M. Hosseinllu , M. Abbasi , O. Safarzadeh , F. Dehghani
{"title":"An intelligent control rod movement strategy for boron-free reactor core using multi-layer perceptron machine learning model","authors":"M. Hosseinllu , M. Abbasi , O. Safarzadeh , F. Dehghani","doi":"10.1016/j.anucene.2025.111405","DOIUrl":null,"url":null,"abstract":"<div><div>The boron-free operation of Small Modular Reactor (SMR) core requires efficient approaches to manage excess reactivity throughout prolonged operational cycles. Adjusting Control Rods (CRs) is the only way to compensate reactivity and regulate<!--> <!-->the reactor power during the operational cycle of boron-free cores. Therefore, developing a proper CR movement strategy throughout the cycle length is crucial for boron-free cores. This study aims to apply data mining methods within machine learning approach to forecast critical CR positions at each burnup level of the boron-free core using a Multi-Layer Perceptron (MLP) model. To achieve this goal, the design of the boron-free core Control Banks (CBs) and their computations, are investigated. Furthermore, the effects of CR movement on core neutron physics parameters are considered.</div><div>The regression values for training, testing, and all datasets are calculated. The results indicate that the prediction of critical CR movement strategy is properly done by the developed MLP model. The trained MLP model operates extremely quickly (less than 1 sec) and can serve as a quick support model for forecasting CR movement strategy. The forecasting results of the developed model, based on known and unknown data, verify a high correlation between forecasted and real values, demonstrating that the performance of the developed model is good and has high accuracy.</div></div>","PeriodicalId":8006,"journal":{"name":"Annals of Nuclear Energy","volume":"218 ","pages":"Article 111405"},"PeriodicalIF":1.9000,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of Nuclear Energy","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0306454925002221","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"NUCLEAR SCIENCE & TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
The boron-free operation of Small Modular Reactor (SMR) core requires efficient approaches to manage excess reactivity throughout prolonged operational cycles. Adjusting Control Rods (CRs) is the only way to compensate reactivity and regulate the reactor power during the operational cycle of boron-free cores. Therefore, developing a proper CR movement strategy throughout the cycle length is crucial for boron-free cores. This study aims to apply data mining methods within machine learning approach to forecast critical CR positions at each burnup level of the boron-free core using a Multi-Layer Perceptron (MLP) model. To achieve this goal, the design of the boron-free core Control Banks (CBs) and their computations, are investigated. Furthermore, the effects of CR movement on core neutron physics parameters are considered.
The regression values for training, testing, and all datasets are calculated. The results indicate that the prediction of critical CR movement strategy is properly done by the developed MLP model. The trained MLP model operates extremely quickly (less than 1 sec) and can serve as a quick support model for forecasting CR movement strategy. The forecasting results of the developed model, based on known and unknown data, verify a high correlation between forecasted and real values, demonstrating that the performance of the developed model is good and has high accuracy.
期刊介绍:
Annals of Nuclear Energy provides an international medium for the communication of original research, ideas and developments in all areas of the field of nuclear energy science and technology. Its scope embraces nuclear fuel reserves, fuel cycles and cost, materials, processing, system and component technology (fission only), design and optimization, direct conversion of nuclear energy sources, environmental control, reactor physics, heat transfer and fluid dynamics, structural analysis, fuel management, future developments, nuclear fuel and safety, nuclear aerosol, neutron physics, computer technology (both software and hardware), risk assessment, radioactive waste disposal and reactor thermal hydraulics. Papers submitted to Annals need to demonstrate a clear link to nuclear power generation/nuclear engineering. Papers which deal with pure nuclear physics, pure health physics, imaging, or attenuation and shielding properties of concretes and various geological materials are not within the scope of the journal. Also, papers that deal with policy or economics are not within the scope of the journal.