论文标题
通过合奏数字双胞胎建模的高级瞬态诊断
Advanced Transient Diagnostic with Ensemble Digital Twin Modeling
论文作者
论文摘要
在过去的几年中，使用机器学习（ML）模型作为减少订购模型（ROM）的数字二线已增长。但是，由于核反应堆瞬变的复杂和非线性性质以及所需的大量任务，单个ML模型在所有任务中概括都是不可行的。在本文中，我们将发行特定的数字TWIN ML模型与合奏结合在一起，以增强预测结果。该合奏还采用了替代状态变量的非概率跟踪方法，以产生对不可观察的安全目标的准确预测。名为Ensemble Digital-Twin建模（EDDM）的独特方法不仅可以从Incorporated Diagnostic Digital-Twin模型中选择最合适的预测，而且还可以减少与培训相关的概括误差，而不是单个模型。
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.
