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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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An analysis was made of the UF6 fueled gas core reactor as a function of cavity reactor criticality and fluid mechanics tests, investigations of uranium optical emission spectra, and radiant heat transfer power plant studies. Data are also given on nuclear and thermodynamic cycle analysis.
Compensating errors between nuclear data observables in a library can adversely impact application simulations. The primary goal of the EUCLID project (Experiments Underpinned by Computational Learning for Improvements in Nuclear Data) is to reduce compensating errors in nuclear data. A new criticality experiment, described in this work, was designed with the specific target nuclear data of 239 Pu fission, inelastic scattering, elastic scattering, capture, nu-bar, and prompt fission neutron spectrum (PFNS). This work will focus on the design and execution of the EUCLID experiment, performed on the Planet vertical lift critical assembly machine at the National Criticality Experiments Research Center (NCERC). The criticality experiment includes two different configurations with very different geometries: one is cube-like to minimize neutron leakage while the other is slab-like to maximize leakage. Having these two widely varying configurations allows the scattering sensitivities of 239 Pu to the neutron multiplication factor to be greatly changed while minimally impacting the other cross section sensitivities. Both configurations utilize the Pu ZPPR (Zero Power Physics Reactor) plates as fuel. The experiments were designed using a D-Optimality criteria, which is an optimization method minimizing the log-determinant of the adjusted nuclear data covariance for the target reactions. These experiments include not only inference of k eff , as done in all critical benchmark experiments, but several other responses as well, such as neutron multiplication measurements and reaction rate ratios. After analysis of the measured data is complete, adjustment of nuclear data will be performed to assess whether the new experimental data successfully reduced compensating errors.
Importance of nuclear data to bias changed significantly when including diverse measurement sets. LLNL pulsed sphere measurements provided information for 9 Be nuclear data above 2 MeV and subcritical benchmarks provided information for 240 Pu nuclear data between 100 keV and 10 MeV. Pulsed sphere leakage spectra and neutron noise observables are differently sensitive to nuclear data compared to critical benchmarks. Leakage spectra are sensitive to nuclear data above 5 MeV. Count rate and Feynman Y are more sensitive to nuclear data. A change in bias can help evaluators identify discrepant nuclear data. In the future, EUCLID plans to perform RAFIEKI analysis with additional benchmark and measurement sets.
NNSA is responsible for managing national nuclear security missions: ensuring a safe, secure, and reliable nuclear deterrent; supplying nuclear fuel to the Navy; and supporting the nation’s nuclear nonproliferation efforts. However, over half of NNSA’s facilities are more than 40 years old, and roughly one-third date back to the Manhattan Project. To execute its critical nuclear security missions, NNSA is making large investments to modernize its nuclear production capabilities. This ramp up represents NNSA’s largest modernization effort since the Cold War. Given the scale of these efforts, NNSA’s Office of Secondary Stage Production Modernization has implemented data driven techniques to prioritize investments and inform strategic decision making. NNSA, with support from its site managing contractors, has developed and implemented an integrated schedule and risk management system to address the issues and limitations with the traditional approach. The multi-year integrated schedules are key to identifying program linkages and managing large portfolios comprised of many different projects and efforts. In conjunction with the integrated schedule, a new program risk management system has also been developed and implemented, which manages program risks and opportunities, along with specific mitigation strategies to reduce or eliminate the risks per timelines that are tracked in the integrated schedule. This paper is supplemented with a follow-on presentation on effective management of program material and throughput modeling.
Many new reactor designs, such as advanced reactors and micro reactors, have materials that lack nuclear data validation. This is also true for many other applications in criticality safety and global security. Both differential and integral experiments are needed to validate cross-section data. Without this, a user cannot have confidence in the predicted results of a radiation-transport code. This work describes an approach called ARCHIMEDES (Application Relevant Critical/Subcritical HEU/Pu-based Integral Measurements for Enhancing Data and Evaluating Sensitivities) to design new criticality experiments that have similar k eff cross-section sensitivities to an application of interest. This process involves simulations to generate cross-section sensitivities to a parameter of interest (such as k eff ), a gap analysis to determine which existing benchmarks are most similar to the application, and an experiment optimization. Recently, there has been a great deal of interest in the reactor physics community on advanced reactors, micro reactors, and accelerator driven systems (ADS). This work will apply the described method to specific examples in this area. The focus of this work will be on the sensitivity study and gap analysis, while future work will include experiment design.
