Predictions of turbulent shear flows using deep neural networks

Srinivasan PA, Guastoni L, Azizpour H, Schlatter P, Vinuesa R (2019)

Publication Type: Journal article

Publication year: 2019

Journal

Physical Review Fluids American Physical Society

Book Volume: 4

Article Number: 054603

Journal Issue: 5

DOI: 10.1103/PhysRevFluids.4.054603

Abstract

In the present work, we assess the capabilities of neural networks to predict temporally evolving turbulent flows. In particular, we use the nine-equation shear flow model by Moehlis et al. [New J. Phys. 6, 56 (2004)NJOPFM1367-263010.1088/1367-2630/6/1/056] to generate training data for two types of neural networks: the multilayer perceptron (MLP) and the long short-term memory (LSTM) networks. We tested a number of neural network architectures by varying the number of layers, number of units per layer, dimension of the input, and weight initialization and activation functions in order to obtain the best configurations for flow prediction. Because of its ability to exploit the sequential nature of the data, the LSTM network outperformed the MLP. The LSTM led to excellent predictions of turbulence statistics (with relative errors of 0.45% and 2.49% in mean and fluctuating quantities, respectively) and of the dynamical behavior of the system (characterized by Poincaré maps and Lyapunov exponents). This is an exploratory study where we consider a low-order representation of near-wall turbulence. Based on the present results, the proposed machine-learning framework may underpin future applications aimed at developing accurate and efficient data-driven subgrid-scale models for large-eddy simulations of more complex wall-bounded turbulent flows, including channels and developing boundary layers.

Authors with CRIS profile

Philipp Schlatter

Involved external institutions

Royal Institute of Technology / Kungliga Tekniska Högskolan (KTH) SE Sweden (SE)

How to cite

APA:

Srinivasan, P.A., Guastoni, L., Azizpour, H., Schlatter, P., & Vinuesa, R. (2019). Predictions of turbulent shear flows using deep neural networks. Physical Review Fluids, 4(5). https://doi.org/10.1103/PhysRevFluids.4.054603

MLA:

Srinivasan, P. A., et al. "Predictions of turbulent shear flows using deep neural networks." Physical Review Fluids 4.5 (2019).

BibTeX: Download