An efficient digital twin based on machine learning SVD autoencoder and generalised latent assimilation for nuclear reactor physics. (15th December 2022) - Gong, Helin; Cheng, Sibo; Chen, Zhang; Li, Qing; Quilodrán-Casas, César; Xiao, Dunhui; Arcucci, Rossella
Abstract: This paper proposes an approach that combines reduced-order models with machine learning in order to create an digital twin to predict the power distribution over the core during the operation stage. The operational digital twin is designed to solve forward problems given input operation parameters, as well as to solve inverse problems given some observations of the power field. The forward model is non-intrusive and realised using SVD autoencoder reduced order model with the combination of machine learning methods, namely, k-nearest-neighbours and decision trees to build the input–output map. For model parameter estimation, the inverse model is based on a generalised latent assimilation method. The proposed approach is able to make use of the non intrusive reduced order model and the online measurements of the power field. The effectiveness in the sense of accuracy and real-time solver of the digital twin is illustrated through a real engineering problem in nuclear reactor physics — reactor core simulation in the life cycle of HPR1000 affected by input parameters, i.e., control rod inserting step, burnup, power level and inlet temperature of the coolant, which shows potential applications for on-line monitoring purpose. Highlights: A real-time operational digital twin is proposed for the prediction of power field in the core. A non-intrusive forward model is built based on SVD-autoencoder and machine learning prediction methods. An inverse model is realised based on a generalised latent assimilation method to overcome the bottleneck of efficient parameter identification.