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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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At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Bayesian parameter estimation and evaluation of the K -ω shear stress transport model for plane impinging jets

Numerical simulations with semi-empirical turbulence models are commonly used to model impinging jets, often used for cooling solid surfaces. In this work, the constants in the k-ω shear stress transport model in ANSYS FLUENT are calibrated to experimental velocity and heat transfer data for a plane turbulent impinging air jet to determine if Kennedy-O'Hagan calibration (Kennedy and O'Hagan 2001 J. R. Stat. Soc. B 63 425–64) can improve predictions of near-surface velocities and surface Nusselt numbers for similar flows. Impinging jets have been proposed to cool the target plates of the divertor in future magnetic fusion energy reactors, where simulations are used to estimate divertor performance. The flat-plate divertor (Wang et al 2009 Fusion Sci. Technol .56 1023–7) uses a plane jet of helium issuing from a B = 0.5 mm slot to cool a surface with radius of curvature of 44 B at a distance 4 B from the slot. Predictions from the calibrated numerical model are compared with independent experimental data at different flow conditions, as well as surface temperature data for a flat plate divertor test section. The contribution of this work is evaluation of the accuracy of a calibrated turbulence model for modest extrapolations in flow geometry and flow conditions for a plane impinging jet.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Laboratory Data on the EUV/Soft-X-Ray Flux Problem of Fe XVIII and Fe XIX

Observations by the spacecraft Extreme Ultraviolet Explorer (EUVE), Far Ultraviolet Spectroscopic Explorer (FUSE), Chandra, and XMM-Newton of Capella (α Aurigae) have encountered problems with the relative intensities of the Fe XVIII and Fe XIX line emission in the soft-X-ray (XUV) and extreme-ultraviolet spectral ranges versus various model predictions based on theoretical atomic data. The reason may either lie in astrophysical phenomena, e.g., at emission or because of absorption by the interstellar medium, in the theoretical atomic data, in one or more spectrometer calibrations, or in the spectral modeling. Here, by measurements using an electron beam ion trap we provide laboratory data obtained under conditions that are reasonably close to stellar emission regions. The laboratory data on Fe XVIII and Fe XIX show line ratios that are rather similar to the observations of Capella. The measurements, therefore, rule out astrophysical phenomena and calibration errors and point to issues with the modeling of the observed Capella emission.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Method for automatic correction of offset drift in online sensors

Abstract Successful operation and optimization of water treatment systems hinge on the availability of high-quality online sensor measurements. Ideally, the available measurements should be simultaneously accurate (i.e., unbiased and precise), representative, voluminous, and timely. This remains a pain-point in current water infrastructures, forming a barrier to a wider adoption of advanced and autonomous control systems. While short-lived symptoms, such as outliers and spikes, can be detected or corrected with state-of-the-art tools for fault detection and identification, it is much more difficult to detect, diagnose, and correct the symptoms of slow faults, such as changes in offset or sensitivity due to drift. The time scale of drift is often longer than the time scales of the system dynamics of interest. Moreover, sensor drift has been shown to occur at the same time and with similar rates when sensors are exposed to the same conditions. This challenges data quality management strategies based on redundancy. In this contribution, we develop a new method, including both a hands-off sensor calibration mechanism and an information-seeking control architecture that can handle the unique challenge of simultaneous and similar drift in online sensors.

Chowdhury, Dhrubajit↗
DOE OSTI2020

A Critical Review of High Burnup Fuel Fragmentation, Relocation, and Dispersal under Loss-Of-Coolant Accident Conditions

High burnup fuel in excess of ~6268 GWd/MTU has been observed to severely fragment when subjected to temperature transient conditions as in a loss-of-coolant accident. In the event of cladding failure, the severely fragmented fuel can relocate into the balloon region and potentially disperse through the burst opening into the reactor’s primary system. This observation has led to implementation of several independent test programs designed to focus on various aspects of fuel fragmentation, relocation, and dispersal. The purpose of this review is to perform a critical, holistic assessment of fuel fragmentation, relocation, and dispersal under loss-of-coolant conditions to identify data gaps in the experimental data base. Phenomena have been identified and well defined through the various testing programs, and general agreement regarding the governing parameters (temperature, burnup, heating rate, cladding deformation, etc.) has been established. However, there is a significant data gap connecting research to commercial application. Furthermore, previous research attempted to evaluate the problem holistically, but has yet to provide a comprehensive understanding of the problem. Major identified data gaps consist of (1) a comprehensive understanding of pretransient fuel rod conditions (fuel temperature, fuel stress, rod internal pressures, microstructure, etc.), (2) definition of prototypic loss-of-coolant accident conditions (heating rate, fuel temperatures, fuel stress), (3) identification of the differences between nuclear and electrical integral loss-of-coolant accident tests, and connection of fuel rod performance (cladding ballooning, burst opening, potential relocation, fragmentation susceptibility, etc.) to fuel fragmentation, relocation, and dispersal severity. Finally, the content of this manuscript provides a clear path for connecting testing and data acquisition to commercial application by providing concrete means for filling data gaps and prioritized testing regimes.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

