Combined fast neutron - gamma ray computed tomography and radiography

Licata, Mauro and Joyce, Malcolm and Joyce, Malcolm (2020) Combined fast neutron - gamma ray computed tomography and radiography. PhD thesis, Lancaster University.

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The combination of real-time fast-neutron and γ-ray assessment for the purpose of tomography and radiography has been investigated using a number of complementary experimental techniques. The research described in this thesis comprises an extensive Monte Carlo simulation study and three experimental approaches, each of which is supported by computer simulations themselves. In the Monte Carlo study, computed by means of MCNP6, actinide materials such as plutonium metal, plutonium oxide, uranium metal, U3O8 and UC2, have been shielded with combinations of lead and high-density polyethylene, then investigated actively with a simulated beam of both fast neutrons and γ rays produced by an americium-beryllium source, and detected by an array of liquid scintillation detectors. This Monte Carlo study demonstrates that, in terms of relative image contrast, the combination of γ and neutron tomography yields to a better discrimination amongst plutonium metal, lead and polyethylene, as well as amongst uranium-based compounds, such as uranium metal and uranium carbide, with the same shielding materials. Less convincing contrast is instead obtained when plutonium oxide and U3O8 are concealed with the same shielding arrangement of lead and polyethylene. The study also shows that a combination of both fast neutron and γ radiation, in several cases, led to a better spatial resolution (order of a few mm) of that achieved using fast neutrons or γ rays in isolation. A similar approach was performed to investigate a variety of materials often associated with conventional explosives and a lithium-based polymer (LiPo). By means of neutron tomography, LiPo and water, hydrogen peroxide, acetone, RDX, TNT, NC have been discerned from one another; whilst the γ tomography approach helps to discern, for instance, RDX from acetone. Experimentally, this technique has been computed, albeit in terms of radiography rather than tomography, using a californium neutron source and single scintillation detector coupled to a real-time, pulse-shape discrimination system. A lithium ion laptop battery was scanned and compared with an X-ray radiograph of the battery itself. These experimental results show that the combined neutron-γ imaging spatial information is comparable to what obtained with the X-ray. In addition, the results show that higher level of image contrast is present in the proximity of the cell batteries, suggesting the potential to identify the spatial lithium polymer distribution within the cell batteries. Furthermore, an alternative approach to investigate a single material type subject to changes in dimension, hypothetically due to corrosion, has been explored. This was conducted assessing both the fast neutron and γ ray flux backscattered by irradiated steel slabs, as a function of their thickness. Such research, carried out with the objective to detect flaws in pipeline sections, not only showed the potential to estimate different thicknesses of steel in isolation, but also showed the potential to measure thicknesses of slabs covered by a layer of materials commonly used for pipelines insulation, such as polyethylene and concrete. Finally, a Monte Carlo study has been completed for an arrangement in which a particle accelerator has been used as the neutron source, with which to explore the potential benefits of combining high-resolution γ-ray spectroscopy, neutron tomography and γ-ray tomography in the same approach. The outcome of this study showed the possibility to identify and localise the distribution of different isotopes of metals, such as 56Fe and 63Cu in a sample. The research presented associated with this aspect of the thesis has potential applications in nuclear safeguards, homeland security, contraband detection and in fields where relatively quick and non-destructive inspections are needed.

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Thesis (PhD)
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22 Sep 2020 16:00
Last Modified:
11 Jun 2024 23:31