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Prognosis of the reactor accident is a crucial way to ensure appropriatestrategies are adopted to avoid radioactive releases. However, there is verylimited research in the field of nuclear industry. In this paper, we propose amethod for accident prognosis based on the Temporal Fusion Transformer (TFT)model with multi-headed self-attention and gating mechanisms. The methodutilizes multiple covariates to improve prediction accuracy on the one hand,and quantile regression methods for uncertainty assessment on the other. Themethod proposed in this paper is applied to the prognosis after loss of coolantaccidents (LOCAs) in HPR1000 reactor. Extensive experimental results show thatthe method surpasses novel deep learning-based prediction methods in terms ofprediction accuracy and confidence. Furthermore, the interference experimentswith different signal-to-noise ratios and the ablation experiments for staticcovariates further illustrate that the robustness comes from the ability toextract the features of static and historical covariates. In summary, this workfor the first time applies the novel composite deep learning model TFT to theprognosis of key parameters after a reactor accident, and makes a positivecontribution to the establishment of a more intelligent and staff-lightmaintenance method for reactor systems.

A Long-term Dependent and Trustworthy Approach to Reactor Accident Prognosis based on Temporal Fusion Transformer