A review of cladding failure thresholds in RIA conditions based on transient reactor test data and the need for continued testing

Transient reactor experiments on light water reactor (LWR) fuel pins had been conducted since the beginning of the nuclear era to help determine core coolability and cladding failure thresholds. During one such test in November of 1993 at the CABRI transient test reactor on a test involving a high burnup fuel rod with a corroded Zircaloy-4 cladding it was first observed that cladding failures could occur prior to a departure from nucleate boiling (pre-DNB) at lower-than-expected peak radial average enthalpies. This paper will present an independent review of the publicly available transient reactor test database on higher burnup LWR pins conducted at the CABRI and NSRR reactors as well as review of a selection of published out of pile mechanical testing methods. The purpose of the review is to determine how well the new regulatory limits are supported by experimental data. The review will identify if additional transient reactor tests could provide additional support for the NRC guidance or identify the need for revisions. The evaluation will consider how far the existing database can be extrapolated when considering low hydrogen zirconium alloy claddings (with and without protective coatings) containing very high burnup (> 70 MWd/kgU) UO2 fuel pellets. Finally, the authors will suggest how out of pile mechanical tests can be used in conjunction with a limited number of transient reactor tests to develop cladding specific failure thresholds in RIA type transients.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Combined analysis of microbial metagenomic and metatranscriptomic sequencing data to assess in situ physiological conditions in the premature infant gut

Microbes alter their transcriptomic profiles in response to the environment. The physiological conditions experienced by a microbial community can thus be inferred using meta-transcriptomic sequencing by comparing transcription levels of specifically chosen genes. However, this analysis requires accurate reference genomes to identify the specific genes from which RNA reads originate. In addition, such an analysis should avoid biases in transcript counts related to differences in organism abundance. In this study we describe an approach to address these difficulties. Sample-specific meta-genomic assembled genomes (MAGs) were used as reference genomes to accurately identify the origin of RNA reads, and transcript ratios of genes with opposite transcription responses were compared to eliminate biases related to differences in organismal abundance, an approach hereafter named the “diametric ratio” method. We used this approach to probe the environmental conditions experienced by Escherichia spp. in the gut of 4 premature infants, 2 of whom developed necrotizing enterocolitis (NEC), a severe inflammatory intestinal disease. We analyzed twenty fecal samples taken from four premature infants (4–6 time points from each infant), and found significantly higher diametric ratios of genes associated with low oxygen levels in samples of infants later diagnosed with NEC than in samples without NEC. We also show this method can be used for examining other physiological conditions, such as exposure to nitric oxide and osmotic pressure. These study results should be treated with caution, due to the presence of confounding factors that might also distinguish between NEC and control infants. Nevertheless, together with benchmarking analyses, we show here that the diametric ratio approach can be applied for evaluating the physiological conditions experienced by microbes in situ. Results from similar studies can be further applied for designing diagnostic methods to detect NEC in its early developmental stages.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Omics-Lethal Human Viruses, West Nile Experiment WCN002

The purpose of this experiment was to evaluate the host response to wild-type West Nile virus (WNV NY99 clone 382) and mutant (WNVE218A) virus infection. Sample data was obtained from mouse cortical neuron tissue and processed for mRNA and miRNA expression analysis. This data set comprises two complete biological replicate experiments conducted in the same conditions and with data processed independently (WCN003). Secondary host-associated viral dataset downloads contain one or more statistically processed (normalization data transformation) quantitative dataset collections resulting in qualitative expression analyses of primary host-pathogen experimental study designs. Transcriptomics dataset downloads have a direct relationship to a primary sample submission corresponding to a specific West Nile virus infection.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Conditional Filamentation of Paraburkholderia elongata 5N - Data Container

