A hybrid Lagrangian-artificial intelligence model for predicting the 3D dispersion of radionuclides in the Persian Gulf, part I: Validation

IF 3.2 3区 工程技术 Q1 NUCLEAR SCIENCE & TECHNOLOGY
Maryam Mohammadi , Ahmad Pirouzmand , Kamal Hadad , Abdorreza Alavi Gharahbagh
{"title":"A hybrid Lagrangian-artificial intelligence model for predicting the 3D dispersion of radionuclides in the Persian Gulf, part I: Validation","authors":"Maryam Mohammadi ,&nbsp;Ahmad Pirouzmand ,&nbsp;Kamal Hadad ,&nbsp;Abdorreza Alavi Gharahbagh","doi":"10.1016/j.pnucene.2025.106023","DOIUrl":null,"url":null,"abstract":"<div><div>Assessing the behavior of radionuclides in the environment following nuclear accidents is critical for maintaining and enhancing the effectiveness of emergency preparedness and response programs. The Persian Gulf (PG), a marine environment with rich oil and gas resources, is one of the world's key waterways. The proximity of several nuclear power plants (NPPs) magnifies the need to study the dispersion of radioactive materials after an accident in this region. This paper is the first part of a research that aims to develop an emergency model to predict the three-dimensional (3D) distribution of radionuclides in the PG. The first part introduces and validates a Lagrangian transport model to predict radionuclide dispersion in the PG. As a necessary step, a hydrodynamic model based on ANNs is implemented to forecast 3D current fields, including baroclinic, wind-induced, and tidal currents. These predicted currents are then used as input for the transport model. The proposed transport model incorporates advection, diffusion, decay, and water-sediment interactions. The calculated results demonstrated the importance of using real wind and tidal data in the transport model. A temporal analysis of radionuclide dispersion in the PG is also conducted.</div></div>","PeriodicalId":20617,"journal":{"name":"Progress in Nuclear Energy","volume":"191 ","pages":"Article 106023"},"PeriodicalIF":3.2000,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Progress in Nuclear Energy","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0149197025004214","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

Assessing the behavior of radionuclides in the environment following nuclear accidents is critical for maintaining and enhancing the effectiveness of emergency preparedness and response programs. The Persian Gulf (PG), a marine environment with rich oil and gas resources, is one of the world's key waterways. The proximity of several nuclear power plants (NPPs) magnifies the need to study the dispersion of radioactive materials after an accident in this region. This paper is the first part of a research that aims to develop an emergency model to predict the three-dimensional (3D) distribution of radionuclides in the PG. The first part introduces and validates a Lagrangian transport model to predict radionuclide dispersion in the PG. As a necessary step, a hydrodynamic model based on ANNs is implemented to forecast 3D current fields, including baroclinic, wind-induced, and tidal currents. These predicted currents are then used as input for the transport model. The proposed transport model incorporates advection, diffusion, decay, and water-sediment interactions. The calculated results demonstrated the importance of using real wind and tidal data in the transport model. A temporal analysis of radionuclide dispersion in the PG is also conducted.
用于预测波斯湾放射性核素三维扩散的混合拉格朗日-人工智能模型,第一部分:验证
评估核事故后放射性核素在环境中的行为对于维持和加强应急准备和响应计划的有效性至关重要。波斯湾拥有丰富的石油和天然气资源,是世界重要的水道之一。由于邻近几座核电站,因此更需要研究该地区发生事故后放射性物质的扩散情况。本文是建立放射性核素三维分布预测应急模型研究的第一部分。第一部分介绍并验证了用于预测放射性核素三维分布的拉格朗日输运模型,作为必要步骤,实现了基于人工神经网络的流体动力学模型,用于预测三维流场,包括斜压流、风致流和潮汐流。这些预测的电流然后被用作输运模型的输入。提出的输运模式包括平流、扩散、衰减和水-沉积物相互作用。计算结果表明了在输运模型中使用实际风、潮资料的重要性。还进行了放射性核素在PG中分散的时间分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Progress in Nuclear Energy
Progress in Nuclear Energy 工程技术-核科学技术
CiteScore
5.30
自引率
14.80%
发文量
331
审稿时长
3.5 months
期刊介绍: Progress in Nuclear Energy is an international review journal covering all aspects of nuclear science and engineering. In keeping with the maturity of nuclear power, articles on safety, siting and environmental problems are encouraged, as are those associated with economics and fuel management. However, basic physics and engineering will remain an important aspect of the editorial policy. Articles published are either of a review nature or present new material in more depth. They are aimed at researchers and technically-oriented managers working in the nuclear energy field. Please note the following: 1) PNE seeks high quality research papers which are medium to long in length. Short research papers should be submitted to the journal Annals in Nuclear Energy. 2) PNE reserves the right to reject papers which are based solely on routine application of computer codes used to produce reactor designs or explain existing reactor phenomena. Such papers, although worthy, are best left as laboratory reports whereas Progress in Nuclear Energy seeks papers of originality, which are archival in nature, in the fields of mathematical and experimental nuclear technology, including fission, fusion (blanket physics, radiation damage), safety, materials aspects, economics, etc. 3) Review papers, which may occasionally be invited, are particularly sought by the journal in these fields.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信