Data-Driven Prediction of Turbulent Flow Statistics Past Bridge Piers in Large-Scale Rivers Using Convolutional Neural Networks
- Title
- Data-Driven Prediction of Turbulent Flow Statistics Past Bridge Piers in Large-Scale Rivers Using Convolutional Neural Networks
- Author
- 강석구
- Keywords
- convolutional neural network; flood flow predictions; large-scale rivers; large-eddy simulation
- Issue Date
- 2022-01
- Publisher
- AMER GEOPHYSICAL UNION
- Citation
- WATER RESOURCES RESEARCH, v. 58, NO. 1, article no. e2021WR030163, Page. 1-23
- 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.
- URI
- https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021WR030163https://repository.hanyang.ac.kr/handle/20.500.11754/176640
- ISSN
- 0043-1397;1944-7973
- DOI
- 10.1029/2021WR030163
- Appears in Collections:
- COLLEGE OF ENGINEERING[S](공과대학) > CIVIL AND ENVIRONMENTAL ENGINEERING(건설환경공학과) > Articles
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