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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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The Surveillance and Test System (SATS) checks the functionality of ATR’s Plant Protection System (PPS). The analogue linearity test checks that the SATS analog and digital conversions are accurate to +/- 61 mVdc. This project analyzes whether this accuracy range can be increased without impacting the uncertainty analysis of the PPS system.
We propose a learning algorithm for discovering unknown parameterized dynamical systems by using observational data of the state variables. Our method is built upon and extends the recent work of discovering unknown dynamical systems, in particular those using deep neural network (DNN). We propose a DNN structure, largely based upon the residual network (ResNet), to not only learn the unknown form of the governing equation but also take into account the random effect embedded in the system, which is generated by the random parameters. Once the DNN model is successfully constructed, it is able to produce system prediction over longer term and for arbitrary parameter values. For uncertainty quantification, it allows us to conduct uncertainty analysis by evaluating solution statistics over the parameter space.
In the existing operations, hydropower contributions to future system flexibility are generally modeled while maintaining traditional operating rules and constraints in supporting grid operation, such as the balancing of variable renewable energy production. Moreover, operation of large scale hydropower systems on major rivers has been investigated for decades, utilizing various systems engineering approaches, with the evolving electric grid, as the result of renewable resources integration, compounded by the changing climate (variability of river flows, intensification of hydrologic cycle resulting in more frequent extreme events) affecting water availability, the need for more advanced stochastic modeling and effective uncertainty analysis approaches have become necessary. The research results supported by this funding and presented in this report provide a new look at hydropower operational flexibility enforced by the changes identified above. Understanding how hydropower operates in response to the underlying uncertainties with respect to the system constraints is crucial in identifying its operational flexibility potentials. In this project, the flexibility of the operating hydropower facility is described by capturing uncertainties in both water and power system and formulating the operations as a multistage stochastic optimization problem. The proposed approach supports short- to seasonal-term operations and planning decision horizons.
Known for their simplistic design and continuous detonation, rotating detonation engines (RDEs) constitute a majority of current pressure gain combustion (PGC) research efforts. Experimental RDE operation times have been continuously extended through the use of rig cooling techniques. As the window of observable behavior is expanded, and as the technology matures toward eventual integration within gas turbines, monitoring techniques must evolve to better match industrial diagnostics. High-speed image analysis techniques prove useful to capture and evaluate the unsteady detonation behavior within the RDE. Traditional image analysis techniques, however, require extensive processing times which prohibit simultaneous monitoring. To better address this problem, a computer vision object detection methodology is proposed to quickly detect individual detonation waves within a single down-axis image. Detonation waves are detected in individual images by the implemented computer vision method You Only Look Once (YOLO) object detection network. In order to detect detonation waves, the network must first be trained using RDE images of interest, for which each required phase of network development is outlined. Detection of waves is improved through proper treatment of the collected image set, variation of Intersection over Union (IoU) and confidence thresholding, and through a parametric study of annotation dimensions. Each detected wave is described by its location and rotational direction, and locations are tracked to calculate wave velocity across each frame, leading to a timestep resolution of 20 µs. Wave velocities are also calculated through a series of frames, leading to a suitable average velocity estimation using as few as 10 frames. Uncertainty analysis accounting for variation in camera framerate, pixel width and annotation centroid locations estimates a total uncertainty of ±4.3% for velocity calculations, using the smallest annotation boxes. This method offers great reductions in processing times, as a step toward real-time monitoring of detonation waves within an RDE. Improving on previous studies, this technique is impartial to wave modes not included in the original training set and calculates wave velocities independent of high-speed pressure data. The ability to isolate waves within predicted bounding boxes will likely facilitate analysis of pixel intensity variation as an estimation of wave strength in future work.
