An Efficient Lagrangian Interpolation Scheme for Computing Flow Maps and Line Integrals using Discrete Velocity Data
We propose a new class of Lagrangian approaches for constructing the flow maps of given dynamical systems. In the case when only discrete velocity data at mesh points is available, an interpolation step will be required. However, in our proposed approaches all particle trajectories share a common gl...
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Veröffentlicht in: | Journal of scientific computing 2018-07, Vol.76 (1), p.120-144 |
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description | We propose a new class of Lagrangian approaches for constructing the flow maps of given dynamical systems. In the case when only discrete velocity data at mesh points is available, an interpolation step will be required. However, in our proposed approaches all particle trajectories share a common global interpolation at each time step and therefore interpolation operations will not increase the overall computational complexity. The old Lagrangian approaches propose to solve the corresponding ordinary differential equations (ODEs)
backward
in time to obtain the
backward
flow map. It is inconvenient and not natural, especially when incorporated with certain computational fluid dynamic solvers, because the velocity field needs to be loaded from the terminal time
backward
to the initial time. In contrast, our proposed approaches for computing the
backward
flow map propose to solve the corresponding ODEs
forward
in time which is more practical. We will also extend the proposed approaches to compute line integrals along any particle trajectory. This paper gives a detailed analysis on the computational complexity and error estimate of the proposed Lagrangian approach. Finally, a wide range of applications of our approaches will be given, including the so-called coherent ergodic partition and the high frequency wave propagations based on geometric optic. |
doi_str_mv | 10.1007/s10915-017-0620-7 |
format | Article |
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backward
in time to obtain the
backward
flow map. It is inconvenient and not natural, especially when incorporated with certain computational fluid dynamic solvers, because the velocity field needs to be loaded from the terminal time
backward
to the initial time. In contrast, our proposed approaches for computing the
backward
flow map propose to solve the corresponding ODEs
forward
in time which is more practical. We will also extend the proposed approaches to compute line integrals along any particle trajectory. This paper gives a detailed analysis on the computational complexity and error estimate of the proposed Lagrangian approach. Finally, a wide range of applications of our approaches will be given, including the so-called coherent ergodic partition and the high frequency wave propagations based on geometric optic.</description><identifier>ISSN: 0885-7474</identifier><identifier>EISSN: 1573-7691</identifier><identifier>DOI: 10.1007/s10915-017-0620-7</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Algorithms ; Complexity ; Computational Mathematics and Numerical Analysis ; Differential equations ; Flow mapping ; Integrals ; Interpolation ; Mathematical and Computational Engineering ; Mathematical and Computational Physics ; Mathematical functions ; Mathematics ; Mathematics and Statistics ; Numerical analysis ; Ordinary differential equations ; Partial differential equations ; Particle trajectories ; Theoretical ; Velocity ; Velocity distribution</subject><ispartof>Journal of scientific computing, 2018-07, Vol.76 (1), p.120-144</ispartof><rights>Springer Science+Business Media, LLC, part of Springer Nature 2017</rights><rights>Springer Science+Business Media, LLC, part of Springer Nature 2017.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c316t-94a5286c6bef4abb081aa8c324c3205a8a1049ed9b1c5f5aefdd38de0a3102bb3</citedby><cites>FETCH-LOGICAL-c316t-94a5286c6bef4abb081aa8c324c3205a8a1049ed9b1c5f5aefdd38de0a3102bb3</cites><orcidid>0000-0001-9413-5691</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10915-017-0620-7$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2918314052?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,776,780,21367,27901,27902,33721,41464,42533,43781,51294</link.rule.ids></links><search><creatorcontrib>You, Guoqiao</creatorcontrib><creatorcontrib>Shi, Renkun</creatorcontrib><creatorcontrib>Xu, Yuhua</creatorcontrib><title>An Efficient Lagrangian Interpolation Scheme for Computing Flow Maps and Line Integrals using Discrete Velocity Data</title><title>Journal of scientific computing</title><addtitle>J Sci Comput</addtitle><description>We propose a new class of Lagrangian approaches for constructing the flow maps of given dynamical systems. In the case when only discrete velocity data at mesh points is available, an interpolation step will be required. However, in our proposed approaches all particle trajectories share a common global interpolation at each time step and therefore interpolation operations will not increase the overall computational complexity. The old Lagrangian approaches propose to solve the corresponding ordinary differential equations (ODEs)
backward
in time to obtain the
backward
flow map. It is inconvenient and not natural, especially when incorporated with certain computational fluid dynamic solvers, because the velocity field needs to be loaded from the terminal time
backward
to the initial time. In contrast, our proposed approaches for computing the
backward
flow map propose to solve the corresponding ODEs
forward
