基于数据库进行乏燃料鉴别的多元统计分析研究
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摘要
近年来,随着国际核军控形势的变化,包含防扩散、防核恐及核安保的多边国际军控合作越来越受到重视.核取证技术作为防扩散、防核恐及核安保的一项核心技术,在对涉核非法活动的威慑、阻止以及响应方面具有重要作用,值得深入研究.目前针对核取证技术的研究较多,主要集中于材料的表征和数据的解读.其中解读作为核取证研究技术中最重要的一环,所面对的对象是多种多样的,包括铀矿石、黄饼、核燃料、乏燃料等,而其中乏燃料由于其潜在的威胁越来越受到重视.本文聚焦于在核取证场景中利用多元统计分析方法进行乏燃料鉴别的研究,主要是利用因子分析、判别分析和回归分析方法对乏燃料组分进行分析,研究各方法的适用范围,并为未来可能的利用数据库进行乏燃料鉴别的工作提供理论依据与可行方案,为相关核取证溯源工作的顺利开展提供支撑.
In recent years, as the international situation about nuclear arms control changes, the multilateral international arms control cooperation including non-proliferation, nuclear terrorism and nuclear security has drawn more and more attention. As a key technology, nuclear forensics plays a significant role in the deterrence,prevention and response to illegitimate nuclear activities, for which it needs studying in depth. At present, there are plenty of researches into nuclear forensics, mostly focusing on the characterization of materials and the interpretation of data. As one of the most important aspects of nuclear forensics research, the interpretation is faced with a variety of different objects, including uranium ore, yellow cake, nuclear fuel, spent nuclear fuel and so on, among which spent nuclear fuel has attracted more and more attention due to its potential threats. In this paper, we primarily focus on the development of multivariate statistical analysis with an aim to interpret the comparative signatures of spent nuclear fuel. A database is established with uranium and plutonium isotopic compositions of spent nuclear fuel samples through simulation. These samples are of different reactor types,initial fuel enrichments and burn-ups. Subsequently, multivariate analysis, including factor analysis,discriminant analysis and regression analysis, are used to the database to validate the feasibility of the identification work. First of all, dimension reduction and visualization work is carried out to determine the possibility for classification by factor analysis. Afterwards, some known samples are assumed to be unknown to further study the possible capabilities of quantitative attribution by conducting factor analysis, including the determination of initial fuel enrichment and burn-up. To eliminate the errors in the identification work and to achieve better outcomes, the discriminant analysis and regression analysis are used to the database to assist with the identification of the reactor type, initial fuel enrichment and burn-up. As revealed by the study, factor analysis is more suitable for the dimension reduction and visualization work, disciminant analysis is more suitable for the identification of reactor type, and regression analysis is more suitable for the identification of initial fuel enrichment and burn-up. Upon the comparison drawn of the three different multivariate analysis methods, a framework for identification process is established to provide a theoretical basis and feasible scheme for the possible identification work of spent nuclear fuel with database, and facilitate the related nuclear forensics work.
In recent years, as the international situation about nuclear arms control changes, the multilateral international arms control cooperation including non-proliferation, nuclear terrorism and nuclear security has drawn more and more attention. As a key technology, nuclear forensics plays a significant role in the deterrence,prevention and response to illegitimate nuclear activities, for which it needs studying in depth. At present, there are plenty of researches into nuclear forensics, mostly focusing on the characterization of materials and the interpretation of data. As one of the most important aspects of nuclear forensics research, the interpretation is faced with a variety of different objects, including uranium ore, yellow cake, nuclear fuel, spent nuclear fuel and so on, among which spent nuclear fuel has attracted more and more attention due to its potential threats. In this paper, we primarily focus on the development of multivariate statistical analysis with an aim to interpret the comparative signatures of spent nuclear fuel. A database is established with uranium and plutonium isotopic compositions of spent nuclear fuel samples through simulation. These samples are of different reactor types,initial fuel enrichments and burn-ups. Subsequently, multivariate analysis, including factor analysis,discriminant analysis and regression analysis, are used to the database to validate the feasibility of the identification work. First of all, dimension reduction and visualization work is carried out to determine the possibility for classification by factor analysis. Afterwards, some known samples are assumed to be unknown to further study the possible capabilities of quantitative attribution by conducting factor analysis, including the determination of initial fuel enrichment and burn-up. To eliminate the errors in the identification work and to achieve better outcomes, the discriminant analysis and regression analysis are used to the database to assist with the identification of the reactor type, initial fuel enrichment and burn-up. As revealed by the study, factor analysis is more suitable for the dimension reduction and visualization work, disciminant analysis is more suitable for the identification of reactor type, and regression analysis is more suitable for the identification of initial fuel enrichment and burn-up. Upon the comparison drawn of the three different multivariate analysis methods, a framework for identification process is established to provide a theoretical basis and feasible scheme for the possible identification work of spent nuclear fuel with database, and facilitate the related nuclear forensics work.
