Published November 2019 | Version v1
Journal article

Solution verification of PANS model for a PWR fuel assembly

  • 1. Texas A&M University (United States)

Description

Highlights: • Application of best practice V&V for PANS model in a PWR fuel bundle with spacer grid. • Low filter PANS model has the same degree of model validation of LES model. • Mesh refinement solution verification is possible for PANS model. - Abstract: Partially averaged Navier-Stokes model is an energy based, filtered representation of the Navier-Stokes equations. As part of the bridging model paradigm family, it is possible to resolve the turbulent space and time scales of interest. The implicit filtering approach enables us to use the PANS model as a classical RANS model as well as large eddy simulation model or even as a direct numerical simulation, without changing the form of the closure model equations. The energy-based filter, unlike large eddy simulations, decouples the closure model relation from the computational grid size. This allow us to perform classical mesh-based refinement solution verification analyses. In the present study we will perform a solution verification study and model validation for a turbulent flow inside a 5 × 5 fuel bundle, with a single spacer grid and split-type mixing vanes. Grid convergence index analysis has been used to estimate the numerical discretization error for local and integral quantities of interest. A posteriori internal consistency criterion is presented in order to verify that the applied spatial discretization is commensurate with the externally imposed energy filter. The results of the PANS model will be compared with large eddy simulation results and PIV data available from literature.

Availability note (English)

Available from http://dx.doi.org/10.1016/j.nucengdes.2019.110213

Additional details

Identifiers

DOI
10.1016/j.nucengdes.2019.110213;
PII
S0029549319302304;

Publishing Information

Journal Title
Nuclear Engineering and Design
Journal Volume
353
Journal Page Range
p. 110213
ISSN
0029-5493
CODEN
NEDEAU

Optional Information

Notes
© 2019 Elsevier B.V. All rights reserved.