Prediction of three-dimensional chemically reacting compressible turbulence based on implicit U-Net enhanced Fourier neural operator

TL;DR

This paper extends the implicit U-Net enhanced Fourier neural operator to predict three-dimensional chemically reacting compressible turbulence, demonstrating faster and more accurate long-term flow dynamics predictions compared to traditional methods.

Contribution

The study introduces an extension of IU-FNO to chemically reacting compressible turbulence, achieving improved prediction speed and accuracy over existing models.

Findings

IU-FNO predicts flow dynamics faster than DSM in LES.

IU-FNO outperforms DSM in predicting energy spectra, PDFs, and spatial structures.

The method shows promise for practical turbulence prediction applications.

Abstract

The accurate and fast prediction of long-term dynamics of turbulence presents a significant challenge for both traditional numerical simulations and machine learning methods. In recent years, the emergence of neural operators has provided a promising approach to address this issue. The implicit U-Net enhanced Fourier neural operator (IU-FNO) has successfully demonstrated long-term stable predictions for three-dimensional incompressible turbulence. In this study, we extend this method to the three-dimensional chemically reacting compressible turbulence. Numerical results show that the IU-FNO model predicts flow dynamics significantly faster than the traditional dynamic Smagorinsky model (DSM) used in large eddy simulation (LES). In terms of prediction accuracy, the IU-FNO framework outperforms the traditional DSM in predicting the energy spectra of velocity, temperature, and density, the probability density functions (PDFs) of vorticity and velocity increments, and instantaneous spatial structures of temperature. Therefore, the IU-FNO represents a highly promising approach for predicting chemically reacting compressible turbulence.