Experimental study and empirical model development for self-leveling behavior of debris bed using gas-injection

Abstract

Studies on the self-leveling behavior of debris bed are crucial in the assessment of core disruptive accident (CDA) that could occur in sodium-cooled fast reactors (SFR). To clarify the mechanisms underlying this behavior, several series of experiments were elaborately designed and conducted within a variety of conditions in recent years, under the close collaboration between Japan Atomic Energy Agency (JAEA) and Kyushu University (Japan). The current contribution, including knowledge from both experimental analyses and empirical model development, is focused on a recently developed comparatively larger-scale experimental facility using gas-injection to simulate the coolant boiling. Compared to the previous investigations, this facility can achieve a much wider range of gas velocities (presently up to a flow rate of around 300 L/min). Based on the experimental observation and quantitative data obtained, influence of various experimental parameters, including gas flow rate (～ 300 L/min), water depth (180 mm and 400 mm), bed volume (3 ～ 7 L), particle size (1 ～ 6 mm), particle density (beads of alumina, zirconia and stainless steel) along with particle shape (spherical and non-spherical) on the leveling is checked and compared. As for the empirical model development, aside from a base model which is restricted to predictions of spherical particles, the status of potential considerations on how to cover more realistic conditions (esp. debris beds formed with non-spherical particles), is also presented and discussed. This work, which gives a large palette of favorable data for a better understanding and an improved estimation of CDAs in SFRs, is expected to benefit future analyses and verifications of computer models developed in advanced fast reactor safety analysis codes.

Keywords

• sodium-cooled fast reactor
• core disruptive accident
• debris bed
• self-leveling
• gas injection
• experimental analyses
• empirical model