Data-scarce surrogate modeling of shock-induced pore collapse process

IF 1.7 4区 工程技术 Q3 MECHANICS Shock Waves Pub Date : 2024-07-13 DOI:10.1007/s00193-024-01177-2
S. W. Cheung, Y. Choi, H. K. Springer, T. Kadeethum
{"title":"Data-scarce surrogate modeling of shock-induced pore collapse process","authors":"S. W. Cheung,&nbsp;Y. Choi,&nbsp;H. K. Springer,&nbsp;T. Kadeethum","doi":"10.1007/s00193-024-01177-2","DOIUrl":null,"url":null,"abstract":"<div><p>Understanding the mechanisms of shock-induced pore collapse is of great interest in various disciplines in sciences and engineering, including materials science, biological sciences, and geophysics. However, numerical modeling of the complex pore collapse processes can be costly. To this end, a strong need exists to develop surrogate models for generating economic predictions of pore collapse processes. In this work, we study the use of a data-driven reduced-order model, namely dynamic mode decomposition, and a deep generative model, namely conditional generative adversarial networks, to resemble the numerical simulations of the pore collapse process at representative training shock pressures. Since the simulations are expensive, the training data are scarce, which makes training an accurate surrogate model challenging. To overcome the difficulties posed by the complex physics phenomena, we make several crucial treatments to the plain original form of the methods to increase the capability of approximating and predicting the dynamics. In particular, physics information is used as indicators or conditional inputs to guide the prediction. In realizing these methods, the training of each dynamic mode composition model takes only around 30 s on CPU. In contrast, training a generative adversarial network model takes 8 h on GPU. Moreover, using dynamic mode decomposition, the final-time relative error is around 0.3% in the reproductive cases. We also demonstrate the predictive power of the methods at unseen testing shock pressures, where the error ranges from 1.3 to 5% in the interpolatory cases and 8 to 9% in extrapolatory cases.</p></div>","PeriodicalId":775,"journal":{"name":"Shock Waves","volume":null,"pages":null},"PeriodicalIF":1.7000,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Shock Waves","FirstCategoryId":"5","ListUrlMain":"https://link.springer.com/article/10.1007/s00193-024-01177-2","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MECHANICS","Score":null,"Total":0}
引用次数: 0

Abstract

Understanding the mechanisms of shock-induced pore collapse is of great interest in various disciplines in sciences and engineering, including materials science, biological sciences, and geophysics. However, numerical modeling of the complex pore collapse processes can be costly. To this end, a strong need exists to develop surrogate models for generating economic predictions of pore collapse processes. In this work, we study the use of a data-driven reduced-order model, namely dynamic mode decomposition, and a deep generative model, namely conditional generative adversarial networks, to resemble the numerical simulations of the pore collapse process at representative training shock pressures. Since the simulations are expensive, the training data are scarce, which makes training an accurate surrogate model challenging. To overcome the difficulties posed by the complex physics phenomena, we make several crucial treatments to the plain original form of the methods to increase the capability of approximating and predicting the dynamics. In particular, physics information is used as indicators or conditional inputs to guide the prediction. In realizing these methods, the training of each dynamic mode composition model takes only around 30 s on CPU. In contrast, training a generative adversarial network model takes 8 h on GPU. Moreover, using dynamic mode decomposition, the final-time relative error is around 0.3% in the reproductive cases. We also demonstrate the predictive power of the methods at unseen testing shock pressures, where the error ranges from 1.3 to 5% in the interpolatory cases and 8 to 9% in extrapolatory cases.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
冲击诱发孔隙坍塌过程的数据稀缺替代模型
了解冲击诱发孔隙塌陷的机理是科学和工程学各学科(包括材料科学、生物科学和地球物理学)的一大兴趣所在。然而,对复杂的孔隙坍塌过程进行数值建模可能成本高昂。为此,我们亟需开发代用模型,以便对孔隙坍塌过程进行经济预测。在这项工作中,我们研究了如何使用数据驱动的降阶模型(即动态模式分解)和深度生成模型(即条件生成对抗网络)来模拟具有代表性的训练冲击压力下的孔隙坍塌过程。由于模拟昂贵,训练数据稀少,因此训练精确的代用模型具有挑战性。为了克服复杂物理现象带来的困难,我们对方法的原始形式进行了一些关键处理,以提高近似和预测动力学的能力。其中,物理信息被用作指导预测的指标或条件输入。在实现这些方法的过程中,每个动态模式组成模型的训练在 CPU 上仅需 30 秒左右。相比之下,在 GPU 上训练一个生成式对抗网络模型需要 8 小时。此外,使用动态模式分解,最终时间相对误差在生殖情况下约为 0.3%。我们还证明了这些方法在未见测试冲击压力时的预测能力,其中内推情况下的误差范围为 1.3%至 5%,外推情况下的误差范围为 8%至 9%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Shock Waves
Shock Waves 物理-力学
CiteScore
4.10
自引率
9.10%
发文量
41
审稿时长
17.4 months
期刊介绍: Shock Waves provides a forum for presenting and discussing new results in all fields where shock and detonation phenomena play a role. The journal addresses physicists, engineers and applied mathematicians working on theoretical, experimental or numerical issues, including diagnostics and flow visualization. The research fields considered include, but are not limited to, aero- and gas dynamics, acoustics, physical chemistry, condensed matter and plasmas, with applications encompassing materials sciences, space sciences, geosciences, life sciences and medicine. Of particular interest are contributions which provide insights into fundamental aspects of the techniques that are relevant to more than one specific research community. The journal publishes scholarly research papers, invited review articles and short notes, as well as comments on papers already published in this journal. Occasionally concise meeting reports of interest to the Shock Waves community are published.
期刊最新文献
An experimental and kinetic modeling study of the autoignition of syngas mixtures behind reflected shock waves Asymmetry of imploding detonations in thin channels Thematic issue on blast exposure research in military training environments Optical measurement of state variables associated with blast wave evolution Influence of fuel inhomogeneity on detonation wave propagation in a rotating detonation combustor
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1