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At least 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Heat Pipe Cooled Microreactors

Microreactor technologies are required to provide reliable carbon-free power generation in remote applications. The heat pipe cooled microreactor concept in particular offers notable advantages due to the passive operation of heat pipes enabling increased reliability and simplicity in a more compact form factor. There is a significant need for experimental work to aid and expedite the deployment of heat pipe microreactors due to their unique technological characteristics. Thus, there has been increased interest in heat pipe experiments by numerous institutions in order to support these efforts. The present work is a comprehensive review of heat pipe experiments, describing instrumentation, methods, phenomena of interest, and recent developments. In addition, legacy work on the operation of high-temperature heat pipes under irradiation is reviewed and discussed. Furthermore, the verification and validation efforts for the flagship heat pipe simulation code Sockeye are reviewed and requirements for future experiments are outlined. Lastly, future directions are proposed for heat pipe experimentation.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Application of recoil-imaging time projection chambers to directional neutron background measurements in the SuperKEKB accelerator tunnel

Gaseous time projection chambers (TPCs) with high readout segmentation are capable of reconstructing detailed 3D ionization distributions of nuclear recoils resulting from elastic neutron scattering. Here, using a system of six compact TPCs with pixel ASIC readout, filled with a 70:30 mixture of He:CO 2 gas, we analyze the first directional measurements of beam-induced neutron backgrounds in the tunnel regions surrounding the Belle II detector at the SuperKEKB e + e – collider. With the use of 3D recoil tracking, we show that these TPCs are capable of maintaining nearly 100% nuclear recoil purity to reconstructed ionization energies (E reco ) as low as 5 keV ee . Using a large sample of Monte-Carlo (MC)-simulated 4 He, 12 C, and 16 O recoil tracks, we find consistency between predicted and measured recoil energy spectra in five of the six TPCs, providing useful validation of the neutron production mechanisms modeled in simulation. Restricting this sample to 4 He recoil tracks with E reco > 40 keV ee , we further demonstrate axial angular resolutions within 8° and we introduce a procedure that under suitable conditions, correctly assigns the vector direction to 91% of these simulated 4 He recoils. Applying this procedure to assign vector directions to measured 4 He recoil tracks, we observe consistency between the angular distributions of observed and simulated recoils, providing first experimental evidence of localized neutron “hotspots” in the accelerator tunnel. Observed rates of nuclear recoils in these TPCs suggest that simulation overestimates the neutron flux from these hotspots. Despite this, we estimate these hotspots to produce the majority of neutron backgrounds in the accelerator tunnel at SuperKEKB’s target luminosity of 6.3 x 10 35 cm –2 s –1 , making them important regions to continue to monitor.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Analytics-at-scale of Sensor Data for Digital Monitoring in Nuclear Plants: 2nd Annual Report

For economic reasons, the nuclear industry is witnessing premature closure of nuclear power plants, despite excellent safety records. Operations and Maintenance (O&M) activities are some of the largest costs in operating legacy light-water plants. By reducing O&M costs, nuclear energy can become more economically competitive with other energy sources. This can be achieved by leveraging machine-learning and artificial intelligence technologies to develop data-driven algorithms to better diagnose potential faults within the system. Improved accuracy of the models can lead to a reduction in unnecessary maintenance, thus reducing costs associated with parts, labor, and unnecessary planned, forced, or extended outages. To address these challenges, the goal of this project is to perform research and development in the area of digital monitoring, i.e., the application of advanced sensor technologies (particularly wireless sensor technologies) and data science based analytic capabilities, to advance online monitoring and predictive maintenance in nuclear plants and improve plant performance (efficiency gain and economic competitiveness). This report summarizes the fiscal year 2020 research progress encompassing (1) different wireless vibration sensor and data indicators used to assess the health of a plant asset; (2) development of diagnostic models for fault detection; and (3) development of prognostic models for estimating the health of the system up to 7 days ahead.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Analytics-at-scale of Sensor Data for Digital Monitoring in Nuclear Plants (3 rd Annual Report)

