Editorial Type:
Article
Article Type:
Research Article

The Multilevel Monte Carlo Method for Simulations of Turbulent Flows

Qingshan Chen Department of Mathematical Sciences, Clemson University, Clemson, South Carolina

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Ju Ming School of Mathematics and Statistics, Huazhong University of Science and Technology, Wuhan, China

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Print Publication:
01 Sep 2018
DOI:
https://doi.org/10.1175/MWR-D-18-0053.1
Page(s):
2933–2947
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Abstract

In this paper, the application of the multilevel Monte Carlo (MLMC) method to numerical simulations of turbulent flows with uncertain parameters is investigated. Several strategies for setting up the MLMC method are presented, and the advantages and disadvantages of each strategy are also discussed. A numerical experiment is carried out using an idealized model for the Antarctic Circumpolar Current (ACC) with uncertain, small-scale bottom topographic features. It is demonstrated that unlike the pointwise solutions, the averaged volume transports are correlated across grid resolutions, and the MLMC method can increase simulation efficiency without losing accuracy in uncertainty assessments.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Ju Ming, jming@hust.edu.cn

Abstract

In this paper, the application of the multilevel Monte Carlo (MLMC) method to numerical simulations of turbulent flows with uncertain parameters is investigated. Several strategies for setting up the MLMC method are presented, and the advantages and disadvantages of each strategy are also discussed. A numerical experiment is carried out using an idealized model for the Antarctic Circumpolar Current (ACC) with uncertain, small-scale bottom topographic features. It is demonstrated that unlike the pointwise solutions, the averaged volume transports are correlated across grid resolutions, and the MLMC method can increase simulation efficiency without losing accuracy in uncertainty assessments.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Ju Ming, jming@hust.edu.cn
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