Detection of sulfur in soil samples using 2.5 MeV neutron activation

Neutron collimator optimization for 14.1 MeV DT neutrons using Monte Carlo and Genetic algorithms

The fast neutrons generated by Deuterium-Tritium (DT) fusion reaction have been widely applied in prompt gamma ray neutron activation analysis measurements. In this study, a multi-layer neutron collimator for DT neutron generator was developed. Genetic algorithm combined with Monte Carlo simulation was used to design a collimator made of iron, lead, graphite, and borated polyethene. Copper foil activations were conducted to determine the fast neutron flux ratios between the beam port and its nearby area and agreed well with those predicted by the simulations. The results demonstrated that a narrower beam was obtained. The fast neutron beam flux was 568 ± 14 s^-1 cm^-2. The neutron flux ratio of the collimator was improved by a factor of 2.36, which could provide a better neutron beam.

Feasibility study of on-line monitoring gadolinium based on neutron induced gamma activation

Gadolinium is a soluble neutron poison for ensuring criticality safety of nuclear facility. A neutron induced gamma activation device was developed for the on-line measurement of gadolinium. The experimental device consisted of an ²⁴¹Am-Be neutron source, six ³He detectors and a liquid scintillation detector. The size of sample container was optimized by using Monte Carlo simulations. Aqueous sample containing gadolinium nitrate were conducted with the device to obtain the calibration curve, and neutron self-shielding effect was also studied to correct the non-linear response. The results showed the minimum detectable concentration (MDC) of gadolinium was 0.426 mg/L. Two test samples were conducted to evaluate the performance of the device. The results demonstrated that the discrepancies were within 10%, which indicate the developed system can be successfully used for on-line monitoring of gadolinium.

Thermal neutron beam optimization for PGNAA applications using Q-learning algorithm and neural network

As a powerful, non-destructive analysis tool based on thermal neutron capture reaction, prompt gamma neutron activation analysis (PGNAA) indeed requires the appropriate neutron source. Neutrons produced by electron Linac-based neutron sources should be thermalized to be appropriate for PGNAA. As a result, thermalization devices (TDs) are used for the usual fast neutron beam to simultaneously maximize the thermal neutron flux and minimize the non- thermal neutron flux at the beam port of TD. To achieve the desired thermal neutron flux, the optimized geometry of TD including the proper materials for moderators and collimator, as well as the optimized dimensions are required. In this context, TD optimization using only Monte Carlo approaches such as MCNP is a multi-parameter problem and time-consuming task. In this work, multilayer perceptron (MLP) neural network has been applied in combination with Q-learning algorithm to optimize the geometry of TD containing collimator and two moderators. Using MLP, both thickness and diameter of the collimator at the beam port of TD have first been optimized for different input electron energies of Linac as well as for moderators’ thickness values and the collimator. Then, the MLP has been learned by the thermal and non-thermal neutron flux simultaneously at the beam port of TD calculated by MCNPX2.6 code. After selecting the optimized geometry of the collimator, a combination of Q-learning algorithm and MLP artificial neural network have been used to find the optimal moderators’ thickness for different input electron energies of Linac. Results verify that the final optimum setup can be obtained based on the prepared dataset in a considerably smaller number of simulations compared to conventional calculation methods as implemented in MCNP.

Improving a PGNAA Technique to Detect Heavy Metals in Solid Samples

Prompt gamma-ray neutron activation analysis (PGNAA) is a useful approach for determining the concentrations of a variety of elements in natural materials, either online or in situ, without affecting their chemical forms in matter. The current research aimed to improve the yield of a portable PGNAA setup using a dc beam of 2.5 MeV neutrons and a CeBr3 detector to record gamma rays from neutron inelastic scatterings. It is impossible to avoid the superimposition of heavy metal gamma rays and those from the detector’s element. However, tests were carried out to improve the signal-to-background ratio. By assessing the minimum detectable concentrations (MDC) of chrome, titanium, and zinc in soil samples, the effectiveness of the new optimization was confirmed. The study shows an improvement in the MDC values.