Unfolding the Neutron Spectra from a Water-Pumping-Injection Multi-layered Concentric Sphere Neutron Spectrometer Using a Self-Adaptive Differential Evolution Algorithm

TL;DR

This paper introduces a self-adaptive differential evolution algorithm for neutron spectrum unfolding from a multi-layered sphere spectrometer, demonstrating improved accuracy and efficiency over existing methods through simulation and comparison with IAEA spectra.

Contribution

The paper presents a novel self-adaptive differential evolution algorithm (SDENUA) for neutron spectrum unfolding, reducing parameter tuning and improving accuracy compared to traditional methods.

Findings

SDENUA achieves less than 12% error in spectrum unfolding.

SDENUA outperforms MAXED and GRAVEL in spectrum agreement.

The method effectively incorporates uncertainties in measurements and response matrices.

Abstract

A self-adaptive differential evolution neutron spectrum unfolding algorithm (SDENUA) was established in this paper to unfold the neutron spectra obtained from a Water-pumping-injection Multi-layered concentric sphere Neutron Spectrometer (WMNS). Specifically, the neutron fluence bounds were estimated to accelerate the algorithm convergence, the minimum error between the optimal solution and the input neutron counts with relative uncertainties was limited to 10-6 to avoid useless calculation. Furthermore, the crossover probability and scaling factor were controlled self-adaptively. FLUKA Monte Carlo was used to simulate the readings of the WMNS under (1) a spectrum of Cf-252 and (2) its spectrum after being moderated, (3) a spectrum used for BNCT, and (4) a reactor spectrum, and the measured neutron counts unfolded by using the SDENUA. The uncertainties of the measured neutron count and the response matrix are considered in the SDENUA, which does not require complex parameter tuning and the priori default spectrum. Results indicate that the solutions of the SDENUA are more in agreement with the IAEA spectra than that of the MAXED and GRAVEL in UMG 3.1, and the errors of the final results calculated by SDENUA are under 12%. The established SDENUA has potential applications for unfolding spectra from the WMNS.