Sag-flownet: self-attention generative network for airfoil flow field prediction
Flow field prediction is essential for airfoil design. It is a time-consuming task to obtain the flow fields around an airfoil. Convolution neural networks (CNN) have been applied for flow field prediction in recent years. However, CNN-based methods rely heavily on convolutional kernels to process i...
Saved in:
Published in | Soft computing (Berlin, Germany) Vol. 28; no. 11-12; pp. 7417 - 7437 |
---|---|
Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.06.2024
Springer Nature B.V |
Subjects | |
Online Access | Get full text |
Cover
Loading…
Abstract | Flow field prediction is essential for airfoil design. It is a time-consuming task to obtain the flow fields around an airfoil. Convolution neural networks (CNN) have been applied for flow field prediction in recent years. However, CNN-based methods rely heavily on convolutional kernels to process information within local neighborhoods, making it difficult to capture global information. In this paper, we propose a novel self-attention generative network referred to as SAG-FlowNet, both for original and optimization airfoil flow field prediction. We investigate the self-attention mechanism with a multi-layer convolutional generative network. We use the self-attention module to capture various information within and between flow fields, and with the help of the attention module, the CNN can utilize the information with stronger relationships regardless of their distances to achieve better flow field prediction results. Through extensive experiments, we explore the proposed SAG-FlowNet performance. The experimental results show that the method has accurate and universal performance for the reconstruction and prediction of the flow field both for original and optimized airfoils. SAG-FlowNet is promising for fast flow field prediction and has potential applications in accelerating airfoil design. |
---|---|
AbstractList | Flow field prediction is essential for airfoil design. It is a time-consuming task to obtain the flow fields around an airfoil. Convolution neural networks (CNN) have been applied for flow field prediction in recent years. However, CNN-based methods rely heavily on convolutional kernels to process information within local neighborhoods, making it difficult to capture global information. In this paper, we propose a novel self-attention generative network referred to as SAG-FlowNet, both for original and optimization airfoil flow field prediction. We investigate the self-attention mechanism with a multi-layer convolutional generative network. We use the self-attention module to capture various information within and between flow fields, and with the help of the attention module, the CNN can utilize the information with stronger relationships regardless of their distances to achieve better flow field prediction results. Through extensive experiments, we explore the proposed SAG-FlowNet performance. The experimental results show that the method has accurate and universal performance for the reconstruction and prediction of the flow field both for original and optimized airfoils. SAG-FlowNet is promising for fast flow field prediction and has potential applications in accelerating airfoil design. |
Author | Deng, Xiaogang Li, Guanxiong Jiang, Yi Wang, Xiao Zhang, Laiping |
