Advanced Transient Diagnostic with Ensemble Digital Twin Modeling
This work addresses the challenge of accurate transient diagnostics in nuclear reactors, which is critical for safety, but it appears incremental as it builds on existing digital-twin approaches with ensemble techniques.
The paper tackles the problem of predicting nuclear reactor transients using machine learning digital twins, where single models struggle with generalization across tasks, by proposing an ensemble method called EDDM that enhances prediction accuracy and reduces generalization error.
The use of machine learning (ML) model as digital-twins for reduced-order-modeling (ROM) in lieu of system codes has grown traction over the past few years. However, due to the complex and non-linear nature of nuclear reactor transients as well as the large range of tasks required, it is infeasible for a single ML model to generalize across all tasks. In this paper, we incorporate issue specific digital-twin ML models with ensembles to enhance the prediction outcome. The ensemble also utilizes an indirect probabilistic tracking method of surrogate state variables to produce accurate predictions of unobservable safety goals. The unique method named Ensemble Diagnostic Digital-twin Modeling (EDDM) can select not only the most appropriate predictions from the incorporated diagnostic digital-twin models but can also reduce generalization error associated with training as opposed to single models.