The response of a scintillation neutron detector of ZnS(Ag) with B was calculated, using the MCNPX Monte Carlo Code. The detector consists of four panels of polymethyl methacrylate (PMMA) and five thin layers of ~0.017cm thickB+ZnS(Ag) in contact with the PMMA. The response was calculated for the bare detector and with different thicknesses of High Density Polyethylene, HDPE, moderator for 29 monoenergetic sources as well as AmBe andCf neutrons sources. In these calculations the reaction rate B(n, α)Li and the neutron fluence in the sensitive area of the detector B+ZnS(Ag) was estimated. Measurements were made at the Neutron Measurements Laboratory, Universidad Politécnica de Madrid, LMN-UPM, to quantify the detections in counts per second in response to aCf neutron source separated 200cm. The MCNPX computations were compared with measurements to estimate the efficiency of ZnS(Ag) for detecting the α that is created in the B(n, α)Li reaction. After validating new models with different geometries it will be possible to improve the detector response trying to achieve a sensitivity of 2.5cps-ngCf comparable with the response requirements for He detectors installed in the Radiation Portal Monitors, RPMs. This type of detector can be considered an alternative to theHe detectors for detection of Special Nuclear Material, SNM.
The construction of the new Neutron Standards Laboratory at CIEMAT (Laboratorio de Patrones Neutrónicos) has been finalised and is ready to provide service. The facility is an ∼8 m×8 m×8 m irradiation vault, following the International Organization for Standardization 8529 recommendations. It relies on several neutron sources: a 5-GBq (5.8× 10(8) s(-1)) (252)Cf source and two (241)Am-Be neutron sources (185 and 11.1 GBq). The irradiation point is located 4 m over the ground level and in the geometrical centre of the room. Each neutron source can be moved remotely from its storage position inside a water pool to the irradiation point. Prior to this, an important task to design the neutron shielding and to choose the most appropriate materials has been developed by the Radiological Security Unit and the Ionizing Radiations Metrology Laboratory. MCNPX was chosen to simulate the irradiation facility. With this information the walls were built with a thickness of 125 cm. Special attention was put on the weak points (main door, air conditioning system, etc.) so that the ambient dose outside the facility was below the regulatory limits. Finally, the Radiation Protection Unit carried out a set of measurements in specific points around the installation with an LB6411 neutron monitor and a Reuter-Stokes high-pressure ion chamber to verify experimentally the results of the simulation.
The Neutron Standards Laboratory of CIEMAT has conducted the characterization of the independent spent fuel storage installation at the Trillo Nuclear Power Plant. At this facility, the spent fuel assemblies are stored in ENSA-DPT type dual purpose casks. Neutron characterization was performed by dosimetry measurements with a neutron survey meter (LB6411) inside the facility, around an individual cask and between stored casks, and outside the facility. Spectra measurements were also performed with a Bonner sphere system in order to determine the integral quantities and validate the use of the neutron monitor at the different positions. Inside the facility, measured neutron spectra and neutron ambient dose equivalent rate are consistent with the casks spatial distribution and neutron emission rates, and measurements with both instruments are consistent with each other. Outside the facility, measured neutron ambient dose equivalent rates are well below the 0.5 μSv/h limit established by the nuclear regulatory authority.
In its broadest sense, the term artificial intelligence indicates the ability of an artifact to perform the same types of functions that characterize human thought. The goal of AI is to use algorithms, heuristics and methodologies based on the ways in which the human brain solves problems. Artificial neural networks recreate the structure of the human brain imitating the learning process. The Artificial neural networks theory has provided an alternative to classical computing for those problems in which traditional methods have delivered results that are not very convincing or not very convenient such as in the case of the neutron spectrometry and dosimetry problem for radiation protection purposes, using the Bonner spheres spectrometer as measurement system, mainly because many problems are encountered when trying to determine the neutron energy spectrum of a measured data. The most delicate part of the spectrometry based on this system is the unfolding process, for which several neutron spectrum unfolding codes have being developed. However, these codes require an initial guess spectrum in order to initiate the unfolding process. Their poor availability and their not easy management for the end user are other associated problems. Artificial Intelligence technology, is an alternative technique that is gaining popularity among researchers in neutron spectrometry research area, since it offers better results compared with the traditional solution methods. In this work, "Synapse", a neutron spectrum unfolding code based on Generalized Regression Artificial Neural Networks technology is presented. The Synapse code is capable to unfold the neutron spectrum and to calculate 15 dosimetric quantities using the count rates, coming from a BSS as the only entrance information. The results obtained show that the Synapse code, based on GRANN technology, is a promising and innovative technological alternative for solving the neutron spectrometry and dosimetry problems.
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