Overview of Dataset This narrative contains assemblies for all Paraburkholderia discussed in Karasz et al. 2022, where their phosphate-solubilizing activity was measured. Assemblies were downloaded from NCBI and annotated with Prokka. These genomes were created in many Narratives and collected here to be more accessible Note: The following naming of assemblies does not correspond with current taxonomic names for the following: Paraburkholderia 5N = Paraburkholderia elongata 5NT Paraburkholderia 1N = Paraburkholderia solitsuga 1NT Paraburkholderia vancouverensis = Paraburkholderia madseniana RL16

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

The Coupled Influence of Thermal Physiology and Biotic Interactions on the Distribution and Density of Ant Species along an Elevational Gradient

A fundamental tenet of biogeography is that abiotic and biotic factors interact to shape the distributions of species and the organization of communities, with interactions being more important in benign environments, and environmental filtering more important in stressful environments. This pattern is often inferred using large databases or phylogenetic signal, but physiological mechanisms underlying such patterns are rarely examined. We focused on 18 ant species at 29 sites along an extensive elevational gradient, coupling experimental data on critical thermal limits, null model analyses, and observational data of density and abundance to elucidate factors governing species’ elevational range limits. Thermal tolerance data showed that environmental conditions were likely to be more important in colder, more stressful environments, where physiology was the most important constraint on the distribution and density of ant species. Conversely, the evidence for species interactions was strongest in warmer, more benign conditions, as indicated by our observational data and null model analyses. Our results provide a strong test that biotic interactions drive the distributions and density of species in warm climates, but that environmental filtering predominates at colder, high-elevation sites. Such a pattern suggests that the responses of species to climate change are likely to be context-dependent and more specifically, geographically-dependent.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Stochastic representation and conditioning of process-based geological model by deep generative and recognition networks

Accurate and realistic geological modeling is the core of oil and gas development and production. In recent years, process-based methods are developed to produce highly realistic geological models by simulating the physical processes that reproduce the sedimentary events and develop the geometry. However, the complex dynamic processes are extremely expensive to simulate, making process-based models difficult to be conditioned to field data. In this work, we propose a comprehensive generative adversarial network framework as a machine-learning-assisted approach for mimicking the outputs of process-based geological models with fast generation. The main objective of our work is to obtain a continuous parametrization of the highly realistic process-based geological models which enables us to calibrate the models and condition the models to data. Numerical results are presented to illustrate the capability of our proposed methodology.

58 GEOSCIENCES↗
DOE OSTI2026

A Feasibility Study on the Integration of Human Performance Data From Diverse Sources Based on the Complexity of a Proceduralized Task

Securing the safety of socio-technical systems including nuclear facilities is the upmost goal to ensure their sustainability because historical records demonstrate that the performance degradation of human operators (e.g., human errors) is one of the crucial contributors to the occurrence of unexpected events resulting in extensive casualties and financial losses. This implies that the collection of human performance data in diverse conditions with which they could be faced during the operation of nuclear facilities. As this collection requires significant resources, it is necessary to resolve how to accomplish it with limited resources. To address this challenge, as suggested in the SHEEP framework, it is indispensable to extract valuable insights after integrating various kinds of human performance data obtained from different sources. However, a practical method to soundly integrate them seems to be still incomplete. Accordingly, the applicability of TACOM (Task Complexity) measure is investigated as a tool to identify useful information based on the integration of human performance data observed from different simulation conditions. As a result, it is expected that the TACOM measure would play an important role in addressing the technical challenge in securing human performance data.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

Reduced-Order Modeling of Hydrogen Releases from Vent Stacks and with Wind Effects

