Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 강석구 | - |
dc.date.accessioned | 2022-11-14T01:28:53Z | - |
dc.date.available | 2022-11-14T01:28:53Z | - |
dc.date.issued | 2022-01 | - |
dc.identifier.citation | WATER RESOURCES RESEARCH, v. 58, NO. 1, article no. e2021WR030163, Page. 1-23 | en_US |
dc.identifier.issn | 0043-1397;1944-7973 | en_US |
dc.identifier.uri | https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021WR030163 | en_US |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/176640 | - |
dc.description.abstract | Prediction of statistical properties of the turbulent flow in large-scale rivers is essential for river flow analysis. The large-eddy simulation (LES) provides a powerful tool for such predictions; however, it requires a very long sampling time and demands significant computing power to calculate the turbulence statistics of riverine flows. In this study, we developed encoder-decoder convolutional neural networks (CNNs) to predict the first- and second-order turbulence statistics of the turbulent flow of large-scale meandering rivers using instantaneous LES results. We train the CNNs using a data set obtained from LES of the flood flow in a large-scale river with three bridge piers-a training testbed. Subsequently, we employed the trained CNNs to predict the turbulence statistics of the flood flow in two different meandering rivers and bridge pier arrangements-validation testbed rivers. The CNN predictions for the validation testbed river flow were compared with the simulation results of a separately done LES to evaluate the performance of the developed CNNs. We show that the trained CNNs can successfully produce turbulence statistics of the flood flow in the large-scale rivers, that is, the validation testbeds. | en_US |
dc.description.sponsorship | This work was supported by the National Science Foundation (grants EAR-0120914 and EAR-1823530). The computational resources were provided by the Civil Engineering Department, Stony Brook Research Computing and Cyberinfrastructure, and the Institute for Advanced Computational Science at Stony Brook University. | en_US |
dc.language | en | en_US |
dc.publisher | AMER GEOPHYSICAL UNION | en_US |
dc.subject | convolutional neural network | en_US |
dc.subject | flood flow predictions | en_US |
dc.subject | large-scale rivers | en_US |
dc.subject | large-eddy simulation | en_US |
dc.title | Data-Driven Prediction of Turbulent Flow Statistics Past Bridge Piers in Large-Scale Rivers Using Convolutional Neural Networks | en_US |
dc.type | Article | en_US |
dc.relation.no | 1 | - |
dc.relation.volume | 58 | - |
dc.identifier.doi | 10.1029/2021WR030163 | en_US |
dc.relation.page | 1-23 | - |
dc.relation.journal | WATER RESOURCES RESEARCH | - |
dc.contributor.googleauthor | Zhang, Zexia | - |
dc.contributor.googleauthor | Flora, Kevin | - |
dc.contributor.googleauthor | Kang, Seokkoo | - |
dc.contributor.googleauthor | Limaye, Ajay B. | - |
dc.contributor.googleauthor | Khosronejad, Ali | - |
dc.sector.campus | S | - |
dc.sector.daehak | 공과대학 | - |
dc.sector.department | 건설환경공학과 | - |
dc.identifier.pid | kangsk78 | - |
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