Machine-Learning Analysis of Moisture Carryover in Boiling Water Reactors

作者：Haoyu Wang, A. Longman, J. T. Gruenwald, James Tusar, Richard B. Vilim · 发表于：Nuclear Technology · 年份：2019 · DOI：10.1080/00295450.2019.1583957 · 被引用次数：5 · 研究领域：Metallurgical Processes and Thermodynamics、Iron and Steelmaking Processes、Fault Detection and Control Systems

Moisture carryover (MCO) is modeled in the General Electric Type-4 boiling water reactor (BWR) using machine-learning methods and data from operating plants. Understanding MCO and the conditions that give rise to an elevated value is important since excessive MCO can damage critical turbine components, can result in elevated dose levels to on-site personnel, and can interfere with late-cycle power management. The analysis of MCO takes into account simplifying reactor symmetries and important geometric dependencies. The plant data are taken from several reactors and were collected over multiple years and multiple fuel cycles. A brief description of the origin of MCO in U.S. BWR plants is given. A machine-learning model is constructed from the data using applicable algorithms and data-reduction techniques. Matching model complexity with available data is one of the more challenging machine-learning tasks. Too many features and too little data will lead to overfitting. The data for each fuel cycle included over 6876 original features, 9 for each fuel bundle. Two approaches are used to reduce the data set into a manageable number of features. The first was an engineering analysis that resulted in the selection of steam quality Q and steam liquid phase velocity VL as the main features driving MCO. Using a Q and a VL for each fuel bundle gives 1528 Q and a VL feature describing the reactor behavior. An analysis of different functional forms of these two variables led to the actual ...