Semi-empirical and semi-quantitative lightweight shielding design algorithm

Song-Chuan Zheng; Qing-Quan Pan; Huan-Wen Lv; Song-Qian Tang; Xiao-Jing Liu

doi:10.1007/s41365-023-01187-2

Your Location：

Home >

Browse articles >

Semi-empirical and semi-quantitative lightweight shielding design algorithm

NUCLEAR ENERGY SCIENCE AND ENGINEERING | Updated：2023-04-20

- Semi-empirical and semi-quantitative lightweight shielding design algorithm
- Nuclear Science and Techniques Vol. 34, Issue 3, Article number: 43(2023)
- Affiliations：
  
  1.College of Smart Energy, Shanghai Jiao Tong University, Shanghai 200240, China
  2.School of Nuclear Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
  3.Science and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu 610200, China
- Author bio：
  
  panqingquan@sjtu.edu.cn;
  xiaojingliu@sjtu.edu.cn
- Funds：
- DOI：10.1007/s41365-023-01187-2
  CLC：
Scan for full text
Song-Chuan Zheng, Qing-Quan Pan, Huan-Wen Lv, et al. Semi-empirical and semi-quantitative lightweight shielding design algorithm. [J]. Nuclear Science and Techniques 34(3):43(2023)
DOI：

Song-Chuan Zheng, Qing-Quan Pan, Huan-Wen Lv, et al. Semi-empirical and semi-quantitative lightweight shielding design algorithm. [J]. Nuclear Science and Techniques 34(3):43(2023) DOI： 10.1007/s41365-023-01187-2.

摘要

Abstract

The lightweight shielding design of small reactors is a popular research topic. Based on a small helium-xenon-cooled solid reactor, the effects of neutron and photon shielding sequence and the number of shielding layers on the radiation dose were first studied. It was found that when photons were shielded first and the number of shielding layers was odd, the radiation dose could be significantly reduced. To reduce the weight of the shielding body, the relative thickness of the shielding layers was optimized using the genetic algorithm. The optimized scheme can reduce the radiation dose by up to 57% and reduce the weight by 11.84%. To determine the total thickness of the shielding layers and avoid the local optimal solution of the genetic algorithm, a series of formulas that describes the relationship between the total thickness and the radiation dose was developed through large-scale calculations. A semi-empirical and semi-quantitative lightweight shielding design algorithm is proposed to integrate the above shielding optimization method that verified by the Monte Carlo method. Finally, a code, SDIC1.0, was developed to achieve the optimized lightweight shielding design for small reactors. It was verified that the difference between the SDIC1.0 and the RMC code is approximately 10% and that the computation time is shortened by 6.3 times.

关键词

Keywords

Small reactorLightweightShielding calculationGenetic algorithm

references

L. Zhang, M. Jia, J. Gong et al. Overview of calculation methods and codes for reactor radiation shielding. Nuclear Electronics & Detection Technology 38, 516-520 (2018). https://kns.cnki.net/kcms/detail/detail.aspx?FileName=HERE201804014&DbName=CJFQ2018https://kns.cnki.net/kcms/detail/detail.aspx?FileName=HERE201804014&DbName=CJFQ2018 (in Chinese)

N. M. Schaeffer, Reactor shielding for nuclear engineers. U.S. Atomic Energy Commission Office of Information Services,1983. doi: 10.2172/4479460http://doi.org/10.2172/4479460

L. Zhang, B. Zhang, Y. Chen, Spatial Adaptive Algorithm for Discrete Ordinate Shielding Calculation. Atomic Energy Science and Technology 52, 2233-2242 (2018). doi: 10.7538/yzk.2018.youxian.0178http://doi.org/10.7538/yzk.2018.youxian.0178 (in Chinese)

M. Paul, A. D. Ankan, H. Deb et al., A Monte Carlo simulation model to determine the effective concrete materials for fast neutron shielding. Radiat. Phys. Chem. 202, 110476 (2023). doi: 10.1016/j.radphyschem.2022.110476http://doi.org/10.1016/j.radphyschem.2022.110476

Y. Qiu, M. Aufiero, K. Wang et al., Development of sensitivity analysis capabilities of generalized responses to nuclear data in Monte Carlo code RMC. Ann. Nucl. Energy 97, 142-152 (2016). doi: 10.1016/j.anucene.2016.07.016http://doi.org/10.1016/j.anucene.2016.07.016

