Dynamic mode decomposition for subcriticality measurement using measured data at single location

Abstract

Dynamic mode decomposition (DMD) is an effective approach for extracting a fundamental mode eigenvalue in subcriticality measurements. According to several previous studies, data for DMD must be obtained at a sufficient number of locations. However, the number of measurement locations is typically limited owing to experimental constraints. In this study, a time-shifting augmentation technique for DMD is employed, where an augmented data matrix is constructed by stacking multiple time-shifted copies of time-series data at a single location. The proposed DMD technique is applied to numerical simulations of subcriticality measurements, such as those involving the pulsed neutron method and Rossi-α method. The fundamental mode eigenvalues of these measurements are accurately extracted via DMD using the augmented data matrix for a single location without introducing the masking time technique.