高级检索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于子空间方法的核数据目标精度评估研究

乔雅馨 吴小飞 侯龙

乔雅馨, 吴小飞, 侯龙. 基于子空间方法的核数据目标精度评估研究[J]. 核动力工程, 2024, 45(6): 39-46. doi: 10.13832/j.jnpe.2024.06.0039
引用本文: 乔雅馨, 吴小飞, 侯龙. 基于子空间方法的核数据目标精度评估研究[J]. 核动力工程, 2024, 45(6): 39-46. doi: 10.13832/j.jnpe.2024.06.0039
Qiao Yaxin, Wu Xiaofei, Hou Long. Research on Nuclear Data Target Accuracy Assessment Based on Subspace Method[J]. Nuclear Power Engineering, 2024, 45(6): 39-46. doi: 10.13832/j.jnpe.2024.06.0039
Citation: Qiao Yaxin, Wu Xiaofei, Hou Long. Research on Nuclear Data Target Accuracy Assessment Based on Subspace Method[J]. Nuclear Power Engineering, 2024, 45(6): 39-46. doi: 10.13832/j.jnpe.2024.06.0039

基于子空间方法的核数据目标精度评估研究

doi: 10.13832/j.jnpe.2024.06.0039
基金项目: 国家重点研发计划资助项目(2022YFB1902600);稳定支持基础科研计划资助
详细信息
    作者简介:

    乔雅馨(1990—),女,博士研究生,现主要从事反应堆物理研究,E-mail: qiao.yaxin@hotmail.com

    通讯作者:

    吴小飞,E-mail: xavierthu@yeah.net

  • 中图分类号: TL32

Research on Nuclear Data Target Accuracy Assessment Based on Subspace Method

  • 摘要: 核数据目标精度评估根据反应堆物理计算响应函数的目标不确定度限制,反向求出核数据的不确定度水平要求,对于引导核数据的研究方向、提升反应堆的经济性和安全性有重要意义。目标精度评估的数学形式是一个非线性规划问题,参与运算的核数据数量庞大,难以在全维度空间内求解。子空间方法是一种有效的特殊降维方法,该方法通过矩阵变换,可以在尽量保留高维信息的前提下,将高维问题转化为低维问题,提高数值计算的稳定性。基于子空间方法的ZPPR-9核数据目标精度评估研究结果表明,对于有效增殖因数目标精度为0.3%的不确定度要求,计算维度可由1170维降低到71维。本研究建立的数值方法能够用于目标精度评估计算。

     

  • 图  1  ZPPR-9堆芯示意图

    Figure  1.  Configuration of ZPPR-9 Critical Core

    图  2  ZPPR-9堆芯R-Z分区图

    Figure  2.  R-Z Model of ZPPR-9 Critical Core

    图  3  238U各反应道计算keff不确定度

    Figure  3.  Uncentainty of keff due to 238U Nuclear Reaction

    图  4  238U非弹性散射截面分群灵敏度及不确定度

    Figure  4.  Sensitivity and Uncertainty of 238U Inelastic Cross Section

    图  5  子空间构建流程

    Figure  5.  Construction Procedures of Subspace

    图  6  估计误差随子空间维度变化关系

    Figure  6.  Relationship between Estimation Error and Subspace Dimension

    表  1  快堆主要参数目标精度

    Table  1.   Target Accuracies of Fast Reactor Major Parameters

    参数名 目标精度
    keff(寿期初)/pcm 300
    功率峰因子(寿期初)/% 2
    燃耗反应性损失/pcm 300
    反应性系数(寿期初)/% 7
    辐照周期末主要核素核子密度/% 2
    辐照周期末其他核素核子密度/% 10
      1pcm=10−5
    下载: 导出CSV

    表  2  各核素考虑反应道及计算不确定度

    Table  2.   Nuclear Reaction of Nuclei and Uncertainty Results

    核素 反应道(MT) 不确定度/%
    235U 2, 4, 16, 18, 102, 452 0.03
    238U 2, 4, 16, 18, 102, 452 1.21
    239Pu 2, 4, 16, 18, 102, 452 0.39
    240Pu 2, 4, 16, 18, 102, 452 0.04
    241Pu 2, 4, 16, 18, 102, 452 0.01
    16O 2, 4, 102 0.04
    23Na 2, 4, 102 0.11
    52Cr 2, 4, 102 0.02
    56Fe 2, 4, 102 0.15
    58Ni 2, 4, 102 0.02
    下载: 导出CSV