Author_xml | – sequence: 1 givenname: Xiao surname: Wang fullname: Wang, Xiao organization: School of Computer Science, Sichuan University – sequence: 2 givenname: Yi surname: Jiang fullname: Jiang, Yi email: yijiang@mail.ustc.edu.cn organization: Institute of System Engineering, Academy of Military Sciences – sequence: 3 givenname: Guanxiong surname: Li fullname: Li, Guanxiong organization: School of Computer Science, Sichuan University – sequence: 4 givenname: Laiping surname: Zhang fullname: Zhang, Laiping organization: Unmanned Systems Research Center, National Innovation Institute of Defense Technology – sequence: 5 givenname: Xiaogang surname: Deng fullname: Deng, Xiaogang organization: School of Computer Science, Sichuan University, Academy of Military Sciences |
BookMark | eNp9kF9LwzAUxYNMcJt-AZ8CPkdvkrZpfZPhPxgouPeQtjejsyYz6dz89rar4JtP98I9v3O4Z0Ymzjsk5JLDNQdQNxEgBWAgJIMiA8EOJ2TKEymZSlQxOe6CqSyRZ2QW4wZAcJXKKXl9M2tmW7932N3SiK1lpuvQdY13dI0Og-maL6T9ee_DO7U-UNME65uWDhi1DbY13Qasm2qAzsmpNW3Ei985J6uH-9XiiS1fHp8Xd0tWCQUdy7gpDeaqxgwBZGW5qDkmhbSJVIKXRW4qYTMubMllaU2l8iwvhVVJLqoikXNyNdpug__cYez0xu-C6xO1hLyPSIfv50SMqir4GANavQ3NhwnfmoMeitNjcbovTh-L04cekiMUe7FbY_iz_of6AW9dc7s |
Cites_doi | 10.1007/s10489-020-01839-5 10.2514/1.J058462 10.1126/science.aaa8415 10.1007/s42979-021-00867-3 10.1146/annurev-fluid-010719-060214 10.1109/38.329096 10.1109/38.144824 10.1016/j.physd.2022.133454 10.1016/j.ast.2013.11.006 10.2514/1.J058291 10.1007/s00162-020-00518-y 10.1017/jfm.2016.803 10.2514/1.J053318 10.1016/j.taml.2020.01.031 10.1109/MSP.2017.2765202 10.1109/TBDATA.2017.2717439 10.1016/j.ejor.2007.10.013 10.1038/nature14539 10.2514/1.C031675 10.1007/s00466-019-01740-0 10.1561/2000000039 10.1115/1.1760520 10.1016/j.compfluid.2019.104393 10.1103/PhysRevFluids.5.104401 10.1007/s00162-021-00580-0 10.1016/j.atmosenv.2006.08.019 10.18653/v1/N18-2074 10.2514/6.2017-3660 10.1007/978-3-319-24574-4_28 10.48550/arXiv.1706.03762 10.1109/CVPR.2015.7298594 10.1007/978-3-319-93025-1_4 10.18653/v1/D16-1244 10.2514/6.2018-1903 10.1109/CVPR.2017.19 10.1109/CVPR.2017.632 10.1109/CVPR.2018.00745 10.1145/2939672.2939738 10.1063/1.5094943 |
ContentType | Journal Article |
Copyright | The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
Copyright_xml | – notice: The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
DBID | AAYXX CITATION JQ2 |
DOI | 10.1007/s00500-023-09602-x |
DatabaseName | CrossRef ProQuest Computer Science Collection |
DatabaseTitle | CrossRef ProQuest Computer Science Collection |
DatabaseTitleList | ProQuest Computer Science Collection |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Engineering Computer Science |
EISSN | 1433-7479 |
EndPage | 7437 |
ExternalDocumentID | 10_1007_s00500_023_09602_x |
GrantInformation_xml | – fundername: National Key Project of China grantid: No. GJXM92579 |
GroupedDBID | -5B -5G -BR -EM -Y2 -~C .86 .VR 06D 0R~ 0VY 1N0 1SB 203 29~ 2J2 2JN 2JY 2KG 2LR 2P1 2VQ 2~H 30V 4.4 406 408 409 40D 40E 5VS 67Z 6NX 8TC 8UJ 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANZL AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO ABAKF ABBBX ABBXA ABDZT ABECU ABFGW ABFTD ABFTV ABHLI ABHQN ABJNI ABJOX ABKAS ABKCH ABKTR ABMNI ABMQK ABNWP ABQBU ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABWNU ABXPI ACAOD ACBXY ACDTI ACGFS ACHSB ACHXU ACIGE ACIPQ ACKNC ACMDZ ACMLO ACOKC ACOMO ACSNA ACTTH ACVWB ACWMK ACZOJ ADHHG ADHIR ADINQ ADKNI ADKPE ADOXG ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFQL AEFTE AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AESTI AETLH AEVLU AEVTX AEXYK AFBBN AFGCZ AFKRA AFLOW AFQWF AFWTZ AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AIMYW AITGF AJBLW AJRNO AJZVZ AKQUC ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMXSW AMYLF AMYQR AOCGG ARAPS ARMRJ ASPBG AVWKF AXYYD AYJHY AZFZN B-. BA0 BDATZ BENPR BGLVJ BGNMA CAG CCPQU COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DU5 EBLON EBS EIOEI EJD ESBYG FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNWQR GQ6 GQ7 GQ8 GXS HCIFZ HF~ HG5 HG6 HMJXF HQYDN HRMNR HVGLF HZ~ I09 IHE IJ- IKXTQ IWAJR IXC IXD IXE IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ K7- KDC KOV LAS LLZTM M4Y MA- N2Q NB0 NPVJJ NQJWS NU0 O9- O93 O9J OAM P2P P9P PF0 PT4 PT5 QOS R89 R9I RIG RNI ROL RPX RSV RZK S16 S1Z S27 S3B SAP SDH SEG SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 TSG TSK TSV TUC U2A UG4 UNUBA UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WK8 YLTOR Z45 Z5O Z7R Z7X Z7Y Z7Z Z81 Z83 Z88 ZMTXR AAYXX CITATION H13 JQ2 |
ID | FETCH-LOGICAL-c270t-61abae87de6e003cf12d1e493f43721b98ac2f612fb13bfac7868b2f7482c943 |
IEDL.DBID | AGYKE |
ISSN | 1432-7643 |
IngestDate | Thu Oct 10 22:44:38 EDT 2024 Thu Sep 12 20:47:02 EDT 2024 Sat Jul 20 01:15:08 EDT 2024 |
IsPeerReviewed | true |
IsScholarly | true |
Issue | 11-12 |
Keywords | Generative networks Flow field Self-attention Prediction |
Language | English |
LinkModel | DirectLink |
MergedId | FETCHMERGED-LOGICAL-c270t-61abae87de6e003cf12d1e493f43721b98ac2f612fb13bfac7868b2f7482c943 |
PQID | 3082705143 |
PQPubID | 2043697 |
PageCount | 21 |
ParticipantIDs | proquest_journals_3082705143 crossref_primary_10_1007_s00500_023_09602_x springer_journals_10_1007_s00500_023_09602_x |
PublicationCentury | 2000 |
PublicationDate | 20240601 |
PublicationDateYYYYMMDD | 2024-06-01 |
PublicationDate_xml | – month: 06 year: 2024 text: 20240601 day: 01 |
PublicationDecade | 2020 |
PublicationPlace | Berlin/Heidelberg |
PublicationPlace_xml | – name: Berlin/Heidelberg – name: Heidelberg |
PublicationSubtitle | A Fusion of Foundations, Methodologies and Applications |
PublicationTitle | Soft computing (Berlin, Germany) |
PublicationTitleAbbrev | Soft Comput |
PublicationYear | 2024 |
Publisher | Springer Berlin Heidelberg Springer Nature B.V |
Publisher_xml | – name: Springer Berlin Heidelberg – name: Springer Nature B.V |
References | Kleijnen (CR21) 2009; 192 CR18 Wu, Liu, An, Chen, Lyu (CR46) 2020; 198 CR17 CR39 CR16 CR38 CR37 CR36 Blocken, Stathopoulos, Carmeliet (CR2) 2007; 41 CR34 CR11 CR33 Morimoto, Fukami, Zhang, Nair, Fukagata (CR30) 2021; 35 Kutz (CR22) 2017; 814 Osher, Fedkiw, Piechor (CR32) 2004; 57 Constantin, Foias (CR7) 2020 Piegl, Tiller (CR35) 1996 Thuerey, Weißenow, Prantl, Hu (CR44) 2020; 58 Brunton, Noack, Koumoutsakos (CR3) 2020; 52 Lamousin, Waggenspack (CR23) 1994; 14 Bhatnagar, Afshar, Pan, Duraisamy, Kaushik (CR1) 2019; 64 Morimoto, Fukami, Maulik, Vinuesa, Fukagata (CR31) 2022; 440 LeCun, Bengio, Hinton (CR24) 2015; 521 Lyu, Kenway, Martins (CR27) 2015; 53 Bryson, Levit (CR4) 1992; 12 Fukami, Hasegawa, Nakamura, Morimoto, Fukagata (CR15) 2021; 2 Maulik, Fukami, Ramachandra, Fukagata, Taira (CR28) 2020; 5 Sun, Wang (CR41) 2020; 10 Deng, Yu (CR10) 2014; 7 Della Vecchia, Daniele, DúAmato (CR9) 2014; 32 CR29 Carta, Corriga, Ferreira, Podda, Recupero (CR5) 2021; 51 Fukami, Fukagata, Taira (CR14) 2020; 34 Taira, Hemati, Brunton, Sun, Duraisamy, Bagheri, Yeh (CR43) 2020; 