Known for their simplistic design and continuous detonation, rotating detonation engines (RDEs) constitute a majority of current pressure gain combustion (PGC) research efforts. Experimental RDE operation times have been continuously extended through the use of rig cooling techniques. As the window of observable behavior is expanded, and as the technology matures toward eventual integration within gas turbines, monitoring techniques must evolve to better match industrial diagnostics. High-speed image analysis techniques prove useful to capture and evaluate the unsteady detonation behavior within the RDE. Traditional image analysis techniques, however, require extensive processing times which prohibit simultaneous monitoring. To better address this problem, a computer vision object detection methodology is proposed to quickly detect individual detonation waves within a single down-axis image. Detonation waves are detected in individual images by the implemented computer vision method You Only Look Once (YOLO) object detection network. In order to detect detonation waves, the network must first be trained using RDE images of interest, for which each required phase of network development is outlined. Detection of waves is improved through proper treatment of the collected image set, variation of Intersection over Union (IoU) and confidence thresholding, and through a parametric study of annotation dimensions. Each detected wave is described by its location and rotational direction, and locations are tracked to calculate wave velocity across each frame, leading to a timestep resolution of 20 µs. Wave velocities are also calculated through a series of frames, leading to a suitable average velocity estimation using as few as 10 frames. Uncertainty analysis accounting for variation in camera framerate, pixel width and annotation centroid locations estimates a total uncertainty of ±4.3% for velocity calculations, using the smallest annotation boxes. This method offers great reductions in processing times, as a step toward real-time monitoring of detonation waves within an RDE. Improving on previous studies, this technique is impartial to wave modes not included in the original training set and calculates wave velocities independent of high-speed pressure data. The ability to isolate waves within predicted bounding boxes will likely facilitate analysis of pixel intensity variation as an estimation of wave strength in future work.
The measurement of Z boson production is presented as a method to determine the integrated luminosity of CMS data sets. The analysis uses proton–proton collision data, recorded by the CMS experiment at the CERN LHC in 2017 at a center-of-mass energy of 13 TeV. Events with Z bosons decaying into a pair of muons are selected. The total number of Z bosons produced in a fiducial volume is determined, together with the identification efficiencies and correlations from the same data set, in small intervals of 20 pb –1 of integrated luminosity, thus facilitating the efficiency and rate measurement as a function of time and instantaneous luminosity. Using the ratio of the efficiency-corrected numbers of Z bosons, the precisely measured integrated luminosity of one data set is used to determine the luminosity of another. For the first time, a full quantitative uncertainty analysis of the use of Z bosons for the integrated luminosity measurement is performed. The uncertainty in the extrapolation between two data sets, recorded in 2017 at low and high instantaneous luminosity, is less than 0.5%. We show that the Z boson rate measurement constitutes a precise method, complementary to traditional methods, with the potential to improve the measurement of the integrated luminosity.
The Annular Core Research Reactor (ACRR) Monte Carlo N-Particle (MCNP) model is used by ACRR reactor operators and experiment designers at Sandia National Laboratories for a variety of computational calculations ranging from reactor kinetics parameter estimates and safety analyses to experimental planning. To understand the dominant source of uncertainty within the MCNP model, perturbations in temperature were applied to individual ACRR MCNP fuel rods. Fuel rod temperatures were randomly sampled from a uniform distribution from operational temperatures to quantify temperature-related uncertainty effects. Stochastic mixing was used to blend the cross sections of the desired temperatures using the MCNP continuous and Thermal Neutron Scattering Treatment [S(α,β)] libraries in ENDF/B-VII.1. Furthermore, this uncertainty analysis produced a 640 row × 640 column correlation and covariance matrix of the neutron energy spectra. Positive covariance was produced around the 1-MeV region and the 0.2-eV region. Correlation was found in the thermal and fast energy regions, but no correlation was observed in the slowing-down energy region because interactions in this region are not dominated by fuel.
A computational fluid dynamics study was performed for a cross-flow marine hydro-kinetic turbine. The analysis was done in three dimensions and used the unsteady Reynolds averaged Navier-Stokes solver in the commercial code STAR-CCM+. The base turbine configuration is the RivGen® Turbine, designed by the Ocean Renewable Power Company (ORPC). A convergence and uncertainty analysis was performed for both the spatial and temporal discretization; this was done using the base configuration which features support struts and helical foils. The proposed study aims to compare the impact of the struts on both power performance and blade loading for helical and straight blades.
