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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Characterization of Infrequent Samples from the Concentration, Storage, and Transfer Facility: F-Area Pump Pit 3 (FPP-3) Sump Sample: December 2021 Sample

In December 2021, SRR-E sent an ~185 mL sample identified as FPP-3 from a F-Area pump pit sump to SRNL for analysis. The sample was pale yellow in color and contained a small amount of suspended solids visually estimated to be less than 1% by volume. SRNL analysis indicated that the sample contained 4.4 E+05 dpm/mL Cs-137, 6.2 E+05 dpm/mL total beta activity, 9.3 E+02 dpm/mL beta activity following cesium removal, and below detectable levels of alpha activity following cesium removal. In addition, the sample contained 0.015 M free OH- and 947 μg total organic carbon/mL.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Performance Improvements of Poincaré Analysis for Exascale Fusion Simulations

Understanding the time-varying magnetic field in a fusion device is critical for the successful design and construction of clean-burning fusion power plants. Poincaré analysis provides a powerful method for the visualization of magnetic fields in fusion devices. However, Poincaré plots can be very computationally expensive making it impractical, for example, to generate these plots in situ during a simulation. In this short paper, we describe a collaboration among computer science and physics researchers to develop a new Poincaré tool that provides a significant reduction in the time to generate analysis results.

Pugmire, Dave↗
DOE OSTI2022

Database-wide hazard modelling of the onset of DIII-D tearing modes with field features

The rate of onset (hazard) of tearing modes is modelled probabilistically using statistical learning algorithms. Axisymmetric energy-density equilibrium fields are taken as raw high-dimensional input features which are reduced with principal component analysis. Signal processing of non-axisymmetric magnetics fluctuation array data provides the target information from which to learn. Model selection, visualization and calibration assessment procedures are detailed. Here, the analysis is deployed at large scale across the DIII-D tokamak database. Standard model selection criteria suggest that the energy-density post-processed feature is a better choice for modelling the onset rate compared to the non-processed equilibrium reconstruction solution. Two example applications of the learned rate function are demonstrated: (i) proximity-to-onset discharge monitoring and (ii) database analysis showing an (expected) observational global trend that the general hazard increases as a plasma performance metric increases. An important connection between the hazard function and its use as a conditional probability generator is reviewed in the Appendix.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

A Science Gateway for the Repeatable Analysis of Machine Learning Predicted Gravity Anomalies

In recent years, deep learning has become an increasingly popular alternative for modeling in geoscience applications due to its scalability and efficiency. However, the interpretability, compute, data volume, and hyperparameter tuning requirements of deep learning models make development and monitoring difficult. Furthermore, model explainability and communicating results obtained by these models to users or domain experts is a challenge, as domain experts in geoscience also need to have a deep understanding of how those models function in order to support their scientific works. Here, we describe a science gateway and machine learning pipeline for predicting gravity anomalies from geophysical data. The gateway, built on open-source technologies, provides a holistic view of the pipeline through interactive visualizations aimed at enabling efficient exploratory data analysis. The repeatability, reproducibility, and monitoring capabilities of this overall system allow us to iterate and analyze at scale. Using this pipeline and gateway, we can repeatedly produce accurate high-resolution gravity anomaly datasets. By describing the underlying technologies, implementation, and results, here we provide a foundation for the broader adoption of science gateways into cross-cutting geoscience and machine learning research projects as a means to improve the scientific discovery and collaboration in the geophysics and computational sciences community.

58 GEOSCIENCES↗
DOE OSTI2021

Optical Microscopy-Guided Laser Ablation Electrospray Ionization Ion Mobility Mass Spectrometry: Ambient Single Cell Metabolomics with Increased Confidence in Molecular Identification

Single cell analysis is a field of increasing interest as new tools are continually being developed to understand intercellular differences within large cell populations. Laser-ablation electrospray ionization mass spectrometry (LAESI-MS) is an emerging technique for single cell metabolomics. Over the years, it has been validated that this ionization technique is advantageous for probing the molecular content of individual cells in situ. Here, we report the integration of a microscope into the optical train of the LAESI source to allow for visually informed ambient in situ single cell analysis. Additionally, we have coupled this ‘LAESI microscope’ to a drift-tube ion mobility mass spectrometer to enable separation of isobaric species and allow for the determination of ion collision cross sections in conjunction with accurate mass measurements. This combined information helps provide higher confidence for structural assignment of molecules ablated from single cells. Here, we show that this system enables the analysis of the metabolite content of Allium cepa epidermal cells with high confidence structural identification together with their spatial locations within a tissue.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

The maximum extent of the filaments and sheets in the cosmic web: an analysis of the SDSS DR17

