In the Precise seismic analysis is essential for the safe structural design of nuclear reactors, but reducing its high computational cost is a practical challenge. This study investigates model order reduction using the POD-Galerkin method to address this issue. Due to the nonlinearity of seismic analysis, the repeated projection of the governing equations in the POD-Galerkin method becomes a computational bottleneck. Therefore, we employ a hyper-reduction technique, the Empirical Cubature Method (ECM), to approximate the projection and accelerate the computation. Both methods are data-driven, requiring an offline stage to compute the full-order model before the fast online stage. For large-scale problems like reactors, this offline stage must be executed in a domain-decomposed parallel environment. However, the offline process for ECM is based on a greedy algorithm, which is inherently serial and difficult to parallelize. Accordingly, this study proposes an approximate parallelization method for the ECM offline stage that can run in a domain-decomposed parallel environment. We will apply this method to a simplified model with geometric nonlinearity to evaluate its impact on computation time and accuracy.
Hirano et al. (Wed,) studied this question.