Experimental investigation of pebble flow dynamics using radioactive particle tracking technique in a scaled-down Pebble Bed Modular Reactor (PBMR)

https://doi.org/10.1016/j.nucengdes.2016.03.031Get rights and content

Highlights

Abstract

The Pebble Bed Modular Reactor (PBMR) is a type of very-high-temperature reactor (VHTR) that is conceptually very similar to moving bed reactors used in the chemical and petrochemical industries. In a PBMR core, nuclear fuel is in the form of pebbles and moves slowly under the influence of gravity. In this work, an integrated experimental and computational study of granular flow in a scaled-down cold flow PBMR was performed. A continuous pebble re-circulation experimental set-up, mimicking the flow of pebbles in a PBMR was designed and developed. An experimental investigation of pebble flow dynamics in a scaled down test reactor was carried out using a non-invasive radioactive particle tracking (RPT) technique that used a cobalt-60 based tracer to mimic pebbles in terms of shape, size and density. A cross-correlation based position reconstruction algorithm and RPT calibration data were used to obtain results about Lagrangian trajectories, the velocity field, and residence time distributions. The RPT technique results a serve as a benchmark data for assessing contact force models used in the discrete element method (DEM) simulations.

Introduction

The VHTR is one of the fourth generation nuclear reactors among six designs and uses gaseous coolant. The VHTR is either a Prismatic Block Reactor (PBR) or Pebble Bed Modular Reactor (PBMR). Both these VHTR designs contain their fuel in the form of TRISO (Tri-isotropic) nuclear fuel particles (Boer, 2009). In a PBMR, nuclear fuel is in the form of spherical pebbles and move slowly in the core under the influence of gravity. The fuel pebbles are continuously re-circulated through the core and are monitored for burn-up. This continuous re-circulation feature eliminates the need to shut down the rector for refueling. The helium gas is used as a coolant to remove the heat from the fuel and it flows through a complex interconnected network of voids formed between pebbles. The flow of pebbles under gravity in a pebble bed reactor is a slow and dense type granular flow. The moving core of a PBMR is a cause of concern from a nuclear safety and performance evaluation point of view, which demands a basic understanding of the physics governing the dense granular flow.

Section snippets

Previous work

An experimental study was performed at Australian Atomic Energy Commission (Gatt, 1973) to track pebble trajectories at pre-defined intervals of time using a collimated detector based radioactive particle tracking (RPT) technique from the outside in recirculated randomly packed beds under different operating conditions and bed parameters. The results showed that the trajectories of pebbles were in a straight line and there was little interference or crossing between pebble trajectories. These

Experimental setup

A cold flow 3-D continuous pebble recirculation experimental set-up was designed, developed and tested (Fig. 1) at Missouri S&T. It has unique features of (i) adjustable flow of pebbles at the bottom of the pebble bed without jamming by developing a unique mechanical design that does not affect the dynamic flow of pebbles in the bed, (ii) the exit pebbles are recirculated back using a continuously moving conveyor, and (iii) the pebbles return at various locations at the top of the bed in a

RPT experiments trajectories results

The Lagrangian trajectory of the radioactive tracer can be reconstructed using calibration curves and the cross-correlation based position reconstruction algorithm. The obtained results about tracer trajectories in two and three dimensions, and the velocity vector plot for different initial seeding positions are shown in Fig. 11, Fig. 12. A plug-type flow was observed in the upper cylindrical region of the reactor for all seeding positions. The tracer seeded at the center followed a shortest

Remarks

The following are the highlights of the work carried out and the key findings of this study:

  • The RPT experiments were carried out around the continuous pebbles recirculation experimental set-up that mimics a cold flow moving bed operation of the Pebble Bed Modular Reactor.

  • A co-60 based radioactive tracer particle mimicking glass marbles in terms of shape, diameter and density was used for RPT experiments.

  • The RPT calibration data that was collected suggested that detector counts are not only a

Acknowledgment

The authors acknowledge the financial support provided by Department of Energy (DOE) Nuclear Energy Research Initiative (NERI) project (NERI-08-043).

References (36)

  • G.P. Deutsch

    Flow of Granular Material from Silos, in Movement of Granular Material and Structure, Fluid Flow and Heat Transfer in Packed Beds

    (1967)
  • A. Shehata

    A New Method for Radioactive Particle Tracking

    (2005)
  • M.Z., Bazant, A.C., Kadak, Pebble flow experiments for pebble bed reactors, in 2nd International Topical meeting on...
  • Xu,Y.H., and Sun,Y.L., Status of the HTR programme in China, in IAEA TCM on High Temperature Gas Cooled Reactor...
  • Z. Wang

    A Dual Measurement System for Radioactive Tracer Pebble Tracking in PBRs

    (2011)
  • C.G.G. Rycroft et al.

    Analysis of granular flow in a pebble bed nuclear reactor

    Phys. Rev. E

    (2006)
  • V.B. Khane

    Experimental and Computational Investigation of Flow of Pebbles in a Pebble Bed Nuclear Reactor

    (2014)
  • M. Vesavikar

    Understanding Hydrodynamics and Performance of Anaerobic Digesters

    (2006)
  • Cited by (30)

    • Neural network prediction of residence time distribution for quasi-2D pebble flow

      2022, Chemical Engineering Science
      Citation Excerpt :

      With regards to pebble bed reactors, several implementations have been attempted. Godfroy et al. have each utilized neural networks in radioactive particle tracking; inputs from multiple radioactivity detectors were combined by a neural network to estimate the locations of pebbles (Godfroy et al., 1997; Khane et al., 2016; Dam and dos Santos, 2021). Neural networks have also been used to assist in heat transfer estimations, as Wu used neural networks to predict the view factor for radiative heat transfer (Wu and Gui, 2020).

    • Transfer learning for radioactive particle tracking

      2022, Chemical Engineering Science
      Citation Excerpt :

      In current practice, calibration of the RPT system for a new monitored domain is performed before an RPT experiment (Larachi et al., 1994; IAEA, 2008). Once all detectors in the RPT array have been placed in their desired positions, the calibration is performed by recording photon counts at each detector for a finite number of known tracer locations (for example, 150 in Larachi et al. (1994), 376 in Khane et al. (2016), 516 in Yadav et al. (2019), and 901 in Chen et al. (2001)). The calibration step is the backbone of RPT and the accuracy of the RPT model relies heavily on the availability of high quality and high resolution calibration data (Yadav et al., 2017).

    View all citing articles on Scopus
    View full text