Q. Pan, J. Rao, S. Huang et al., Improved adaptive variance reduction algorithm based on RMC code for deep penetration problems. Ann. Nucl. Energy 137, 1-8 (2020). doi: 10.1016/j.anucene.2019.107113http://doi.org/10.1016/j.anucene.2019.107113

H.O. Tekin, T. Manici, Simulations of mass attenuation coefficients for shielding materials using the MCNP-X code. Nucl. Sci. Tech. 28, 95 (2017). doi: 10.1007/s41365-017-0253-4http://doi.org/10.1007/s41365-017-0253-4

Q. Pan, T. Zhang, X. Liu et al., SP3-Coupled Global Variance Reduction Method Based On RMC Code. Nucl. Sci. Tech. 32, 122 (2021). doi: 10.1007/s41365-021-00973-0http://doi.org/10.1007/s41365-021-00973-0

L. Deng, G. Li, B. Zhang et al. A high fidelity general purpose 3-D Monte Carlo particle transport program JMCT3.0. Nucl. Sci. Tech. 33, 108 (2022). doi: 10.1007/s41365-022-01092-0http://doi.org/10.1007/s41365-022-01092-0

R. Li, X. Zhang, S. Liu et al. Research on deep penetration shielding calculation using Monte Carlo particle transport code cosRMC. Atomic Energy Science and Technology 55(S1), 82-87 (2021). doi: 10.7538/yzk.2020.youxian.0752http://doi.org/10.7538/yzk.2020.youxian.0752 (in Chinese)

C. Cao, P. Cao, Q. Gan, A novel method for rapid calculation of moderated neutron spectrum and its application in deep penetration. Ann. Nucl. Energy 168, 108895 (2022). doi: 10.1016/j.anucene.2021.108895http://doi.org/10.1016/j.anucene.2021.108895

Q. Pan, N. An, T. Zhang et al., Single-step Monte Carlo criticality algorithm. Computer Physics Communications 279, 108439 (2022). doi: 10.1016/j.cpc.2022.108439http://doi.org/10.1016/j.cpc.2022.108439

X. Li, Y. Song, J. Mao, Z. Zhang, Many-objective rapid optimization of reactor shielding design based on NSGA – III. Ann. Nucl. Energy 177, 109322 (2022). doi: 10.1016/j.anucene.2022.109322http://doi.org/10.1016/j.anucene.2022.109322

Z. Chen, Z. Zhang, J. Xie et al. Metaheuristic optimization method for compact reactor radiation shielding design based on genetic algorithm. Ann. Nucl. Energy 134, 318-329 (2019). doi: 10.1016/j.anucene.2019.06.031http://doi.org/10.1016/j.anucene.2019.06.031

Y. Song, Z. Zhang, J. Mao et al. Research on fast intelligence multi-objective optimization method of nuclear reactor radiation shielding. Ann. Nucl. Energy 149, 107771 (2020). doi: 10.1016/j.anucene.2020.107771http://doi.org/10.1016/j.anucene.2020.107771

Z. Chen, Z. Zhang, J. Xie et al. Multi-objective optimization strategies for radiation shielding design with genetic algorithm. Computer Physics Communications 260, 107267 (2021). doi: 10.1016/j.cpc.2020.107267http://doi.org/10.1016/j.cpc.2020.107267

X. Li, W. Li, T. Jiao et al. Component Optimization of Shielding Materials Based on Multi-objective Genetic Algorithm. Annal Report of China Institute of Atomic Energy 40, 111-112 (2020). https://kns.cnki.net/kcms/detail/detail.aspx?FileName=FSFH202001015&DbName=CJFQ2020https://kns.cnki.net/kcms/detail/detail.aspx?FileName=FSFH202001015&DbName=CJFQ2020 (in Chinese)

X. Wu, Y. Yang, S. Han et al., Multi-objective optimization method for nuclear reactor radiation shielding design based on PSO algorithm. Ann. Nucl. Energy 160, 108404 (2021). doi: 10.1016/j.anucene.2021.108404http://doi.org/10.1016/j.anucene.2021.108404

Y. Song, J. Mao, Z. Zhang et al. A novel multi-objective shielding optimization method: DNN-PCA-NSGA-Ⅱ. Ann. Nucl Energy 161, 108461 (2021). doi: 10.1016/j.anucene.2021.108461http://doi.org/10.1016/j.anucene.2021.108461

