With the recent rapid development of artificial intelligence (AI) and wide applications in many areas, some fundamental questions in turbulence research can be addressed, such as: 'Can turbulence be learned by AI? If so, how and why?' In order to provide answers to these questions, we applied deep learning to the prediction of turbulent heat transfer based only on wall information using data obtained from direct numerical simulations (DNS) of turbulent channel flow. Through this attempt, we investigated whether deep learning could help to improve our understanding of the physics of turbulent heat transfer. Under the assumption that the wall-normal local heat flux can be explicitly expressed through a multilayer nonlinear network in terms of the nearby wall-shear stresses and wall pressure fluctuations, we applied convolutional neural networks (CNNs) to predict the local heat flux. After optimizing the network hyperparameters using a grid searching method, we found that the network can predict the heat flux very accurately with a correlation coefficient of 0.980 between the DNS and the prediction by CNN for the trained Reynolds number, Reτ = 180, and shows similar accuracy at a Reynolds number three times higher than the trained number. This result indicates that relationships between the local heat flux and the nearby inputs are quite insensitive to the Reynolds number within the tested range. In addition, observing the gradient maps of the trained network, we identified the part of the input data that is essential for the local heat flux prediction and the spatial relationship between the local heat flux and the nearby input fields. In addition to obtaining an understanding of the underlying physics, we investigated whether our model could be utilized for turbulence modelling.
|Journal||Journal of Fluid Mechanics|
|Publication status||Published - 2020 Jan 1|
Bibliographical noteFunding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (2017R1E1A1A03070282).
© Cambridge University Press 2019
All Science Journal Classification (ASJC) codes
- Condensed Matter Physics
- Mechanics of Materials
- Mechanical Engineering