English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Classical eddy viscosity models add a viscosity term with turbulent viscositycoefficient whose specification varies from model to model. Turbulent viscositycoefficient approximations of unknown accuracy are typically constructed bysolving associated systems of nonlinear evolution equations or by data drivenapproaches such as deep neural networks. Often eddy viscosity modelsover-diffuse, so additional fixes are added. This process increases modelcomplexity and decreases model comprehensibility, leading to the following twoquestions: Is an eddy viscosity model needed? Does the eddy viscosity modelfail? This report derives a posteriori computable conditions that answer thesetwo questions.

Diagnostics for Eddy Viscosity Models of Turbulence Including Data-Driven/Neural Network Based Parameterizations