    表  3  子空间方法目标精度评估结果

    Table  3.   Target Accuracy Assessment Results Based on Subspace Method

    核素 反应道(MT) 能量区间/eV 原始
    不确定度/%
    目标
    不确定度/%
    238U 4 [1.35×106,2.23×106] 20.58 2.09
    238U 4 [2.23×106,3.68×106] 19.42 2.06
    238U 4 [3.68×106,6.07×106] 20.14 2.9
    238U 4 [8.21×105,1.35×106] 16.94 2.89
    238U 4 [6.07×106,1.00×107] 30.3 7.02
    239Pu 102 [5.53×103,9.12×103] 16.54 4.26
    239Pu 102 [3.35×103,5.53×103] 16.54 4.81
    238U 2 [1.35×106,2.23×106] 18.78 5.97
    239Pu 4 [4.98×105,8.21×105] 32.61 10.93
    23Na 4 [4.98×105,8.21×105] 17.54 6.02
    238U 4 [6.74×104,1.11×105] 19.18 6.66
    238U 18 [5.53×103,9.12×103] 408.03 142.12
    56Fe 4 [1.35×106,2.23×106] 11.98 4.4
    239Pu 102 [2.03×103,3.35×103] 10.73 4
    239Pu 4 [1.11×105,1.83×105] 43.73 16.36
    239Pu 4 [6.74×104,1.11×105] 50 20.06
    56Fe 102 [4.98×105,8.21×105] 25.24 10.48
    239Pu 102 [4.09×104,6.74×104] 11.4 4.79
    238U 2 [2.23×106,3.68×106] 15.25 6.55
    239Pu 4 [3.02×105,4.98×105] 32.71 14.15
    下载: 导出CSV

