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At least 1,243 records · Page 69

Machine Learning technique for isotopic determination of radioisotopes using HPGe γ-ray spectra

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.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Correlated characterization of 20 Ne-implanted targets using nuclear reaction analysis, Rutherford backscattering spectrometry, and ion transport modeling

In this article, we present the preparation and characterization of a large sample of implanted noble gas targets for use in precision nuclear astrophysics measurements with intense proton beams. Tantalum and titanium backings were prepared using wet-acid etching, outgassed via resistive heating, and implanted with 20 Ne + beams from differing ion sources. These experimental targets were investigated using both nuclear reaction analysis techniques on the 1169-keV resonance in 20 Ne(p, $\gamma$) 21 Na and Rutherford backscattering spectrometry analysis with 2-MeV α-particle beams. Results from these analyses reveal small target-to-target variations in stoichiometry, while exhibiting excellent agreement independent of ion-beam analysis method. We also present a self-consistent validation of the nuclear reaction analysis results using ion transport simulations in TRIM-2013 that rely on input parameters from SIMNRA scattering-yield fits to Rutherford backscattering spectra. In addition to a complete description of the implantation profile, this analysis method provides an alternate solution for characterizing a large sample of implanted targets when no suitable resonances are available for nuclear reaction analysis.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Advanced silicon tracking detector developments for the future Electron-Ion Collider

The proposed Electron-Ion Collider (EIC) will operate high-luminosity high-energy electron+proton and electron+nucleus collisions at the collision energies from 20 GeV to 141 GeV to solve several fundamental questions in the high energy and nuclear physics fields. Its instantaneous luminosity can reach 10 33-34 cm -2 s -1 and the bunching crossing rate is around 10 ns. The EIC project has received CD1 approval from the US DOE in 2021 and moves toward the machine design and preparation for construction. To realize various particle measurements with high precision at the future EIC, a low material-budget and high-granularity silicon vertex and tracking detector with fine spatial and momentum resolutions and nearly 4π solid angle coverage is desired. The Monolithic Active Pixel Sensor (MAPS) and AC Coupled Low Gain Avalanche Diode (AC-LGAD) technologies stand out of several advanced technology options for the EIC silicon vertex and tracking detector subsystems. The MAPS technology has advanced features of low material budget, low power consumption, good radiation resistance and fine spatial resolution. The AC-LGAD technology can achieve fast timing resolution. Latest studies and progress of the EIC silicon vertex and tracking detector conceptual design, performance validations in simulation and ongoing MAPS and AC-LGAD R&D will be shown. Furthermore, schedule and plan of the EIC project detector development will be discussed as well.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Leveraging Radiofrequency Identification Success Beyond Hazardous Material Inventory Management at a National Laboratory

Effective inventory management can be overshadowed by conflicting priorities in organizational procedures, particularly in research-focused institutions such as national laboratories that handle expensive, delicate, and hazardous materials. Here, this study investigated the potential of radiofrequency identification (RFID) technology, currently used for hazardous chemical inventory, in applications with higher metal interference and absorption, specifically pressure release device (PRD) compliance and nuclear container management, at Lawrence Livermore National Laboratory (LLNL). This study was done to document best practices to enhance inventory identification speeds for inventory reconciliation and inventory recall and to explore optimal configurations for RFID implementation compared to traditional manual methods of equipment management. Tests were conducted to determine the ideal RFID tag orientation (read at angles of 0°, 90°, and 270°), various container layouts (linear, separated, curved, operational), and ID methods such as manual, barcode, and RFID performing three trials per method per orientation. Results indicated that 0° was the optimal read angle for minimizing metallic interference, and the operational and curved arrangements significantly outperformed the linear and separated configurations in read speed. 3D printed mounts were developed and tested, increasing the read range of the RFID reader by up to 235% in cases of high metallic interference. The RFID technology demonstrated an average speed increase of 65% over a simplified manual identification, which supports the conclusion that RFID is a more efficient method for large hazardous inventory management and equipment reconciliation. Additionally, capturing meta-data, such as location and date, can be used to query for inventory recall and automated updating of record information.

