Matches in SemOpenAlex for { <https://semopenalex.org/work/W4387255249> ?p ?o ?g. }
Showing items 1 to 89 of
89
with 100 items per page.
- W4387255249 abstract "Data-driven turbulence modeling has been extensively studied in recent years. To date, only high-fidelity data from the mean flow field have been used for Reynolds-averaged Navier–Stokes (RANS) modeling, while the instantaneous turbulence fields from direct numerical simulation and large eddy simulation simulations have not been utilized. In this paper, a new framework is proposed to augment machine learning RANS modeling with features extracted from instantaneous turbulence flow data. A conditional generative model is trained to model the probability distribution of the local instantaneous turbulence field given local mean flow features. Then, the generative model is transferred to machine learning RANS modeling. The present work is mainly focused on generating a local instantaneous turbulence field using conditional generative adversarial networks (CGANs). Several GANs are trained first on the turbulence data from channel flow and periodic hill flow to generate complete one-dimensional and two-dimensional turbulence fields. Then, a CGAN is trained on the periodic hill flow data to generate local turbulence fields. Statistical analysis is performed on the generated samples from the GAN models. The first and second moments, the two-point correlation, and the energy spectra conform well to those of real turbulence. Finally, the information learned by the CGAN is used for machine learning RANS modeling by multitask learning, and the feasibility of the framework proposed in this paper is initially verified." @default.
- W4387255249 created "2023-10-03" @default.
- W4387255249 creator A5044217858 @default.
- W4387255249 creator A5053646906 @default.
- W4387255249 date "2023-10-01" @default.
- W4387255249 modified "2023-10-03" @default.
- W4387255249 title "Local turbulence generation using conditional generative adversarial networks toward Reynolds-averaged Navier–Stokes modeling" @default.
- W4387255249 cites W1014719144 @default.
- W4387255249 cites W1675759519 @default.
- W4387255249 cites W1885185971 @default.
- W4387255249 cites W1965957586 @default.
- W4387255249 cites W1974097079 @default.
- W4387255249 cites W1983406208 @default.
- W4387255249 cites W2049839207 @default.
- W4387255249 cites W2059954760 @default.
- W4387255249 cites W2105355027 @default.
- W4387255249 cites W2110418811 @default.
- W4387255249 cites W2111851584 @default.
- W4387255249 cites W2344479506 @default.
- W4387255249 cites W2490045648 @default.
- W4387255249 cites W2811020507 @default.
- W4387255249 cites W2946794331 @default.
- W4387255249 cites W2962757926 @default.
- W4387255249 cites W2994070579 @default.
- W4387255249 cites W3013108861 @default.
- W4387255249 cites W3042535854 @default.
- W4387255249 cites W3091186176 @default.
- W4387255249 cites W3093998008 @default.
- W4387255249 cites W3096831136 @default.
- W4387255249 cites W3104657094 @default.
- W4387255249 cites W3120515765 @default.
- W4387255249 cites W3181354245 @default.
- W4387255249 cites W3201527114 @default.
- W4387255249 cites W3210404677 @default.
- W4387255249 cites W4225136312 @default.
- W4387255249 cites W4251485344 @default.
- W4387255249 cites W622177816 @default.
- W4387255249 doi "https://doi.org/10.1063/5.0166031" @default.
- W4387255249 hasPublicationYear "2023" @default.
- W4387255249 type Work @default.
- W4387255249 citedByCount "0" @default.
- W4387255249 crossrefType "journal-article" @default.
- W4387255249 hasAuthorship W4387255249A5044217858 @default.
- W4387255249 hasAuthorship W4387255249A5053646906 @default.
- W4387255249 hasConcept C11413529 @default.
- W4387255249 hasConcept C121332964 @default.
- W4387255249 hasConcept C121448008 @default.
- W4387255249 hasConcept C121864883 @default.
- W4387255249 hasConcept C150711758 @default.
- W4387255249 hasConcept C15476950 @default.
- W4387255249 hasConcept C189223162 @default.
- W4387255249 hasConcept C196558001 @default.
- W4387255249 hasConcept C204573209 @default.
- W4387255249 hasConcept C32526432 @default.
- W4387255249 hasConcept C38349280 @default.
- W4387255249 hasConcept C41008148 @default.
- W4387255249 hasConcept C57879066 @default.
- W4387255249 hasConceptScore W4387255249C11413529 @default.
- W4387255249 hasConceptScore W4387255249C121332964 @default.
- W4387255249 hasConceptScore W4387255249C121448008 @default.
- W4387255249 hasConceptScore W4387255249C121864883 @default.
- W4387255249 hasConceptScore W4387255249C150711758 @default.
- W4387255249 hasConceptScore W4387255249C15476950 @default.
- W4387255249 hasConceptScore W4387255249C189223162 @default.
- W4387255249 hasConceptScore W4387255249C196558001 @default.
- W4387255249 hasConceptScore W4387255249C204573209 @default.
- W4387255249 hasConceptScore W4387255249C32526432 @default.
- W4387255249 hasConceptScore W4387255249C38349280 @default.
- W4387255249 hasConceptScore W4387255249C41008148 @default.
- W4387255249 hasConceptScore W4387255249C57879066 @default.
- W4387255249 hasFunder F4320321001 @default.
- W4387255249 hasIssue "10" @default.
- W4387255249 hasLocation W43872552491 @default.
- W4387255249 hasOpenAccess W4387255249 @default.
- W4387255249 hasPrimaryLocation W43872552491 @default.
- W4387255249 hasRelatedWork W1971780915 @default.
- W4387255249 hasRelatedWork W2035176602 @default.
- W4387255249 hasRelatedWork W2037490010 @default.
- W4387255249 hasRelatedWork W2076130803 @default.
- W4387255249 hasRelatedWork W2970648050 @default.
- W4387255249 hasRelatedWork W3139121707 @default.
- W4387255249 hasRelatedWork W3167942138 @default.
- W4387255249 hasRelatedWork W4206332009 @default.
- W4387255249 hasRelatedWork W4226180565 @default.
- W4387255249 hasRelatedWork W4386769830 @default.
- W4387255249 hasVolume "35" @default.
- W4387255249 isParatext "false" @default.
- W4387255249 isRetracted "false" @default.
- W4387255249 workType "article" @default.