Construction of low-dimensional system reproducing low-Reynolds-number turbulence by machine learning
Fluid Dynamics
2018-03-23 v1
Abstract
In a dissipative system, there exists the (global) attractor which has finite fractal dimensions. The flow on the attractor can be parametrized by a finite number of parameters (Temmam 1987). Using machine learning we demonstrate how to construct precise low-dimensional governing equations which are valid in some range of Reynolds number for low-Reynolds-number turbulence in plane Couette flow.
Keywords
Cite
@article{arxiv.1803.08206,
title = {Construction of low-dimensional system reproducing low-Reynolds-number turbulence by machine learning},
author = {Masaki Shimizu and Genta Kawahara},
journal= {arXiv preprint arXiv:1803.08206},
year = {2018}
}
Comments
Submitted to Phys. Rev. E on July 16, 2017 https://www.jstage.jst.go.jp/article/jsmefed/2016/0/2016_1004/_article https://doi.org/10.1299/jsmefed.2016.1004