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

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.

Application of artificial intelligence techniques in modeling attenuation behavior of ionization radiation: a review

Introduction Shielding of ionizing radiations,which are gamma rays,neutrons,and X-rays,can be achieved by attenuating its intensity using different materials.Protection is therefore crucial in ensuring the safety of lives and essential equipment in areas such as nuclear power plants,radiotherapy facilities,space exploration,and others.Artificial Intelligent technologies have become desirable in modeling shielding materials’attenuation behavior due to their unique advantages.Objective The overview aims to present the recent application of AI technologies in modeling the radiation attenuation behavior of materials.Methods A total of 41 relevant articles were obtained using Scopus and web of science databases.The search was restricted to articles and conference papers published within the last two decades.Results From the overview,it was realized that AI techniques can predict the attenuation properties of shielding materials and optimize the shield design.The methods can be grouped into predictive models which are:fuzzy logic,Support Vector Regression,Neural Networks,and optimization models which include Genetic algorithms,Ant Colony,and Particle Swarm Optimization.Neural networks are the most robust and widely used technique.The predictive models are used in predicting parameters such as attenuation coefficient,buildup factor,shield thickness,and radiation dose rates,whiles the optimization techniques are employed in single and multi-objective attenuator designs.Conclusion In the overview,the accuracies and complexities of the various AI techniques have been discussed giving insight into their prospects.The AI techniques are easy to model compared to conventional methods and can save computational time when coupled with conventional statistical and deterministic models or employed as a standalone technique.