An integrated heterogeneous computing framework for ensemble simulations of laser-induced ignition
An integrated computational framework is introduced to study complex engineering systems through physics-based ensemble simulations on heterogeneous supercomputers. The framework is primarily designed for the quantitative assessment of laser-induced ignition in rocket engines. We develop and combine...
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creator | Maeda, Kazuki Teixeira, Thiago Wang, Jonathan M Hokanson, Jeffrey M Melone, Caetano Di Renzo, Mario Jones, Steve Urzay, Javier Iaccarino, Gianluca |
description | An integrated computational framework is introduced to study complex
engineering systems through physics-based ensemble simulations on heterogeneous
supercomputers. The framework is primarily designed for the quantitative
assessment of laser-induced ignition in rocket engines. We develop and combine
an implicit programming system, a compressible reacting flow solver, and a data
generation/management strategy on a robust and portable platform. We
systematically present this framework using test problems on a hybrid CPU/GPU
machine. Efficiency, scalability, and accuracy of the solver are
comprehensively assessed with canonical unit problems. Ensemble data management
and autoencoding are demonstrated using a canonical diffusion flame case.
Sensitivity analysis of the ignition of a turbulent, gaseous fuel jet is
performed using a simplified, three-dimensional model combustor. Our approach
unifies computer science, physics and engineering, and data science to realize
a cross-disciplinary workflow. The framework is exascale-oriented and can be
considered a benchmark for future computational science studies of real-world
systems. |
doi_str_mv | 10.48550/arxiv.2202.02319 |
format | Article |
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engineering systems through physics-based ensemble simulations on heterogeneous
supercomputers. The framework is primarily designed for the quantitative
assessment of laser-induced ignition in rocket engines. We develop and combine
an implicit programming system, a compressible reacting flow solver, and a data
generation/management strategy on a robust and portable platform. We
systematically present this framework using test problems on a hybrid CPU/GPU
machine. Efficiency, scalability, and accuracy of the solver are
comprehensively assessed with canonical unit problems. Ensemble data management
and autoencoding are demonstrated using a canonical diffusion flame case.
Sensitivity analysis of the ignition of a turbulent, gaseous fuel jet is
performed using a simplified, three-dimensional model combustor. Our approach
unifies computer science, physics and engineering, and data science to realize
a cross-disciplinary workflow. The framework is exascale-oriented and can be
considered a benchmark for future computational science studies of real-world
systems.</description><identifier>DOI: 10.48550/arxiv.2202.02319</identifier><language>eng</language><subject>Computer Science - Computational Engineering, Finance, and Science ; Computer Science - Distributed, Parallel, and Cluster Computing ; Physics - Data Analysis, Statistics and Probability ; Physics - Fluid Dynamics</subject><creationdate>2022-02</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2202.02319$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2202.02319$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Maeda, Kazuki</creatorcontrib><creatorcontrib>Teixeira, Thiago</creatorcontrib><creatorcontrib>Wang, Jonathan M</creatorcontrib><creatorcontrib>Hokanson, Jeffrey M</creatorcontrib><creatorcontrib>Melone, Caetano</creatorcontrib><creatorcontrib>Di Renzo, Mario</creatorcontrib><creatorcontrib>Jones, Steve</creatorcontrib><creatorcontrib>Urzay, Javier</creatorcontrib><creatorcontrib>Iaccarino, Gianluca</creatorcontrib><title>An integrated heterogeneous computing framework for ensemble simulations of laser-induced ignition</title><description>An integrated computational framework is introduced to study complex
engineering systems through physics-based ensemble simulations on heterogeneous
supercomputers. The framework is primarily designed for the quantitative
assessment of laser-induced ignition in rocket engines. We develop and combine
an implicit programming system, a compressible reacting flow solver, and a data
generation/management strategy on a robust and portable platform. We
systematically present this framework using test problems on a hybrid CPU/GPU
machine. Efficiency, scalability, and accuracy of the solver are
comprehensively assessed with canonical unit problems. Ensemble data management
and autoencoding are demonstrated using a canonical diffusion flame case.
