已入深夜,您辛苦了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!祝你早点完成任务,早点休息,好梦!

Fast image processing method for coal particle cluster box dimension measurement and its application in diffusion coefficient testing

分形维数 扩散 反向 维数(图论) 粒子(生态学) 投影(关系代数) 分形 图像处理 反问题 计算机科学 算法 生物系统 数学 图像(数学) 数学分析 人工智能 几何学 化学 物理 热力学 地质学 有机化学 纯数学 海洋学 生物
作者
Jingjing Liu,Ruihang Liu,Haoxiang Zhang,He Jiang,Qiqi Kou,Deqiang Cheng,Jiansheng Qian
出处
期刊:Fuel [Elsevier BV]
卷期号:352: 129050-129050 被引量:1
标识
DOI:10.1016/j.fuel.2023.129050
摘要

The diffusion coefficient is the key parameter used to characterize methane diffusion behavior in coal. The commonly used analytical solutions for diffusion coefficients require simplifying the particle shape to regular shapes, which greatly deviates from the actual shape of coal particles. The influence of coal particle shape on the diffusion coefficient is currently not well understood. Based on image processing techniques and inverse problem-based numerical simulation, a new method is proposed in this paper to determine the diffusion coefficients of irregular coal particles, and a feasible solution for quantitatively assessing the influence of particle shapes on the diffusion coefficient is provided. The implementation of this method is based on two aspects: the fast quantitative characterization of the shape features of coal particle clusters and inverse problem-based numerical simulation to solve the diffusion coefficient of irregularly shaped coal particles. First, image processing technology is used to quickly and accurately extract the 2D projection contour of batch particles, and an algorithm based on fractal theory is designed to obtain the box dimension of batch contours, which achieves the quantitative characterization for the shape features of coal particle clusters. At the same time, a comparison with Fraclab is conducted to validate the scientific effectiveness of the box dimension algorithm. Then, 3 coal particles with representative fractal dimensions are selected and their diffusion coefficients are obtained using the inverse problem numerical method. The results indicate that the desorption curves obtained through inverse problem optimization for the 3 particles are excellently consistent with the experimental data, with fitting degrees R2 all exceeding 99%. Therefore, the diffusion coefficients can be effectively obtained. Meanwhile, the influence of shape on the diffusion coefficient is analyzed quantitatively. The results show that the diffusion coefficient increases with the box dimension of the contour, and the particle shape has a considerable impact on determining the gas diffusion coefficient.

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
壮观复天完成签到 ,获得积分10
1秒前
聪明凌萱发布了新的文献求助10
2秒前
3秒前
NexusExplorer应助鼠标采纳,获得30
5秒前
大模型应助友好的东蒽采纳,获得10
5秒前
5秒前
zc发布了新的文献求助10
7秒前
隐形曼青应助tumiao采纳,获得10
9秒前
美满听白完成签到,获得积分10
10秒前
yy发布了新的文献求助10
12秒前
12秒前
13秒前
Owen应助yan采纳,获得10
16秒前
16秒前
123456发布了新的文献求助10
17秒前
20秒前
22秒前
25秒前
我在完成签到,获得积分10
27秒前
大家好完成签到 ,获得积分10
28秒前
29秒前
30秒前
coco完成签到 ,获得积分10
32秒前
三维码完成签到,获得积分10
34秒前
35秒前
英姑应助123456采纳,获得10
35秒前
Jackson发布了新的文献求助10
36秒前
忐忑的雅柔完成签到,获得积分10
36秒前
yy完成签到,获得积分10
37秒前
逆光完成签到,获得积分20
37秒前
Lalalili完成签到,获得积分10
37秒前
heluoyu完成签到,获得积分10
37秒前
38秒前
39秒前
张海新发布了新的文献求助10
39秒前
40秒前
41秒前
谭2113完成签到,获得积分10
41秒前
zc完成签到,获得积分10
44秒前
逆光发布了新的文献求助10
46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
Bend stiffness of submarine cables – an experimental and numerical investigation 5000
Advanced Weaponeering Fourth Edition, Volume 2 1000
Weaponeering: An Introduction Fourth Edition, Volume 1 1000
悉尼大学博士学位论文,题目:Modelling and testing of one-sided stitched laminated composites. 作者:Kristopher P. Plain 700
Matrix Methods in Data Mining and Pattern Recognition Second Edition 610
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 7542462
求助须知:如何正确求助?哪些是违规求助? 9126339
关于积分的说明 19498230
捐赠科研通 7138502
什么是DOI,文献DOI怎么找? 3258401
关于科研通互助平台的介绍 2425743
邀请新用户注册赠送积分活动 2246552