58 CR26 CR48 CR25 CR47 Dropkin, Custodio, Henoch, Johari (CR12) 2012; 49 CR45 CR20 CR42 Jordan, Mitchell (CR19) 2015; 349 CR40 Chen, Shi, Zhang, Wu, Guizani (CR6) 2017; 7 Creswell, White, Dumoulin, Arulkumaran, Sengupta, Bharath (CR8) 2018; 35 Dumoulin, Visin (CR13) 2016; 1050 SL Brunton (9602_CR3) 2020; 52 JN Kutz (9602_CR22) 2017; 814 L Piegl (9602_CR35) 1996 9602_CR29 M Morimoto (9602_CR30) 2021; 35 A Creswell (9602_CR8) 2018; 35 P Della Vecchia (9602_CR9) 2014; 32 9602_CR45 JP Kleijnen (9602_CR21) 2009; 192 9602_CR25 9602_CR47 9602_CR26 9602_CR48 Z Lyu (9602_CR27) 2015; 53 Y LeCun (9602_CR24) 2015; 521 S Osher (9602_CR32) 2004; 57 L Deng (9602_CR10) 2014; 7 MI Jordan (9602_CR19) 2015; 349 H Wu (9602_CR46) 2020; 198 S Bhatnagar (9602_CR1) 2019; 64 9602_CR20 L Sun (9602_CR41) 2020; 10 9602_CR42 M Morimoto (9602_CR31) 2022; 440 9602_CR40 K Taira (9602_CR43) 2020; 58 9602_CR16 9602_CR38 9602_CR17 9602_CR39 9602_CR18 A Dropkin (9602_CR12) 2012; 49 V Dumoulin (9602_CR13) 2016; 1050 9602_CR34 K Fukami (9602_CR14) 2020; 34 9602_CR36 9602_CR37 S Bryson (9602_CR4) 1992; 12 K Fukami (9602_CR15) 2021; 2 P Constantin (9602_CR7) 2020 R Maulik (9602_CR28) 2020; 5 HJ Lamousin (9602_CR23) 1994; 14 M Chen (9602_CR6) 2017; 7 S Carta (9602_CR5) 2021; 51 9602_CR11 9602_CR33 B Blocken (9602_CR2) 2007; 41 N Thuerey (9602_CR44) 2020; 58 |
References_xml | – volume: 51 start-page: 889 year: 2021 end-page: 905 ident: CR5 article-title: A multi-layer and multi-ensemble stock trader using deep learning and deep reinforcement learning publication-title: Appl Intell doi: 10.1007/s10489-020-01839-5 contributor: fullname: Recupero – volume: 58 start-page: 998 issue: 3 year: 2020 end-page: 1022 ident: CR43 article-title: Modal analysis of fluid flows: applications and outlook publication-title: AIAA J doi: 10.2514/1.J058462 contributor: fullname: Yeh – ident: CR45 – ident: CR18 – ident: CR47 – volume: 349 start-page: 255 issue: 6245 year: 2015 end-page: 260 ident: CR19 article-title: Machine learning: trends, perspectives, and prospects publication-title: Science doi: 10.1126/science.aaa8415 contributor: fullname: Mitchell – year: 1996 ident: CR35 publication-title: The NURBS book contributor: fullname: Tiller – volume: 2 start-page: 1 year: 2021 end-page: 16 ident: CR15 article-title: Model order reduction with neural networks: application to laminar and turbulent flows publication-title: SN Comput Sci doi: 10.1007/s42979-021-00867-3 contributor: fullname: Fukagata – ident: CR39 – ident: CR16 – ident: CR37 – volume: 52 start-page: 477 year: 2020 end-page: 508 ident: CR3 article-title: Machine learning for fluid mechanics publication-title: Annu Rev Fluid Mech doi: 10.1146/annurev-fluid-010719-060214 contributor: fullname: Koumoutsakos – volume: 14 start-page: 59 issue: 6 year: 1994 end-page: 65 ident: CR23 article-title: NURBS-based free-form deformations publication-title: IEEE Comput Graph Appl doi: 10.1109/38.329096 contributor: fullname: Waggenspack – volume: 12 start-page: 25 issue: 4 year: 1992 end-page: 34 ident: CR4 article-title: The virtual wind tunnel publication-title: IEEE Comput Graph Appl doi: 10.1109/38.144824 contributor: fullname: Levit – ident: CR33 – volume: 440 year: 2022 ident: CR31 article-title: Assessments of epistemic uncertainty using Gaussian stochastic weight averaging for fluid-flow regression publication-title: Physica D doi: 10.1016/j.physd.2022.133454 contributor: fullname: Fukagata – volume: 32 start-page: 103 issue: 1 year: 2014 end-page: 110 ident: CR9 article-title: An airfoil shape optimization technique coupling PARSEC parameterization and evolutionary algorithm publication-title: Aerosp Sci Technol doi: 10.1016/j.ast.2013.11.006 contributor: fullname: DúAmato – ident: CR29 – volume: 58 start-page: 25 issue: 1 year: 2020 end-page: 36 ident: CR44 article-title: Deep learning methods for Reynolds-averaged Navier–Stokes simulations of airfoil flows publication-title: AIAA J doi: 10.2514/1.J058291 contributor: fullname: Hu – ident: CR40 – ident: CR25 – ident: CR42 – volume: 1050 start-page: 23 year: 2016 ident: CR13 article-title: A guide to convolution arithmetic for deep learning publication-title: Statistics contributor: fullname: Visin – volume: 34 start-page: 497 year: 2020 end-page: 519 ident: CR14 article-title: Assessment of supervised machine learning methods for fluid flows publication-title: Theor Comput Fluid Dyn doi: 10.1007/s00162-020-00518-y contributor: fullname: Taira – volume: 814 start-page: 1 year: 2017 end-page: 4 ident: CR22 article-title: Deep learning in fluid dynamics publication-title: J Fluid Mech doi: 10.1017/jfm.2016.803 contributor: fullname: Kutz – ident: CR48 – volume: 53 start-page: 968 issue: 4 year: 2015 end-page: 985 ident: CR27 article-title: Aerodynamic shape optimization investigations of the common research model wing benchmark publication-title: AIAA J doi: 10.2514/1.J053318 contributor: fullname: Martins – year: 2020 ident: CR7 publication-title: Navier–Stokes equations contributor: fullname: Foias – volume: 10 start-page: 161 issue: 3 year: 2020 end-page: 169 ident: CR41 article-title: Physics-constrained Bayesian neural network for fluid flow reconstruction with sparse and noisy data publication-title: Theor Appl Mech Lett doi: 10.1016/j.taml.2020.01.031 contributor: fullname: Wang – volume: 35 start-page: 53 issue: 1 year: 2018 end-page: 65 ident: CR8 article-title: Generative adversarial networks: an overview publication-title: IEEE Signal Process Mag doi: 10.1109/MSP.2017.2765202 contributor: fullname: Bharath – volume: 7 start-page: 750 issue: 4 year: 2017 end-page: 758 ident: CR6 article-title: Deep feature learning for medical image analysis with convolutional autoencoder neural network publication-title: IEEE Trans Big Data doi: 10.1109/TBDATA.2017.2717439 contributor: fullname: Guizani – volume: 192 start-page: 707 issue: 3 year: 2009 end-page: 716 ident: CR21 article-title: Kriging metamodeling in simulation: a review publication-title: Eur J Oper Res doi: 10.1016/j.ejor.2007.10.013 contributor: fullname: Kleijnen – ident: CR38 – ident: CR17 – volume: 521 start-page: 436 issue: 7553 year: 2015 end-page: 444 ident: CR24 article-title: Deep learning publication-title: Nature doi: 10.1038/nature14539 contributor: fullname: Hinton – ident: CR11 – volume: 49 start-page: 1345 issue: 5 year: 2012 end-page: 1355 ident: CR12 article-title: Computation of flow field around an airfoil with leading-edge protuberances publication-title: J Aircr doi: 10.2514/1.C031675 contributor: fullname: Johari – volume: 64 start-page: 525 year: 2019 end-page: 545 ident: CR1 article-title: Prediction of aerodynamic flow fields using convolutional neural networks publication-title: Comput Mech doi: 10.1007/s00466-019-01740-0 contributor: fullname: Kaushik – ident: CR34 – ident: CR36 – volume: 7 start-page: 197 issue: 3–4 year: 2014 