This work updates neutron reactions on 63,65 Cu. It also discusses the motivations of the research which is to correct deficiencies in benchmark performance since critical assembly configurations use Cu as a reflector. This presentation discusses the incorporation of URR parameters fit to 2017 63 Cu(n, γ) measurement by Weigand et al., as well as preliminary work on angular distributions and states that validation against copper-sensitive ICSBEP benchmarks is ongoing. The presentation concludes by stating that R -matrix analysis coupled to consistent average parameters used in the URR have been performed for 63,65 Cu isotopes, that future work will focus on the angular distributions, and that an analysis of additional data measured on natural Cu sample is in progress.
Detailed descriptions are presented of the input data for each of the MSFC computer codes applied to the analysis of a realistic nuclear propelled vehicle. The analytical techniques employed include cross section data, preparation, one and two dimensional discrete ordinates transport, point kernel, and single scatter methods.
The ability to rapidly detect and characterize the chemistry of trace quantities of inorganic species has multiple potential applications, including nuclear safeguards, nuclear forensics, and environmental monitoring. Some mass spectrometric methods currently exist, but they are costly and time-intensive, often destroying chemical information in the process of ionization. Ambient mass spectrometry is a relatively new analytical method in which samples are introduced to the mass spectrometer from atmospheric pressure, rather than from high vacuum. Ambient MS can operate with gentler ionization methods such as electrospray ionization (ESI) and paper-spray ionization (PSI), reducing fragmentation and preserving the chemical information of the analyte. This may be useful for determining the intended purpose of material in a sample. Strontium, part of nuclear fallout, is analyzed here to test these new analytical methods for viability. Electrospray ionization mass spectrometry functions by spraying the sample as a solution through a needle charged to a few kV. A nebulizing gas flows out along with the sample to create the spray before droplets enter a drying chamber with heated drying gas to remove the solvent molecules. From there bare analyte ions enter the mass spectrometer. This method has the advantage of relatively fast sample preparation and stable ionization. More stable ionization produces higher-quality data. Paper-spray ionization mass spectrometry operates by applying sample to a paper triangle. The paper triangle is then electrified in a metal clamp to a few kV before spray solvent is applied. The spray solvent transports analyte in the sample to the tip, where it is ionized and enters the mass spectrometer. One advantage of this method is reduced sample preparation - an analyst can even swipe a surface and cut a triangle directly from the swipe. The data analysis serves to rapidly process tens of samples all at once. The mass spectrometer outputs a file containing a few hundred spectra for each sample. The pipeline processes each one to identify peaks of interest. A full palette of data analysis metrics are generated, including plots of the raw data and plots of calculated values such as isotopic ratios. The pipeline filters the raw data to remove noise and background peaks, as well as peaks representing detector saturation. A heat map is generated to show the overall spectrum in mass-time- intensity space. Post-processing, we obtained the desired calibration curves, demonstrating that both ESI-MS and PSI-MS are viable methods for quantifying trace amounts of inorganics while retaining their chemical speciation information. ESI-MS demonstrated a linear response region of 100 ppb to 20 ppm, with a limit-of-detection of around 100 ppb. At top left is an example spectrum with the {sup 88}SrNO{sub 3} peak identified to show the retention of chemical speciation. PSI-MS showed a linear response region of around 1 ppb to 1 ppm, with a limit-of-detection of near 1 ppb. PSI-MS produced more erratic results than ESI-MS, likely due to the variability in ionization resulting from manual positioning of the paper triangle near the MS inlet. Future efforts will focus on improving the repeatability of PSI-MS as well as analyzing other species of interest such as cerium and uranium. Improvements will also be made to create more reliable data extraction and intelligent data filtering.
