IMPROVEMENT OF FINITE ELEMENT MESH QUALITY BY USING GEOMETRICAL QUALITY MEASURES AND OPTIMIZATION

多边形网格 有限元法 形状优化 度量(数据仓库) 数学优化 计算机科学 反向 联轴节(管道) 数学 几何学 机械工程 结构工程 工程类 数据挖掘 计算机图形学(图像)
作者
Marko Kegl,Boštjan Harl,Dejan Dinevski
出处
期刊:Proceedings of International Structural Engineering and Construction [ISEC Press]
卷期号:2 (1)
标识
DOI:10.14455/isec.res.2015.213
摘要

This paper discusses possible procedures to improve finite element meshes in order to enable an accurate and stable numerical analysis processes. In general, the process of mesh improvement has to address three main aspects: mesh untangling (removal or fix of inverted finite elements), improvement of element shape, and making the element sizes more uniform. This paper focusses on the last two aspects: improving shape and size uniformity. This problem is addressed from purely geometrical aspect and by engaging optimization methods; thus, stress/strain related finite element quality measures are not considered. Two various element quality measures are discussed with emphasis on their coupling with an adequate optimization procedure. These two measures are the inverse mean ratio measure and the size measure. The first one addresses the shape and size of finite elements, but concentrates more on the shape. The later one, on the other hand, addresses only finite element size. Since mesh improvement tasks are typical multi-objective optimization problems, the paper also addresses briefly the procedure how to transform the multi-objective problem into a usual single-objective one that can be solved by employing standard optimization techniques. In this work a gradient-based approximation method was employed to do the optimization. The discussed theory is numerically tested on simply deformed meshes.

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
搞怪的思卉完成签到,获得积分10
1秒前
梅夕阳完成签到,获得积分10
2秒前
超级的冷菱完成签到 ,获得积分10
4秒前
C_Li完成签到,获得积分10
5秒前
ruuuu完成签到,获得积分10
5秒前
林结衣完成签到,获得积分10
7秒前
冷酷孤风完成签到,获得积分10
9秒前
明明如月完成签到,获得积分10
10秒前
GONTUYZ完成签到 ,获得积分10
10秒前
洛莫完成签到,获得积分10
11秒前
眼睛大樱桃完成签到,获得积分10
14秒前
言非离完成签到,获得积分10
16秒前
17秒前
彭于晏应助C_Li采纳,获得10
19秒前
风格完成签到,获得积分10
21秒前
Song完成签到 ,获得积分10
22秒前
来福发布了新的文献求助10
22秒前
holly完成签到 ,获得积分10
23秒前
999完成签到,获得积分10
24秒前
对方正在看文献完成签到,获得积分10
28秒前
诚心的映梦完成签到,获得积分20
32秒前
mjc完成签到 ,获得积分10
35秒前
珠珠完成签到 ,获得积分10
36秒前
a2480896完成签到,获得积分10
43秒前
FashionBoy应助科研通管家采纳,获得10
44秒前
SciGPT应助科研通管家采纳,获得10
44秒前
44秒前
44秒前
44秒前
乐乐应助科研通管家采纳,获得10
44秒前
44秒前
zhaonana完成签到 ,获得积分10
46秒前
徐慕源完成签到,获得积分10
46秒前
星辰大海应助a2480896采纳,获得10
50秒前
cl完成签到 ,获得积分10
51秒前
xixixi完成签到 ,获得积分10
51秒前
雪落你看不见完成签到,获得积分10
55秒前
Criminology34应助笨笨映寒采纳,获得10
57秒前
SQL完成签到 ,获得积分10
58秒前
1分钟前
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
PowerCascade: A Synthetic Dataset for Cascading Failure Analysis in Power Systems 2000
Signals, Systems, and Signal Processing 610
Unlocking Chemical Thinking: Reimagining Chemistry Teaching and Learning 555
Photodetectors: From Ultraviolet to Infrared 500
On the Dragon Seas, a sailor's adventures in the far east 500
Yangtze Reminiscences. Some Notes And Recollections Of Service With The China Navigation Company Ltd., 1925-1939 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 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6355858
求助须知:如何正确求助?哪些是违规求助? 8170527
关于积分的说明 17201202
捐赠科研通 5411774
什么是DOI,文献DOI怎么找? 2864385
邀请新用户注册赠送积分活动 1841922
关于科研通互助平台的介绍 1690224