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  4. Large-eddy simulations with ClimateMachine v0.2.0: a new open-source code for atmospheric simulations on GPUs and CPUs

Large-eddy simulations with ClimateMachine v0.2.0: a new open-source code for atmospheric simulations on GPUs and CPUs

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Author(s)
Sridhar, Akshay
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Tissaoui, Yassine
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Marras, Simone
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Shen, Zhaoyi
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Kawczynski, Charles
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Byrne, Simon
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Pamnany, Kiran
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Waruszewski, Maciej
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Gibson, Thomas H
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Kozdon, Jeremy E
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Date Issued
August 12, 2022
Journal
Geoscientific Model Development
Publisher
Copernicus GmbH
Citation
Sridhar, A., Tissaoui, Y., Marras, S., Shen, Z., Kawczynski, C., Byrne, S., Pamnany, K., Waruszewski, M., Gibson, T. H., Kozdon, J. E., Churavy, V., Wilcox, L. C., Giraldo, F. X., and Schneider, T.: Large-eddy simulations with ClimateMachine v0.2.0: a new open-source code for atmospheric simulations on GPUs and CPUs, Geosci. Model Dev., 15, 6259–6284.
Version
Final published version
Abstract
We introduce ClimateMachine, a new open-source atmosphere modeling framework which uses the Julia language and is designed to be scalable on central processing units (CPUs) and graphics processing units (GPUs). ClimateMachine uses a common framework both for coarser-resolution global simulations and for high-resolution, limited-area large-eddy simulations (LESs). Here, we demonstrate the LES configuration of the atmosphere model in canonical benchmark cases and atmospheric flows using a total energy-conserving nodal discontinuous Galerkin (DG) discretization of the governing equations. Resolution dependence, conservation characteristics, and scaling metrics are examined in comparison with existing LES codes. They demonstrate the utility of ClimateMachine as a modeling tool for limited-area LES flow configurations.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution
https://creativecommons.org/licenses/by/4.0/
Persistent DSpace Link
https://dspace.mit.edu/handle/1721.1/175483
DOI of Published Version
https://doi.org/10.5194/gmd-15-6259-2022
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