DEEP LEARNING SURROGATE FOR TURBULENT FLOW
The example embodiments are directed to a system and method for predicting a flow about an object through the use of a predictive model instead of a machine simulation. Traditional CFD simulations can take hours, even days. The example embodiments provide a predictive model that can predict a CFD fl...
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creator | EDGAR, Marc REIMANN, Johan BARR, Brian Chandler |
description | The example embodiments are directed to a system and method for predicting a flow about an object through the use of a predictive model instead of a machine simulation. Traditional CFD simulations can take hours, even days. The example embodiments provide a predictive model that can predict a CFD flow in seconds which greatly improves design time. In one example, the method may include receiving input data comprising shape parameters of a geometric object and flow parameters associated with the geometric object, predicting, via execution of a predictive model, a computational fluid dynamic (CFD) flow about the geometric object based on the shape parameters and the flow parameters included in the input data, and outputting one or more attributes of the predicted CFD flow about the geometric object via a display device. |
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Traditional CFD simulations can take hours, even days. The example embodiments provide a predictive model that can predict a CFD flow in seconds which greatly improves design time. In one example, the method may include receiving input data comprising shape parameters of a geometric object and flow parameters associated with the geometric object, predicting, via execution of a predictive model, a computational fluid dynamic (CFD) flow about the geometric object based on the shape parameters and the flow parameters included in the input data, and outputting one or more attributes of the predicted CFD flow about the geometric object via a display device.</description><language>eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; PHYSICS</subject><creationdate>2020</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20201210&DB=EPODOC&CC=US&NR=2020387579A1$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76547</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20201210&DB=EPODOC&CC=US&NR=2020387579A1$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>EDGAR, Marc</creatorcontrib><creatorcontrib>REIMANN, Johan</creatorcontrib><creatorcontrib>BARR, Brian Chandler</creatorcontrib><title>DEEP LEARNING SURROGATE FOR TURBULENT FLOW</title><description>The example embodiments are directed to a system and method for predicting a flow about an object through the use of a predictive model instead of a machine simulation. Traditional CFD simulations can take hours, even days. The example embodiments provide a predictive model that can predict a CFD flow in seconds which greatly improves design time. In one example, the method may include receiving input data comprising shape parameters of a geometric object and flow parameters associated with the geometric object, predicting, via execution of a predictive model, a computational fluid dynamic (CFD) flow about the geometric object based on the shape parameters and the flow parameters included in the input data, and outputting one or more attributes of the predicted CFD flow about the geometric object via a display device.</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2020</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZNBycXUNUPBxdQzy8_RzVwgODQryd3cMcVVw8w9SCAkNcgr1cfULUXDz8Q_nYWBNS8wpTuWF0twMym6uIc4euqkF-fGpxQWJyal5qSXxocFGBkYGxhbmpuaWjobGxKkCAPA0Jck</recordid><startdate>20201210</startdate><enddate>20201210</enddate><creator>EDGAR, Marc</creator><creator>REIMANN, Johan</creator><creator>BARR, Brian Chandler</creator><scope>EVB</scope></search><sort><creationdate>20201210</creationdate><title>DEEP LEARNING SURROGATE FOR TURBULENT FLOW</title><author>EDGAR, Marc ; REIMANN, Johan ; BARR, Brian Chandler</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_US2020387579A13</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng</language><creationdate>2020</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>EDGAR, Marc</creatorcontrib><creatorcontrib>REIMANN, Johan</creatorcontrib><creatorcontrib>BARR, Brian Chandler</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>EDGAR, Marc</au><au>REIMANN, Johan</au><au>BARR, Brian Chandler</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>DEEP LEARNING SURROGATE FOR TURBULENT FLOW</title><date>2020-12-10</date><risdate>2020</risdate><abstract>The example embodiments are directed to a system and method for predicting a flow about an object through the use of a predictive model instead of a machine simulation. Traditional CFD simulations can take hours, even days. The example embodiments provide a predictive model that can predict a CFD flow in seconds which greatly improves design time. In one example, the method may include receiving input data comprising shape parameters of a geometric object and flow parameters associated with the geometric object, predicting, via execution of a predictive model, a computational fluid dynamic (CFD) flow about the geometric object based on the shape parameters and the flow parameters included in the input data, and outputting one or more attributes of the predicted CFD flow about the geometric object via a display device.</abstract><oa>free_for_read</oa></addata></record> |
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subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING PHYSICS |
title | DEEP LEARNING SURROGATE FOR TURBULENT FLOW |
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