Flow field prediction method and system

The invention provides a flow field prediction method and system, and relates to the technical field of fluid mechanics, and the method comprises the steps: obtaining corresponding flow field data of a research object under different geometric shapes and working conditions, and constructing a flow f...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: CHEN MING, HUANG RUOYI, DING QIANXUE, YIN SHUWEI, PEI JUAN, DU FENGLEI, QIU ZHILIANG, LI XIAOFENG, HONG YUN, OU YANG, CHEN GUODONG, ZHANG YI, CHENG SHUO, ZHOU JINGYI, HUANG CHENGPENG, HUANG MINGQUAN, ZHANG LEI, WANG XINGYUE, CAO JUAN, LI JIN, ZHAI LIANG, FU XIAOCHENG, GU JUNJIE, ZHU YI, KANG YIBO, WANG JUN, XIAN HAOYANG, PU XIANG, JIANG HAOYU, WANG XUE
Format: Patent
Sprache:chi ; eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title
container_volume
creator CHEN MING
HUANG RUOYI
DING QIANXUE
YIN SHUWEI
PEI JUAN
DU FENGLEI
QIU ZHILIANG
LI XIAOFENG
HONG YUN
OU YANG
CHEN GUODONG
ZHANG YI
CHENG SHUO
ZHOU JINGYI
HUANG CHENGPENG
HUANG MINGQUAN
ZHANG LEI
WANG XINGYUE
CAO JUAN
LI JIN
ZHAI LIANG
FU XIAOCHENG
GU JUNJIE
ZHU YI
KANG YIBO
WANG JUN
XIAN HAOYANG
PU XIANG
JIANG HAOYU
WANG XUE
description The invention provides a flow field prediction method and system, and relates to the technical field of fluid mechanics, and the method comprises the steps: obtaining corresponding flow field data of a research object under different geometric shapes and working conditions, and constructing a flow field data set; converting the flow field data in the flow field data set into a storage structure based on a grid topology connection diagram; establishing a flow field prediction model based on a graph convolutional neural network, and training the flow field prediction model by using the converted flow field data set; and inputting geometric parameters and working condition parameters to be predicted, and performing flow field prediction by using the flow field prediction model. According to the method, the information of the grid topology connection and the characteristics of variable geometry and variable working conditions are combined, and the flow field can be quickly predicted under the variable geometry an
format Patent
fullrecord <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_CN117556725A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>CN117556725A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_CN117556725A3</originalsourceid><addsrcrecordid>eNrjZFB3y8kvV0jLTM1JUSgoSk3JTC7JzM9TyE0tychPUUjMS1EoriwuSc3lYWBNS8wpTuWF0twMim6uIc4euqkF-fGpxQWJyal5qSXxzn6GhuampmbmRqaOxsSoAQCU5ifi</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Flow field prediction method and system</title><source>esp@cenet</source><creator>CHEN MING ; HUANG RUOYI ; DING QIANXUE ; YIN SHUWEI ; PEI JUAN ; DU FENGLEI ; QIU ZHILIANG ; LI XIAOFENG ; HONG YUN ; OU YANG ; CHEN GUODONG ; ZHANG YI ; CHENG SHUO ; ZHOU JINGYI ; HUANG CHENGPENG ; HUANG MINGQUAN ; ZHANG LEI ; WANG XINGYUE ; CAO JUAN ; LI JIN ; ZHAI LIANG ; FU XIAOCHENG ; GU JUNJIE ; ZHU YI ; KANG YIBO ; WANG JUN ; XIAN HAOYANG ; PU XIANG ; JIANG HAOYU ; WANG XUE</creator><creatorcontrib>CHEN MING ; HUANG RUOYI ; DING QIANXUE ; YIN SHUWEI ; PEI JUAN ; DU FENGLEI ; QIU ZHILIANG ; LI XIAOFENG ; HONG YUN ; OU YANG ; CHEN GUODONG ; ZHANG YI ; CHENG SHUO ; ZHOU JINGYI ; HUANG CHENGPENG ; HUANG MINGQUAN ; ZHANG LEI ; WANG XINGYUE ; CAO JUAN ; LI JIN ; ZHAI LIANG ; FU XIAOCHENG ; GU JUNJIE ; ZHU YI ; KANG YIBO ; WANG JUN ; XIAN HAOYANG ; PU XIANG ; JIANG HAOYU ; WANG XUE</creatorcontrib><description>The invention provides a flow field prediction method and system, and relates to the technical field of fluid mechanics, and the method comprises the steps: obtaining corresponding flow field data of a research object under different geometric shapes and working conditions, and constructing a flow field data set; converting the flow field data in the flow field data set into a storage structure based on a grid topology connection diagram; establishing a flow field prediction model based on a graph convolutional neural network, and training the flow field prediction model by using the converted flow field data set; and inputting geometric parameters and working condition parameters to be predicted, and performing flow field prediction by using the flow field prediction model. According