dc.contributor.author | Maulik, Romit | |
dc.contributor.author | San, Omer | |
dc.contributor.author | Rasheed, Adil | |
dc.contributor.author | Vedula, Prakash | |
dc.date.accessioned | 2019-04-09T07:33:47Z | |
dc.date.available | 2019-04-09T07:33:47Z | |
dc.date.created | 2019-01-09T18:28:31Z | |
dc.date.issued | 2018 | |
dc.identifier.citation | Physics of fluids. 2018, 30 (12), -?. | nb_NO |
dc.identifier.issn | 1070-6631 | |
dc.identifier.uri | http://hdl.handle.net/11250/2593792 | |
dc.description.abstract | In this article, we demonstrate the use of artificial neural networks as optimal maps which are utilized for convolution and deconvolution of coarse-grained fields to account for sub-grid scale turbulence effects. We demonstrate that an effective eddy-viscosity is predicted by our purely data-driven large eddy simulation framework without explicit utilization of phenomenological arguments. In addition, our data-driven framework precludes the knowledge of true sub-grid stress information during the training phase due to its focus on estimating an effective filter and its inverse so that grid-resolved variables may be related to direct numerical simulation data statistically. The proposed predictive framework is also combined with a statistical truncation mechanism for ensuring numerical realizability in an explicit formulation. Through this, we seek to unite structural and functional modeling strategies for modeling non-linear partial differential equations using reduced degrees of freedom. Both a priori and a posteriori results are shown for a two-dimensional decaying turbulence case in addition to a detailed description of validation and testing. A hyperparameter sensitivity study also shows that the proposed dual network framework simplifies learning complexity and is viable with exceedingly simple network architectures. Our findings indicate that the proposed framework approximates a robust and stable sub-grid closure which compares favorably to the Smagorinsky and Leith hypotheses for capturing the theoretical k3 scaling in Kraichnan turbulence | nb_NO |
dc.description.abstract | Data-driven deconvolution for large eddy simulations of Kraichnan turbulence | nb_NO |
dc.language.iso | eng | nb_NO |
dc.title | Data-driven deconvolution for large eddy simulations of Kraichnan turbulence | nb_NO |
dc.type | Journal article | nb_NO |
dc.type | Peer reviewed | nb_NO |
dc.description.version | acceptedVersion | nb_NO |
dc.source.pagenumber | -? | nb_NO |
dc.source.volume | 30 | nb_NO |
dc.source.journal | Physics of fluids | nb_NO |
dc.source.issue | 12 | nb_NO |
dc.identifier.doi | 10.1063/1.5079582 | |
dc.identifier.cristin | 1653614 | |
cristin.unitcode | 7401,90,26,0 | |
cristin.unitname | Mathematics and Cybernetics | |
cristin.ispublished | true | |
cristin.fulltext | postprint | |
cristin.qualitycode | 2 | |