A computational fluid dynamics study was performed for a cross-flow marine hydrokinetic turbine. The analysis was done in three dimensions and used the unsteady Reynolds-averaged Navier-Stokes solver in the commercial code STAR-CCM+. The base turbine configuration is the RivGen(R) Turbine, designed by the Ocean Renewable Power Company. A convergence and uncertainty analysis was performed for both the spatial and temporal discretization; this was done using the base configuration, which features support struts and helical foils. Both struts and helical blades introduce three-dimensional flow effects, influencing the complex flow phenomenon of dynamic stall. The study compares the relative impact of struts on power performance and blade loading for both helical and straight blades, and found that for this turbine the relative loss in power from struts was lower with helical blades.
This project aimed to develop advanced high-temperature additive components that contribute towards the DOE’s goal for advanced gas turbines that are capable of at least 65% efficiency in combined cycle application. The objective was to leverage state-of-the-art additive manufacturing to develop an innovative stage 1 turbine nozzle (S1N) that can provide cooling flow savings while maintaining the component durability expected in today’s gas turbines. The program had two phases. Phase I was a conceptual phase for novel advanced cooling designs enabled by additive manufacturing, as well as proposals for validation. Phase II included execution of the Phase I conceptual design, including manufacturing of prototype hardware and validation within an environment that is similar to engine operation. During Phase I of the program, the team devised a concept to reduce cooling air usage. The cooling air used in the side walls is filmed out along the side walls, while the cooling air used in the airfoil is eventually directed to near-wall channels and exits holes along the airfoil trailing end. During this program, the team performed additive trials to analyze the geometric limitations of additive manufacturing. This helped the team understand minimum wall thicknesses, hole sizes, and cooling channel dimensions among other limits. Phase II of this program pushed GE Vernova beyond its previous experience of designing and manufacturing an additively manufactured hot gas path component. Modern hot gas path components utilize material chemistries that are traditionally hard to weld, such as cast Renè 108, and exhibit solidification cracking when additively manufactured using Direct Metal Laser Melting (DMLM). Note that AM108 is a powder form of Renè 108. A S1N with advanced cooling is larger and more complex than parts previously built by additive manufacturing and required new learnings to resolve risks around solidification cracking. Finally, the team validated the design in a combustion rig that replicated operation in a gas turbine. In order to properly quantify the benefits of the new additive design, a baseline was also tested in the rig and operated under the same conditions. In addition, an uncertainty analysis was done to quantify any sources of error that could impact the results. At the end of the validation effort, it was determined that the additive nozzle exceeded the 15% reduction in cooling flow goal even with the worst-case assumptions for uncertainty.
To evaluate radioactive tracer dilution and gamma spectroscopy as a safeguards technique for monitoring the mass of liquid salt in molten salt systems, the gamma spectroscopy data of salt samples from the pyroprocessing facilities in Idaho National Laboratory were acquired and analyzed. The primary focus is the uncertainty analysis of the gamma radioactivity of 154 Eu isotope in the molten salts. The facilities include an electrorefiner for pyroprocessing spent oxide fuels (SOF-ER) and Mark-IV ER for processing the metallic fuels from Experimental Breeder Reactor-II (EBR-II). The relative gamma radioactivity uncertainty was consistently at 3% for 137 Cs for both Mark-IV ER and SOF-ER salts. However, for 154 Eu, it was 8% for SOF-ER salt and 7% for Mark-IV ER salt. Here, the main reason for the higher uncertainty for 154 Eu is believed to be the lower counting statistics in gamma spectroscopy. To improve the gamma radioactivity uncertainty of isotope 154 Eu, a longer data acquisition time (12 h instead of 4 h) and a higher count rate gamma detector for gamma spectroscopy were tested and it was found that the uncertainty for 154 Eu was significantly improved from 7% or 8–3%.
Accurate event locations are important for many endeavors in seismology, and understanding the factors that contribute to uncertainties in those locations is complex. In this article, we present a case study that takes an in-depth look at the accuracy and precision possible for locating nine shallow earthquakes in the Rock Valley fault zone in southern Nevada. These events are targeted by the Rock Valley Direct Comparison phase of the Source Physics Experiment, as candidates for the colocation of a chemical explosion with an earthquake hypocenter to directly compare earthquake and explosion sources. For this comparison, it is necessary to determine earthquake hypocenters as accurately as possible so that different source types have nearly identical locations. Our investigations include uncertainty analysis from different sets of phase arrivals, stations, velocity models, and location algorithms. For a common set of phase arrivals and stations, we find that epicentral locations from different combinations of velocity models and algorithms are within 600 m of one another in most cases. Event depths exhibit greater uncertainties, but focusing on the S-P times at the nearest station allows for estimates within approximately 500 m.
