High Efficiency Stirling Power Conversion Technologies for Radioisotope and Fission Nuclear Power Systems
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We report the production of surrogate nuclear explosive debris involves the dissolution of irradiated fissile material, elemental isolation, and recombination based on required fission product ratios. Gamma-ray spectrometry is used following each step to assess the radioactive fission product inventory. A high-purity germanium detector is used to measure each sample, and the detector efficiencies are determined prior to sample measurements. Through radiochemical techniques, elements are isolated and recombined into samples with specific fission product ratios. As a result, a matrix-based approach can account for imperfect chemical separations to calculate the proper mixture volumes to obtain intended volatile-to-refractory fission product ratios, or R-values.
A method for depositing actinides directly onto a 4H-SiC Schottky barrier diode (SBD) detector as a field deployable actinides sensor was developed to enable off-site analysis, shortening the time to obtain critical information such as elements, concentration, and/or isotopic ratio related to the radiological situation. A thin film of Hg was first electrodeposited onto the Pt contact of the SiC diode, followed by chronoamperometric deposition of microgram levels of actinides under conventional control conditions. The 4H-SiC diode maintained consistent functionality through the electrodeposition process and showed resolved alpha-energy spectra that contained accurate isotopic information demonstrating the feasibility of a combined chemical and radiological sensor for field actinide detection and quantification.
Cryogenic decay energy spectrometry provides high energy resolution and enables absolute decay counting, offering an alternative measurement technique for radiochronometry. A cryogenic decay energy spectrometry experiment was conducted using a magnetic microcalorimeter to determine the age of a plutonium sample. The energy resolution was measured at 0.05% from 5 to 6 MeV. The time since sample purification was determined using the measured concentration ratio of the 241 Am/ 241 Pu radiochronometer. Sample age estimates based on 241 Pu alpha-decay and beta-decay counts, along with 241 Am decay counts, align with the expected sample age within expanded uncertainty (k = 2), supporting the accuracy of cryogenic decay energy spectrometry as a radiochronometric method.
It is well known that national security relies on several layers of protection. One of the most important is the traffic control at borders and ports that exploits Radiation Portal Monitors (RPMs) to detect and deter potential smuggling attempts. Most portal monitors rely on plastic scintillators to detect gamma rays. Despite their poor energy resolution, their cost effectiveness and the possibility of growing them in large sizes make them the gamma-ray detector of choice in RPMs. Unmixing algorithms applied to organic scintillator spectra can be used to reliably identify the bare and unshielded radionuclides that triggered an alarm, even with fewer than 1000 detected counts and in the presence of two or three nuclides at the same time. In this work, we experimentally studied the robustness of a state-of-the-art unmixing algorithm to different radiation background spectra, due to varying atmospheric conditions, in the 16 °C to 28 °C temperature range. In the presence of background, the algorithm is able to identify the nuclides present in unknown radionuclide mixtures of three nuclides, when at least 1000 counts from the sources are detected. With fewer counts available, we found larger differences of approximately 35.9% between estimated nuclide fractions and actual ones. In these low count rate regimes, the uncertainty associated by our algorithm with the identified fractions could be an additional valuable tool to determine whether the identification is reliable or a longer measurement to increase the signal-to-noise ratio is needed. Moreover, the algorithm identification performances are consistent throughout different data sets, with negligible differences in the presence of background types of different intensity and spectral shape.
γ-ray spectroscopy is a quantitative, non-destructive technique that may be utilized for the identification and quantitative isotopic estimation of radionuclides. Traditional methods of isotopic determination have various challenges that contribute to statistical and systematic uncertainties in the estimated isotopics. Furthermore, these methods typically require numerous pre-processing steps, and have only been rigorously tested in laboratory settings with limited shielding. Here, in this work, we examine the application of a number of machine learning based regression algorithms as alternatives to conventional approaches for analyzing γ-ray spectroscopy data in the Emergency Response arena. This approach not only eliminates many steps in the analysis procedure, and therefore offers potential to reduce this source of systematic uncertainty, but is also shown to offer comparable performance to conventional approaches in the Emergency Response Application.
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An initial number of fissions on a fissile/fissionable material, defined by ENDF library availability, is used to produce an initial inventory of fission products (FPs). The FPs are decayed using full, analytical solutions to the Bateman equations to calculate the FP inventory at a user-specified time. Output can contain information about activity, mass, dose rate, and gamma-ray intensities based on user-defined parameters, given as .txt or .xlsx file formats. Full execution and display of results takes 5-10 seconds, depending on the complexity of the input parameters.
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