Core protection system using DNBR Power Trip Limit evaluation model based on universal approximation theorem

The core protection system uses a universal approximation theorem-based DNBR PTL evaluation model with an artificial neural network to predict DNBR PTL in real-time, addressing the limitations of existing systems by enabling flexible implementation on various platforms and ensuring rapid reactor shutdown to prevent fuel damage.

KR102997469B1Active Publication Date: 2026-07-29KEPCO NUCLEAR FUEL CO LTD
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Patent Information

Application Number
KR1020250105153
Authority / Receiving Office
KR · KR
Patent Type
Patents
Current Assignee / Owner
Filing Date
2025-07-31
Publication Date
2026-07-29
Estimated Expiration
2045-07-31

AI Technical Summary

Technical Problem

Existing core protection systems in nuclear reactors face limitations in accurately estimating the Departure from Nucleate Boiling Ratio (DNBR) under various operating conditions, particularly due to complex algorithms that hinder implementation on digital logic circuit-based FPGAs and digital computers, and lack real-time protection against fuel cladding damage.

Method used

A core protection system using a universal approximation theorem-based DNBR PTL evaluation model, comprising a core operation information collection unit, a DNBR PTL evaluation unit, and a shutdown determination unit, which employs an artificial neural network with one or more hidden layers to predict DNBR PTL in real-time, applying uncertainty corrections and enabling rapid reactor shutdown when the DNBR limit is reached.

Benefits of technology

The system provides flexible, real-time protection against nuclear fuel damage by accurately predicting DNBR PTL under varying conditions, suitable for both computer-based PLC equipment and digital logic circuit-based FPGAs, ensuring stable and rapid reactor protection.

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Abstract

The present invention relates to a core protection system using a DNBR PTL evaluation model based on a universal approximation theorem, and to a core protection system that determines whether to shut down a reactor by inferring a Power Trip Limit (PTL) that reaches a limit value (DL; DNBR Limit) of the Departure from Nucleate Boiling Ratio (DNBR) through an artificial neural network model to which the universal approximation theorem is applied. According to the present invention, the DNBR PTL can be predicted quickly and stably under various operating conditions using only a single hidden-layer-based neural network structure, by including a core operation information collection unit (100) that collects values ​​of an ex-core neutron flux or in-core neutron flux having multiple axial levels, a core inlet temperature, pressure, flow rate, output, and radial peak output factor (Fr); a DNBR PTL evaluation unit (200) that calculates the DNBR PTL by an artificial neural network model including one or more hidden layers using the collected core operation information as input; and a shutdown determination unit (300) that generates a reactor shutdown signal when the current operating output value collected from the core operation information collection unit (100) is higher than the calculated PTL.
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