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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 (79th percentile)

Mentioned by

news
15 news outlets
blogs
1 blog
twitter
9 X users
googleplus
1 Google+ user
video
1 YouTube creator

Citations

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57 Dimensions

Readers on

mendeley
89 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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 9 X users 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 89 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 1 1%
Unknown 88 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 27 30%
Researcher 18 20%
Student > Doctoral Student 8 9%
Student > Master 6 7%
Professor > Associate Professor 4 4%
Other 8 9%
Unknown 18 20%
Readers by discipline Count As %
Engineering 22 25%
Physics and Astronomy 12 13%
Earth and Planetary Sciences 6 7%
Mathematics 6 7%
Environmental Science 5 6%
Other 13 15%
Unknown 25 28%
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
#333,105
of 25,382,440 outputs
Outputs from Science Advances
#2,544
of 12,215 outputs
Outputs of similar age
#7,037
of 326,430 outputs
Outputs of similar age from Science Advances
#44
of 212 outputs
Altmetric has tracked 25,382,440 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 12,215 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 120.3. This one has done well, scoring higher than 79% 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 326,430 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 212 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 79% of its contemporaries.