end-page: 387 ident: CR10 article-title: Deep learning: methods and applications publication-title: Found Trends Signal Process doi: 10.1561/2000000039 contributor: fullname: Yu – volume: 57 start-page: B15 issue: 3 year: 2004 end-page: B15 ident: CR32 article-title: Level set methods and dynamic implicit surfaces publication-title: Appl Mech Rev doi: 10.1115/1.1760520 contributor: fullname: Piechor – volume: 198 year: 2020 ident: CR46 article-title: A deep learning approach for efficiently and accurately evaluating the flow field of supercritical airfoils publication-title: Comput Fluids doi: 10.1016/j.compfluid.2019.104393 contributor: fullname: Lyu – ident: CR26 – volume: 5 issue: 10 year: 2020 ident: CR28 article-title: Probabilistic neural networks for fluid flow surrogate modeling and data recovery publication-title: Phys Rev Fluids doi: 10.1103/PhysRevFluids.5.104401 contributor: fullname: Taira – volume: 35 start-page: 633 issue: 5 year: 2021 end-page: 658 ident: CR30 article-title: Convolutional neural networks for fluid flow analysis: toward effective metamodeling and low dimensionalization publication-title: Theor Comput Fluid Dyn doi: 10.1007/s00162-021-00580-0 contributor: fullname: Fukagata – ident: CR20 – volume: 41 start-page: 238 issue: 2 year: 2007 end-page: 252 ident: CR2 article-title: CFD simulation of the atmospheric boundary layer: wall function problems publication-title: Atmos Environ doi: 10.1016/j.atmosenv.2006.08.019 contributor: fullname: Carmeliet – ident: 9602_CR20 – volume: 14 start-page: 59 issue: 6 year: 1994 ident: 9602_CR23 publication-title: IEEE Comput Graph Appl doi: 10.1109/38.329096 contributor: fullname: HJ Lamousin – ident: 9602_CR39 doi: 10.18653/v1/N18-2074 – volume: 198 year: 2020 ident: 9602_CR46 publication-title: Comput Fluids doi: 10.1016/j.compfluid.2019.104393 contributor: fullname: H Wu – ident: 9602_CR47 doi: 10.2514/6.2017-3660 – volume: 49 start-page: 1345 issue: 5 year: 2012 ident: 9602_CR12 publication-title: J Aircr doi: 10.2514/1.C031675 contributor: fullname: A Dropkin – ident: 9602_CR26 – ident: 9602_CR37 doi: 10.1007/978-3-319-24574-4_28 – volume: 10 start-page: 161 issue: 3 year: 2020 ident: 9602_CR41 publication-title: Theor Appl Mech Lett doi: 10.1016/j.taml.2020.01.031 contributor: fullname: L Sun – ident: 9602_CR45 doi: 10.48550/arXiv.1706.03762 – volume: 35 start-page: 53 issue: 1 year: 2018 ident: 9602_CR8 publication-title: IEEE Signal Process Mag doi: 10.1109/MSP.2017.2765202 contributor: fullname: A Creswell – volume: 7 start-page: 197 issue: 3–4 year: 2014 ident: 9602_CR10 publication-title: Found Trends Signal Process doi: 10.1561/2000000039 contributor: fullname: L Deng – volume: 57 start-page: B15 issue: 3 year: 2004 ident: 9602_CR32 publication-title: Appl Mech Rev doi: 10.1115/1.1760520 contributor: fullname: S Osher – ident: 9602_CR42 doi: 10.1109/CVPR.2015.7298594 – volume: 53 start-page: 968 issue: 4 year: 2015 ident: 9602_CR27 publication-title: AIAA J doi: 10.2514/1.J053318 contributor: fullname: Z Lyu – ident: 9602_CR29 doi: 10.1007/978-3-319-93025-1_4 – volume: 51 start-page: 889 year: 2021 ident: 9602_CR5 publication-title: Appl Intell doi: 10.1007/s10489-020-01839-5 contributor: fullname: S Carta – ident: 9602_CR36 – ident: 9602_CR11 – ident: 9602_CR33 doi: 10.18653/v1/D16-1244 – ident: 9602_CR34 – volume: 58 start-page: 25 issue: 1 year: 2020 ident: 9602_CR44 publication-title: AIAA J doi: 