Open Access
ABSTRACT
Estimation of Turbulent Flow from Wall Information via Machine Learning
Yousuke Shimoda1, Takahiro Matsumori1, Kazuki Sato1, Tatsuro Hirano1, Naoya Fukushima1,*
1 Department of Prime Mover Engineering, Tokai University, Kitakaname 4-1-1, Hiratsuka, Kanagawa 2591292, Japan.
* Corresponding Author: Naoya Fukushima. Email: -tokai.ac.jp
The International Conference on Computational & Experimental Engineering and Sciences 2021, 23(1), 16-16. https://doi.org/10.32604/icces.2021.08337
Abstract
Along with rapid development of computer technologies, a wide range
of turbulent flows have been investigated by direct numerical simulations and the
big databases have been built throughout the world. From the DNS results, we
can investigate turbulent characteristics in three-dimensional space and time. In
the laboratory experiment, we can apply sophisticated laser diagnostics technique
to measure flow field non-invasively in research. On actual equipment, it is very
difficult to get the flow field data away from the wall. We can measure only wall
information, such as wall shear stresses and pressure. When we predict
turbulence from wall information, we can improve performance of thermal-fluid
equipment and control turbulence better. In recent years, machine learning has
achieved remarkable success in various research and developments fields. In this
study, we apply a machine learning approach to wall turbulence in order to
predict the flow field only from wall information. Direct numerical simulations
of turbulent channel flow have been conducted to make training, validation and
test datasets. We use pressure and shear stresses on a wall as input data since
they can be measured by wall sensors in order to predict the flow field away
from the wall with machine learning. Finally, we evaluate the usability of
pressure and shear stresses on wall to predict the flow field.
Keywords
Cite This Article
Shimoda, Y., Matsumori, T., Sato, K., Hirano, T., Fukushima, N. (2021). Estimation of Turbulent Flow from Wall Information via Machine Learning.
The International Conference on Computational & Experimental Engineering and Sciences, 23(1), 16–16.