Abstract

The performance of nonstructural components in nuclear power plants (NPPs), which is primarily based on experience and historical data, has been attracting increased interest from researchers following the Fukushima Daiichi nuclear disaster in 2011. This disaster demonstrated the importance of using batteries in NPPs as an auxiliary power system, where such systems can provide the necessary power to mitigate the risk of serious accidents. However, little research has been conducted on such nonstructural components (e.g., auxiliary battery power systems) to evaluate their performance following the post-Fukushima safety requirements, recommended by several nuclear regulators worldwide [e.g., Nuclear Regulatory Commission (NRC), and Nuclear Safety Commission (NSC)]. To address this research gap, the current study investigates the lateral performance of an auxiliary battery power system similar to those currently existing/operational in NPPs in Canada. The rack system was experimentally tested under displacement-controlled quasi-static cyclic fully-reversed loading that simulates lateral seismic demands, following the FEMA 461 guidelines “Interim testing protocol for determining the seismic performance characteristics of structural and nonstructural components”. Following a brief summary of the experimental program, the test results are presented in terms of the rack hysteretic response, damage sequence, stiffness degradation, ductility capacity, member strains, and local deformations. Subsequently, a simplified mechanistic model and a concentrated plasticity model in OpenSees have been developed and calibrated using the experimental results. The results show that without detailed modeling of the rack system connections (i.e., L-shaped connection and sliding nuts), incorrect performance prediction of such systems may result. The findings of the current study can be utilized, within the next generation of performance-based seismic design approaches, to enhance the robustness and improve the reliability of damage state predictions of auxiliary battery power systems in critical facilities.

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