10.2514/1.J058291 contributor: fullname: N Thuerey – ident: 9602_CR48 doi: 10.2514/6.2018-1903 – volume: 192 start-page: 707 issue: 3 year: 2009 ident: 9602_CR21 publication-title: Eur J Oper Res doi: 10.1016/j.ejor.2007.10.013 contributor: fullname: JP Kleijnen – ident: 9602_CR25 doi: 10.1109/CVPR.2017.19 – volume: 2 start-page: 1 year: 2021 ident: 9602_CR15 publication-title: SN Comput Sci doi: 10.1007/s42979-021-00867-3 contributor: fullname: K Fukami – volume-title: The NURBS book year: 1996 ident: 9602_CR35 contributor: fullname: L Piegl – volume-title: Navier–Stokes equations year: 2020 ident: 9602_CR7 contributor: fullname: P Constantin – volume: 34 start-page: 497 year: 2020 ident: 9602_CR14 publication-title: Theor Comput Fluid Dyn doi: 10.1007/s00162-020-00518-y contributor: fullname: K Fukami – volume: 58 start-page: 998 issue: 3 year: 2020 ident: 9602_CR43 publication-title: AIAA J doi: 10.2514/1.J058462 contributor: fullname: K Taira – volume: 12 start-page: 25 issue: 4 year: 1992 ident: 9602_CR4 publication-title: IEEE Comput Graph Appl doi: 10.1109/38.144824 contributor: fullname: S Bryson – ident: 9602_CR18 doi: 10.1109/CVPR.2017.632 – volume: 1050 start-page: 23 year: 2016 ident: 9602_CR13 publication-title: Statistics contributor: fullname: V Dumoulin – volume: 349 start-page: 255 issue: 6245 year: 2015 ident: 9602_CR19 publication-title: Science doi: 10.1126/science.aaa8415 contributor: fullname: MI Jordan – volume: 440 year: 2022 ident: 9602_CR31 publication-title: Physica D doi: 10.1016/j.physd.2022.133454 contributor: fullname: M Morimoto – ident: 9602_CR17 doi: 10.1109/CVPR.2018.00745 – ident: 9602_CR40 – ident: 9602_CR16 doi: 10.1145/2939672.2939738 – volume: 814 start-page: 1 year: 2017 ident: 9602_CR22 publication-title: J Fluid Mech doi: 10.1017/jfm.2016.803 contributor: fullname: JN Kutz – volume: 52 start-page: 477 year: 2020 ident: 9602_CR3 publication-title: Annu Rev Fluid Mech doi: 10.1146/annurev-fluid-010719-060214 contributor: fullname: SL Brunton – volume: 32 start-page: 103 issue: 1 year: 2014 ident: 9602_CR9 publication-title: Aerosp Sci Technol doi: 10.1016/j.ast.2013.11.006 contributor: fullname: P Della Vecchia – volume: 7 start-page: 750 issue: 4 year: 2017 ident: 9602_CR6 publication-title: IEEE Trans Big Data doi: 10.1109/TBDATA.2017.2717439 contributor: fullname: M Chen – volume: 64 start-page: 525 year: 2019 ident: 9602_CR1 publication-title: Comput Mech doi: 10.1007/s00466-019-01740-0 contributor: fullname: S Bhatnagar – volume: 35 start-page: 633 issue: 5 year: 2021 ident: 9602_CR30 publication-title: Theor Comput Fluid Dyn doi: 10.1007/s00162-021-00580-0 contributor: fullname: M Morimoto – volume: 41 start-page: 238 issue: 2 year: 2007 ident: 9602_CR2 publication-title: Atmos Environ doi: 10.1016/j.atmosenv.2006.08.019 contributor: fullname: B Blocken – volume: 521 start-page: 436 issue: 7553 year: 2015 ident: 9602_CR24 publication-title: Nature doi: 10.1038/nature14539 contributor: fullname: Y LeCun – volume: 5 issue: 10 year: 2020 ident: 9602_CR28 publication-title: Phys Rev Fluids doi: 10.1103/PhysRevFluids.5.104401 contributor: fullname: R Maulik – ident: 9602_CR38 doi: 10.1063/1.5094943 |
SSID | ssj0021753 |
Score | 2.4228861 |
Snippet | Flow field prediction is essential for airfoil design. It is a time-consuming task to obtain the flow fields around an airfoil. Convolution neural networks... |
SourceID | proquest crossref springer |
