High-energy neutron shielding analysis using curve fitting and machine learning: a Monte Carlo-based study in concrete and Ferro-Boron configurations

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GÖKCAN A. O., SARIYER D.

Annals of Nuclear Energy, cilt.232, 2026 (SCI-Expanded, Scopus) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 232
  • Basım Tarihi: 2026
  • Doi Numarası: 10.1016/j.anucene.2026.112244
  • Dergi Adı: Annals of Nuclear Energy
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Chemical Abstracts Core, Compendex, Environment Index, INSPEC
  • Anahtar Kelimeler: Curve fitting, Decision Tree, FLUKA, High-energy neutron, Linear Regression, Machine Learning, Monte Carlo simulation, Random Forest
  • Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
  • Manisa Celal Bayar Üniversitesi Adresli: Evet

Özet

This study aims to develop an effective shielding approach to control secondary neutron radiation generated under abnormal conditions in high-energy proton accelerators. Neutrons, being neutral, deeply penetrate matter and pose risks to radiation protection and biological safety. Neutron dose distributions were obtained using FLUKA Monte Carlo simulations for four shield–environment setups (standard concrete–air, standard concrete–shield, ferro-boron–air, ferro-boron–shield) and four proton energies (50, 100, 250, 1000 MeV). The data were analyzed using a classical exponential attenuation model via curve fitting and then used to train regression models based on Linear Regression, Decision Tree, and Random Forest algorithms. Model performance was evaluated with R2, RMSE, MAE, and 5-fold cross-validation. Random Forest achieved the highest accuracy, especially in shielded setups (R2 ≈ 0.92–0.97). These results show that combining machine learning with physics-based modeling reduces computation time while maintaining high accuracy, providing a practical framework for radiation shielding design.