平均方法比平均方法要好得多

Lívia Boda, I. Faragó, T. Kalmár-Nagy
{"title":"平均方法比平均方法要好得多","authors":"Lívia Boda, I. Faragó, T. Kalmár-Nagy","doi":"10.32973/jcam.2021.003","DOIUrl":null,"url":null,"abstract":"Operator splitting is a powerful method for the numerical investigation of complex time-dependent models, where the stationary (elliptic) part consists of a sum of several structurally simpler sub-operators. As an alternative to the classical splitting methods, a new splitting scheme is proposed here, the Average Method with sequential splitting. In this method, a decomposition of the original problem is sought in terms of commuting matrices. Wedemonstrate that third-order accuracy can be achieved with the Average Method. The computational performance of the method is investigated, yielding run times 1-2 orders of magnitude faster than traditional methods.","PeriodicalId":47168,"journal":{"name":"Journal of Applied and Computational Mechanics","volume":"7 1","pages":""},"PeriodicalIF":2.8000,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The average method is much better than average\",\"authors\":\"Lívia Boda, I. Faragó, T. Kalmár-Nagy\",\"doi\":\"10.32973/jcam.2021.003\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Operator splitting is a powerful method for the numerical investigation of complex time-dependent models, where the stationary (elliptic) part consists of a sum of several structurally simpler sub-operators. As an alternative to the classical splitting methods, a new splitting scheme is proposed here, the Average Method with sequential splitting. In this method, a decomposition of the original problem is sought in terms of commuting matrices. Wedemonstrate that third-order accuracy can be achieved with the Average Method. The computational performance of the method is investigated, yielding run times 1-2 orders of magnitude faster than traditional methods.\",\"PeriodicalId\":47168,\"journal\":{\"name\":\"Journal of Applied and Computational Mechanics\",\"volume\":\"7 1\",\"pages\":\"\"},\"PeriodicalIF\":2.8000,\"publicationDate\":\"2021-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Applied and Computational Mechanics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.32973/jcam.2021.003\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MECHANICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Applied and Computational Mechanics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.32973/jcam.2021.003","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MECHANICS","Score":null,"Total":0}
引用次数: 0

摘要

算子分裂是一种用于复杂时相关模型数值研究的有效方法,其中平稳(椭圆)部分由几个结构更简单的子算子组成。为了替代传统的分割方法,本文提出了一种新的分割方案,即序列分割的平均方法。该方法利用可交换矩阵对原问题进行分解。我们证明了用平均方法可以达到三阶精度。研究了该方法的计算性能,其运行时间比传统方法快1-2个数量级。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
The average method is much better than average
Operator splitting is a powerful method for the numerical investigation of complex time-dependent models, where the stationary (elliptic) part consists of a sum of several structurally simpler sub-operators. As an alternative to the classical splitting methods, a new splitting scheme is proposed here, the Average Method with sequential splitting. In this method, a decomposition of the original problem is sought in terms of commuting matrices. Wedemonstrate that third-order accuracy can be achieved with the Average Method. The computational performance of the method is investigated, yielding run times 1-2 orders of magnitude faster than traditional methods.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
6.90
自引率
3.20%
发文量
0
审稿时长
8 weeks
期刊介绍: The Journal of Applied and Computational Mechanics aims to provide a medium for dissemination of innovative and consequential papers on mathematical and computational methods in theoretical as well as applied mechanics. Manuscripts submitted to the journal undergo a blind peer reviewing procedure conducted by the editorial board. The Journal of Applied and Computational Mechanics devoted to the all fields of solid and fluid mechanics. The journal also welcomes papers that are related to the recent technological advances such as biomechanics, electro-mechanics, advanced materials and micor/nano-mechanics. The scope of the journal includes, but is not limited to, the following topic areas: -Theoretical and experimental mechanics- Dynamic systems & control- Nonlinear dynamics and chaos- Boundary layer theory- Turbulence and hydrodynamic stability- Multiphase flows- Heat and mass transfer- Micro/Nano-mechanics- Structural optimization- Smart materials and applications- Composite materials- Hydro- and aerodynamics- Fluid-structure interaction- Gas dynamics
期刊最新文献
Stress-based dimensional reduction and dual-mixed hp finite elements for elastic plates Post-extrapolation for specified time-step results without interpolation in MOC-based 1D hydraulic transients and gas release computations Application of the p-version of FEM to hierarchic rod models with reference to mechanical contact problems Evaluation of the SU2 Open-Source Code for a Hypersonic Flow at Mach Number 5 Solutions for the vibration and stability problems of heterogenous beams with three supports using Green functions
×
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