S.T. Asbury, Multi-grid genetic algorithms for optimal radiation shield design. University of Michigan, 2012. https://www.proquest.com/dissertations-theses/multi-grid-genetic-algorithms-optimal-radiation/docview/1027166861/se-2https://www.proquest.com/dissertations-theses/multi-grid-genetic-algorithms-optimal-radiation/docview/1027166861/se-2

Z. Zhang, S. Zhao, Z. Chen et al, Study on radiation shielding optimization method based on multi-objective evolutionary genetic algorithm. Nucl. Power Engineering. 41(S1):124-129 (2020). https://kns.cnki.net/kcms/detail/detail.aspx?FileName=HDLG2020S1026&DbName=DKFX2020https://kns.cnki.net/kcms/detail/detail.aspx?FileName=HDLG2020S1026&DbName=DKFX2020 (in Chinese)

S. Treanţă, M.A. Jiménez, T. Antczak, A necessary and sufficient condition on the equivalence between local and global optimal solutions in variational control problems. Nonlinear Analysis 191, 111640 (2020). doi: 10.1016/j.na.2019.111640http://doi.org/10.1016/j.na.2019.111640

W. Jiang. To investigate the mechanism of ant colony algorithm to solve the local optimal. Intelligent Computer and Applications 4(03):53-54 (2014). https://kns.cnki.net/kcms/detail/detail.aspx?FileName=DLXZ201403015&DbName=CJFQ2014https://kns.cnki.net/kcms/detail/detail.aspx?FileName=DLXZ201403015&DbName=CJFQ2014 (in Chinese)

X. Yao, Y. Liu, G. Lin, Evolutionary programming made faster. IEEE T. Evolutionary Computation 3(2), 82-102 (1999). doi: 10.1109/4235.771163http://doi.org/10.1109/4235.771163

Z. Wu, S.J. Hsieh, J. Li, Sensor deployment based on fuzzy graph considering heterogeneity and multiple-objectives to diagnose manufacturing system. Robotics and Computer-Integrated Manufacturing 29, 192-208 (2013). doi: 10.1016/j.rcim.2012.05.004http://doi.org/10.1016/j.rcim.2012.05.004.

S. Yang, Research on the intelligent radiation shielding design method using the genetic algorithm. North China Electric Power University, 2012. (in Chinese) https://kns.cnki.net/kcms2/article/abstract?v=3uoqIhG8C447WN1SO36whHG-SvTYjkCc7dJWN_daf9c2-IbmsiYfKooOoHjsFfe_W2iqf9AXDEUF1Yba2z9YNSKw4tdCKVwN&uniplatform=NZKPThttps://kns.cnki.net/kcms2/article/abstract?v=3uoqIhG8C447WN1SO36whHG-SvTYjkCc7dJWN_daf9c2-IbmsiYfKooOoHjsFfe_W2iqf9AXDEUF1Yba2z9YNSKw4tdCKVwN&uniplatform=NZKPT

Y. Lei, S. Zhang, X. Li et al, MATLAB genetic algorithm toolbox and application. Xidian University Press, 2014 (in Chinese) https://www.wenkunet.com/d-6558574.htmlhttps://www.wenkunet.com/d-6558574.html

Views

Downloads

CSCD

Alert me when the article has been cited

Submit

Tools

Publicity Resources

Adaptive discontinuous finite element quadrature sets over an icosahedron for discrete ordinates method

Numerical study of scattering legendre moments and effect of anisotropic scattering on S_N shielding calculation

Two-plane painting injection scheme for BRing of HIAF

Optimization of a dynamic uncertain causality graph for fault diagnosis in nuclear power plant

GA-based dynamical correction of dispersion coefficients in Lagrangian puff model

Related Author

No data

Related Institution

School of Nuclear Science and Engineering, North China Electric Power University

China Nuclear Power Engineering Co. Ltd.

China Institute of Atomic Energy

Institute of Modern Physics, Chinese Academy of Sciences

University of Chinese Academy of Sciences

Address：Editorial Office of NST, 2019 Jialuo Road, Jiading, Shanghai 201800, China Postal code：201800
Email：nst@sinap.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 沪ICP备05005479号-1 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