In this paper, we have developed a methodology to estimate the spatiotemporal distribution of radiation air dose rates around the Fukushima Daiichi Nuclear Power Plant (FDNPP). In our exploratory data analysis, we found that (1) the temporal evolution of dose rates is composed of a log-linear decay trend and fluctuations of air dose rates that are spatially correlated among adjacent monitoring posts; and (2) the slope of the log-linear environmental decay trend can be represented as a function of the apparent initial dose rates, coordinate position, land-use type, and soil type. From these observations, we first estimated the log-linear decay trend at each location based on these predictors, using the random forest method. We then developed a modified Kalman filter coupled with a Gaussian process model to estimate the dose-rate time series at a given location and time. We applied this method to the Fukushima evacuation zone (as of March 2017), which included 17 monitoring post locations (with monitoring datasets collected between 2014 and 2018) and generated a time series of dose-rate maps. Our results show that this approach allows us to produce accurate spatial and temporal predictions of radiation dose-rate maps using limited spatiotemporal measurements.
This is a presentation to the INL AI/ML Activities Workshop in May 2023 discussing the application of AI/ML in nuclear operating experience program and low power shutdown initiating event study.
The calculation of the spectrum of antineutrinos ($\bar{v}_e$) from a reactor is a complicated problem requiring several nuclear data and physics inputs. Many of these have been investigated thoroughly to improve calculations and properly account for uncertainties. One input which has heretofore escaped consideration is the fission-yield distribution between ground and isomeric states. Here, in this work, we explore the effect of incorporating newly evaluated isomeric yield ratios (IYR) for 43 fission products into summation calculations and identify the disproportionate importance of certain isotopes, particularly at higher energies. Our analysis indicates that updated IYRs contribute to a significant increase in the $\bar{v}_e$ spectrum around and above 7 MeV, with increases of more than 50% at higher energies. Through a detailed sensitivity study, we highlight a number of isotopes for which the IYR has a substantial effect on the $\bar{v}_e$ spectrum. This work stresses the critical role of isomeric yields in calculations of reactor $\bar{v}_e$ spectra and points to the necessity for their accurate experimental determination, especially for key fission products, in order to refine our understanding and address the observed anomalies between measured and calculated $\bar{v}_e$ spectra.
Securing enough data has been a main challenge in human reliability analysis (HRA). Many researchers and institutes have made a lot of efforts for collecting HRA data to produce reasonable human error probabilities (HEPs) as well as reduce the uncertainty of HRA quantification. Representatively, U.S. Nuclear Regulatory Commission (U.S. NRC), Korea Atomic Energy Research Institute (KAERI) and Idaho National Laboratory (INL) have led lots of empirical research regarding the HRA data collection. The U.S. NRC and KAERI have mainly carried out full-scope simulator research collecting HRA data through experiments using full-scope simulators with actual operators. In contrast, INL has experimentally collected the data using simplified simulators and student operators. INL has proposed the Simplified Human Error Experimental Program (SHEEP) framework to complement full-scope data collection efforts by suggesting a way to infer full-scope data based on experimental data collected from students operating simplified simulators, specifically the Rancor Microworld Simulator (Rancor) and the Compact Nuclear Simulator (CNS). The aim of the SHEEP framework is to lower the entry point for collecting useful HRA data by securing large sample sizes at a reasonable amount of cost and labor while also guaranteeing a high degree of freedom when designing experiments. The authors’ previous research investigated whether data collected from the SHEEP framework could support a representative full-scope study. Besides, human performance differences between professional and student operators when using Rancor and CNS have been analyzed to understand the lack of fidelity of the simplified simulators and student operators within the SHEEP study. As a follow up research, this study experimentally investigates students’ learning effects and the performance trends over a certain period when using Rancor. This study aims to find out 1) how much training or education is required to collect HRA data from non-experts (i.e., students) when using Rancor and 2) how much differences there are in human performance measures between students and professional operators. In this study, a longitudinal experiment is developed. The four experiment trials with two weeks interval are carried out for sixteen undergraduate students majoring nuclear engineering at Chosun University. Totally four scenarios randomly selected from ten Rancor scenarios are used in each experiment trial. Four human performance measurements (i.e., workload, situation awareness, time and error) are considered in the experiment. Lastly, the trend of students’ performance is compared with operator data having been collected from the previous experiment.
