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!

Machine-learned constitutive relations for mechanoluminescent ZnS:Cu-PDMS composites

本构方程 材料科学 经验模型 弹性体 实验数据 智能材料 聚二甲基硅氧烷 机械工程 计算机科学 复合材料 数学 工程类 结构工程 有限元法 模拟 统计
作者
George Hoover,Andy Huang,Donghyeon Ryu
出处
期刊:Smart Materials and Structures [IOP Publishing]
卷期号:32 (10): 105025-105025 被引量:2
标识
DOI:10.1088/1361-665x/acf256
摘要

Abstract Materials with novel properties, such as emerging smart materials, offer a design challenge to researchers who want to make use of their unique behaviors. The complex nature of these material responses can be difficult to model from a physics-based understanding as a full description of the multi-physics, multi-scale, and non-linear phenomena requires expertise from various scientific disciplines. Some new smart materials, such as the mechanoluminescent (ML) copper-doped zinc sulfide (ZnS:Cu)-embedded in polydimethylsiloxane (PDMS) (ZnS:Cu–PDMS), lack a constitutive model or an agreement on the mechanisms of action behind the unique material properties. As constitutive equations are essential to engineer devices, with existing knowledge gap in underlying physics of smart materials, a viable approach is to use empirical data for deriving constitutive equations. However, it is challenging to derive constitutive equations on non-linear, multi-variate, and multi-physics relationship using conventional data processing approaches due to the size and complexity of the empirical data. In this work, a machine learning framework is proposed for ones to derive constitutive equations using empirical data for novel materials. The framework is validated by creating constitutive models for ZnS:Cu–PDMS elastomeric composites undergoing a variety of tensile load patterns. To avoid confinement of the models to the programming environment, in which they are developed, numerical fits of the machine-learned models are created as constitutive equations for the non-linear, multi-variate, and multi-physics ML properties. These models can be used when designing ML ZnS:Cu–PDMS to develop devices to harness the unique ML properties.
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
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

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
miles发布了新的文献求助10
1秒前
LiuJ完成签到 ,获得积分10
1秒前
2秒前
完美世界应助大头采纳,获得10
4秒前
王大壮完成签到,获得积分10
9秒前
9秒前
9秒前
12秒前
13秒前
yaling发布了新的文献求助10
14秒前
15秒前
17秒前
18秒前
tt发布了新的文献求助10
19秒前
酷波er应助momo采纳,获得10
19秒前
19秒前
20秒前
搞怪熊猫完成签到,获得积分10
23秒前
简约生活完成签到,获得积分10
25秒前
Accelerator完成签到,获得积分10
25秒前
漂亮白枫发布了新的文献求助10
25秒前
26秒前
威廉兰尼斯特完成签到,获得积分10
26秒前
FashionBoy应助Arui采纳,获得10
29秒前
李健应助tt采纳,获得10
31秒前
呆萌安双完成签到 ,获得积分10
32秒前
Ray发布了新的文献求助10
32秒前
CipherSage应助大面包采纳,获得10
35秒前
L.C.完成签到,获得积分10
36秒前
大个应助漂亮白枫采纳,获得10
37秒前
38秒前
38秒前
万能图书馆应助dapis采纳,获得10
39秒前
40秒前
41秒前
恋雅颖月应助miles采纳,获得10
42秒前
42秒前
岂曰无衣完成签到 ,获得积分10
42秒前
大模型应助victoria采纳,获得10
44秒前
大头发布了新的文献求助10
46秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3989857
求助须知:如何正确求助?哪些是违规求助? 3531994
关于积分的说明 11255679
捐赠科研通 3270758
什么是DOI,文献DOI怎么找? 1805053
邀请新用户注册赠送积分活动 882195
科研通“疑难数据库(出版商)”最低求助积分说明 809208