Vibrational analysis of the complex structure of reactor internals using the finite element method leads to considerable computational expense. Additionally, fluid-structure interaction (FSI) effects due to liquid coolant result in a large number of fluid elements. Here, we describe a model reduction method based on Guyan theory to solve these complex numerical problems efficiently. The master degrees of freedom selection process, which is based on the shapes of vibrational modes, is discussed. We consider the structural characteristics of the cylindrical parts of the reactor, and include FSI effects. To verify the model reduction method, several numerical examples of simple cylindrical shells are described with and without the coolant. Practical application to the internals of an advanced pressurized reactor 1400 (APR1400) is discussed with various different conditions. Keywords: model reduction method; Guyan reduction; master degrees of freedom selection; reactor internals; fluid-structure interaction
IntroductionVibrational analysis is an important aspect of structural integrity assessments of nuclear reactors, and it can be used to identify the response of the reactor internals to vibration sources such as earthquakes and flowinduced vibrations. The finite element method (FEM) is widely used in vibrational analysis, and the accuracy of the results depends on the quality of the model. Park et al. recently described FE models of reactor internals using a scaled-down model and provided an experimental verification of the technique [1]. Although these FE models provide accurate results, the complex structural form of the reactor internals and the fluid elements used to describe the fluid-structure interaction (FSI) leads to a large number of elements and nodes, which in turn leads to significant computational expense. For this reason, Choi et al. constructed a simplified FE model of reactor internals using one-dimensional elements and reported computationally efficient seismic analyses [2]. However, the use of such a simplified model has significant limitations in terms of the geometry.Recently, model reduction methods have been reported to reduce the overall computational costs, with applications in fields, including automobiles, aircraft,