to the method, the information of the grid topology connection and the characteristics of variable geometry and variable working conditions are combined, and the flow field can be quickly predicted under the variable geometry an</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; ELECTRIC DIGITAL DATA PROCESSING ; PHYSICS</subject><creationdate>2024</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&amp;date=20240213&amp;DB=EPODOC&amp;CC=CN&amp;NR=117556725A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25542,76289</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&amp;date=20240213&amp;DB=EPODOC&amp;CC=CN&amp;NR=117556725A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>CHEN MING</creatorcontrib><creatorcontrib>HUANG RUOYI</creatorcontrib><creatorcontrib>DING QIANXUE</creatorcontrib><creatorcontrib>YIN SHUWEI</creatorcontrib><creatorcontrib>PEI JUAN</creatorcontrib><creatorcontrib>DU FENGLEI</creatorcontrib><creatorcontrib>QIU ZHILIANG</creatorcontrib><creatorcontrib>LI XIAOFENG</creatorcontrib><creatorcontrib>HONG YUN</creatorcontrib><creatorcontrib>OU YANG</creatorcontrib><creatorcontrib>CHEN GUODONG</creatorcontrib><creatorcontrib>ZHANG YI</creatorcontrib><creatorcontrib>CHENG SHUO</creatorcontrib><creatorcontrib>ZHOU JINGYI</creatorcontrib><creatorcontrib>HUANG CHENGPENG</creatorcontrib><creatorcontrib>HUANG MINGQUAN</creatorcontrib><creatorcontrib>ZHANG LEI</creatorcontrib><creatorcontrib>WANG XINGYUE</creatorcontrib><creatorcontrib>CAO JUAN</creatorcontrib><creatorcontrib>LI JIN</creatorcontrib><creatorcontrib>ZHAI LIANG</creatorcontrib><creatorcontrib>FU XIAOCHENG</creatorcontrib><creatorcontrib>GU JUNJIE</creatorcontrib><creatorcontrib>ZHU YI</creatorcontrib><creatorcontrib>KANG YIBO</creatorcontrib><creatorcontrib>WANG JUN</creatorcontrib><creatorcontrib>XIAN HAOYANG</creatorcontrib><creatorcontrib>PU XIANG</creatorcontrib><creatorcontrib>JIANG HAOYU</creatorcontrib><creatorcontrib>WANG XUE</creatorcontrib><title>Flow field prediction method and system</title><description>The invention provides a flow field prediction method and system, and relates to the technical field of fluid mechanics, and the method comprises the steps: obtaining corresponding flow field data of a research object under different geometric shapes and working conditions, and constructing a flow field data set; converting the flow field data in the flow field data set into a storage structure based on a grid topology connection diagram; establishing a flow field prediction model based on a graph convolutional neural network, and training the flow field prediction model by using the converted flow field data set; and inputting geometric parameters and working condition parameters to be predicted, and performing flow field prediction by using the flow field prediction model. According to the method, the information of the grid topology connection and the characteristics of variable geometry and variable working conditions are combined, and the flow field can be quickly predicted under the variable geometry an</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>ELECTRIC DIGITAL DATA PROCESSING</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2024</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZFB3y8kvV0jLTM1JUSgoSk3JTC7JzM9TyE0tychPUUjMS1EoriwuSc3lYWBNS8wpTuWF0twMim6uIc4euqkF-fGpxQWJyal5qSXxzn6GhuampmbmRqaOxsSoAQCU5ifi</recordid><startdate>20240213</startdate><enddate>20240213</enddate><creator>CHEN MING</creator><creator>HUANG RUOYI</creator><creator>DING QIANXUE</creator><creator>YIN SHUWEI</creator><creator>PEI JUAN</creator><creator>DU FENGLEI</creator><creator>QIU ZHILIANG</creator><creator>LI XIAOFENG</creator><creator>HONG YUN</creator><creator>OU YANG</creator><creator>CHEN GUODONG</creator><creator>ZHANG YI</creator><creator>CHENG SHUO</creator><creator>ZHOU JINGYI</creator><creator>HUANG CHENGPENG</creator><creator>HUANG MINGQUAN</creator><creator>ZHANG LEI</creator><creator>WANG XINGYUE</creator><creator>CAO JUAN</creator><creator>LI JIN</creator><creator>ZHAI LIANG</creator><creator>FU XIAOCHENG</creator><creator>GU JUNJIE</creator><creator>ZHU YI</creator><creator>KANG YIBO</creator><creator>WANG JUN</creator><creator>XIAN HAOYANG</creator><creator>PU XIANG</creator><creator>JIANG HAOYU</creator><creator>WANG XUE</creator><scope>EVB</scope></search><sort><creationdate>20240213</creationdate><title>Flow