A Data-driven approach to predicting neutron penetration through media

被引:0
|
作者
Weiss, Abdullah G. [1 ,3 ]
Aranguren, Begona [1 ]
Butt, Moiz I. [1 ]
Tsvetkov, Pavel V. [1 ]
Kimber, Mark L. [1 ,2 ]
McDeavitt, Sean M. [1 ]
机构
[1] Texas A&M Univ, Dept Nucl Engn, College Stn, TX 77843 USA
[2] Texas A&M Univ, Dept Mech Engn, College Stn, TX 77843 USA
[3] Natl Inst Stand & Technol, Ctr Neutron Res, Gaithersburg, MD 20899 USA
关键词
Neutron Attenuation; Irradiation; Neutron Moderation; Radiation Shielding; Neutron Interactions; MCNP; MODERATOR;
D O I
10.1016/j.anucene.2023.109953
中图分类号
TL [原子能技术]; O571 [原子核物理学];
学科分类号
0827 ; 082701 ;
摘要
A novel methodology is developed to predict the neutron permeation through media, enabling easier selection of shields and attenuators. The methodology relies on generalized metrics parametrized from MCNP & REG;6.2 simulations where the average neutron energy and relative intensity are tallied through various media. This work builds on previously developed neutron energy attenuation coefficients by discussing accompanying neutron permeation coefficients. Both metrics are used to accurately predict the intensity of neutrons penetrating any medium and the average penetrating neutron energy, allowing for a simplified and generalized approach to predicting neutron penetration. Benchmarks with increasing complexity are used to demonstrate the applicability of the metrics to any geometry and medium. The benchmarks revealed 1.29-3.25% deviation from results in higher fidelity simulations. The data-driven methodology enables streamlined approaches to analyze complex neutronattenuating media and presents a computationally efficient alternative to iterative neutronics simulations for optimizing neutron shielding and moderation setups.
引用
收藏
页数:10
相关论文
共 50 条
  • [31] A data-driven approach to predicting diabetes and cardiovascular disease with machine learning
    Dinh, An
    Miertschin, Stacey
    Young, Amber
    Mohanty, Somya D.
    BMC MEDICAL INFORMATICS AND DECISION MAKING, 2019, 19 (01)
  • [32] A Data-Driven Approach for Predicting the Remaining Useful Life of Steam Generators
    Hoang-Phuong Nguyen
    Fauriat, William
    Zio, Enrico
    Liu, Jie
    2018 3RD INTERNATIONAL CONFERENCE ON SYSTEM RELIABILITY AND SAFETY (ICSRS), 2018, : 255 - 260
  • [33] A data-driven approach to predicting diabetes and cardiovascular disease with machine learning
    An Dinh
    Stacey Miertschin
    Amber Young
    Somya D. Mohanty
    BMC Medical Informatics and Decision Making, 19
  • [34] A Data-Driven Approach to Predicting Septic Shock in the Intensive Care Unit
    Yee, Christopher R.
    Narain, Niven R.
    Akmaev, Viatcheslav R.
    Vemulapalli, Vijetha
    BIOMEDICAL INFORMATICS INSIGHTS, 2019, 11
  • [35] Teaching Grammar Through Data-Driven Learning (DDL) Approach
    Nugraha, Sidik Indra
    Miftakh, Fauzi
    Wachyudi, Kelik
    PROCEEDINGS OF THE NINTH INTERNATIONAL CONFERENCE ON APPLIED LINGUISTICS (CONAPLIN 9), 2016, 82 : 300 - 303
  • [36] Rare disease knowledge enrichment through a data-driven approach
    Shen, Feichen
    Zhao, Yiqing
    Wang, Liwei
    Mojarad, Majid Rastegar
    Wang, Yanshan
    Liu, Sijia
    Liu, Hongfang
    BMC MEDICAL INFORMATICS AND DECISION MAKING, 2019, 19 (1)
  • [37] Unlocking competitiveness through scent names: A data-driven approach
    Meng, Hua
    Zamudio, Cesar
    Jewell, Robert D.
    BUSINESS HORIZONS, 2018, 61 (03) : 385 - 395
  • [38] Rare disease knowledge enrichment through a data-driven approach
    Feichen Shen
    Yiqing Zhao
    Liwei Wang
    Majid Rastegar Mojarad
    Yanshan Wang
    Sijia Liu
    Hongfang Liu
    BMC Medical Informatics and Decision Making, 19
  • [39] Predicting mud weight in carbonate formations using seismic data: A data-driven approach
    Peshkov, Georgy
    Khemraev, Kerim
    Safonov, Sergey
    Bukhanov, Nikita
    Alali, Ammar
    Abughaban, Mahmoud
    GEOENERGY SCIENCE AND ENGINEERING, 2025, 250
  • [40] Effective Promotional Strategies Selection in Social Media: A Data-Driven Approach
    Kuang, Kun
    Jiang, Meng
    Cui, Peng
    Luo, Hengliang
    Yang, Shiqiang
    IEEE TRANSACTIONS ON BIG DATA, 2018, 4 (04) : 487 - 501