{"created":"2025-01-19T01:35:15.562648+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00233717","sets":["934:1119:11590:11591"]},"path":["11591"],"owner":"44499","recid":"233717","title":["A Cascadic Parareal Method for Parallel-in-Time Simulation of Compressible Supersonic Flow"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-03-26"},"_buckets":{"deposit":"f38c23eb-5e5e-46c0-b371-f98bbbf46976"},"_deposit":{"id":"233717","pid":{"type":"depid","value":"233717","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"A Cascadic Parareal Method for Parallel-in-Time Simulation of Compressible Supersonic Flow","author_link":["635455","635457","635456","635458"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"A Cascadic Parareal Method for Parallel-in-Time Simulation of Compressible Supersonic Flow"},{"subitem_title":"A Cascadic Parareal Method for Parallel-in-Time Simulation of Compressible Supersonic Flow","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"parallel-in-time, Parallel-in-Space/Time, Cascadic Parareal, compressible fluid simulation, supersonic flow","subitem_subject_scheme":"Other"}]},"item_type_id":"3","publish_date":"2024-03-26","item_3_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"Karlsruhe Institute of Technology"},{"subitem_text_value":"The University of Tokyo"}]},"item_3_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Karlsruhe Institute of Technology","subitem_text_language":"en"},{"subitem_text_value":"The University of Tokyo","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/233717/files/IPSJ-TACS1701004.pdf","label":"IPSJ-TACS1701004.pdf"},"date":[{"dateType":"Available","dateValue":"2026-03-26"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-TACS1701004.pdf","filesize":[{"value":"11.3 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"5"},{"tax":["include_tax"],"price":"0","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"16"},{"tax":["include_tax"],"price":"0","billingrole":"11"},{"tax":["include_tax"],"price":"0","billingrole":"14"},{"tax":["include_tax"],"price":"0","billingrole":"15"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"5c4d55ff-fb25-4eca-b2d6-777b46829a79","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Information Processing Society of Japan"}]},"item_3_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Yen-Chen, Chen"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kengo, Nakajima"}],"nameIdentifiers":[{}]}]},"item_3_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Yen-Chen, Chen","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kengo, Nakajima","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_3_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11833852","subitem_source_identifier_type":"NCID"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_6501","resourcetype":"journal article"}]},"item_3_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1882-7829","subitem_source_identifier_type":"ISSN"}]},"item_3_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"Cascadic Parareal is a parallel-in-time (PinT) method developed to improve the parallel performance for explicit time-dependent problems. It is more efficient than other PinT methods when explicit methods are used for solving. Cascadic Parareal has been proven to accelerate a one-dimensional advection problem and a two-dimensional compressible flow simulation faster compared to spatial parallelism with more than 64 cores in the previous works. However, Cascadic Parareal has also demonstrated slow convergence and produced unstable results for supersonic flow simulations. The instability is caused by unstable supersonic flow results calculated on the coarse meshes. In the present work, we introduce an improvement for Cascadic Parareal using local mesh refinement (LMR) to improve its accuracy for supersonic flow simulations. Numerical experiments in this research demonstrate that the LMR method can improve the convergence rate and accuracy of Cascadic Parareal for supersonic flow simulations. The numerical experiments of the present work show that the improved PinT method can provide stable and more accurate simulations for supersonic flow, and the compute time performance of the PinT algorithm can outperform simple spatial parallelism.\n------------------------------\nThis is a preprint of an article intended for publication Journal of\nInformation Processing(JIP). This preprint should not be cited. This\narticle should be cited as: Journal of Information Processing Vol.32(2024) (online)\n------------------------------","subitem_description_type":"Other"}]},"item_3_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Cascadic Parareal is a parallel-in-time (PinT) method developed to improve the parallel performance for explicit time-dependent problems. It is more efficient than other PinT methods when explicit methods are used for solving. Cascadic Parareal has been proven to accelerate a one-dimensional advection problem and a two-dimensional compressible flow simulation faster compared to spatial parallelism with more than 64 cores in the previous works. However, Cascadic Parareal has also demonstrated slow convergence and produced unstable results for supersonic flow simulations. The instability is caused by unstable supersonic flow results calculated on the coarse meshes. In the present work, we introduce an improvement for Cascadic Parareal using local mesh refinement (LMR) to improve its accuracy for supersonic flow simulations. Numerical experiments in this research demonstrate that the LMR method can improve the convergence rate and accuracy of Cascadic Parareal for supersonic flow simulations. The numerical experiments of the present work show that the improved PinT method can provide stable and more accurate simulations for supersonic flow, and the compute time performance of the PinT algorithm can outperform simple spatial parallelism.\n------------------------------\nThis is a preprint of an article intended for publication Journal of\nInformation Processing(JIP). This preprint should not be cited. This\narticle should be cited as: Journal of Information Processing Vol.32(2024) (online)\n------------------------------","subitem_description_type":"Other"}]},"item_3_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌コンピューティングシステム（ACS）"}],"bibliographicIssueDates":{"bibliographicIssueDate":"2024-03-26","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"17"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":233717,"updated":"2025-01-19T10:00:45.120606+00:00","links":{}}