João D. Talon, Aquilino S. Martinez, Alessandro C. Gonçalves
{"title":"Continuous mapping of nuclear reactor core power using artificial neural network even in the presence of inactive detectors","authors":"João D. Talon, Aquilino S. Martinez, Alessandro C. Gonçalves","doi":"10.1016/j.net.2024.07.007","DOIUrl":null,"url":null,"abstract":"Monitoring the radial power distribution during the operation of a pressurized light water reactor (PWR) is crucial for ensuring safe operating conditions and achieving high levels of fuel burnup. This paper introduces a methodology utilizing Artificial Neural Networks (ANN) for reconstructing the radial power distribution in the core of a Pressurized Water Reactor (PWR) with a hot full power of 1876 MWth, such as the Angra 1 reactor. This approach uses measurements from Self-Powered Neutron Detectors (SPND), simulated through the SERPENT code. The use of ANN demonstrated good accuracy in predicting the radial power distribution with an average relative error of 1.27%, considering 36 active detectors, with maximum relative error of 6.99%. Moreover, the proposed process demonstrated robust performance, even when measurements from one, two, or three SPND detectors were unavailable, resulting in errors of 1.24%, 1.13 %, and 1.09%, respectively. Therefore, the methodology ensures a reliable reconstruction of the radial power distribution, even when SPND detector measurements are unavailable, enabling the optimization of detector use and contributing to the increase of operational safety margins.","PeriodicalId":19272,"journal":{"name":"Nuclear Engineering and Technology","volume":null,"pages":null},"PeriodicalIF":2.6000,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nuclear Engineering and Technology","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1016/j.net.2024.07.007","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
Monitoring the radial power distribution during the operation of a pressurized light water reactor (PWR) is crucial for ensuring safe operating conditions and achieving high levels of fuel burnup. This paper introduces a methodology utilizing Artificial Neural Networks (ANN) for reconstructing the radial power distribution in the core of a Pressurized Water Reactor (PWR) with a hot full power of 1876 MWth, such as the Angra 1 reactor. This approach uses measurements from Self-Powered Neutron Detectors (SPND), simulated through the SERPENT code. The use of ANN demonstrated good accuracy in predicting the radial power distribution with an average relative error of 1.27%, considering 36 active detectors, with maximum relative error of 6.99%. Moreover, the proposed process demonstrated robust performance, even when measurements from one, two, or three SPND detectors were unavailable, resulting in errors of 1.24%, 1.13 %, and 1.09%, respectively. Therefore, the methodology ensures a reliable reconstruction of the radial power distribution, even when SPND detector measurements are unavailable, enabling the optimization of detector use and contributing to the increase of operational safety margins.
期刊介绍:
Nuclear Engineering and Technology (NET), an international journal of the Korean Nuclear Society (KNS), publishes peer-reviewed papers on original research, ideas and developments in all areas of the field of nuclear science and technology. NET bimonthly publishes original articles, reviews, and technical notes. The journal is listed in the Science Citation Index Expanded (SCIE) of Thomson Reuters.
NET covers all fields for peaceful utilization of nuclear energy and radiation as follows:
1) Reactor Physics
2) Thermal Hydraulics
3) Nuclear Safety
4) Nuclear I&C
5) Nuclear Physics, Fusion, and Laser Technology
6) Nuclear Fuel Cycle and Radioactive Waste Management
7) Nuclear Fuel and Reactor Materials
8) Radiation Application
9) Radiation Protection
10) Nuclear Structural Analysis and Plant Management & Maintenance
11) Nuclear Policy, Economics, and Human Resource Development