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Predictions of turbulent shear flows using deep neural networks
KTH, School of Engineering Sciences (SCI), Mechanics. KTH, School of Engineering Sciences (SCI), Centres, Linné Flow Center, FLOW. KTH, School of Electrical Engineering and Computer Science (EECS). KTH, Centres, SeRC - Swedish e-Science Research Centre.
KTH, Centres, SeRC - Swedish e-Science Research Centre. KTH Mech, Linne FLOW Ctr, SE-10044 Stockholm, Sweden.;Swedish E Sci Res Ctr SeRC, SE-10044 Stockholm, Sweden..
KTH, Centres, SeRC - Swedish e-Science Research Centre. KTH, School of Electrical Engineering and Computer Science (EECS).ORCID iD: 0000-0001-5211-6388
KTH, Centres, SeRC - Swedish e-Science Research Centre. KTH, School of Engineering Sciences (SCI), Mechanics. KTH, School of Engineering Sciences (SCI), Centres, Linné Flow Center, FLOW.ORCID iD: 0000-0001-9627-5903
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2019 (English)In: Physical Review Fluids, E-ISSN 2469-990X, Vol. 4, no 5, article id 054603Article in journal (Refereed) Published
Abstract [en]

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)] 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 Poincare 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.

Place, publisher, year, edition, pages
AMER PHYSICAL SOC , 2019. Vol. 4, no 5, article id 054603
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Physical Sciences
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URN: urn:nbn:se:kth:diva-252606DOI: 10.1103/PhysRevFluids.4.054603ISI: 000467744500004OAI: oai:DiVA.org:kth-252606DiVA, id: diva2:1321964
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QC 20190610

Available from: 2019-06-10 Created: 2019-06-10 Last updated: 2019-06-10Bibliographically approved

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Azizpour, HosseinSchlatter, PhilippVinuesa, Ricardo

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Srinivasan, P. A.Guastoni, L.Azizpour, HosseinSchlatter, PhilippVinuesa, Ricardo
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MechanicsLinné Flow Center, FLOWSchool of Electrical Engineering and Computer Science (EECS)SeRC - Swedish e-Science Research Centre
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