This is a lightweight, easy to use Python analysis package for analyzing public low-energy nuclear reaction data relevant to understanding basic nuclear processes, often of relevance to Solar Fusion, to improve our theoretical understanding of them, and enable connections with input from lattice QCD. It performs the analysis in a Bayesian Framework and supports Bayesian Model Averaging to provide a robust uncertainty quantification.
Relations between neutron star properties that do not depend on the nuclear equation of state offer insights on neutron star physics and have practical applications in data analysis. Such relations are obtained by fitting to a range of phenomenological or nuclear physics equation of state models, each of which may have varying degrees of accuracy. In this study we revisit commonly used relations and reassess them with a very flexible set of phenomenological nonparametric equation of state models that are based on Gaussian processes. Our models correspond to two sets: equations of state which mimic hadronic models, and equations of state with rapidly changing behavior that resemble phase transitions. Here we quantify the accuracy of relations under both sets and discuss their applicability with respect to expected upcoming statistical uncertainties of astrophysical observations. We further propose a goodness-of-fit metric which provides an estimate for the systematic error introduced by using the relation to model a certain equation-of-state set. Overall, the nonparametric distribution is more poorly fit with existing relations, with the I–Love–Q relations retaining the highest degree of universality. Fits degrade for relations involving the tidal deformability, such as the binary-Love and compactness-Love relations, and when introducing phase transition phenomenology. For most relations, systematic errors are comparable to current statistical uncertainties under the nonparametric equation of state distributions.
Characterizing neutron activation of stainless steel is a critical task for many applications, and yet little activation data is available for this alloy. To address the need, a series of neutron activation experiments have been performed using the Flattop critical assembly at the National Criticality Experiments Research Center. Several experimental conditions were probed for complete understanding of the neutron environment, including foil composition and position within the assembly. The experimental results reported here will aid in the production of stainless steel benchmarks for a range of nuclear data applications including those used in nuclear forensics analysis.
This experiment was carried out during three balloon flights which provided a total exposure of 3500 + or - 60 sq m sec sterad at an average depth of 4.8 g/sq cm The detector, in which the development of cascade showers in a 33.7 rl absorber was sampled by 10 scintillation counters and 216 Geiger-Muller tubes, was calibrated at the Cornell Electron Synchrotron, the separation of cosmic electrons from the nuclear background was confirmed by extensive analysis of data from the flights, from the calibration and from ground level exposure. The spectral intensity of primary cosmic ray electrons were found in particles/sq m sec sterad GeV. Similarly, the ground level spectrum of secondary cosmic ray electrons was also found. The steepness of the spectrum of cosmic electrons relative to that of nuclei implies one of the following conclusions: either the injection spectrum of electrons is steeper than that of nuclei, or the electron spectrum has been steepened by Compton/synchrotron losses in the energy range covered by the experiment.
The Nuclear Science User Facilities (NSUF) first gathered and reported metric data for Fiscal Year (FY) 2014. The goal of the FY 2014 NSUF Metric Report was to establish metric measures and to provide baseline data to evaluate future performance of the NSUF. Each metric goal and objective were established through NSUF discussions with Department of Energy (DOE) Idaho Operations Office and Office of Nuclear Energy staff. This report provides the data, analysis, and conclusions for the FY 2019 metrics.
As part of the Scientific Discovery through Advanced Computing (SciDAC) program, the Quantum Chromodynamics Nuclear Tomography (QuantOM) project aims to analyze data from Deep Inelastic Scattering (DIS) experiments conducted at Jefferson Lab and the upcoming Electron Ion Collider. The DIS data analysis is performed on an event-level by combining the input from theoretical and experimental nuclear physics into a single, composable workflow. The optimization itself (I.e. fitting the experimental data with theoretical predictions) is carried out by a machine / deep learning algorithm. The size of the acquired DIS data as well as the complexity of the workflow itself require that the analysis is performed across multiple GPUs on high performance computing systems, such as Polaris at Argonne National Laboratory. This presentation discusses the novelties and challenges that came along with parallelizing this workflow. Recent results are compared to common distributed training techniques.