Time: June 21th, 2021 10:00
Location: Room 209, Science Buidling, Tsinghua Univeristy
This talk introduces an approach that combines reduced-order models with machine learning in order to create physics-informed digital twins to predict high-dimensional output quantities of interest, such as neutron flux and power distributions in nuclear reactor cores. The digital twin is designed to solve forward problems given input parameters, as well as to solve inverse problems given some extra measurements. Offline, we use reduced-order modeling, namely, the proper orthogonal decomposition (POD) to produce physics-based computational models that are accurate enough for fast predictive digital twins. The machine learning techniques, namely, k-nearest-neighbors (KNN) and decision trees (DT) are used to formulate the input-parameter- dependent coefficients of the reduced basis, whereafter the high-fidelity fields are able to be reconstructed. Online, we use the real time input parameters to rapidly reconstruct the neutron field in the core based on the adapted physics-based digital twin. The framework is presented through a real engineering problem in nuclear reactor physics - reactor core simulation in the life cycle of HPR1000 governed by the two-group neutron diffusion equations affected by input parameters, i.e., burnup, control rod inserting step, power level and temperature of the coolant, which shows, however, potential applications for on-line monitoring purpose.