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A deep-learning approach for reconstructing 3D turbulent flows from 2D observation data
Pusan Natl Univ, Sch Mech Engn, Busandaehak ro, Pusan 46241, South Korea..
Pusan Natl Univ, Sch Mech Engn, Busandaehak ro, Pusan 46241, South Korea..
Univ Politecn Valencia, Inst Univ Matemat Pura & Aplicada, Valencia 46022, Spain..
KTH, School of Engineering Sciences (SCI), Centres, Linné Flow Center, FLOW. KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Fluid Mechanics and Engineering Acoustics.ORCID iD: 0000-0001-6570-5499
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2023 (English)In: Scientific Reports, E-ISSN 2045-2322, Vol. 13, no 1Article in journal (Refereed) Published
Abstract [en]

Turbulence is a complex phenomenon that has a chaotic nature with multiple spatio-temporal scales, making predictions of turbulent flows a challenging topic. Nowadays, an abundance of high-fidelity databases can be generated by experimental measurements and numerical simulations, but obtaining such accurate data in full-scale applications is currently not possible. This motivates utilising deep learning on subsets of the available data to reduce the required cost of reconstructing the full flow in such full-scale applications. Here, we develop a generative-adversarial-network (GAN)-based model to reconstruct the three-dimensional velocity fields from flow data represented by a cross-plane of unpaired two-dimensional velocity observations. The model could successfully reconstruct the flow fields with accurate flow structures, statistics and spectra. The results indicate that our model can be successfully utilised for reconstructing three-dimensional flows from two-dimensional experimental measurements. Consequently, a remarkable reduction in the complexity of the experimental setup and the storage cost can be achieved.

Place, publisher, year, edition, pages
Springer Nature , 2023. Vol. 13, no 1
National Category
Fluid Mechanics
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URN: urn:nbn:se:kth:diva-328284DOI: 10.1038/s41598-023-29525-9ISI: 000984284300036PubMedID: 36781944Scopus ID: 2-s2.0-85147970587OAI: oai:DiVA.org:kth-328284DiVA, id: diva2:1763256
Note

QC 20230607

Available from: 2023-06-07 Created: 2023-06-07 Last updated: 2025-02-09Bibliographically approved

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Vinuesa, Ricardo

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