EJUSTCO: Monte Carlo radiation transport code hybrid with ANN model for gamma-ray shielding simulation

Joseph Konadu Boahen; Ahmed S. G. Khalil; Mohsen A. Hassan; Samir A. Elsagheer Mohamed

doi:10.1007/s41365-023-01297-x

Your Location：

Home >

Browse articles >

EJUSTCO: Monte Carlo radiation transport code hybrid with ANN model for gamma-ray shielding simulation

NUCLEAR ENERGY SCIENCE AND ENGINEERING | Updated：2023-10-13

- EJUSTCO: Monte Carlo radiation transport code hybrid with ANN model for gamma-ray shielding simulation
- Nuclear Science and Techniques Vol. 34, Issue 9, Article number: 144(2023)
- Affiliations：
  
  1.Materials Science and Engineering Department, School of Innovative Design Engineering, Egypt-Japan University of Science and Technology (E-JUST), 179 New Borg El-Arab City, Egypt.
  2.Faculty of Engineering, Aswan University, Egypt
  3.Physics Department, Environmental and Smart Technology Group, Faculty of Science, Fayoum University, 63514 Fayoum, Egypt
- Author bio：
  
  * Joseph Konadu Boahen joseph.boahen@ejust.edu.eg
  Mohsen A. Hassan mohsen.khozami@ejust.edu.eg
- Funds：
- DOI：10.1007/s41365-023-01297-x
  CLC：
Scan for full text
Joseph Konadu Boahen, Ahmed S. G. Khalil, Mohsen A. Hassan, et al. EJUSTCO: Monte Carlo radiation transport code hybrid with ANN model for gamma-ray shielding simulation. [J]. Nuclear Science and Techniques 34(9):144(2023)
DOI：

Joseph Konadu Boahen, Ahmed S. G. Khalil, Mohsen A. Hassan, et al. EJUSTCO: Monte Carlo radiation transport code hybrid with ANN model for gamma-ray shielding simulation. [J]. Nuclear Science and Techniques 34(9):144(2023) DOI： 10.1007/s41365-023-01297-x.

摘要

Abstract

Gamma ray shielding is essential to ensure the safety of personnel and equipment in facilities and environments where radiation exists. The Monte Carlo technique is vital for analyzing the gamma-ray shielding capabilities of materials. In this study, a simple Monte Carlo code, EJUSTCO, is developed to cd simulate gamma radiation transport in shielding materials for academic purposes. The code considers the photoelectric effect, Compton (incoherent) scattering, pair production, and photon annihilation as the dominant interaction mechanisms in the gamma radiation shielding problem. Variance reduction techniques, such as the Russian roulette, survival weighting, and exponential transformation, are incorporated into the code to improve computational efficiency. Predicting the exponential transformation parameter typically requires trial and error as well as expertise. Herein, a deep learning neural network is proposed as a viable method for predicting this parameter for the first time. The model achieves an MSE of 0.00076752 and an ,R,-value of 0.99998. The exposure buildup factors and radiation dose rates due to the passage of gamma radiation with different source energies and varying thicknesses of lead, water, iron, concrete, and aluminum in single-, double-, and triple-layer material systems are validated by comparing the results with those of MCNP, ESG, ANS-6.4.3, MCBLD, MONTEREY MARK (M), PENELOPE, and experiments. Average errors of 5.6%, 2.75%, and 10% are achieved for the exposure buildup factor in single-, double-, and triple-layer materials, respectively. A significant parameter that is not considered in similar studies is the gamma ray albedo. In the EJUSTCO code, the total number and energy albedos have been computed. The results are compared with those of MCNP, FOTELP, and PENELOPE. In general, the EJUSTCO-developed code can be employed to assess the performance of radiation shielding materials because the validation results are consistent with theoretical, experimental, and literary results.

关键词

Keywords

Monte CarloGamma raysShieldingArtificial Neural NetworkSimulation.

references

R. M. Lokhande, V. Vinayak, S. V. Mukhamale, et al., Gamma radiation shielding characteristics of various spinel ferrite nanocrystals: a combined experimental and theoretical investigation. RSC Adv. 11, 7925-7937 (2021). doi: 10.1039/d0ra08372khttp://doi.org/10.1039/d0ra08372k

J. K. Boahen, S. A. E. Mohamed, A. S. G. Khalil, et al., Finite Element Formulation and Simulation of Gamma Ray Attenuation of Single and Multilayer Materials Using Lead, Tungsten and EPDM. Mater. Sci. Forum. 1069, 87-94 (2022). doi: 10.4028/p-ol4895http://doi.org/10.4028/p-ol4895

R. Li, S. Liu, X. Zhang, et al., Nuclides selection method for nuclear reactor shielding based on non-dominated sorting. Ann. Nucl. Energy. 182, 109633 (2023). doi: 10.1016/j.anucene.2022.109633http://doi.org/10.1016/j.anucene.2022.109633

H. Hirayama, H. Nakashima, M. Morishima, et al., Progress and prospects of calculation methods for radiation shielding. J. Nucl. Sci. Technol. 52, 1339-1361 (2015). doi: 10.1080/00223131.2015.1021283http://doi.org/10.1080/00223131.2015.1021283

O. Gencel, The application of artificial neural networks technique to estimate mass attenuation coefficient of shielding barrier. 4, 743-751 (2009).

L. Deng, G. Li, B. Y. 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

C. J. Werner, J. Armstrong, F. B. Brown, et al., “MCNP User’s Manual Code Version 6.2,” Los Alamos Natl. Lab., (2017), p. 746.

Geant4 Collaboration. Introduction to Geant4 Release 11.0 Geant4 Collaboration. 2021.