42 ENGINEERING↗

Neutron Response of the EJ-254 Boron-Loaded Plastic Scintillator

Organic scintillators doped with capture agents provide a detectable signal for neutrons over a broad energy range. This work characterizes the fast and slow neutron response of EJ-254, an organic plastic scintillator with 5 loading by weight. For fast neutrons, the primary mechanism for light generation in organic scintillators is n-p elastic scattering. To study the fast neutron response, the proton light yield of EJ-254 was measured at the 88-Inch Cyclotron at Lawrence Berkeley National Laboratory. Using a broad-spectrum neutron source and a double time-of-flight technique, the EJ-254 proton light yield was obtained over the energy range of approximately 270 keV to 4.5 MeV and determined to be in agreement with other plastic scintillators comprised of the same polymer base. To isolate the slow neutron response, an AmBe source with polyethylene moderator was made incident on the EJ-254 scintillator surrounded by an array of EJ-309 observation detectors. Events in the EJ-254 target coincident with the signature 477.6 keV γ ray (resulting from de-excitation of the residual 7Li nucleus following boron neutron capture) were identified. Pulse shape discrimination was used to evaluate the temporal differences in the response of EJ-254 scintillation signals arising from γ-ray and fast/slow neutron interactions. Clear separation between γ-ray and fast neutrons signals was not achieved and the neutron capture feature was observed to overlap both the γ-ray and fast neutron bands. Taking into account the electron light nonproportionality, the neutron capture light yield in EJ-254 was determined to be 89.4±1.1 keVee.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Spectral Data Fusion From Handheld Laser-Induced Breakdown Spectroscopy (LIBS) and X-ray Fluorescence (XRF) Analyzers for Improved Detection of Cerium in a Simulated Dispersal Accident

Here, this work implements a mid-level data fusion methodology on spectral data from handheld X-ray fluorescence and laser-induced breakdown spectroscopy analyzers to quantify plutonium surrogate (CeO 2 ) contamination in soil samples for the first time. Spectral data from each analyzer were used independently to train supervised machine learning regressions to predict Ce concentration. Fused features from both data sets were then used to train the same models, comparing prediction performance by evaluating model precision and sensitivity. Fusing principal component scores from the two sensors yielded an order of magnitude improvement in precision and sensitivity of predictions made with an artificial neural network, compared to predictions made by models trained on independent sensor data. As a result, a boosted ensemble trained on the fused spectral features yielded an ideal predictor with root-mean-squared error on the order of 10 –6 and calculated limit of detection order 10 –5 wt %.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Integrated Risk-Informed Condition Based Maintenance Capability and Automated Platform: Technical Report 1

Due to continuing global energy market trends, driven heavily by the abundant preserves of natural gas, there is an immediate need to reduce costs associated with operation and maintenance (O&M) for the current domestic nuclear power industry and for future reactor developments. This is to ensure that nuclear power generation remains an economically competitive and viable option in the energy market. O&M costs include labor-intensive preventive maintenance (PM) programs, which involve manually-performed inspection, calibration, testing, and maintenance of plant assets at periodic frequency and time-based replacement of assets, irrespective of their condition. This has resulted in an expensive, labor-centric business model to achieve high capacity factors. Fortunately, there are technologies (advanced sensors, data analytics, and risk assessment methodologies) that can enable the transition from a labor-centric business model to a technology-centric business model. The technology-centric business model will result in a significant reduction of PM activities, laying the foundation for real-time condition assessment of plant assets, reducing overall labor and part costs. To enable this transition, PKMJ Technical Services LLC is partnering with the U.S. Department of Energy’s Idaho National Laboratory (operated by the Battelle Energy Alliance, LLC) and the Public Services Enterprise Group (PSEG) Nuclear, LLC in the Integrated Risk-Informed Condition-Based Maintenance Capability and Automated Platform Project. In this report, the configuration of a digital cloud platform using Microsoft Azure is discussed, data from the PSEG Salem Nuclear Generating Station Units 1 & 2 are imported into a digital cloud platform, and the data is used for an evaluation of several key areas: cost impact analysis, risk-informed model development, and preventive maintenance strategy optimization. First, the cost impact analysis reviews which plant assets are potential good candidates for condition-based monitoring. Next, INL utilized the data in their local environment to develop the risk-informed model; which provides estimates of failure rates and probability of failures of assets based upon their past performance. The developed model is performed on assets selected from the cost impact analysis. Lastly, engineers assess the preventive maintenance strategy for the selected assets at PSEG against maintenance strategies in the nuclear industry for similar assets to potentially identify acceptable justification for the extension of current maintenance frequencies.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

