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 Finite-Volume based POD-Galerkin reduced order modeling strategy forsteady-state Reynolds averaged Navier--Stokes (RANS) simulation is extended forlow-Prandtl number flow. The reduced order model is based on a full order modelfor which the effects of buoyancy on the flow and heat transfer arecharacterized by varying the Richardson number. The Reynolds stresses arecomputed with a linear eddy viscosity model. A single gradient diffusionhypothesis, together with a local correlation for the evaluation of theturbulent Prandtl number, is used to model the turbulent heat fluxes. Thecontribution of the eddy viscosity and turbulent thermal diffusivity fields areconsidered in the reduced order model with an interpolation based data-drivenmethod. The reduced order model is tested for buoyancy-aided turbulent liquidsodium flow over a vertical backward-facing step with a uniform heat fluxapplied on the wall downstream of the step. The wall heat flux is incorporatedwith a Neumann boundary condition in both the full order model and the reducedorder model. The velocity and temperature profiles predicted with the reducedorder model for the same and new Richardson numbers inside the range ofparameter values are in good agreement with the RANS simulations. Also, thelocal Stanton number and skin friction distribution at the heated wall arequalitatively well captured. Finally, the reduced order simulations, performedon a single core, are about $10^5$ times faster than the RANS simulations thatare performed on eight cores.

A POD-Galerkin reduced order model of a turbulent convective buoyant flow of sodium over a backward-facing step