The Measurement of Uranium Subcritical and Critical (MUSIC) experiment was a series of measurements of critical and subcritical configurations of bare highly enriched uranium. The goal was to compare measurement methods, analysis techniques, and simulation methods across regimes of criticality and to provide high-quality validation of 235 U nuclear data. A benchmark evaluation of the two critical configurations of the MUSIC experiments will soon be published in the release of the International Criticality Safety Benchmark Evaluation Project Handbook. The recent execution of the experiment aids in proper quantification of model simplifications and all uncertainties associated with the experiment. Historical benchmark evaluations are heavily relied on for uranium nuclear data validation despite the fact that the same level of documentation and comparable uncertainty analysis may not be present. The MUSIC evaluation is less likely to include “unknown unknowns” that could impede accurately modeling the system. Presented are both highly detailed and very simplified models, which represent the experimental configurations accurately, aiding the users of the benchmark for nuclear data or transport code validation. The sensitivities of k eff to nuclear data and nuclear data–related uncertainties are very similar between this experiment and previous bare uranium sphere experiments. In addition, the nuclear data uncertainties to any nuclides other than 235 U are small. For all these reasons, the recently evaluated MUSIC benchmark critical configurations could prove very useful for 235 U nuclear data validation. Currently, major libraries have good agreement with the experimental results, within 200 pcm for all nuclear data libraries, and within one standard deviation of the experimental result for most. Suggested nuclear data adjustments based on MUSIC and Lady Godiva are also presented, with posterior improvements to both the agreement in k eff and the uncertainty associated with the nuclear data.
Due to improvements in well development, shale gas production has gained importance recently, especially in the United States. To improve gas production and develop a better understanding of shale reservoirs, researchers are conducting field tests to monitor CO 2 storage and enhanced gas recovery. Reservoir simulations can then utilize these field results for sensitivity studies, uncertainty analysis, history matching, gas production forecasts, and CO 2 storage capacity estimations. One such field test was performed for the Chattanooga Shale formation in Morgan County, Tennessee. Approximately 463 tonnes (510 tons) of CO 2 was successfully injected into a hydraulically fractured horizontal well over a thirteen-day period in March 2014. The injection test was achieved in four steps: pre-injection, injection, soaking, and flowback. In this paper, those steps were modeled with a reservoir simulator to match the historic production and injection rates with gas component compositions and forecast the production for five years. The reservoir fluid was modeled as a multi-component gas, including CH 4 , C 2 H 6 , C 3 H 8 , N 2 , and CO 2 . Langmuir constants, reservoir pressure, and fracture network volume were adjusted to match simulation results with observed production rates and gas composition. Results showed that, under the same reservoir conditions, each gas component behaves differently by way of compositional changes in production. CH4 behaves like N2, while CO 2 behaves like heavier hydrocarbons such as C 2 H 6 and C 3 H 8 . CO 2 plume results showed that, after injection, the produced CO 2 is mostly derived from fractured limestone (i.e., Fort Payne Formation) because the injected CO 2 is not adsorbed in limestone but rather by shale formations.
Experimentally measuring the pressure gain through the method of equivalent available pressure is prone to many experimental uncertainties. This study performs a detailed uncertainty analysis of the thrust and pressure gain measurements utilizing data from a H2/air operated rotating detonation combustor with an axial air inlet and a 50% exit constriction. The measured (negative) pressure gain values agree with the results in literature for similar inlet-to-exit area ratios. Specific considerations are given to the uncertainty in thrust measurement introduced by the base drag correction on the inner-body. The assumed unity Mach number utilized in the equivalent available pressure methodology is evaluated experimentally with two different methods using time-averaged measurements at the exhaust plane. A definitive demonstration of gain will be challenging given the assessed uncertainties, but recommendations are given to increase the precision of the equivalent available pressure methodology to estimate pressure gain.
