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.

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
1秒前
科研通AI6.1应助Andrew采纳,获得10
1秒前
1秒前
鱼鱼鱼完成签到,获得积分10
2秒前
善学以致用应助咿呀喂采纳,获得10
3秒前
lvbowen完成签到,获得积分10
3秒前
魔幻的白柏完成签到,获得积分10
3秒前
5秒前
kokp发布了新的文献求助10
5秒前
白白不喽发布了新的文献求助10
5秒前
桔子完成签到 ,获得积分10
6秒前
lyt发布了新的文献求助10
6秒前
花开花落发布了新的文献求助10
7秒前
7秒前
无花果应助鱼仔采纳,获得10
8秒前
ChenGY应助cx采纳,获得10
8秒前
孤独小懒虫完成签到,获得积分10
9秒前
yinghuayu96完成签到,获得积分20
10秒前
科研通AI6.1应助火柴采纳,获得10
10秒前
研友_VZG7GZ应助mookie采纳,获得10
11秒前
Fran07完成签到,获得积分10
11秒前
12秒前
善学以致用应助花开花落采纳,获得10
12秒前
FashionBoy应助Labor2025采纳,获得10
12秒前
话语完成签到,获得积分10
12秒前
万能图书馆应助Stanford采纳,获得10
15秒前
大力一德完成签到,获得积分10
16秒前
缓慢平蓝发布了新的文献求助10
16秒前
16秒前
苏苏完成签到,获得积分20
17秒前
pluto应助饼干玮玮采纳,获得10
17秒前
许多多完成签到,获得积分10
18秒前
66发布了新的文献求助10
19秒前
CipherSage应助新羽采纳,获得10
19秒前
19秒前
星辰大海应助漂亮寻云采纳,获得10
19秒前
英俊的铭应助炫酷发卡采纳,获得10
20秒前
SciGPT应助过氧化氢采纳,获得10
20秒前
清夕雨发布了新的文献求助30
21秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Kinesiophobia : a new view of chronic pain behavior 3000
Les Mantodea de guyane 2500
Molecular Biology of Cancer: Mechanisms, Targets, and Therapeutics 2000
Signals, Systems, and Signal Processing 510
Discrete-Time Signals and Systems 510
Brittle Fracture in Welded Ships 500
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5945327
求助须知:如何正确求助?哪些是违规求助? 7098629
关于积分的说明 15899396
捐赠科研通 5077392
什么是DOI,文献DOI怎么找? 2730361
邀请新用户注册赠送积分活动 1690413
关于科研通互助平台的介绍 1614604