Downwinding for Preserving Strong Stability in Explicit Integrating Factor Runge--Kutta Methods

Leah Isherwood, Zachary J. Grant, S. Gottlieb
{"title":"Downwinding for Preserving Strong Stability in Explicit Integrating Factor Runge--Kutta Methods","authors":"Leah Isherwood, Zachary J. Grant, S. Gottlieb","doi":"10.4310/PAMQ.2018.V14.N1.A1","DOIUrl":null,"url":null,"abstract":"Strong stability preserving (SSP) Runge-Kutta methods are desirable when evolving in time problems that have discontinuities or sharp gradients and require nonlinear non-inner-product stability properties to be satisfied. Unlike the case for L2 linear stability, implicit methods do not significantly alleviate the time-step restriction when the SSP property is needed. For this reason, when handling problems with a linear component that is stiff and a nonlinear component that is not, SSP integrating factor Runge--Kutta methods may offer an attractive alternative to traditional time-stepping methods. The strong stability properties of integrating factor Runge--Kutta methods where the transformed problem is evolved with an explicit SSP Runge--Kutta method with non-decreasing abscissas was recently established. In this work, we consider the use of downwinded spatial operators to preserve the strong stability properties of integrating factor Runge--Kutta methods where the Runge--Kutta method has some decreasing abscissas. We present the SSP theory for this approach and present numerical evidence to show that such an approach is feasible and performs as expected. However, we also show that in some cases the integrating factor approach with explicit SSP Runge--Kutta methods with non-decreasing abscissas performs nearly as well, if not better, than with explicit SSP Runge--Kutta methods with downwinding. In conclusion, while the downwinding approach can be rigorously shown to guarantee the SSP property for a larger time-step, in practice using the integrating factor approach by including downwinding as needed with optimal explicit SSP Runge--Kutta methods does not necessarily provide significant benefit over using explicit SSP Runge--Kutta methods with non-decreasing abscissas.","PeriodicalId":283112,"journal":{"name":"arXiv: Numerical Analysis","volume":"86 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv: Numerical Analysis","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4310/PAMQ.2018.V14.N1.A1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5

Abstract

Strong stability preserving (SSP) Runge-Kutta methods are desirable when evolving in time problems that have discontinuities or sharp gradients and require nonlinear non-inner-product stability properties to be satisfied. Unlike the case for L2 linear stability, implicit methods do not significantly alleviate the time-step restriction when the SSP property is needed. For this reason, when handling problems with a linear component that is stiff and a nonlinear component that is not, SSP integrating factor Runge--Kutta methods may offer an attractive alternative to traditional time-stepping methods. The strong stability properties of integrating factor Runge--Kutta methods where the transformed problem is evolved with an explicit SSP Runge--Kutta method with non-decreasing abscissas was recently established. In this work, we consider the use of downwinded spatial operators to preserve the strong stability properties of integrating factor Runge--Kutta methods where the Runge--Kutta method has some decreasing abscissas. We present the SSP theory for this approach and present numerical evidence to show that such an approach is feasible and performs as expected. However, we also show that in some cases the integrating factor approach with explicit SSP Runge--Kutta methods with non-decreasing abscissas performs nearly as well, if not better, than with explicit SSP Runge--Kutta methods with downwinding. In conclusion, while the downwinding approach can be rigorously shown to guarantee the SSP property for a larger time-step, in practice using the integrating factor approach by including downwinding as needed with optimal explicit SSP Runge--Kutta methods does not necessarily provide significant benefit over using explicit SSP Runge--Kutta methods with non-decreasing abscissas.
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
显式积分因子Runge—Kutta方法中保持强稳定性的下卷
强稳定保持(SSP)龙格-库塔方法适用于具有不连续或急剧梯度的随时间演化问题,并要求满足非线性非内积稳定性。与L2线性稳定性的情况不同,当需要SSP性质时,隐式方法不能显著减轻时间步长限制。因此,当处理具有刚性线性分量和非刚性非线性分量的问题时,SSP积分因子龙格-库塔方法可能是传统时间步进方法的一个有吸引力的替代方案。本文建立了积分因子Runge—Kutta方法的强稳定性,该方法将变换问题演化为具有非减少横坐标的显式SSP Runge—Kutta方法。在这项工作中,我们考虑使用下风空间算子来保持积分因子Runge—Kutta方法的强稳定性,其中Runge—Kutta方法具有一些下降的横坐标。我们提出了这种方法的SSP理论,并提出了数值证据来表明这种方法是可行的,并按预期执行。然而,我们也表明,在某些情况下,具有非下降横坐标的显式SSP Runge- Kutta方法的积分因子方法与具有下卷的显式SSP Runge- Kutta方法相比,即使不是更好,也几乎一样好。总之,虽然下卷方法可以严格地证明可以保证更大时间步长的SSP性质,但在实践中,使用积分因子方法,根据需要包括最优显式SSP Runge- Kutta方法的下卷,并不一定比使用非递减横坐标的显式SSP Runge- Kutta方法提供显著的好处。
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
自引率
0.00%
发文量
0
期刊最新文献
Finite Element Methods for Elliptic Distributed Optimal Control Problems with Pointwise State Constraints (Survey) A Comparison Study of Deep Galerkin Method and Deep Ritz Method for Elliptic Problems with Different Boundary Conditions On the Convergence of Physics Informed Neural Networks for Linear Second-Order Elliptic and Parabolic Type PDEs A novel iterative penalty method to enforce boundary conditions in Finite Volume POD-Galerkin reduced order models for fluid dynamics problems Linear and nonlinear fractional elliptic problems
×
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