Data driven Z-FFR physical modeling

Abstract

The Z-FFR (Z-Pinch Driven Fusion Fission Hybrid Reactor) is an important innovative design concept. The high uncertainty of the operating process of the pulsed power unit and the physical process of fusion and the absence of some theoretical and experimental conditions make it difficult to establish a high-precision mechanistic model, and it is difficult to obtain an accurate mathematical model of a complex, dynamic system. A data-driven physical modelling approach is urgently needed to replace the mechanistic models obtained with the aid of extensive simulations and experiments. The approach includes the creation of functional modules, the packaging of sub-modules, the configuration of module interfaces and the configuration of analytical models. Based on the actual needs of Z-FFR design and operation monitoring, the online analysis can be autonomously configured to accommodate different experimental data through machine learning, enabling anomaly detection, trend prediction, model design evaluation and operation assessment during the experimental process.