{"title":"使用替代模型检测有缺陷的压水堆燃料棒","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":"{\"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}","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
摘要
及时准确地检测出有缺陷的燃料棒至关重要,因为有缺陷的燃料释放出的放射性裂变产物会导致一次回路污染和辐射照射。由于物理现象的复杂性,用于故障诊断的模型可能难以构建,因此最近越来越多地使用数据驱动的代理模型来检测和描述缺陷燃料棒:它们利用计算数据库来学习和预测新的未知数据。在本文中,我们提出了一种基于神经网络的异常检测方法,考虑到物理计算可能是 CPU 密集型的,从而克服了这一问题。我们建立了一个裂变产物释放和冷却剂活性计算的物理模型,并用它来生成一个代用活性模型,这样就可以用少量的 CPU 时间生成一个更大的数据库。然后,利用这个更大的计算数据库,训练了一个用于异常检测的递归自动编码器。该网络对缺陷状态进行分类的准确率为 100%,时间精度也很高。为了更好地了解这种方法的局限性,还进行了敏感性分析,即在缺陷发生时降低活动增加和添加噪声。这种方法可以帮助现有和未来反应堆的操作人员及时预测缺陷燃料棒,避免发电厂在运行和经济方面受到挫折。本文所描述的工作是在 R2CA(减少设计基础和扩展事故的放射性后果)项目内进行的,该项目由 HORIZON 2020 提供资助,并由 IRSN(法国)负责协调。
Using a surrogate model for the detection of defective PWR fuel rods
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.