A multiscale and multicriteria Generative Adversarial Network to synthesize 1-dimensional turbulent fields

TL;DR

This paper presents a novel multiscale, multicriteria GAN model grounded in turbulence theory to generate realistic 1D turbulent velocity fields, capturing energy distribution, cascade, and intermittency.

Contribution

It introduces a physics-informed GAN architecture with multiscale optimization criteria based on turbulence statistics, ensuring realistic turbulent field synthesis.

Findings

Successfully reproduces turbulence energy spectra

Captures intermittency and cascade phenomena

Validated with wind tunnel data

Abstract

This article introduces a new Neural Network stochastic model to generate a 1-dimensional stochastic field with turbulent velocity statistics. Both the model architecture and training procedure ground on the Kolmogorov and Obukhov statistical theories of fully developed turbulence, so guaranteeing descriptions of 1) energy distribution, 2) energy cascade and 3) intermittency across scales in agreement with experimental observations. The model is a Generative Adversarial Network with multiple multiscale optimization criteria. First, we use three physics-based criteria: the variance, skewness and flatness of the increments of the generated field that retrieve respectively the turbulent energy distribution, energy cascade and intermittency across scales. Second, the Generative Adversarial Network criterion, based on reproducing statistical distributions, is used on segments of different length of the generated field. Furthermore, to mimic multiscale decompositions frequently used in turbulence's studies, the model architecture is fully convolutional with kernel sizes varying along the multiple layers of the model. To train our model we use turbulent velocity signals from grid turbulence at Modane wind tunnel.