Superstatistical turbulence models
Christian Beck
TL;DR
This paper reviews superstatistical turbulence models, focusing on extending the Sawford model with superstatistics and comparing its predictions with experimental data to improve understanding of turbulent flow statistics.
Contribution
It introduces a superstatistical extension of the Sawford model and evaluates its effectiveness against experimental turbulence data.
Findings
Superstatistical models better capture turbulence statistics.
The extended Sawford model aligns well with experimental results.
Superstatistics provides a promising framework for turbulence modeling.
Abstract
Recently there has been some progress in modeling the statistical properties of turbulent flows using simple superstatistical models. Here we briefly review the concept of superstatistics in turbulence. In particular, we discuss a superstatistical extension of the Sawford model and compare with experimental data.
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Taxonomy
TopicsStatistical Mechanics and Entropy · Advanced Statistical Methods and Models · Statistics Education and Methodologies