Revisiting the bias factor methodologies for the validation of fast test reactors
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The statistical methods used for antineutrino detection will need to be improved to effectively monitor the inventory of next-generation nuclear reactors. In this sensitivity study, we evaluate machine learning models compared to previously used statistical approaches to identify diversion scenarios in a simulated Advanced Fast Reactor (AFR)-100. A chi-square goodness-of-fit technique, which individually compares the simulated antineutrino yields to the expected antineutrino yield, resulted in precise but low diversion detection probability. Various support vector machine (SVM) models were applied with diverse training datasets to evaluate the robustness of the method towards unexpected or “unseen” diversion scenarios. Furthermore, our results indicate that while the SVM models significantly improved the detection probability of near-field antineutrino-based safeguards, up to a probability of ~0.04, for the simulated small reactor, the detection system still needs improvements to reach the 0.2 detection limit established by the International Atomic Energy Agency.
Special Nuclear Material (SNM) including 95% 235 U and 239 Pu is capable of being identified by utilizing a quasi-forward directional AmBe source from Patent No: US20190013109A1 using Prompt Gamma Neutron Activation Analysis (PGNAA) simulated with Monte-Carlo N-Particle transport 6.2 (MCNP). HPGe and LaBr 3 detector arrays were used to identify and quantify the peak-to-background and peak-to-total ratios of the associated photon spectra from the SNM encased in a polyethylene shield. The conducted simulations varied the volume of the SNM and neutron source strength in the MCNP data card to conduct an uncertainty analysis. Photopeaks identified include K-shell X-rays from 235 U and 239 Pu, 61 keV from fission, 2.223 MeV prompt gamma from hydrogen, 511 keV annihilation, and a single and double escape peak from the prompt gamma interaction from hydrogen. Relationships between peak-to-total and peak-to-background as a function of SNM and particle history were investigated to aid in the analysis. The capabilities of both detector systems to acquire well resolved photopeak with a 5% relative error or less, with a 1 Ci source activity, and a peak-to-background ratio of 1.15. Finally, this was determined to take 326 s for LaBr 3 and 163 s for HPGe which is comparable to current methods for material detection which take between 100 and 900 s to acquire.
Source location of Special Nuclear Material (SNM) encompassing 95% 235 U and 239 Pu is identified by utilizing a directional source from Patent No: US20190013109A1 using Prompt Gamma Neutron Activation Analysis (PGNAA) and neutron spectroscopy simulated with Monte-Carlo N-Particle transport 6.2 (MCNP). BC-408, HPGe, LaBr 3 detector arrays were used to identify the location of the SNM using total counts incident on each detector, and PGNAA photopeaks from HPGe and LaBr 3 detector arrays in a polyethylene shield. The conducted simulations varied the volume and location of the SNM in the MCNP input files to observe how the source location method behaved. PGNAA photopeaks used for source identification include 61 keV from fission, 2.223 MeV prompt gamma from hydrogen, 511 keV annihilation, and a single and double escape peaks from the prompt gamma interaction from hydrogen. The capabilities of each detector systems to acquire well resolved photopeaks with a 1% relative error or less, and total relative error for F4 and F8 tallies were less than 0.015% relative error. Source predictions of the SNM with uneven amounts of polyethylene shielding between the source and detectors was observed to overpredict and give invalid source location predictions. Source locations of the SNM with even amounts of polyethylene material between the source and each detector were found to be valid. With a 1 Ci 241 Am source activity, it was determined that 1630 s were needed to obtain the results for each detector system with the quasi-forward directional AmBe source. Coupling source and material identification together would increase acquisition time but would only require one system to determine.
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The growing conditions and key plant physiological processes of the most important staple crops for the human and animal food chains depend on climate conditions and are affected by climate extremes such as heat stress and drought.
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