    表  4  直接筛选法目标精度评估结果

    Table  4.   Target Accuracy Assessment Results Based on Direct Screening Method

    核素 反应道(MT) 能量区间/eV 原始
    不确定度/%
    目标
    不确定度/%
    238U 4 [1.35×106,2.23×106] 20.58 0.98
    238U 4 [2.23×106,3.68×106] 19.42 0.96
    238U 2 [1.35×106,2.23×106] 18.78 1.08
    238U 4 [3.68×106,6.07×106] 20.14 1.35
    238U 2 [2.23×106,3.68×106] 15.25 1.18
    238U 4 [8.21×105,1.35×106] 16.94 1.34
    238U 2 [8.21×105,1.35×106] 9.32 0.93
    238U 4 [6.07×106,1.00×107] 30.3 3.26
    239Pu 102 [5.53×103,9.12×103] 16.54 1.94
    238U 2 [3.68×106,6.07×106] 13.19 1.66
    238U 4 [6.74×104,1.11×105] 19.18 2.52
    239Pu 102 [3.35×103,5.53×103] 16.54 2.18
    238U 18 [5.53×103,9.12×103] 408.03 54.04
    239Pu 4 [4.98×105,8.21×105] 32.61 4.78
    23Na 4 [4.98×105,8.21×105] 17.54 2.6
    56Fe 102 [4.98×105,8.21×105] 25.24 4.13
    56Fe 4 [1.35×106,2.23×106] 11.98 1.98
    239Pu 4 [1.11×105,1.83×105] 43.73 7.24
    239Pu 102 [2.03×103,3.35×103] 10.73 1.82
    56Fe 102 [3.02×105,4.98×105] 25.2 4.42
    下载: 导出CSV
  • [1] 汤涛,周涛. 不确定性量化的高精度数值方法和理论[J]. 中国科学: 数学,2015, 45(7): 891-928.
    [2] SULLIVAN T J. Introduction to uncertainty quantification[M]. Cham: Springer, 2015: 1-6.
    [3] SALVATORES M, JACQMIN R. International evaluation co-operation volume 26: Uncertainty and target accuracy assessment for innovative systems using recent covariance data evaluations[R]. Vienna: OECD/NEA, 2008.
    [4] ALIBERTI G, PALMIOTTI G, SALVATORES M, et al. Nuclear data sensitivity, uncertainty and target accuracy assessment for future nuclear systems[J]. Annals of Nuclear Energy, 2006, 33(8): 700-733. doi: 10.1016/j.anucene.2006.02.003
    [5] USACHEV L N, BOBKOV Y G. Planning an optimum set of microscopic experiments and evaluation to obtain a given accuracy in reactor parameter calculations: INDC(CCP)-19/U[R]. Vienna: INDC, 1972.
    [6] 刘勇,曹良志,吴宏春,等. 核数据敏感性与不确定性分析及其在目标精度评估中的应用[J]. 原子能科学技术,2019, 53(1): 86-93. doi: 10.7538/yzk.2018.youxian.0136
    [7] 朱帅涛. 快能谱反应堆多群核数据的调整与精度评估方法研究[D]. 北京: 华北电力大学(北京),2020.
    [8] DENSMORE J D, MCKINNEY G W, HENDRICKS J S. Correction to the MCNP perturbation feature for cross-section dependent tallies: LA-13374[R]. Los Alamos, New Mexico: Los Alamos National Laboratory, 1997.
    [9] KIEDROWSKI B C. MCNP6.1 k-eigenvalue sensitivity capability: a user’s guide: LA-UR-13-22251[R]. Los Alamos: Los Alamos National Laboratory, 2013.
    [10] MACFARLANE R, MUIR D W, BOICOURT R M, et al. The NJOY nuclear data processing system, version 2016: LA-UR-17-20093[R]. Los Alamos: Los Alamos National Laboratory, 2017.
    [11] RUSSI T M. Uncertainty quantification with experimental data and complex system models[D]. Berkeley: University of California, Berkeley, 2010.
    [12] CONSTANTINE P G, DOW E, WANG Q Q. Active subspace methods in theory and practice: applications to Kriging surfaces[J]. SIAM Journal on Scientific Computing, 2014, 36(4): A1500-A1524. doi: 10.1137/130916138
    [13] ABDEL-KHALIK H S. Adaptive core simulation[D]. Raleigh: North Carolina State University, 2004.
    [14] CONSTANTINE P G. Active subspaces: emerging ideas for dimension reduction in parameter studies[M]. Philadelphia: Society for Industrial and Applied Mathematics, 2015: 21-44.
    [15] HU X Z, CHEN X Q, ZHAO Y, et al. Active subspace approach to reliability and safety assessments of small satellite separation[J]. Acta Astronautica, 2017, 131: 159-165. doi: 10.1016/j.actaastro.2016.10.042
    [16] LI J C, CAI J S, QU K. Surrogate-based aerodynamic shape optimization with the active subspace method[J]. Structural and Multidisciplinary Optimization, 2019, 59(2): 403-419. doi: 10.1007/s00158-018-2073-5
    [17] ABDEL-KHALIK H S, BANG Y, WANG C J. Overview of hybrid subspace methods for uncertainty quantification, sensitivity analysis[J]. Annals of Nuclear Energy, 2013, 52: 28-46. doi: 10.1016/j.anucene.2012.07.020
    [18] LEWIS A, SMITH R, WILLIAMS B. Gradient free active subspace construction using Morris screening elementary effects[J]. Computers & Mathematics with Applications, 2016, 72(6): 1603-1615.
    [19] KHUWAILEH B A, ABDEL-KHALIK H S. Subspace-based inverse uncertainty quantification for nuclear data assessment[J]. Nuclear Data Sheets, 2015, 123: 57-61. doi: 10.1016/j.nds.2014.12.010
    [20] HALKO N, MARTINSSON P G, TROPP J A. Finding structure with randomness: probabilistic algorithms for constructing approximate matrix decompositions[J]. SIAM Review, 2011, 53(2): 217-288. doi: 10.1137/090771806
    [21] LUO W, LI B. Combining eigenvalues and variation of eigenvectors for order determination[J]. Biometrika, 2016, 103(4): 875-887. doi: 10.1093/biomet/asw051
    [22] JOLLIFFE I T. Principal component analysis[M]. 2nd ed. New York: Springer, 2002: 10-28.
    [23] ISHIKAWA M, IKEGAMI T, SANDA T. ZPPR benchmarks for large LMFBR core physics from JUPITER cooperative program between United States and Japan[J]. Nuclear Science and Engineering, 2014, 178(3): 335-349. doi: 10.13182/NSE14-9
    [24] IWAI T, SUGINO K, ISHIKAWA M. Development of the ZPPR-9 core benchmark problem: PNC TN9410 98-079[R]. Japan: Power Reactor and Nuclear Fuel Development Corporation, 1998.
    [25] SALVATORES M, PALMIOTTI G, ALIBERTI G, et al. Methods and issues for the combined use of integral experiments and covariance data: NEA-NSC-WPEC-DOC-2013-445[R]. Vienna: OECD/NEA, 2013.
    [26] TADA K, YAMAMOTO A, KUNIEDA S, et al. Nuclear data processing code FRENDY version 2: JAEA-Data/Code 2022-009[R]. Japan: Japan Atomic Energy Agency, 2023.
  • 加载中
图(6) / 表(4)
计量
  • 文章访问数:  19
  • HTML全文浏览量:  3
  • PDF下载量:  1
  • 被引次数: 0
出版历程
  • 收稿日期:  2023-12-30
  • 修回日期:  2024-01-30
  • 刊出日期:  2024-12-17

目录

    /

    返回文章
    返回