引文
[1] IAEA 2012 Nuclear Forensics Support:Reference Manual(Vienna:IAEA)pp3-34
[2] AAAS 2008 Nuclear Forensics Role, State of the Art, and Program Needs(New York:AAAS Publication Services)pp3-29
[3] Robel M, Kris to M J, Heller M A 2009 Institute of Nuclear Materials Management Annual Meeting Tucson, USA, July12-16, 2009 p414001
[4] IAEA 2015 Nuclear Forensics in Support of Investigations Implement Guide(Vienna:IAEA)pp27-28
[5] Su J H 2014 2014 International Arms Control and Disarma ment(Beijing:World Affairs Press)p29(in Chinese)[苏佳杭2014 2014国际军备控制与裁军(北京:世界知识出版社)第29页]
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[24] Maraini F, Balestrieri F, Bucci R, Magrf A, Marini D 2004Chemom. Intell. Lab. Syst. 73 85
[25] Fisher R 1936 Ann. Hum. Genet. 7 178
[26] Hui X, Sun J 2006 Lecture Notes in Artificial Intelligence(Berlin:Spring-Verlag)p274
[27] Cortes C, Vapnik V 1995 Int. J. Mach. Learn. Cybern. 20 273
[28] Gao H X 2005 Applied Multivariate Statistical Analysis(Beijing:Peking University Press)pp293-321(in Chinese)[高惠璇2005应用多元统计分析(北京:北京大学出版社)第293—321页]
[29] Xu X F, Fu Y G, Zhu J Y, Li R, Tian D F, Wu J, Li K B2017 Acta Phys. Sin.66 082801(in Chinese)[徐雪峰,付元光,朱剑钰,李瑞,田东风,伍钧,李凯波2017物理学报66 082801]
[30] Shi X M, Zhang B A 2010 Nucl. Power Eng. 31 1(in Chinese)[师学明,张本爱2010核动力工程31 1]
[31] Su J H, Wu J, Hu S D 2019 Ann. Nucl. Energ. 126 43
[2] AAAS 2008 Nuclear Forensics Role, State of the Art, and Program Needs(New York:AAAS Publication Services)pp3-29
[3] Robel M, Kris to M J, Heller M A 2009 Institute of Nuclear Materials Management Annual Meeting Tucson, USA, July12-16, 2009 p414001
[4] IAEA 2015 Nuclear Forensics in Support of Investigations Implement Guide(Vienna:IAEA)pp27-28
[5] Su J H 2014 2014 International Arms Control and Disarma ment(Beijing:World Affairs Press)p29(in Chinese)[苏佳杭2014 2014国际军备控制与裁军(北京:世界知识出版社)第29页]
[6] Croff A 1983 Nucl. Technol. 62 335
[7] Nicolaou G 2006 J. Environ. Radioact. 86 313
[8] Nicolaou G 2008 J. Environ. Radioact. 99 1708
[9] Nicolaou G 2008 J. Radioanal. Nucl. Chem. 279 503
[10] Nicolaou G 2014 Ann. Nucl. Energy 72 130
[11] Robel M, Kristo M J 2008 J. Environ. Radioact. 99 1789
[12] Jones A, Turner P, Zimmerman C, Goulermas J Y 2014 Anal.Chem. 86 5399
[13] Dayman K, Coble J, Orton C, Schwantes J 2014 Nucl.Instrum. Meth. Phys. Res., Sect. A 735 624
[14] Coble J, Orton C, Schwantes J 2017 Nucl. Instrum. Methods Phys. Res. Sect. A 850 18
[15] Belkin M, Niyogi P 2003 P. Neural Comput. 15 1373
[16] Breiman L 2001 Mach. Learn. 45 5
[17] Parzen E 1962 Ann. Math. Stat. 33 1065
[18] Pan Z W 2008 J. Complexity 24 606
[19] Raymenm M, Sanschagrin P, Punch W, Venkataraman S,Goodman E, Kuhn L 1997 Am. J. Mol. Biol. 265 445
[20] Kowalski B, Bender C 1972 Anal. Chem. 44 1405
[21] Cover T, Hart P 1967 IEEE Trans. Inf. Theory 13 21
[22] Yi T, Lander E 1993 Am. J. Mol. Biol. 232 1117
[23] Wu W, Mallet Y, Walczak B, Penninckx W, Masssart D,Heuerding S 1996 Anal. Chim. Acta 329 257
[24] Maraini F, Balestrieri F, Bucci R, Magrf A, Marini D 2004Chemom. Intell. Lab. Syst. 73 85
[25] Fisher R 1936 Ann. Hum. Genet. 7 178
[26] Hui X, Sun J 2006 Lecture Notes in Artificial Intelligence(Berlin:Spring-Verlag)p274
[27] Cortes C, Vapnik V 1995 Int. J. Mach. Learn. Cybern. 20 273
[28] Gao H X 2005 Applied Multivariate Statistical Analysis(Beijing:Peking University Press)pp293-321(in Chinese)[高惠璇2005应用多元统计分析(北京:北京大学出版社)第293—321页]
[29] Xu X F, Fu Y G, Zhu J Y, Li R, Tian D F, Wu J, Li K B2017 Acta Phys. Sin.66 082801(in Chinese)[徐雪峰,付元光,朱剑钰,李瑞,田东风,伍钧,李凯波2017物理学报66 082801]
[30] Shi X M, Zhang B A 2010 Nucl. Power Eng. 31 1(in Chinese)[师学明,张本爱2010核动力工程31 1]
[31] Su J H, Wu J, Hu S D 2019 Ann. Nucl. Energ. 126 43