Monte Carlo and Machine Learning-Based Evaluation of Fe-Enriched Al Alloys for Nuclear Radiation Shielding Applications.

Abstract

This study presents a hybrid computational investigation into the radiation shielding behavior of Fe-enriched Al-based alloys (Al-Fe-Mo-Si-Zr) for potential use in nuclear applications. Four alloy compositions with varying Fe contents (7.21, 6.35, 5.47, and 4.58 wt%) were analyzed using a combination of Monte Carlo simulations, machine learning (ML) predictions based on multilayer perceptrons (MLPs), EpiXS, and SRIM-based charged particle transport modeling. Key photon interaction parameters-including mass attenuation coefficient (MAC), half-value layer (HVL), buildup factors, and effective atomic number (Z_eff)-were calculated across a wide energy range (0.015-15 MeV). Results showed that the 7.21Fe alloy exhibited a maximum MAC of 12 cm²/g at low energies and an HVL of 0.19 cm at 0.02 MeV, indicating improved gamma attenuation with increasing Fe content. The ML model accurately predicted MAC values in agreement with Monte Carlo and XCOM data, validating the applicability of AI-assisted modeling in material evaluation. SRIM calculations demonstrated enhanced charged particle shielding: the projected range of 10 MeV protons decreased from ~55 µm (low Fe) to ~50 µm (high Fe), while alpha particle penetration reduced accordingly. In terms of fast neutron attenuation, the 7.21Fe alloy reached a maximum removal cross-section (Σ_R) of 0.08164 cm^-1, showing performance comparable to conventional materials like concrete. Overall, the results confirm that Fe-rich Al-based alloys offer a desirable balance of lightweight design, structural stability, and dual-function radiation shielding, making them strong candidates for next-generation protective systems in high-radiation environments.