Repository logo

Infoscience

  • English
  • French
Log In
Logo EPFL, École polytechnique fédérale de Lausanne

Infoscience

  • English
  • French
Log In
  1. Home
  2. Academic and Research Output
  3. Journal articles
  4. Application of superstatistical analysis on fluctuant surface shear in particle-laden turbulence boundary layer
 
research article

Application of superstatistical analysis on fluctuant surface shear in particle-laden turbulence boundary layer

Li, Guang  
•
He, Wei
•
Yang, Bo
Show more
January 1, 2022
European Physical Journal E

We report on an application of superstatistics to particle-laden turbulent flow. Four flush-mounted hot-film wall shear sensors were used to record the fluctuations of the wall shear stress in sand-laden flow. By comparing the scaling exponent in sand-free with that in sand-laden flows, we found that the sand-laden flow is more intermittent. By applying the superstatistics analysis to the friction velocity, we found that the large time scale is smaller when the flow is sand-laden. The probability density of a fluctuating energy dissipation rate measured in sand-laden flow follows a log-normal distribution with higher variances than for sand-free flow. The variance of this dissipation rate is a power law of the corresponding time scale. The prediction based on the superstatistics model is consistent with our structure function exponents sigma(n) for sand-free flow. Nevertheless, it overestimates sigma(n) for sand-laden flow, especially at higher Reynolds numbers.

  • Files
  • Details
  • Metrics
Type
research article
DOI
10.1140/epje/s10189-021-00159-x
Web of Science ID

WOS:000746617400003

Author(s)
Li, Guang  
He, Wei
Yang, Bo
Yu, Hongxiang
Huang, Ning
Herrmann, Hans J.
Zhang, Jie
Date Issued

2022-01-01

Publisher

SPRINGER

Published in
European Physical Journal E
Volume

45

Issue

1

Start page

5

Subjects

Chemistry, Physical

•

Materials Science, Multidisciplinary

•

Physics, Applied

•

Polymer Science

•

Chemistry

•

Materials Science

•

Physics

•

extended self-similarity

•

reynolds-number

•

numerical simulations

•

lagrangian statistics

•

acceleration

•

velocity

•

fluid

•

dunes

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LSPN  
CRYOS  
Available on Infoscience
January 31, 2022
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/185036
Logo EPFL, École polytechnique fédérale de Lausanne
  • Contact
  • infoscience@epfl.ch

  • Follow us on Facebook
  • Follow us on Instagram
  • Follow us on LinkedIn
  • Follow us on X
  • Follow us on Youtube
AccessibilityLegal noticePrivacy policyCookie settingsEnd User AgreementGet helpFeedback

Infoscience is a service managed and provided by the Library and IT Services of EPFL. © EPFL, tous droits réservés