An improved pressure drop correlation for modeling localized effects in a pebble bed reactor

David Reger, Elia Merzari, Paolo Balestra, Sebastian Schunert, Yassin Hassan, Haomin Yuan

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Advances in the development of pebble bed reactors (PBRs) has created a desire for accurate and cost-effective simulation tools for design scoping studies and safety analysis. The current state-of-the-art for these simulations is the use of porous media models, although these models rely on correlations to capture the effects of flow features that are not explicitly modeled. One of the areas where correlation accuracy is currently lacking is in the near-wall region of the bed. In this region, the presence of the wall causes the pebbles to pack more orderly, drastically changing the geometry and flow behavior in this region. This work presents a new generalized pressure drop correlation for PBRs based on the KTA equation. A high-to-low methodology is applied, where large eddy simulation (LES) is performed on two beds of 1568 and 1700 pebbles to generate a high-fidelity dataset. The flow fields are then averaged in time and separated into concentric rings of 0.05 Dpeb width. Average porosity, velocity, and pressure drop are extracted for each ring and the friction and form losses are calculated. The Reynolds number range for this study is 625–10,000, and thus the form losses are dominant over the friction losses. The form losses across the rings are investigated, and a correction term for the form loss calculation is determined and applied to the KTA equation to drastically improve the capability of modeling localized porosity effects in a porous media code. The improved correlation reduces near-wall velocity prediction error from over 50% with the KTA correlation to around 5%. Agreement in pressure drop prediction between LES and porous media simulations is also improved.

Original languageEnglish (US)
Article number112123
JournalNuclear Engineering and Design
StatePublished - Mar 2023

All Science Journal Classification (ASJC) codes

  • Nuclear and High Energy Physics
  • Materials Science(all)
  • Nuclear Energy and Engineering
  • Safety, Risk, Reliability and Quality
  • Waste Management and Disposal
  • Mechanical Engineering


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