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Introduction

The Core Operating Limits Supervisory System (COLSS) is an important component of commercial reactor core monitoring systems (CMS), developed by Combustion Engineering, Inc.. It collects reactor coolant measurements and in-core neutron detector signals and calculates multiple core safety parameters in real-time. The COLSS conservatively calculates lumped one-dimensional axial power distribution and multiplies penalties to estimate safety parameter. Ivakhnenko developed the General Method of Data Handling (GMDH) that is the machine learning algorithm to build regression model. This study aims to model the 3-D Assembly Power Distribution (APD) and increase the margin of the most critical safety parameter of the CMS, the minimum Departure from Nucleate Boiling Ratio (MDNBR), by replacing the conservative penalty with model uncertainty. The training data for GMDH are produced using 3-D whole-core two step code STREAM/RAST-K, which has been developed in UNIST. The methods and results of each procedure, including input data acquisition, GMDH training, and uncertainty evaluation, have been explained. Two GMDH models have been developed: one for the 3-D assembly power distribution and the other for the hot-pin’s power distribution (HPD). This paper also explains various ways to apply these regression models on the COLSS to increase the operational margin of MDNBR.