W. Nelson and Y. Namito, The EGS4 code system: solution of gamma-ray and electron transport problems. Int. Conf. Supercomput. Nucl. Appl., 1990.

A. Cook and G. C. Meggitt, Radiological Protection. Energy Dig. 8, 16-19 (1979) doi: 10.4324/9780203020746-18http://doi.org/10.4324/9780203020746-18

D. Trubey, New Gamma-Ray Buildup Factor Data for Point Kernel Calculations. 1988.

A. Kratsios, The Universal Approximation Property. Ann. Math. Artif. Intell. 89, 435-469 (2021). doi: 10.1007/s10472-020-09723-1http://doi.org/10.1007/s10472-020-09723-1

V. Ljubenov, R. D. Simović, S. Marković, et al., Total reflection coefficients of low-energy photons presented as universal functions. Nucl. Technol. Radiat. Prot. 25, 100-106 (2010). doi: 10.2298/NTRP1002100Lhttp://doi.org/10.2298/NTRP1002100L

J. Wood, Computational Methods in Reactor Shielding, 1st ed. New York: Pergamon Press, 1982.

T. Y. Huang, Z. G. Li, K. Wang, et al., Hybrid windowed networks for on-the-fly Doppler broadening in RMC code. Nucl. Sci. Tech. 32, 1-13 (2021). doi: 10.1007/s41365-021-00901-2http://doi.org/10.1007/s41365-021-00901-2

M. Salman, Evaluation of the Absorption, Scattering and Overall Probability of Gamma Rays in Lead and Concrete Interactions. 4, 191–199 (2021).

N. Tsoulfanidis and S. Landsberger, Measurement & Detection of Radiation. 2015.

M. Sharifzadeh, H. Afarideh, H. Khalafi, et al., A Matlab-based Monte Carlo algorithm for transport of gamma-rays in matter. Monte Carlo Methods Appl. 21, 77-90 (2015). doi: 10.1515/mcma-2014-0011http://doi.org/10.1515/mcma-2014-0011

M. J. Berger, J. H. Hubbell, S. M. Seltzer, et al., XCOM: Photon Cross Sections Database. 1998.

A. Haghighat, Monte Carlo Methods for Particle Transport. CRC. Press. (2016). doi: 10.1201/b17934http://doi.org/10.1201/b17934

A. F. Bielajew, Fundamentals of the Monte Carlo method for neutral and charged particle transport. Sci. York, 2000.

E. D. Cashwell and C. J. Everett, A Practical Manual on the Monte Carlo Method for Random Walk. 1957.

S. García-Pareja, A. M. Lallena, and F. Salvat, Variance-Reduction Methods for Monte Carlo Simulation of Radiation Transport. Front. Phys. 9, 1-13 (2021). doi: 10.3389/fphy.2021.718873http://doi.org/10.3389/fphy.2021.718873

R. Dastres and M. Soori, Artificial Neural Network Systems. Int. J. Imaging Robot. 21, 13-25 (2021).

Y. Upadhyay, Introduction to FeedForward Neural Networks. Towards. Data. Science. (2019).

C. Li, Y. Song, Z. Zhang, et al., A novel and high-precision method for calculating the γ -ray build-up factor for multilayer Shields. 2021, 8860762 (2021). doi: 10.1155/2021/8860762http://doi.org/10.1155/2021/8860762

A. Quesada, 5 algorithms to train a neural network. Artificial Intelligence Techniques, Ltd, 2022. https://www.neuraldesigner.com/blog/5_algorithms_to_train_a_neural_networkhttps://www.neuraldesigner.com/blog/5_algorithms_to_train_a_neural_network (accessed Jun. 05, 2022).

S. Basterrech, S. Mohammed, G. Rubino, et al., Levenberg - Marquardt training algorithms for random neural networks. Comput. J. 54, 125-135 (2011). doi: 10.1093/comjnl/bxp101http://doi.org/10.1093/comjnl/bxp101

U. T. Lin and S. H. Jiang, A dedicated empirical formula for γ-ray buildup factors for a point isotropic source in stratified shields. Radiat. Phys. Chem. 48, 389-401 (1996). doi: 10.1016/0969-806X(95)00461-6http://doi.org/10.1016/0969-806X(95)00461-6

A. Kiyani, A. A. Karami, M. Bahiraee, et al. Calculation of gamma buildup factors for point sources. Adv. Mater. Res. 2, 93-98 (2013).

ANSI/ANS-6.4.3,Gamma-Ray Attenuation Coefficients and Buildup Factors for Engineering Materials. American Nuclear Society, 1991.

A. Das and T. Singh, Development of a new Monte Carlo based transport code to calculate photon exposure build-up factors in various shielding arrangements. Radiat. Phys. Chem. 194, 110028 (2022). doi: 10.1016/j.radphyschem.2022.110028http://doi.org/10.1016/j.radphyschem.2022.110028

E. Aslani-amoli, Dissertation. University of Missori-ROLLA. (1973).

Views

Downloads

CSCD

Alert me when the article has been cited

Submit

Tools

Publicity Resources

Development of three dimensional discrete ordinates-Monte Carlo coupled system

Hi’CT: a pixel sensor-based device for ion tomography

Parallel computing approach for efficient 3-D X-ray-simulated image reconstruction

Assessment of the induced radioactivity in the treatment room of the Heavy Ion Medical Machine in Wuwei using PHITS

Machine learning-based analyses for total ionizing dose effects in bipolar junction transistors

Related Author

No data

Related Institution

Nuclear and Radiation Safety Center of Ministry of Environmental Protection

Institute of Modern Physics, Chinese Academy of Sciences

Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College

School of Nuclear Science and Technology, University of Chinese Academy of Sciences

Advanced Energy Science and Technology Guangdong Laboratory

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