BISON validation to in situ cladding burst test and high-burnup LOCA experiments
The process for developing and qualifying nuclear fuels for commercial nuclear application requires fundamental material development, characterization, and design; out-of-pile testing on unirradiated materials; integral fuel rod irradiations, testing, and postirradiation examinations; and transient analyses. The historical approach depends on the generation of large empirical datasets and series of integral fuel rod irradiations, and this approach ultimately takes ~20 years—or sometimes longer—to acquire data through extensive sequential testing. Thus, the qualification and eventual deployment of new fuel systems constitute a long process. However, recent technological advancements have provided researchers the opportunity to perform out-of-cell, in situ measurements to assess material performance for the duration of the experiment. One such example of this capability is the use of digital image coordination and thermal imaging to assess Zircaloy cladding performance under a simulated loss-of-coolant accident (LOCA) transient condition. In situ measurements generally provide high-fidelity strain, strain rates, and temperature surface maps. This is critical for the US nuclear industry, which is actively developing a technical basis to support extending the peak rod average burnup from 62 to ~75 GWd/tU and the deployment of accident-tolerant fuel. However, the US Nuclear Regulatory Commission (NRC) outlined in its research information letter several technical issues that the industry must address before extending burnup. One topic of specific interest is understanding the cladding balloon and rupture geometry during the LOCA heat-up phase. By leveraging these advanced in situ capabilities, this work used in situ data generated from a simulated LOCA to better understand high-temperature creep and its effect on Zircaloy balloon and rupture performance. Here, this work used the BISON fuel performance code to assess the high-temperature creep model predictions with in situ data.