An iterative prediction method for designing the moderator used for the boron neutron capture therapy. Issue 1 (7th December 2021) - Zhang, Ruiqin; Yu, Yangyi; Zhang, Zhi; Yang, Yigang
Abstract: Purpose: To conduct research related to slow neutrons, fast neutrons must be mode‐rated and shifted to the desired energy region. Methods: In this research, an iterated prediction method, in which the neutron transportation properties of all materials were characterized by a reflection matrix, R, and a transmission matrix, T, was proposed to bypass a time‐consuming Monte Carlo simulation and predict the performance of the moderator, including the epithermal neutron flux and the dose of fast neutrons and gamma rays, used for boron neutron capture therapy (BNCT). To find the optimal solution in the huge parameter space, a genetic algorithm combined with transmission and reflection matrices was utilized. Results: The results showed that a 70‐loop iteration was able to find a design for the moderator of BNCT with almost 80 % higher epithermal neutron flux per kilowatt than that of the empirically optimized moderator that was previously reported in the literature. Compared with the Monte Carlo method, this method had the advantage of reducing the calculation time and statistical errors. Conclusion: The genetic algorithm with matrices (GAM) method can be used to find an optimal solution in a huge parameter space without brute‐force calculations. It could be a promising method for designing the moderator for thermal or epithermal neutron usages.