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

A predictive, point-cloud tracer is presented that determines with aquantified uncertainty the Lagrangian motion of a group of point-particleswithin a finite region. The tracer assumes a random forcing within confidenceintervals to account for the empiricism of data-driven force models andstochasticity related to the chaotic nature of the subcloud scale dynamics. Itbuilds on the closed Subgrid Particle-Averaged Reynolds Stress-Equivalent(SPARSE) formulation presented in Dom\'inguez-V\'azquez~\textit{et al.}[\textit{Int. J. Multiph. Flow.} 161, 104375] that assumes a deterministicforcing. SPARSE describes the first two moments of particle clouds with momentequations in closed-form, with a theoretical third-order convergence rate withrespect to the standard deviations of the cloud variables. The cloud modelalleviates computational cost and enhances the convergence rate as compared toMonte Carlo (MC) based point-particle methods. The randomness in the forcingmodel leads to virtual stresses that correlate random forcing and fieldfluctuations. These stresses strain and rotate the random cloud as compared toa deterministically forced cloud and thus determine to what extent the randomforcing propagates into the confidence intervals of the dispersed solution. Insymmetric flows the magnitude of the virtual stress is zero. Tests inanalytical carrier-fields and in a decaying homogeneous isotropic turbulenceflow computed with a discontinuous Galerkin (DG) compressible DNS solver areperformed to verify and validate the SPARSE method for randomly forcedparticles.

SPARSE-R: A point-cloud tracer with random forcing