An Overview of Emerging Nuclear Sensor Technologies: Challenges, Advancements and Applications

Nuclear sensors are essential for detecting and measuring nuclear radiation in various applications, including nuclear power plants, medical imaging, and environmental monitoring. Traditional nuclear sensors have served these fields for decades, but recent advancements in emerging sensor technologies offer novel improvements in accuracy, sensitivity, and reliability. This review presents an up-to-date overview of recent progress in the advancements of nuclear sensor technologies, their diverse applications, challenges in implementation, and opportunities for future research.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Feasibility of an Active Interrogation System to Classify Waste with He-4 Neutron Spectroscopy

This work investigates a 4 He-detector active interrogation system that leverages neutron spectroscopy to classify nuclear waste streams. MCNP models tested the concept through the simulation of a D-D neutron generator, an array of 4 He detectors, and various waste compositions. The fast-neutron Differential Die-Away signature was augmented with a neutron-energy discrimination signature. This signature isolates induced fission neutrons, the energy of which is greater than that of the D-D monoenergetic spectrum. With the incorporation of this spectroscopic technique, the measurement time decreased by 3–9% (depending on the degree of neutron moderation and absorption presented by the sample), demonstrating how neutron spectroscopy can enhance active interrogation methods. The reduced measurement times would have significant financial and logistical benefits for facilities with large footprints of low-level waste production.

4He detectors↗

TRANSPORTATION CONSIDERATIONS FOR USED FUEL AND ADVANCED NUCLEAR ENERGY TECHNOLOGIES

This study focuses on requirements and challenges pertaining to advanced transportable nuclear energy systems, which are currently in development and feature varying degrees of mobility. These reactors mostly use high-assay low-enriched uranium (HALEU) fuel, and only limited refueling is required during their lifecycles. Due to the smaller source terms, higher burnup fuel, and potentially higher thermal efficiency involved, the waste inventory will be less than that generated by conventional light-water reactors. However, the relevant regulatory requirements must be met when transporting HALEU fuel (whether fresh or used) inside the reactor vessel—mainly the fuel material transportation, siting, and physical protection requirements outlined in US Nuclear Regulatory Commission (NRC) regulations 10 CFR 71 and 73. Spent nuclear fuel (SNF), as well as irradiated fuel (used during any interval of reactor operation), contains highly radioactive materials that generate heat and necessitate special precautions during transportation. This study addresses the challenges that this incurs, and outlines potential solutions.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Pressurizer dynamic model and emulated programmable logic controllers for nuclear power plants cybersecurity investigations

This work demonstrates the functionality of the pressurizer using a fast-running three regions, non-equilibrium model and control programs of emulated PLCs. The state variables from an integrated model of primary loop in a representative PWR plant are communicated to the pressurizer’s emulated PLCs using a synchronized data transfer function. In turn, the PLCs communicate back instructions to the pressurizer model to adjust the pressure and water level to remain within preprogramed setpoints. Pressurizer model simultaneously solves the coupled mass and energy conservation equations in the saturated vapor and saturated and subcooled liquid regions using fixed step solver. Results demonstrate the response of the pressurizer model linked to an emulated pressure and water level PLCs in a simulated transient involving surge-in and surge-out events. The 50 ms response delay time for the emulated PLC insignificantly affects operation and predictions of the pressurizer model.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