Filaments and sheets are striking visual patterns in cosmic web. The maximum extent of these large-scale structures are difficult to determine due to their structural variety and complexity. We construct a volume-limited sample of galaxies in a cubic region from the SDSS, divide it into smaller subcubes and shuffle them around. We quantify the average filamentarity and planarity in the 3D galaxy distribution as a function of the density threshold and compare them with those from the shuffled realizations of the original data. The analysis is repeated for different shuffling lengths by varying the size of the subcubes. The average filamentarity and planarity in the shuffled data show a significant reduction when the shuffling scales are smaller than the maximum size of the genuine filaments and sheets. We observe a statistically significant reduction in these statistical measures even at a shuffling scale of $\sim 130 \, {{\, \rm Mpc}}$, indicating that the filaments and sheets in three dimensions can extend up to this length scale. They may extend to somewhat larger length scales that are missed by our analysis due to the limited size of the SDSS data cube. We expect to determine these length scales by applying this method to deeper and larger surveys in future.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Impacts of PV Module Connector Failures on Cost and Performance of Utility Scale Photovoltaic Systems

The reliability, cost and performance of electrical connectors are a concern in all types of electrical systems, and demands on connectors used on photovoltaic (PV) systems include that connectors maintain electrical conductivity and physical strength, endure ultraviolet sunlight and high ambient temperature, and resist moisture and chemical intrusion over a very long (>25 year) performance period. Connector failures increase operation and maintenance (O&M) costs and reduce plant production, but connector failure can also cause safety and liability problems, which are of greater concern. This work results from a three-year collaboration between Sandia National Laboratories (SNL), the Electric Power Research Institute (EPRI), and the National Renewable Energy Laboratory (NREL) and funded by the U.S. Department of Energy (DOE) Solar Energy Technology Office (SETO) under Agreements #39035 and #38531 "Connector Reliability Across the US Solar Sector." a multi-pronged investigation of PV connector health across the US (see https://energy.sandia.gov/pvconnectors/). This report presents derivation of a Techno-Economic Analysis (TEA) that models failure modes and frequencies (how often failure occurs), estimates O&M costs and lost production associated with connector failures, and then calculates the effect that PV module connectors can have on Levelized Cost of Energy (LCOE). The model is informed with initial data from quantitative assessment of failure rates, root causes and mechanisms, in-situ diagnostics and data collection, lab-based forensics, and interviews with PV connector manufacturers and plant operators. SNL conducted site inspections at multiple utility-scale sites in different climates and subjected field samples of new, used, and degraded connectors to visual and electrical characterization. EPRI conducted metallurgical analysis of the pin and sleeve conductors to study failure-induced morphological and compositional changes. There is in general a shortage of statistically valid data, but data from PVROM database maintained by SNL was sufficient to ascertain failure rates and lost production as well as provide qualitative insight in its curated maintenance records. This report details the structure of the mathematical model but the sources of data to inform the model will continue to evolve. Analysis of a 100 MW PV plant is provided as an example of the use of the model, with results indicating that connectors are responsible for Annualized O&M Costs of $\$$71,933/year; Annualized Unit O&M Costs of $\$$0.72/kW/year; that a Reserve Account of $\$$187,220 should be available to fund repairs related to connectors; that connectors add $\$$1,494,004 to the Net Present Value of the O&M Costs (project life); and that O&M related to connectors adds about $\$$0.00088/kWh to the Levelized Cost of Energy. The impact of this model is to provide a tool to make the US solar sector more robust by quantifying and monetizing the reliability risks to utility-scale PV systems posed by poorly installed, mismatched and/or poorly designed and manufactured connectors. The TEA provides a model incorporating failure statistics, O&M cost data, and lost production into a single figure of merit, informing decisions and enabling practitioners to optimize cost and performance trade-offs. Stakeholders include connector manufacturers, system designers and equipment specifiers, standards bodies, installers and O&M providers, investors and insurance underwriters. This report supports continued growth of PV predicated on assurances that properly installed and maintained PV system connectors are safe and reliable. The project team is proposing future work including accelerated testing of connectors and expanding the approach taken here to other PV system components, such as TEA for rapid shut-down devices.

14 SOLAR ENERGY↗
DOE OSTI2022

Near real-time streaming analysis of big fusion data

Experiments on fusion plasmas produce high-dimensional data time series with ever-increasing magnitude and velocity, but turn-around times for analysis of this data have not kept up. For example, many data analysis tasks are often performed in a manual, ad-hoc manner some time after an experiment. In this article, we introduce the Delta framework that facilitates near real-time streaming analysis of big and fast fusion data. By streaming measurement data from fusion experiments to a high-performance compute center, Delta allows computationally expensive data analysis tasks to be performed in between plasma pulses. This article describes the modular and expandable software architecture of Delta and presents performance benchmarks of individual components as well as of an example workflow. Focusing on a streaming analysis workflow where electron cyclotron emission imaging (ECEi) data is measured at KSTAR on the National Energy Research Scientific Computing Center's (NERSC's) supercomputer we routinely observe data transfer rates of about 4 Gigabit per second. In NERSC, a demanding turbulence analysis workflow effectively utilizes multiple nodes and graphical processing units and executes them in under 5 min. We further discuss how Delta uses modern database systems and container orchestration services to provide web-based real-time data visualization. For the case of ECEi data we demonstrate how data visualizations can be augmented with outputs from machine learning models. Here, by providing session leaders and physics operators, results of higher-order data analysis using live visualizations may make more informed decisions on how to configure the machine for the next shot.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Destructive Examination of Shipping Package 9975-02731