Energy flow and balance in convergent systems beyond petapascal energy densities controls the fate of late-stage stars and the potential for controlling thermonuclear inertial fusion ignition. Timeresolved x-ray self-emission imaging combined with a Bayesian inference analysis is used to describe the energy flow and the potential information stored in the rebounding spherical shock at 0.22 petaPascal (2.2 Gbar or billions of atmospheres pressure). This analysis, together with a simple mechanical model, describes the trajectory of the shell and the time history of the pressure at the fuel-shell interface, ablation pressure, and energy partitioning including kinetic energy of the shell and internal energy of the fuel. The techniques used here provide a fully self-consistent uncertainty analysis of integrated implosion data, a thermodynamic-path independent measurement of pressure in the petaPascal range, and can be used to deduce the energy flow in a wide variety of implosion systems to petapascal energy densities.
Energy flow and balance in convergent systems beyond petapascal energy densities controls the fate of late-stage stars and the potential for controlling thermonuclear inertial fusion ignition. Time-resolved x-ray self-emission imaging combined with a Bayesian inference analysis is used to describe the energy flow and the potential information stored in the rebounding spherical shock at 0.22 PPa (2.2 Gbar or billions of atmospheres pressure). This analysis, together with a simple mechanical model, describes the trajectory of the shell and the time history of the pressure at the fuel-shell interface, ablation pressure, and energy partitioning including kinetic energy of the shell and internal energy of the fuel. Here, the techniques used here provide a fully self-consistent uncertainty analysis of integrated implosion data, a thermodynamic-path independent measurement of pressure in the petapascal range, and can be used to deduce the energy ow in a wide variety of implosion systems to petapascal energy densities.
The thin ~15 in. diameter highly enriched uranium (HEU) metal plates were assembled to delayed criticality at the Oak Ridge Critical Experiments Facility (ORCEF) in 1969 with various thicknesses of polyethylene (varying from 1/16 to 2$\frac{3}{8}$ inches) between uranium metal plates. The average 235 U enrichment was 93.27 wt. %. These unreflected critical configurations contained 4$\frac{2}{3}$ to 20$\frac{5}{6}$ thin 15 in. diameter HEU metal plates (on loan from Los Alamos National Laboratory [LANL] and shipped to Oak Ridge National Laboratory [ORNL] on June 3, 1969). Depending on the thickness of polyethylene, the enriched uranium masses varying from 28,053 to 135,148 grams. Fractional plate sections consisted of the appropriate number of 60° pie sections. In addition to the measurement at delayed criticality, subcritical measurements were also performed by the inverse kinetic rod drop method. Prompt neutron decay constant measurements were also performed by the Rossi alpha and randomly pulsed neutron method using a time-tagged spontaneous fission californium neutron source; these are briefly reported here. At the time of these measurements in 1969, the thin HEU metal plates were in near-pristine condition with extremely little oxidation, allowing better descriptions of the uranium plates than the use of these plates in a heavily oxidized and deteriorated condition in recent reflected benchmark experiments at the LANL facility at the Nevada Test Site with these same thin highly enriched uranium metal plates. This report documents the experimental information for the measurements performed so that later researchers can perform the required uncertainty and calculational analyses and documentation to use these data for an International Nuclear Criticality Safety Benchmark Evaluation Program (ICSBEP) or a Nuclear Energy Agency (NEA) benchmark. Data from the experiments described should be acceptable for use as criticality safety benchmark experiments for the ICSBEP and the NEA nuclear criticality safety benchmark program once the uncertainty analysis on the measured neutron multiplication factors is completed. Additional data—such as the dimensional inspection reports, uranium isotopic information, and other relevant particulars—should be retrieved from the Y-12 Plant or LANL and incorporated in the final ICSBEP benchmark. Based on previous ICSBEP benchmarks with this enriched uranium metal at ORCEF, the uncertainties in $k_{eff}$ could be as low as ± 0.0002 for some configurations. Other experiments with smaller-diameter than 15 in. diameter HEU metal plates have been benchmarked in HEU-METFAST-001. The prompt neutron time decay measurements could be the basis for an International Reactor Physics Benchmark Program. Preparation of the present report is part of an effort at ORNL to document more than 15 undocumented critical and subcritical experiments enumerated in ORNL/TM-2019/18 and performed by ORNL at ORCEF and other US Department of Energy critical experiments facilities using more than 500 operational days of critical facility time. This work for this report publication was supported by the Nuclear Criticality, Radiation Transport and Safety NCSP Program at ORNL.