Sensitivity analysis of the ignition of a turbulent, gaseous fuel jet is
performed using a simplified, three-dimensional model combustor. Our approach
unifies computer science, physics and engineering, and data science to realize
a cross-disciplinary workflow. The framework is exascale-oriented and can be
considered a benchmark for future computational science studies of real-world
systems.</description><subject>Computer Science - Computational Engineering, Finance, and Science</subject><subject>Computer Science - Distributed, Parallel, and Cluster Computing</subject><subject>Physics - Data Analysis, Statistics and Probability</subject><subject>Physics - Fluid Dynamics</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotz71uwyAYhWGWDlXaC-gUbsAu8GGMxyjqnxSpS3YL8IeLakMEdn_uvk3a6QxHeqWHkDvOaqmbht2b_BU-aiGYqJkA3l0Tu4s0xAXHbBYc6BsumNOIEdNaqEvzaV1CHKnPZsbPlN-pT5liLDjbCWkJ8zqZJaRYaPJ0MgVzFeKwut9WGGM4Xzfkypup4O3_bsjx8eG4f64Or08v-92hMqrtKm2UktZa3kgtwHHnpLZWdwawtUIqrj0yjkMLQgN0VvKWC44OfAeqaTlsyPYve0H2pxxmk7_7M7a_YOEHl3JQ7w</recordid><startdate>20220204</startdate><enddate>20220204</enddate><creator>Maeda, Kazuki</creator><creator>Teixeira, Thiago</creator><creator>Wang, Jonathan M</creator><creator>Hokanson, Jeffrey M</creator><creator>Melone, Caetano</creator><creator>Di Renzo, Mario</creator><creator>Jones, Steve</creator><creator>Urzay, Javier</creator><creator>Iaccarino, Gianluca</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20220204</creationdate><title>An integrated heterogeneous computing framework for ensemble simulations of laser-induced ignition</title><author>Maeda, Kazuki ; Teixeira, Thiago ; Wang, Jonathan M ; Hokanson, Jeffrey M ; Melone, Caetano ; Di Renzo, Mario ; Jones, Steve ; Urzay, Javier ; Iaccarino, Gianluca</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a679-8a664bbb154823c1cc48bb89a3e7b24618fe01ed7328339b417121ec3f9365713</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Computer Science - Computational Engineering, Finance, and Science</topic><topic>Computer Science - Distributed, Parallel, and Cluster Computing</topic><topic>Physics - Data Analysis, Statistics and Probability</topic><topic>Physics - Fluid Dynamics</topic><toplevel>online_resources</toplevel><creatorcontrib>Maeda, Kazuki</creatorcontrib><creatorcontrib>Teixeira, Thiago</creatorcontrib><creatorcontrib>Wang, Jonathan M</creatorcontrib><creatorcontrib>Hokanson, Jeffrey M</creatorcontrib><creatorcontrib>Melone, Caetano</creatorcontrib><creatorcontrib>Di Renzo, Mario</creatorcontrib><creatorcontrib>Jones, Steve</creatorcontrib><creatorcontrib>Urzay, Javier</creatorcontrib><creatorcontrib>Iaccarino, Gianluca</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Maeda, Kazuki</au><au>Teixeira, Thiago</au><au>Wang, Jonathan M</au><au>Hokanson, Jeffrey M</au><au>Melone, Caetano</au><au>Di Renzo, Mario</au><au>Jones, Steve</au><au>Urzay, Javier</au><au>Iaccarino, Gianluca</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An integrated heterogeneous computing framework for ensemble simulations of laser-induced ignition</atitle><date>2022-02-04</date><risdate>2022</risdate><abstract>An integrated computational framework is introduced to study complex
engineering systems through physics-based ensemble simulations on heterogeneous
supercomputers. The framework is primarily designed for the quantitative
assessment of laser-induced ignition in rocket engines. We develop and combine
an implicit programming system, a compressible reacting flow solver, and a data
generation/management strategy on a robust and portable platform. We
systematically present this framework using test problems on a hybrid CPU/GPU
machine. Efficiency, scalability, and accuracy of the solver are
comprehensively assessed with canonical unit problems. Ensemble data management
and autoencoding are demonstrated using a canonical diffusion flame case.
Sensitivity analysis of the ignition of a turbulent, gaseous fuel jet is
performed using a simplified, three-dimensional model combustor. Our approach
unifies computer science, physics and engineering, and data science to realize
a cross-disciplinary workflow. The framework is exascale-oriented and can be
considered a benchmark for future computational science studies of real-world
systems.</abstract><doi>10.48550/arxiv.2202.02319</doi><oa>free_for_read</oa></addata></record> |
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subjects | Computer Science - Computational Engineering, Finance, and Science Computer Science - Distributed, Parallel, and Cluster Computing Physics - Data Analysis, Statistics and Probability Physics - Fluid Dynamics |
title | An integrated heterogeneous computing framework for ensemble simulations of laser-induced ignition |
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