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| DOI | 10.1029/2020GL087005 |
| Deep Learning Emulation of Subgrid-Scale Processes in Turbulent Shear Flows | |
| Pal A. | |
| 发表日期 | 2020 |
| ISSN | 0094-8276 |
| 卷号 | 47期号:12 |
| 英文摘要 | Deep neural networks (DNNs) are developed from a data set obtained from the dynamic Smagorinsky model to emulate the subgrid-scale (SGS) viscosity (νsgs) and diffusivity (κsgs) for turbulent stratified shear flows encountered in the oceans and the atmosphere. These DNNs predict νsgs and κsgs from velocities, strain rates, and density gradients such that the evolution of the kinetic energy budget and density variance budget terms is similar to the corresponding values obtained from the original dynamic Smagorinsky model. These DNNs also compute νsgs and κsgs ∼2–4 times quicker than the dynamic Smagorinsky model resulting in a ∼2–2.5 times acceleration of the entire simulation. This study demonstrates the feasibility of deep learning in emulating the subgrid-scale (SGS) phenomenon in geophysical flows accurately in a cost-effective manner. In a broader perspective, deep learning-based surrogate models can present a promising alternative to the traditional parameterizations of the subgrid-scale processes in climate models. ©2020. American Geophysical Union. All Rights Reserved. |
| 英文关键词 | Budget control; Climate models; Cost effectiveness; Deep neural networks; Kinetic energy; Kinetics; Shear flow; Strain rate; Cost effective; Density gradients; Dynamic Smagorinsky models; Geophysical flows; Stratified shear flow; Sub-grid scale process; Surrogate model; Turbulent shear flows; Deep learning; artificial neural network; climate modeling; diffusivity; energy budget; feasibility study; kinetic energy; shear flow; stress-strain relationship; turbulence; viscosity |
| 语种 | 英语 |
| 来源期刊 | Geophysical Research Letters
 |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/170227 |
| 作者单位 | National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, United States; Department of Mechanical Engineering, Indian Institute of Technology Kanpur, Kanpur, India |
| 推荐引用方式 GB/T 7714 | Pal A.. Deep Learning Emulation of Subgrid-Scale Processes in Turbulent Shear Flows[J],2020,47(12). |
| APA | Pal A..(2020).Deep Learning Emulation of Subgrid-Scale Processes in Turbulent Shear Flows.Geophysical Research Letters,47(12). |
| MLA | Pal A.."Deep Learning Emulation of Subgrid-Scale Processes in Turbulent Shear Flows".Geophysical Research Letters 47.12(2020). |
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