@Article{icces.2021.08337,
AUTHOR = {Yousuke Shimoda, Takahiro Matsumori, Kazuki Sato, Tatsuro Hirano, Naoya Fukushima},
TITLE = {Estimation of Turbulent Flow from Wall Information via Machine Learning},
JOURNAL = {The International Conference on Computational \& Experimental Engineering and Sciences},
VOLUME = {23},
YEAR = {2021},
NUMBER = {1},
PAGES = {16--16},
URL = {http://www.techscience.com/icces/v23n1/42040},
ISSN = {1933-2815},
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.},
DOI = {10.32604/icces.2021.08337}
}