Nuclear power plants collect and store large volumes of heterogeneous data from various components and systems. With recent advances in machine learning (ML) techniques, these data can be leveraged to develop diagnostic and short-term forecasting models to better predict future equipment condition. Maintenance operations can then be planned in advance whenever degraded performance is predicted, thus resulting in fewer unplanned outages and the optimization of maintenance activities. This enables lower maintenance costs and improves the overall economics of nuclear power. This report primarily focuses on developing a short-term forecasting process that leverages a feature selection process to distill large volumes of heterogeneous data and predict specific equipment parameters. A variety of feature selection methods, including Shapley Additive Explanations (SHAP) and variance inflation factor (VIF), were used to select the optimal features as inputs for three ML methods: long short-term memory (LSTM) networks, support vector regression (SVR), and random forest (RF). Each combination of model and input features was used to predict a pump bearing temperature both 1 and 24 hours in advance, based on actual plant system data. The optimal inputs for the LSTM and SVR were selected using the SHAP values, while the optimal input for the RF consisted solely of the response variable itself. Each model produced similar 1-hour-ahead predictions, with root mean square errors (RMSEs) of roughly 0.006. For the 24-hour-ahead predictions, differences could be seen between LSTM, SVR, and RF, as reflected by model performances of 0.036 ± 0.014, 0.0026 ± 0, and 0.063 ± 0.004 RMSE, respectively. As big data and continuous online monitoring become more widely available, the proposed feature selection process can be used for many applications beyond the prediction of process parameters within nuclear infrastructure. This report summarizes the Fiscal Year 2021 research progress encompassing the (1) data cleaning and feature selection necessary for ML applications; (2) development of short-term forecasting models to predict future plant process parameters for both single and multiple time steps ahead; and (3) validation of the feature selection methods and short-term forecasting models given new data from different systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

History of and Aftermath from the Withdrawal of the Intermediate-Range Nuclear Forces Treaty

On February 1, 2019, the United States and Russia withdrew from the three-decades old Intermediate-Range Nuclear Forces (INF) treaty. Events precipitating the withdrawal were allegations by both the United States and Russia of a variety of treaty violations. Until that point, the treaty had been a centerpiece of arms control and a key agreement of the global security architecture. The absence of such a pillar has the potential destabilize the status quo of arms control, creating significant uncertainty in global nuclear stability and security. In this paper, we present a historical review as overture to an analysis on the impacts of this development on force structure. This analysis examines the changes in U.S., Russian, and Chinese nuclear forces which may occur as a result of the treaty's demise. The article concludes with commentary on potential actions to preserve stability in a post-INF world.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2023

Neutron Leakage Spectra of the EUCLID Experiment [Abstract]

Integral experiments with sub-critical and critical configurations of special nuclear material are performed in support of nuclear data validation and adjustment. Different nuclear data evaluations may have different values for individual cross sections due to uncertainties in (or lack of) differential experiments, but compensating errors in these data sets can lead to the same k eff results for one application while vastly different for another application. One example of this is 239 Pu, where both ENDF/B-VIII.0 and JEFF-3.3 correctly compute k eff of the Jezebel critical assembly, but the individual contributions from each reaction are vastly different. To help resolve this specific case, the Experiments Underpinned by Computational Learning for Improvements in Nuclear Data (EUCLID) project used machine learning to design a set of experiments to help resolve the compensating errors in 239 Pu. A total of six responses were measured during the experimental campaign, which constrain the data in ways that k eff alone cannot and will be used for adjustment of the nuclear data. One of these responses is the neutron leakage spectrum, which recent work has shown to be useful for constraining the prompt fission neutron spectrum and inelastic scattering. The neutron leakage spectra were measured utilizing a 3 in. right cylinder EJ-301D detector. The measured signal in the detector was deconvoluted using spectrum unfolding techniques, which are presented and compared to simulations.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Contrastive Machine Learning with Gamma Spectroscopy Data Augmentations for Detecting Shielded Radiological Material Transfers

Data analysis techniques can be powerful tools for rapidly analyzing data and extracting information that can be used in a latent space for categorizing observations between classes of data. Machine learning models that exploit learned data relationships can address a variety of nuclear nonproliferation challenges like the detection and tracking of shielded radiological material transfers. The high resource cost of manually labeling radiation spectra is a hindrance to the rapid analysis of data collected from persistent monitoring and to the adoption of supervised machine learning methods that require large volumes of curated training data. Instead, contrastive self-supervised learning on unlabeled spectra can enhance models that are built on limited labeled radiation datasets. This work demonstrates that contrastive machine learning is an effective technique for leveraging unlabeled data in detecting and characterizing nuclear material transfers demonstrated on radiation measurements collected at an Oak Ridge National Laboratory testbed, where sodium iodide detectors measure gamma radiation emitted by material transfers between the High Flux Isotope Reactor and the Radiochemical Engineering Development Center. Label-invariant data augmentations tailored for gamma radiation detection physics are used on unlabeled spectra to contrastively train an encoder, learning a complex, embedded state space with self-supervision. A linear classifier is then trained on a limited set of labeled data to distinguish transfer spectra between byproducts and tracked nuclear material using representations from the contrastively trained encoder. The optimized hyperparameter model achieves a balanced accuracy score of 80.30%. Any given model—that is, a trained encoder and classifier—shows preferential treatment for specific subclasses of transfer types. Regardless of the classifier complexity, a supervised classifier using contrastively trained representations achieves higher accuracy than using spectra when trained and tested on limited labeled data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Epithermal Neutron Resonance Analysis Using a Compact D-T Generator

