Deep learning approach in multi-scale prediction of turbulent mixing-layer

TL;DR

This paper introduces a deep learning GAN-based method to predict small-scale turbulent flow structures from blurred large-scale data, enabling efficient multi-resolution analysis in fluid dynamics.

Contribution

It presents a novel GAN approach that predicts detailed 3D turbulent structures from filtered flow data, reducing computational costs and utilizing unprocessed simulation data.

Findings

Successfully predicts small-scale structures from blurred data

Achieves 3D detailed flow predictions with less computational effort

Demonstrates potential for multi-resolution analysis in turbulence modeling

Abstract

Achievement of solutions in Navier-Stokes equation is one of challenging quests, especially for its closure problem. For achievement of particular solutions, there are variety of numerical simulations including Direct Numerical Simulation (DNS) or Large Eddy Simulation (LES). These methods analyze flow physics through efficient reduced-order modeling such as proper orthogonal decomposition or Koopman method, showing prominent fidelity in fluid dynamics. Generative adversarial network (GAN) is a reprint of neurons in brain as combinations of linear operations, using competition between generator and discriminator. Current paper propose deep learning network for prediction of small-scale movements with large-scale inspections only, using GAN. Therefore DNS result of three-dimensional mixing-layer was filtered blurring out the small-scaled structures, then is predicted of its detailed structures, utilizing Generative Adversarial Network (GAN). This enables multi-resolution analysis being asked to predict fine-resolution solution with only inspection of blurry one. Within the grid scale, current paper present deep learning approach of modeling small scale features in turbulent flow. The presented method is expected to have its novelty in utilization of unprocessed simulation data, achievement of 3D structures in prediction by processing 3D convolutions, and predicting precise solution with less computational costs.