Monte Carlo Modelling and Simulation of Gamma-Ray Dose Rates from Selected Radioactive Sources
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Abstract
This study presents a comprehensive investigation of external gamma-ray dose rates from Cs-137, Co-60, and Ir-192 sources, considering variations in source-to-detector distance and lead shielding thickness using Monte Carlo simulations with the GEANT4 toolkit. Dose rates were evaluated for two activity levels, 5MBq representing standard operational conditions and 10 MBq for model validation, at three distances (0.5 m, 1.5 m, and 3 m) and four lead thicknesses (0, 1, 2, and 5 cm). The results show a systematic decrease in dose rate with increasing distance, consistent with the inverse square law, and an exponential reduction with increasing lead thickness, reflecting photon attenuation within the shielding material. Energy-dependent attenuation effects were observed, with lower-energy photons from Cs-137 experiencing more effective reduction compared to higher-energy photons from C0-60, while Ir-192 exhibited intermediate attenuation characteristics, highlighting the influence of photon energy on shielding efficiency. Across identical geometrical configurations and activity levels, Co-60 produced the highest dose rates, followed by Ir-192, and then Cs-137, indicating a direct correlation between photon energy and external dose magnitude. The consistency of distance-dependent and thickness-dependent trends confirms the physical accuracy and computational reliability of the GEANT4-based Monte Carlo model. These findings provide a quantitative basis for optimizing shielding design, defining safe operational distances, and implementing effective radiation protection strategies in medical, industrial, and research environments in accordance with international safety standards. Furthermore, the study demonstrates that Monte Carlo simulations serve as a high-fidelity predictive tool for evidence-based radiological safety planning, supporting both practical applications and future research developments in complex radiological scenarios.
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