{"title":"Using a surrogate model for the detection of defective PWR fuel rods","authors":"","doi":"10.1016/j.anucene.2024.110779","DOIUrl":null,"url":null,"abstract":"<div><p>Timely and accurate detection of defective fuel rods is critical as the release of radioactive fission products from defective fuels can lead to primary circuit contamination and radiation exposure. Due to the complexity of the physical phenomena, models for fault diagnosis can be difficult to construct and recently data driven surrogate models have being increasingly used to detect and characterize defective fuel rods: they make use of a computational database to learn from and make predictions about new unknown data. In this paper, we present a method for the elaboration of an anomaly detector based on neural networks, taking into account the fact that physical computation can be CPU intensive and thus overcome this issue. A physical model for fission products release and coolant activity calculation was built and used to generate a surrogate activity model that enables the generation of a bigger database in small amount of CPU times. Then using this bigger computational database, a recurrent autoencoder was trained for anomaly detection. The network classifies the defect status with 100% accuracy and a good time precision. A sensitivity analysis with lower activity increase at defect onset and addition of noise was conducted in order to better understand the limits of this method. Such methods can be useful for operators of the existing as well as future reactors to make timely predictions of defective fuel rods and avoid operational and economic setbacks for power plants. The work described in this paper was carried out within the R2CA (Reduction of Radiological Consequences of design basis and extension Accidents) project, funded in HORIZON 2020 and coordinated by IRSN (France).</p></div>","PeriodicalId":8006,"journal":{"name":"Annals of Nuclear Energy","volume":null,"pages":null},"PeriodicalIF":1.9000,"publicationDate":"2024-08-05","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/S0306454924004420","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
Timely and accurate detection of defective fuel rods is critical as the release of radioactive fission products from defective fuels can lead to primary circuit contamination and radiation exposure. Due to the complexity of the physical phenomena, models for fault diagnosis can be difficult to construct and recently data driven surrogate models have being increasingly used to detect and characterize defective fuel rods: they make use of a computational database to learn from and make predictions about new unknown data. In this paper, we present a method for the elaboration of an anomaly detector based on neural networks, taking into account the fact that physical computation can be CPU intensive and thus overcome this issue. A physical model for fission products release and coolant activity calculation was built and used to generate a surrogate activity model that enables the generation of a bigger database in small amount of CPU times. Then using this bigger computational database, a recurrent autoencoder was trained for anomaly detection. The network classifies the defect status with 100% accuracy and a good time precision. A sensitivity analysis with lower activity increase at defect onset and addition of noise was conducted in order to better understand the limits of this method. Such methods can be useful for operators of the existing as well as future reactors to make timely predictions of defective fuel rods and avoid operational and economic setbacks for power plants. The work described in this paper was carried out within the R2CA (Reduction of Radiological Consequences of design basis and extension Accidents) project, funded in HORIZON 2020 and coordinated by IRSN (France).
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.