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Science Advances

A variational approach to probing extreme events in turbulent dynamical systems

Overview of attention for article published in Science Advances, September 2017
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (97th percentile)
  • Good Attention Score compared to outputs of the same age and source (77th percentile)

Mentioned by

news
15 news outlets
blogs
1 blog
twitter
9 tweeters
googleplus
1 Google+ user
video
1 video uploader

Citations

dimensions_citation
55 Dimensions

Readers on

mendeley
86 Mendeley
citeulike
1 CiteULike
Title
A variational approach to probing extreme events in turbulent dynamical systems
Published in
Science Advances, September 2017
DOI 10.1126/sciadv.1701533
Pubmed ID
Authors

Mohammad Farazmand, Themistoklis P. Sapsis

Abstract

Extreme events are ubiquitous in a wide range of dynamical systems, including turbulent fluid flows, nonlinear waves, large-scale networks, and biological systems. We propose a variational framework for probing conditions that trigger intermittent extreme events in high-dimensional nonlinear dynamical systems. We seek the triggers as the probabilistically feasible solutions of an appropriately constrained optimization problem, where the function to be maximized is a system observable exhibiting intermittent extreme bursts. The constraints are imposed to ensure the physical admissibility of the optimal solutions, that is, significant probability for their occurrence under the natural flow of the dynamical system. We apply the method to a body-forced incompressible Navier-Stokes equation, known as the Kolmogorov flow. We find that the intermittent bursts of the energy dissipation are independent of the external forcing and are instead caused by the spontaneous transfer of energy from large scales to the mean flow via nonlinear triad interactions. The global maximizer of the corresponding variational problem identifies the responsible triad, hence providing a precursor for the occurrence of extreme dissipation events. Specifically, monitoring the energy transfers within this triad allows us to develop a data-driven short-term predictor for the intermittent bursts of energy dissipation. We assess the performance of this predictor through direct numerical simulations.

Twitter Demographics

Twitter Demographics

The data shown below were collected from the profiles of 9 tweeters who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 86 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 1 1%
Unknown 85 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 26 30%
Researcher 17 20%
Student > Doctoral Student 8 9%
Student > Master 6 7%
Professor > Associate Professor 4 5%
Other 7 8%
Unknown 18 21%
Readers by discipline Count As %
Engineering 18 21%
Physics and Astronomy 12 14%
Earth and Planetary Sciences 6 7%
Mathematics 6 7%
Environmental Science 5 6%
Other 13 15%
Unknown 26 30%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 125. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 29 November 2017.
All research outputs
#285,952
of 23,151,189 outputs
Outputs from Science Advances
#2,140
of 10,040 outputs
Outputs of similar age
#6,822
of 316,588 outputs
Outputs of similar age from Science Advances
#48
of 213 outputs
Altmetric has tracked 23,151,189 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 10,040 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 121.5. This one has done well, scoring higher than 78% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 316,588 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 97% of its contemporaries.
We're also able to compare this research output to 213 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 77% of its contemporaries.