Nuclear material identification and isotopic composition analysis are important capabilities for safeguards and the verification of nuclear arms control and nonproliferation treaties. Epithermal neutron resonance imaging has been proposed for spent fuel assay measurements, and the development of a mobile neutron imaging system could provide such a tool for on-site applications. This technique is highly sensitive to isotopes of interest for safeguards applications (e.g. 235,238 U, 238,239,240 Pu) and yields unique transmission spectra for a target containing actinides and some mid- and high-Z elements. Paired with a position-sensitive neutron detector, such a system could provide spatial information of the target isotopic composition. Recent progress in the development of high intensity neutron sources could make mobile epithermal neutron imaging designs increasingly feasible to serve as a tool for nuclear inspectors. In this work, Monte Carlo (MC) simulations were performed to further optimize a previous design for an epithermal neutron resonance-based analysis apparatus using a D-T portable neutron generator. Additionally, experimental work is being performed to test the feasibility of this method. The MC simulations of moderation time distributions for different moderator compositions and thicknesses were measured to provide a multi-group correction for neutron time-of-flight energy reconstruction.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Superconducting nanowires as high-rate photon detectors in strong magnetic fields

Superconducting nanowire single photon detectors are capable of single-photon detection across a large spectral range, with near unity detection efficiency, picosecond timing jitter, and sub-10 µm position resolution at rates as high as 10 9 counts/s. In an effort to bring this technology into nuclear physics experiments, we fabricate Niobium Nitride nanowire detectors using novel material preparation methods and test their performance in strong magnetic fields. Furthermore, we demonstrate that these devices are capable of detection of 400 nm wavelength photons with saturated internal quantum efficiency at liquid helium temperatures and in magnetic fields potentially up to 8 T at high rates and with nearly zero dark counts.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Radiation effects induced by the energetic protons in 8x8x32 mm3 CdZnTe detectors

We report CdZnTe (CZT) detectors have been used in the past and are being actively considered for future gamma-ray space telescopes. One of the challenges of operating CZT detectors in low Earth orbit is radiation damage caused by energetic protons. Previous studies concluded that fluences of 10 8 -10 9 p/cm 2 are sufficient to cause shifting of peak positions and degradations of energy resolution. Degradation of detectors after proton irradiation has been extensively investigated to determine their radiation resistance limits and performance recovering procedures for their applications in space and nuclear nonproliferation. Here we present the study of radiation effects induced by 100 MeV protons in 3-cm long CdZnTe detectors that we recently proposed for the gamma-ray telescope GECCO. The goal this study is to evaluate the feasibility of using such long CZT detectors in future space missions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Data Exploration of UAV Flights Recorded on Smartphones

The increasing accessibility and technological advancements of unmanned aerial vehicles (UAVs) pose legitimate concerns to nuclear non-proliferation efforts. To deter and combat unwanted or nefarious UAV activity near or around nuclear sensitive sites, the ability to quickly and easily detect drones becomes vital. Our team is exploring the use of ubiquitous smartphones as multi-modal sensors (microphone, accelerometer, gyroscope, etc.) to meet this challenge. In conjunction with Idaho National Lab, two UAV flight tests and one stationary in-air test were conducted and recorded with eight Samsung Galaxy S8 smartphones. This work presents initial characterization and annotation of acoustic signals associated with UAV activity.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2021

Comparison of MCNP thermal scattering laws to inelastic neutron scattering data

Neutron transport computations employ thermal scattering laws (TSLs) to account for the influence of material structure and collective excitations at low energy (≲ 10 eV). Recent work has been undertaken to improve TSL accuracy and expand the number of available materials in the Evaluated Nuclear Data File (ENDF). However, comparison of TSL to measured data is often sparse. In this work, we present a method to evaluate TSL accuracy in the Monte Carlo N-Particle (MCNP) code by simulating inelastic neutron scattering (INS) measurement and comparing to data. The method is briefly described with example application to the recently released ENDF-VIII TSL library for polyethylene as well as a cryogenic PE TSL we are separately developing.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Linear and Non-linear Optical Characterization of Glass and Sapphire for Optical Instrumentation of Advanced Fission Reactors

The linear and nonlinear optical properties of glass and sapphire are investigated to determine the material’s suitability to support optical instrumentation for advanced fission reactors. A post-irradiation examination experiment consisting of a Z-scan, measuring nonlinear optical absorption and refraction, and a spectrometer, measuring linear optical absorption was developed and used. Radiation-induced negative nonlinear optical absorption was observed for the first time in fused silica, sapphire, and borosilicate glass under both types of thermal annealing conditions and the respective effects of thermal annealing are reported.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Direct analysis of cotton swipes for plutonium isotope determination by microextraction-ICP-MS

