A multiscale and multicriteria Generative Adversarial Network to synthesize 1-dimensional turbulent fields
This work addresses the need for synthetic turbulent field generation in fluid dynamics research, offering a physics-informed approach that is incremental in applying GANs to this domain.
The authors tackled the problem of generating 1D turbulent fields with accurate velocity statistics by developing a multiscale and multicriteria Generative Adversarial Network, achieving results that align with experimental observations for energy distribution, cascade, and intermittency across scales.
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.