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基于共轭离散纵标的减方差方法

郑征 丁谦学 周岩

郑征, 丁谦学, 周岩. 基于共轭离散纵标的减方差方法[J]. 强激光与粒子束, 2018, 30: 026004. doi: 10.11884/HPLPB201830.170223
引用本文: 郑征, 丁谦学, 周岩. 基于共轭离散纵标的减方差方法[J]. 强激光与粒子束, 2018, 30: 026004. doi: 10.11884/HPLPB201830.170223
Zheng Zheng, Ding Qianxue, Zhou Yan. Variance reduction method based on adjoint discrete ordinate[J]. High Power Laser and Particle Beams, 2018, 30: 026004. doi: 10.11884/HPLPB201830.170223
Citation: Zheng Zheng, Ding Qianxue, Zhou Yan. Variance reduction method based on adjoint discrete ordinate[J]. High Power Laser and Particle Beams, 2018, 30: 026004. doi: 10.11884/HPLPB201830.170223

基于共轭离散纵标的减方差方法

doi: 10.11884/HPLPB201830.170223
详细信息
    作者简介:

    郑征(1985—), 男,工程师,从事核电厂屏蔽设计和分析工作; zhengzheng@snerdi.com.cn

  • 中图分类号: TL328

Variance reduction method based on adjoint discrete ordinate

  • 摘要: 对于深穿透类型的屏蔽计算,为了得到较为可信的统计结果,蒙特卡罗方法(MC方法)需要模拟大量的粒子,巨大的计算时间是其存在的主要问题。源偏倚和权窗技巧能够有效降低深穿透问题的计数误差。开展了基于共轭离散纵标(SN)的MC减方差方法研究,根据SN方法的共轭注量率计算并生成了源偏倚和权窗参数,编写了JMCT程序的源抽样子程序,并且在秦山一期测量值基础上进行了验证,成功应用到CAP1400压力容器快中子注量率和堆腔中子和光子剂量率计算中。数值结果表明,对于深穿透屏蔽计算问题,和无偏的MC方法相比,基于共轭SN的MC减方差方法能够在保证结果精度的前提下,提高计算效率1~2个量级。
  • 图  1  基于共轭SN的MC减方差方法流程图

    Figure  1.  Flowchart of MC variance reduction method based on adjoint SN

    图  2  秦山一期JMCT计算模型示意图

    Figure  2.  JMCT model for phase Ⅰ of Qianshan Nuclear Power Plant

    图  3  秦山一期堆芯中平面JSNT网格划分示意图

    Figure  3.  JSNT mesh for core mid-plane of phase Ⅰ of Qianshan Nuclear Power Plant

    图  4  秦山一期前100万无偏和偏倚源粒子分布示意图

    Figure  4.  First million unbiased and biased source particles for phase Ⅰ of Qianshan Nuclear Power Plant

    图  5  CAP1400压力容器内表面快中子注量率JMCT和JSNT计算模型示意图

    Figure  5.  JMCT and JSNT models for CAP1400 RPV calculation

    图  6  CAP1400压力容器内表面JSNT共轭源和JMCT前100万源粒子示意图

    Figure  6.  Adjoint source of JSNT and first million biased source particles of JMCT

    图  7  CAP1400压力容器内表面快中子注量率(E≥1.0 MeV)分布

    Figure  7.  FNFR distribution (E≥1.0 MeV) on inner RPV of CAP1400

    图  8  CAP1400堆腔剂量率JMCT和JSNT计算模型示意图

    Figure  8.  JMCT and JSNT models for CAP1400 cavity dose rate calculation

    表  1  秦山一期快中子注量率(E≥1.0 MeV)计数误差小于0.05时计算总时间和加速比

    Table  1.   Calculation time and speedup for different cases with fast neutron fluence rate (FNFR) tally error less than 0.05

    case geometry type number of mesh fast neutron fluence rate/(109 n·cm-2·s-1) JSNT time/min JMCT time/min total time T/min speedup (T_bias/ T_unbias)
    measure - - 7.41 - - - -
    1(unbiased) - - 6.08 - 19 814.12 19 814.12 1
    2 xyz 393 120 6.48 19.66 3 575.56 3 595.22 6
    3 xyz 393 120 6.35 19.66 197.92 217.58 91
    4 xyz 393 120 6.41 19.66 30.00 49.66 399
    5 rtz 402 948 5.92 80.83 100.78 181.61 109
    6 rtz 823 368 6.44 161.66 429.62 591.28 34
    7 rtz 1 857 492 6.53 397.59 185.07 582.66 34
    下载: 导出CSV

    表  2  CAP1400压力容器内表面快中子注量率各个计数误差区间内的网格数目

    Table  2.   Number of mesh in each tally error bin on inner RPV of CAP1400

    tally error number of mesh
    upper lower unbiased biased
    0.00 0.01 0 0
    0.01 0.02 0 1
    0.02 0.03 0 311
    0.03 0.04 0 350
    0.04 0.05 343 119
    0.05 0.06 322 48
    0.06 0.07 114 16
    0.07 0.08 50 8
    0.08 0.09 21 1
    0.09 0.10 7 1
    0.10 1.00 3 5
    下载: 导出CSV

    表  3  CAP1400堆腔剂量率中子和光子剂量率及加速比

    Table  3.   Neutron dose rate, photon dose rate and speedup for CAP1400 cavity dose rate calculation

    dose rate/(mSv·h-1) tally error/10-2 sum of time/min M/10-2 JMCT speedup(M_biased/M_ref)
    case1: particle type neutron 2.82×106 7.14 10 612.57 1.85 1
    biased source (reference) photon 6.82×104 5.35 10 612.57 3.29 1
    case2: neutron and photon neutron 2.80×106 1.30 10 384.53 57 31
    fluence rate response function photon 6.50×104 3.15 10 384.53 9.70 3
    case3: neutron and photon neutron 2.82×106 1.15 10 064.27 75.1 41
    dose rate response function photon 6.27×104 1.74 10 064.27 32.8 10
    下载: 导出CSV
  • [1] Wagner J C. Acceleration of Monte Carlo shielding calculations with an automated variance reduction technique and parallel processing[R]. University Park, 1997.
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    [4] Blakeman E D, Peplow D E, Wagner J C, et al. PWR facility dose modeling using MCNP5 and the CADIS/ADVANTG variance-reduction methodology[R]. ORNL/TM-2007/133, 2007.
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    [6] Wagner J C, Peplow P E, Evans T M. Automated variance reduction applied to nuclear well-logging problems[J]. Nuclear Technology, 2009, 168: 799-809. doi: 10.13182/NT09-A9309
    [7] Vasiliev A, Ferroukhi H, Zimmermann M A, et al. Development of a CASMO-4/SIMULATE-3/MCNPX calculation scheme for PWR fast neutron fluence analysis and validation against RPV scraping test data[J]. Annals of Nuclear Energy, 2007, 34: 615-627. doi: 10.1016/j.anucene.2007.02.020
    [8] Fero A H. SORCERY user manual[R]. LTR- R EA-06-74, 2006.
    [9] X-5 Monte Carlo Team, MCNP - Ageneral Monte Carlo N-Particle transport code[R]. LA-UR-03-1987, 2003.
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出版历程
  • 收稿日期:  2017-06-20
  • 修回日期:  2017-08-20
  • 刊出日期:  2018-02-15

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