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.

Automated summary

In this study, a simple Monte Carlo code, EJUSTCO, is developed for simulating gamma radiation transport in shielding materials, and a deep learning neural network is proposed to predict exponential transformation parameter. The developed code can be used to assess the performance of radiation shielding materials, and the validation results are consistent with theoretical, experimental, and literary results.