Towards developing high-fidelity yet compact skeletal mechanisms: An effective, efficient and expertise-free strategy for systematic mechanism reduction

还原(数学) 机制(生物学) 生化工程 忠诚 计算机科学 生物系统 工程类 数学 生物 物理 电信 几何学 量子力学
作者
Han Li,Wenming Yang
出处
期刊:Chemical Engineering Journal [Elsevier BV]
卷期号:428: 132117-132117 被引量:4
标识
DOI:10.1016/j.cej.2021.132117
摘要

This study explored the potential of integrating reduction methods with different features for systematic mechanism reduction. Based on our two recently developed/optimized reduction methods, sensitivity analysis method and unimportant reaction elimination, a generalized strategy for systematic reduction of large detailed mechanisms was proposed. This strategy was firstly used to reduce the detailed mechanism for n-dodecane containing 2,115 species. A compact skeletal mechanism with 150 species was obtained, and close agreement with the detailed mechanism was achieved. Next, the detailed mechanism for a three-component biodiesel surrogate with 3,299 species was successfully reduced to a skeletal one with 179 species. Extensive validations were performed to show the high fidelity of the obtained skeletal mechanism. After demonstrating the effectiveness and reduction capability of the proposed strategy, its efficient and expertise-free features were elaborated. This strategy not only greatly lowers the threshold for reducing large detailed mechanisms, but also significantly saves the computational cost and human time effort in mechanism reduction process, while exhibiting satisfactory reduction capability to generate high-fidelity yet compact skeletal mechanisms.

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
丘比特应助xuan采纳,获得10
刚刚
22222222发布了新的文献求助10
1秒前
研友_VZG7GZ应助耍酷橘子采纳,获得10
2秒前
Sheep完成签到,获得积分10
2秒前
daishuheng发布了新的文献求助10
3秒前
太叔如冰发布了新的文献求助10
3秒前
zhao发布了新的文献求助30
3秒前
林夕发布了新的文献求助10
4秒前
四叶草哦发布了新的文献求助10
4秒前
5秒前
5秒前
5秒前
在水一方应助樂楽采纳,获得10
5秒前
5秒前
Fin完成签到,获得积分10
5秒前
5秒前
无限凡之发布了新的文献求助10
5秒前
lyf完成签到,获得积分10
8秒前
情怀应助daishuheng采纳,获得10
8秒前
emerald完成签到,获得积分10
8秒前
今后应助卷就完了采纳,获得10
9秒前
10秒前
10秒前
Yummy发布了新的文献求助10
10秒前
香蕉觅云应助WYC采纳,获得10
10秒前
西女木木完成签到,获得积分10
11秒前
丘比特应助冯冯采纳,获得10
11秒前
鱼刺鱼刺卡应助hduGL采纳,获得40
11秒前
丰富语蕊应助种田采纳,获得10
11秒前
zkx发布了新的文献求助30
11秒前
12秒前
12秒前
甜甜发布了新的文献求助10
13秒前
13秒前
14秒前
14秒前
科研通AI6.3应助吐泡泡采纳,获得10
15秒前
taotie完成签到,获得积分10
15秒前
耍酷橘子发布了新的文献求助10
15秒前
16秒前
高分求助中
Markov Chain Monte Carlo 10000
(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
Common Foundations of American and East Asian Modernisation: From Alexander Hamilton to Junichero Koizumi 5000
How to Use Machine Learning in Chemistry: An Introduction 1000
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
Discerning Saints: Moralization of Intrinsic Motivation and Selective Prosociality at Work 500
Handbuch Trainingswissenschaft – Trainingslehre 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7583093
求助须知:如何正确求助?哪些是违规求助? 9161776
关于积分的说明 19604859
捐赠科研通 7165133
什么是DOI,文献DOI怎么找? 3266207
关于科研通互助平台的介绍 2431164
邀请新用户注册赠送积分活动 2257518