Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Predictions of turbulent shear flows using deep neural networks
KTH, Skolan för teknikvetenskap (SCI), Mekanik. KTH, Skolan för teknikvetenskap (SCI), Centra, Linné Flow Center, FLOW. KTH, Skolan för elektroteknik och datavetenskap (EECS). KTH, Centra, SeRC - Swedish e-Science Research Centre.
KTH, Centra, 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, Centra, SeRC - Swedish e-Science Research Centre. KTH, Skolan för elektroteknik och datavetenskap (EECS).ORCID-id: 0000-0001-5211-6388
KTH, Centra, SeRC - Swedish e-Science Research Centre. KTH, Skolan för teknikvetenskap (SCI), Mekanik. KTH, Skolan för teknikvetenskap (SCI), Centra, Linné Flow Center, FLOW.ORCID-id: 0000-0001-9627-5903
Vise andre og tillknytning
2019 (engelsk)Inngår i: Physical Review Fluids, E-ISSN 2469-990X, Vol. 4, nr 5, artikkel-id 054603Artikkel i tidsskrift (Fagfellevurdert) 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.

sted, utgiver, år, opplag, sider
AMER PHYSICAL SOC , 2019. Vol. 4, nr 5, artikkel-id 054603
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-252606DOI: 10.1103/PhysRevFluids.4.054603ISI: 000467744500004OAI: oai:DiVA.org:kth-252606DiVA, id: diva2:1321964
Merknad

QC 20190610

Tilgjengelig fra: 2019-06-10 Laget: 2019-06-10 Sist oppdatert: 2019-06-10bibliografisk kontrollert

Open Access i DiVA

Fulltekst mangler i DiVA

Andre lenker

Forlagets fulltekst

Personposter BETA

Azizpour, HosseinSchlatter, PhilippVinuesa, Ricardo

Søk i DiVA

Av forfatter/redaktør
Srinivasan, P. A.Guastoni, L.Azizpour, HosseinSchlatter, PhilippVinuesa, Ricardo
Av organisasjonen
I samme tidsskrift
Physical Review Fluids

Søk utenfor DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric

doi
urn-nbn
Totalt: 25 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf