EGU21-402, updated on 03 Mar 2021
https://doi.org/10.5194/egusphere-egu21-402
EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Stable and accurate a posteriori LES of 2D turbulence with convolutional neural networks: Backscatter analysis and generalization via transfer learning

Yifei Guan, Ashesh Chattopadhyay, Adam Subel, and Pedram Hassanzadeh
Yifei Guan et al.
  • Rice University, Mechanical Engineering, United States of America (yg62@rice.edu)

In large eddy simulations (LES), the subgrid-scale effects are modeled by physics-based or data-driven methods. This work develops a convolutional neural network (CNN) to model the subgrid-scale effects of a two-dimensional turbulent flow. The model is able to capture both the inter-scale forward energy transfer and backscatter in both a priori and a posteriori analyses. The LES-CNN model outperforms the physics-based eddy-viscosity models and the previous proposed local artificial neural network (ANN) models in both short-term prediction and long-term statistics. Transfer learning is implemented to generalize the method for turbulence modeling at higher Reynolds numbers. Encoder-decoder network architecture is proposed to generalize the model to a higher computational grid resolution.

How to cite: Guan, Y., Chattopadhyay, A., Subel, A., and Hassanzadeh, P.: Stable and accurate a posteriori LES of 2D turbulence with convolutional neural networks: Backscatter analysis and generalization via transfer learning, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-402, https://doi.org/10.5194/egusphere-egu21-402, 2021.

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