SourceType | Aggregation Database Publisher |
StartPage | 7417 |
SubjectTerms | Accuracy Airfoils Application of Soft Computing Artificial Intelligence Artificial neural networks Computational Intelligence Control Deep learning Engineering Flow nets Fluid dynamics Mathematical Logic and Foundations Mechatronics Modules Multilayers Neural networks Optimization Performance prediction Pressure distribution Reynolds number Robotics Velocity |
Title | Sag-flownet: self-attention generative network for airfoil flow field prediction |
URI | https://link.springer.com/article/10.1007/s00500-023-09602-x https://www.proquest.com/docview/3082705143 |
Volume | 28 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LT8MwDLZgu8CBN2I8phy4QdCadk3LbaANBAIhMSQ4VXkixLRNo0iIX4-TthvPA-c2kWo79ufa_gKwzxFFKxsL6sAEjZRmNI2VoNrYdovpNDH-f8fVdXx-F13ct-9nc9y-2b2qSHpHPZ11c0wlLYohhjrUzSgCx3rbEX7VoN45e7jsTvOsknwSkQCCR4y45azM77t8jUczkPmtLurDTW8Z-tXQTtFl8nz0mssj9f6Tw_E_X7ICSyX8JJ3CXlZhzgzXYLm62oGUJ30NFj_xFK7Dza14pHaACbvJj8mLGVjqaDl9oyR59MTVzmuSYdFTThAIE_E0saOnAXHLiO-TI-OJqwq5RRvQ73X7p-e0vIqBKsZbOWpSSGESrk1s0A8oGzAdmCgNrav7BTJNhGIW0ZKVQSitUDyJE8ksjxKm0ijchNpwNDRbQLTiqQmEjRlmMkxKwXU75jrWqDfJw6ABB5U-snFBuJFNqZW95DKUXOYll701YLdSWVYevpfMMfBwx-seNuCwUsHs8d-7bf_v9R1YYAhxisaxXajlk1ezhxAll000yd7JyXWzNM0mzN-xzgf6ct99 |
link.rule.ids | 315,786,790,27955,27956,41114,41556,42183,42625,52144,52267 |
linkProvider | Springer Nature |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LTwIxEJ4YOKgHUdSIzx68aQnbXba73owRUcGYCAmemj6JkSCBJTH-ett94CN64LzbpjvTznyzM_MV4JRaFC1NyLEDEziQiuA4lBwrbZoNouJIp_87ug9hux_cDZqDvClsVlS7FynJ1FIvmt0cVUkDWx-DHewm2CLHcuA8fAnKlzfP99eLQCtnn7RQwKJH63LzZpm_Z_npkL5Q5q_EaOpvWhXoFyvNykxe6_NE1OXHLxLHZT9lEzZyAIousx2zBSt6XIVKcbkDys96Fda_MRVuw-MTH2IzsiG7Ti7QTI8MdsScaakkGqbU1c5uonFWVY4sFEb8ZWreXkbIDUNppRyaTF1eyA3agV7runfVxvllDFgS2kisLrngOqJKh9paAmk8ojwdxL5xmT9PxBGXxFi8ZITnC8MljcJIEEODiMg48HehNH4b6z1AStJYe9yExMYyRAhOVTOkKlRWcYL6Xg3OCoWwSUa5wRbkyqnkmJUcSyXH3mtwWOiM5cdvxhwHD3XM7n4NzgsVfD3-f7b95V4_gdV2r9thnduH-wNYIxbwZGVkh1BKpnN9ZAFLIo7z_fkJ5V_g7A |
linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LS8NAEB6kgujBR1WsVt2DN11tNmk28VbU-hZBBT2FfRaxxFIjiL_e2TxqFT2I52SXzczuzDeZmW8BtjiiaGVDQR2YoIHSjMahElQb224xHUcm_99xeRWe3AVn9-37sS7-vNq9SkkWPQ2OpSnN9gba7o0a3xxtSYuiv6EOgjOKKHIywGMb1GCyc_xwfjQKukomSoQFiCTR_ZaNMz_P8tU5fSLOb0nS3Pd050BUqy5KTp52XzO5q96_ETr-57PmYbYEpqRT7KQFmDBpHeaqSx9IaQPqMDPGYLgI1zeiR20fQ3mT7ZMX07fUEXbmJZSkl1NaO3tK0qLanCBEJuJxaJ8f-8QNI3kFHRkMXb7IDVqC2-7R7cEJLS9poIrxVoY6FlKYiGsTGrQQynpMeyaIfesygp6MI6GYRRxlpedLKxSPwkgyy4OIqTjwl6GWPqdmBYhWPDaesCHDGIdJKbhuh1yHGpUoue81YLtSTjIoqDiSEelyLrkEJZfkkkveGtCs9JeUx_Ilcdw83DG--w3YqdTx-fj32Vb_9vomTF0fdpOL06vzNZhmiIOK6rIm1LLhq1lHHJPJjXKrfgAGcOnH |
openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Sag-flownet%3A+self-attention+generative+network+for+airfoil+flow+field+prediction&rft.jtitle=Soft+computing+%28Berlin%2C+Germany%29&rft.au=Wang%2C+Xiao&rft.au=Jiang%2C+Yi&rft.au=Li%2C+Guanxiong&rft.au=Zhang%2C+Laiping&rft.date=2024-06-01&rft.issn=1432-7643&rft.eissn=1433-7479&rft.volume=28&rft.issue=11-12&rft.spage=7417&rft.epage=7437&rft_id=info:doi/10.1007%2Fs00500-023-09602-x&rft.externalDBID=n%2Fa&rft.externalDocID=10_1007_s00500_023_09602_x |
thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1432-7643&client=summon |
thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1432-7643&client=summon |
thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1432-7643&client=summon |