field prediction method and system</title><author>CHEN MING ; HUANG RUOYI ; DING QIANXUE ; YIN SHUWEI ; PEI JUAN ; DU FENGLEI ; QIU ZHILIANG ; LI XIAOFENG ; HONG YUN ; OU YANG ; CHEN GUODONG ; ZHANG YI ; CHENG SHUO ; ZHOU JINGYI ; HUANG CHENGPENG ; HUANG MINGQUAN ; ZHANG LEI ; WANG XINGYUE ; CAO JUAN ; LI JIN ; ZHAI LIANG ; FU XIAOCHENG ; GU JUNJIE ; ZHU YI ; KANG YIBO ; WANG JUN ; XIAN HAOYANG ; PU XIANG ; JIANG HAOYU ; WANG XUE</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN117556725A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2024</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>ELECTRIC DIGITAL DATA PROCESSING</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>CHEN MING</creatorcontrib><creatorcontrib>HUANG RUOYI</creatorcontrib><creatorcontrib>DING QIANXUE</creatorcontrib><creatorcontrib>YIN SHUWEI</creatorcontrib><creatorcontrib>PEI JUAN</creatorcontrib><creatorcontrib>DU FENGLEI</creatorcontrib><creatorcontrib>QIU ZHILIANG</creatorcontrib><creatorcontrib>LI XIAOFENG</creatorcontrib><creatorcontrib>HONG YUN</creatorcontrib><creatorcontrib>OU YANG</creatorcontrib><creatorcontrib>CHEN GUODONG</creatorcontrib><creatorcontrib>ZHANG YI</creatorcontrib><creatorcontrib>CHENG SHUO</creatorcontrib><creatorcontrib>ZHOU JINGYI</creatorcontrib><creatorcontrib>HUANG CHENGPENG</creatorcontrib><creatorcontrib>HUANG MINGQUAN</creatorcontrib><creatorcontrib>ZHANG LEI</creatorcontrib><creatorcontrib>WANG XINGYUE</creatorcontrib><creatorcontrib>CAO JUAN</creatorcontrib><creatorcontrib>LI JIN</creatorcontrib><creatorcontrib>ZHAI LIANG</creatorcontrib><creatorcontrib>FU XIAOCHENG</creatorcontrib><creatorcontrib>GU JUNJIE</creatorcontrib><creatorcontrib>ZHU YI</creatorcontrib><creatorcontrib>KANG YIBO</creatorcontrib><creatorcontrib>WANG JUN</creatorcontrib><creatorcontrib>XIAN HAOYANG</creatorcontrib><creatorcontrib>PU XIANG</creatorcontrib><creatorcontrib>JIANG HAOYU</creatorcontrib><creatorcontrib>WANG XUE</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>CHEN MING</au><au>HUANG RUOYI</au><au>DING QIANXUE</au><au>YIN SHUWEI</au><au>PEI JUAN</au><au>DU FENGLEI</au><au>QIU ZHILIANG</au><au>LI XIAOFENG</au><au>HONG YUN</au><au>OU YANG</au><au>CHEN GUODONG</au><au>ZHANG YI</au><au>CHENG SHUO</au><au>ZHOU JINGYI</au><au>HUANG CHENGPENG</au><au>HUANG MINGQUAN</au><au>ZHANG LEI</au><au>WANG XINGYUE</au><au>CAO JUAN</au><au>LI JIN</au><au>ZHAI LIANG</au><au>FU XIAOCHENG</au><au>GU JUNJIE</au><au>ZHU YI</au><au>KANG YIBO</au><au>WANG JUN</au><au>XIAN HAOYANG</au><au>PU XIANG</au><au>JIANG HAOYU</au><au>WANG XUE</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Flow field prediction method and system</title><date>2024-02-13</date><risdate>2024</risdate><abstract>The invention provides a flow field prediction method and system, and relates to the technical field of fluid mechanics, and the method comprises the steps: obtaining corresponding flow field data of a research object under different geometric shapes and working conditions, and constructing a flow field data set; converting the flow field data in the flow field data set into a storage structure based on a grid topology connection diagram; establishing a flow field prediction model based on a graph convolutional neural network, and training the flow field prediction model by using the converted flow field data set; and inputting geometric parameters and working condition parameters to be predicted, and performing flow field prediction by using the flow field prediction model. According to the method, the information of the grid topology connection and the characteristics of variable geometry and variable working conditions are combined, and the flow field can be quickly predicted under the variable geometry an</abstract><oa>free_for_read</oa></addata></record>
fulltext fulltext_linktorsrc
identifier
ispartof
issn
language chi ; eng
recordid cdi_epo_espacenet_CN117556725A
source esp@cenet
subjects CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
PHYSICS
title Flow field prediction method and system
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-05T14%3A18%3A49IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=CHEN%20MING&rft.date=2024-02-13&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3ECN117556725A%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true