Here, the physical release behavior of hydrogen is important to understand from a safety and design perspective. The consequences of unignited pressurized gaseous hydrogen plumes exiting vent stacks were considered by extending and modifying existing general hydrogen plume models. Entrainment, vent stack backpressure, and the flow regime of hydrogen exiting the vent were found to be significant factors affecting plume shape and size, but further investigation and validation with unchoked, low-Froude-number flows is recommended to improve the model’s robustness. Additionally, models for the effects of wind on unignited plume momentum and entrainment were added to explore this behavior. Wind was assumed to increase mixing of hydrogen with the ambient air, and to affect the momentum of the released jet. Introducing wind into the plume model led to a shorter plume for all wind and jet directions. A high counter-flowing wind led to non-physical results and challenges in interpreting the visualization. The proposed jet plume wind sub-models (specifically entrainment coefficients) were fit and compared to experimental data of different releases of hydrogen into a wind tunnel, but the quantity of data available and experimental conditions were limited. Thus, collection of more empirical data and for a wider range of conditions is recommended for improvement of the proposed computational models. Developing reduced-order models for these physical phenomena can improve accessibility to predicted physical behavior and the rate at which hydrogen systems can be safely designed and deployed.

entrainment↗
DOE OSTI2024

Inverse prediction of PuO2 processing conditions using Bayesian seemingly unrelated regression with functional data

Over the past decade, a variety of innovative methodologies have been developed to better characterize the relationships between processing conditions and the physical, morphological, and chemical features of special nuclear material (SNM). Different processing conditions generate SNM products with different features, which are known as “signatures” because they are indicative of the processing conditions used to produce the material. These signatures can potentially allow a forensic analyst to determine which processes were used to produce the SNM and make inferences about where the material originated. This article investigates a statistical technique for relating processing conditions to the morphological features of PuO 2 particles. We develop a Bayesian implementation of seemingly unrelated regression (SUR) to inverse-predict unknown PuO 2 processing conditions from known PuO 2 features. Model results from simulated data demonstrate the usefulness of the technique. Applied to empirical data from a bench-scale experiment specifically designed with inverse prediction in mind, our model successfully predicts nitric acid concentration, while results for Pu concentration and precipitation temperature were equivalent to a simple mean model. Our technique compliments other recent methodologies developed for forensic analysis of nuclear material and can be generalized across the field of chemometrics for application to other materials.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Rasterization with Data-Parallel Primitives

Parallel rasterization can suffer from race conditions during fragment generation, which is traditionally addressed by using specialized hardware accessible via vendor graphics APIs. Unfortunately, graphics APIs are increasingly problematic on high-performance computers, either because they are not provided or because of concerns about dependencies with in situ visualization. In response, we present a hardware-agnostic rasterization algorithm that handles race conditions using only data-parallel primitives (DPPs), enabling efficient rendering on HPC systems without graphics API dependencies and aligning with recent efforts to deliver visualization software with DPPs. Our evaluation consists of three phases: (1) evaluating portability across different CPU and GPU architectures, (2) evaluating competitiveness with a community standard, and (3) evaluating performance across varying workloads and available parallelism. The supporting experiments run on both AMD and NVIDIA GPUs, considering data sets as large as 460 million triangles and 160 million pixels. While performance generally falls short of graphics API baselines, it achieves interactive frame rates on most workloads. As a result, we conclude our approach is a viable solution for rasterization on high-performance computers since our approach is portably performant across different architectures without the need for specialized vendor support.

Buckley, Makani [University of Oregon] (ORCID:0009↗
DOE OSTI2020

Prognosis of Wind Turbine Gearbox Bearing Failures Using SCADA and Modeled Data

Predictive maintenance and condition monitoring systems for wind turbines have seen increased adoption to minimize downtime, reducing operation and maintenance costs. On today’s wind power plants, the integrated supervisory control and data acquisition (SCADA) system provides low- frequency operational data that can be leveraged to quantify a wind turbine’s health. The aim of this study is to utilize machine-learning techniques to predict axial cracking failures in wind turbine gearbox bearings up to 1 month ahead of time. The failures are assumed to have occurred when the investigated bearing was replaced. While current SCADA systems show the overall condition of a wind turbine, often they do not allow for the investigation of specific gearbox bearings’ health. To enrich bearing fault signatures, additional data are computed through physics-based models using gearbox design information. Based on SCADA data, modeled data, and bearing failure log data from an actual wind plant, the performances of different machine-learning models on unseen data are then evaluated using industry-standard metrics such as precision, recall, and F1 score. Results show the overall system performance enhancement in predicting bearing failure when modeled data are included with SCADA data. The reduction in terms of false alarms is about 50%, and improvement in terms of precision and F1 score is about 33% and 12% respectively, based on the best modeling case in this study.

49 EE - Wind and Water Power Program - Wind (EE-4W↗