in time which is more practical. We will also extend the proposed approaches to compute line integrals along any particle trajectory. This paper gives a detailed analysis on the computational complexity and error estimate of the proposed Lagrangian approach. Finally, a wide range of applications of our approaches will be given, including the so-called coherent ergodic partition and the high frequency wave propagations based on geometric optic.</description><subject>Algorithms</subject><subject>Complexity</subject><subject>Computational Mathematics and Numerical Analysis</subject><subject>Differential equations</subject><subject>Flow mapping</subject><subject>Integrals</subject><subject>Interpolation</subject><subject>Mathematical and Computational Engineering</subject><subject>Mathematical and Computational Physics</subject><subject>Mathematical functions</subject><subject>Mathematics</subject><subject>Mathematics and Statistics</subject><subject>Numerical analysis</subject><subject>Ordinary differential equations</subject><subject>Partial differential equations</subject><subject>Particle trajectories</subject><subject>Theoretical</subject><subject>Velocity</subject><subject>Velocity distribution</subject><issn>0885-7474</issn><issn>1573-7691</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp1kDtPwzAURi0EEqXwA9gsMQeu83TGqi1QKYiBx2rdOE5wldrBdoT670kpEhODdZdzPkuHkGsGtwyguPMMSpZFwIoI8hii4oTMWFYkUZGX7JTMgPMsKtIiPScX3m8BoORlPCNhYei6bbXUygRaYefQdBoN3Zig3GB7DNoa-iI_1E7R1jq6tLthDNp09L63X_QJB0_RNLTSRv1Y00Tv6egPyEp76VRQ9F31VuqwpysMeEnO2olRV793Tt7u16_Lx6h6ftgsF1UkE5aHqEwxi3ku81q1KdY1cIbIZRKn04MMOTJIS9WUNZNZm6FqmybhjQJMGMR1nczJzXF3cPZzVD6IrR2dmb4Uccl4wlLI4oliR0o6671TrRic3qHbCwbiEFcc44oprjjEFcXkxEfHT6zplPtb_l_6BmkifcU</recordid><startdate>20180701</startdate><enddate>20180701</enddate><creator>You, Guoqiao</creator><creator>Shi, Renkun</creator><creator>Xu, Yuhua</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><orcidid>https://orcid.org/0000-0001-9413-5691</orcidid></search><sort><creationdate>20180701</creationdate><title>An Efficient Lagrangian Interpolation Scheme for Computing Flow Maps and Line Integrals using Discrete Velocity Data</title><author>You, Guoqiao ; Shi, Renkun ; Xu, Yuhua</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c316t-94a5286c6bef4abb081aa8c324c3205a8a1049ed9b1c5f5aefdd38de0a3102bb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Algorithms</topic><topic>Complexity</topic><topic>Computational Mathematics and Numerical Analysis</topic><topic>Differential equations</topic><topic>Flow mapping</topic><topic>Integrals</topic><topic>Interpolation</topic><topic>Mathematical and Computational Engineering</topic><topic>Mathematical and Computational Physics</topic><topic>Mathematical functions</topic><topic>Mathematics</topic><topic>Mathematics and Statistics</topic><topic>Numerical analysis</topic><topic>Ordinary differential equations</topic><topic>Partial differential equations</topic><topic>Particle trajectories</topic><topic>Theoretical</topic><topic>Velocity</topic><topic>Velocity distribution</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>You, Guoqiao</creatorcontrib><creatorcontrib>Shi, Renkun</creatorcontrib><creatorcontrib>Xu, Yuhua</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><jtitle>Journal of scientific computing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>You, Guoqiao</au><au>Shi, Renkun</au><au>Xu, Yuhua</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An Efficient Lagrangian Interpolation Scheme for Computing Flow Maps and Line Integrals using Discrete Velocity Data</atitle><jtitle>Journal of scientific computing</jtitle><stitle>J Sci Comput</stitle><date>2018-07-01</date><risdate>2018</risdate><volume>76</volume><issue>1</issue><spage>120</spage><epage>144</epage><pages>120-144</pages><issn>0885-7474</issn><eissn>1573-7691</eissn><abstract>We propose a new class of Lagrangian approaches for constructing the flow maps of given dynamical systems. In the case when only discrete velocity data at mesh points is available, an interpolation step will be required. However, in our proposed approaches all particle trajectories share a common global interpolation at each time step and therefore interpolation operations will not increase the overall computational complexity. The old Lagrangian approaches propose to solve the corresponding ordinary differential equations (ODEs)
backward
in time to obtain the
backward
flow map. It is inconvenient and not natural, especially when incorporated with certain computational fluid dynamic solvers, because the velocity field needs to be loaded from the terminal time
backward
to the initial time. In contrast, our proposed approaches for computing the
backward
flow map propose to solve the corresponding ODEs
forward
in time which is more practical. We will also extend the proposed approaches to compute line integrals along any particle trajectory. This paper gives a detailed analysis on the computational complexity and error estimate of the proposed Lagrangian approach. Finally, a wide range of applications of our approaches will be given, including the so-called coherent ergodic partition and the high frequency wave propagations based on geometric optic.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s10915-017-0620-7</doi><tpages>25</tpages><orcidid>https://orcid.org/0000-0001-9413-5691</orcidid></addata></record> |
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subjects | Algorithms Complexity Computational Mathematics and Numerical Analysis Differential equations Flow mapping Integrals Interpolation Mathematical and Computational Engineering Mathematical and Computational Physics Mathematical functions Mathematics Mathematics and Statistics Numerical analysis Ordinary differential equations Partial differential equations Particle trajectories Theoretical Velocity Velocity distribution |
title | An Efficient Lagrangian Interpolation Scheme for Computing Flow Maps and Line Integrals using Discrete Velocity Data |
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