Jerald A. Caton
出处
期刊:Energy Conversion and Management [Elsevier]
卷期号:58: 84-93 被引量:107
标识
DOI:10.1016/j.enconman.2012.01.005
摘要

Recent advancements have demonstrated new combustion modes for internal combustion engines that exhibit low nitric oxide emissions and high thermal efficiencies. These new combustion modes involve various combinations of stratification, lean mixtures, high levels of EGR, multiple injections, variable valve timings, two fuels, and other such features. Although the exact combination of these features that provides the best design is not yet clear, the results (low emissions with high efficiencies) are of major interest. The current work is directed at determining some of the fundamental thermodynamic reasons for the relatively high efficiencies and to quantify these factors. Both the first and second laws are used in this assessment. An automotive engine (5.7 l) which included some of the features mentioned above (e.g., high compression ratios, lean mixtures, and high EGR) was evaluated using a thermodynamic cycle simulation. These features were examined for a moderate load (bmep = 900 kPa), moderate speed (2000 rpm) condition. By the use of lean operation, high EGR levels, high compression ratio and other features, the net indicated thermal efficiency increased from 37.0% to 53.9%. These increases are explained in a step-by-step fashion. The major reasons for these improvements include the higher compression ratio and the dilute charge (lean mixture, high EGR). The dilute charge resulted in lower temperatures which in turn resulted in lower heat loss. In addition, the lower temperatures resulted in higher ratios of the specific heats which account for a more effective conversion of thermal energy to work. Other thermodynamic features are described.
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
1秒前
科研通AI2S应助zyh采纳,获得10
1秒前
榆木小鸟完成签到 ,获得积分10
1秒前
科研通AI5应助徐徐采纳,获得10
1秒前
2秒前
2秒前
zee完成签到 ,获得积分20
2秒前
单薄明雪完成签到,获得积分10
2秒前
3秒前
万能图书馆应助Godspeed采纳,获得10
3秒前
孟陬十一发布了新的文献求助10
3秒前
vivi猫小咪完成签到,获得积分10
3秒前
3秒前
bkagyin应助amumu采纳,获得10
4秒前
南方姑娘发布了新的文献求助10
4秒前
4秒前
4秒前
4秒前
丘比特应助Wu采纳,获得10
4秒前
5秒前
乐乐应助luuuuuing采纳,获得30
5秒前
6秒前
丘比特应助anan采纳,获得10
6秒前
6秒前
动人的老黑完成签到 ,获得积分10
7秒前
星星泡饭发布了新的文献求助10
7秒前
8秒前
Silence完成签到,获得积分10
8秒前
yan儿发布了新的文献求助10
9秒前
pearl完成签到,获得积分10
10秒前
hahah发布了新的文献求助10
10秒前
请叫我风吹麦浪应助胖豆采纳,获得10
10秒前
无花果应助幸福胡萝卜采纳,获得10
10秒前
11秒前
卡卡发布了新的文献求助10
11秒前
wanci应助风趣的天真采纳,获得10
11秒前
Silence发布了新的文献求助10
11秒前
清爽老九发布了新的文献求助100
11秒前
12秒前
衔尾蛇发布了新的文献求助10
12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 3527742
求助须知:如何正确求助?哪些是违规求助? 3107867
关于积分的说明 9286956
捐赠科研通 2805612
什么是DOI,文献DOI怎么找? 1540026
邀请新用户注册赠送积分活动 716884
科研通“疑难数据库(出版商)”最低求助积分说明 709762