This study demonstrates a method for determining the isotopic composition of low-level (sub-pg) plutonium (Pu) directly from a cotton swipe. Environmental sample (ES) swipes are routinely employed as a tool for monitoring activities in nuclear facilities. Traditional ES swipe analysis, as employed in nuclear safeguards, utilizes laborious ashing, digestion, and chemical separation procedures prior to mass spectrometric (MS) analysis. Here, an innovative sample introduction technique employing a microextraction probe to extract Pu directly from the swipe surface is described. The microextraction probe lowers onto the swipe surface, seals on a small area (8 mm 2 ), and delivers solvent (2% HNO 3 ) to extract actinide material that may be present. The extracted analyte is subsequently directed into a sector field inductively coupled plasma (ICP)-MS for isotope ratio determination. This microextraction-ICP-MS method successfully determined the isotopic composition ( 240 Pu/ 239 Pu and 242 Pu/ 239 Pu) of three Pu certified reference materials (CRM 136, 137, and 138) that were deposited (1 pg) onto ES swipes. The percent relative difference from the certified value, uncorrected for instrumental fractionation, was <2% for the 240 Pu/ 239 Pu ratio on all three CRMs and <10% for the 242 Pu/ 239 Pu ratio on CRM 136 and 138. Here, the percent relative standard deviation, an estimate of the sample-to-sample isotopic precision, was <4% for the 240 Pu/ 239 Pu and <15% for the 242 Pu/ 239 Pu. Method limits of detection were determined, based on measurements of an enriched 244 Pu material, to be ~7 fg. Additionally, a mixed uranium (U) and Pu deposition was made to determine the method's ability to simultaneously extract U and Pu and determine the isotopic composition of both analytes.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Erratum to: Data Fusion to Support Integrated Nuclear Detonation Detection [Slides]

The original document (LA-UR-22-29547) contained minor equation errors that approximate correct equations that couple the multi-sensor, serial system detector thresholds for a seismic Rayleigh wave detector and an acoustic energy detector. Those errors appeared on slides 62-67. This erratum associates the following slides with the erroneous slides. The numbers of the erroneous slides are marked at the upper right in small text in orange. A result of those errors over-predict the performance of the two-sensor serial network, that is, the former document provides an optimistic estimate of system performance.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Hybrid Modeling of a Circulating Water Pump Motor

To achieve high capacity factors, the nuclear fleet has relied on labor-intensive and time-consuming operation and preventive maintenance programs for plant systems. Manually performed inspection, calibration, testing, and maintenance of plant assets at periodic frequencies, along with the time-based replacement of assets irrespective of condition, have resulted in a costly, labor-centric business model. Fortunately, there are technologies that can eliminate unnecessary preventive maintenance activities by deploying risk-informed predictive maintenance, enabling the transition to a technology-centric business model.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

First measurement of the nuclear-recoil ionization yield in silicon at 100 eV

We measured the nuclear--recoil ionization yield in silicon with a cryogenic phonon-sensitive gram-scale detector. Neutrons from a mono-energetic beam scatter off of the silicon nuclei at angles corresponding to energy depositions from 4 keV down to 100 eV, the lowest energy probed so far. The results show no sign of an ionization production threshold above 100 eV. These results call for further investigation of the ionization yield theory and a comprehensive determination of the detector response function at energies below the keV scale.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Machine-Learning Enabled Evaluation of Probability of Piping Degradation In Secondary Systems of Nuclear Power Plants

The transition to condition-based, risk-informed automated maintenance will contribute to a significant reduction of operations and maintenance costs that account for the majority of nuclear power generation costs. Furthermore, of the operations and maintenance costs in U.S. plants, approximately 80% are labor costs. To address the issue of rising operating costs and economic viability, technologies used to perform online monitoring of piping and other secondary system structural components in commercial nuclear power plants (NPPs) are under evaluation. These online monitoring systems have the potential to identify when a more detailed inspection is needed using real time measurements, rather than at a pre-determined inspection interval thus reducing the maintenance cost. This paper describes distributed high-temperature stable fiber sensors fabricated in optical fibers through a roll-to-roll laser direct writing process using femtosecond lasers. Using phase-sensitive optical time domain reflectometry, distributed acoustic and vibration sensors can be developed and deployed to critical components and systems in NPPs to perform active measurements with spatial resolution down to 0.5-meter throughout the piping systems. Complex acoustic and vibration signatures harnessed by distributed sensors are registered and analyzed by artificial intelligence algorithms for degradation detection and flaw identification. Piping elbows with machined-in flaws were instrumented with fiber sensors. High-spatial-resolution data were used to develop and validate machine learning algorithms, including both linear and nonlinear regression, and classification. Additionally, classification and sensor analysis were also performed for data analysis. The paper concludes with recommendations and future work on applications of machine learning enabled high-resolution fiber sensors for piping degradation monitoring in current or future NPPs.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