Destructive and non-destructive examinations were performed on the components of the package 9975-02731 as part of a comprehensive Savannah River Site surveillance program for plutonium materials stored in the K-Area Complex (KAC). The package stored nuclear materials in KAC for 13.8 years. Inspection of the package during opening in KAC noted one non-conforming condition: a large discolored area on the upper fiberboard subassembly. The package was subsequently transferred to Savannah River National Laboratory for more detailed examination. In addition to the observation noted in KAC, the following conditions were observed: (1) Axial gap of 1.021 inch slightly exceeded the 1 inch maximum criterion, and (2) Lead shield height exceeded drawing tolerance of 24.7 inch by 0.019 inch. Visual examination of the upper fiberboard assembly and analysis of the discolored fiberboard indicate the discoloration was due to wood glue residue. The physical, thermal, and mechanical properties of the fiberboard met the acceptance criteria and/or were within the range of previously tested fiberboard and baseline samples. The visual, microscopic and tensile testing results were consistent with previous O-ring and baseline Viton results. The lead shield surface was covered with corrosion product as observed on other shields but there was no evidence of excessive blistering, flaking, or spalling. Besides minor and expected corrosion, no evidence of a degraded condition was found in this package. Examination of the 9975-02731 corroborates existing evidence that the 9975 package is robust and the components can perform their expected functions.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

A deep learning approach for semantic segmentation of unbalanced data in electron tomography of catalytic materials

In computed TEM tomography, image segmentation represents one of the most basic tasks with implications not only for 3D volume visualization, but more importantly for quantitative 3D analysis. In case of large and complex 3D data sets, segmentation can be an extremely difficult and laborious task, and thus has been one of the biggest hurdles for comprehensive 3D analysis. Heterogeneous catalysts have complex surface and bulk structures, and often sparse distribution of catalytic particles with relatively poor intrinsic contrast, which possess a unique challenge for image segmentation, including the current state-of-the-art deep learning methods. To tackle this problem, we apply a deep learning-based approach for the multi-class semantic segmentation of a γ-Alumina/Pt catalytic material in a class imbalance situation. Specifically, we used the weighted focal loss as a loss function and attached it to the U-Net’s fully convolutional network architecture. We assessed the accuracy of our results using Dice similarity coefficient (DSC), recall, precision, and Hausdorff distance (HD) metrics on the overlap between the ground-truth and predicted segmentations. Our adopted U-Net model with the weighted focal loss function achieved an average DSC score of 0.96 ± 0.003 in the γ-Alumina support material and 0.84 ± 0.03 in the Pt NPs segmentation tasks. We report an average boundary-overlap error of less than 2 nm at the 90th percentile of HD for γ-Alumina and Pt NPs segmentations. The complex surface morphology of γ-Alumina and its relation to the Pt NPs were visualized in 3D by the deep learning-assisted automatic segmentation of a large data set of high-angle annular dark-field (HAADF) scanning transmission electron microscopy (STEM) tomography reconstructions.

36 MATERIALS SCIENCE↗
DOE OSTI2020

GIS Visualization of Transportation Energy Consumption

Transportation is witnessing unprecedented transformation, where emerging technologies are disrupting the way we travel. Be it sharing economy, or micro-mobility, the landscape of urban transportation is undergoing a much-needed paradigm shift. In order to capture and model these shifts, researchers need to be agile in their studies of the current and future transportation landscape. The need for agility in turn calls for sophisticated, intuitive, and reliable means to visualize and share transportation data. Travel is inherently spatial, so methods of visualizing transportation information should also be rooted in spatial analysis. Geographic Information Systems (GIS) are the premier technological framework to analyze and display spatial data. GIS tools have been used in the past to depict flow of vehicles, and display of network conditions (speed, congestion, etc.). However, in an ever-changing technological landscape, spanning advancements in vehicle as well as information systems, depicting vehicle flows falls short of providing a comprehensive picture of the impact these advancements have on travel related energy consumption. To address this issue, this research effort presents a web-based mapping application to visualize how travel related energy flows across a city. The application is being developed by National Renewable Energy Laboratory researchers to integrate various transportation energy consumption models within a GIS schema. This has the goal of enabling rapid analysis of energy impacts of the dramatically evolving transportation environment. The application is being developed using a Python framework for ArcGIS Online. Using road network data from the City of Columbus, Ohio and traffic data from CATT Laboratory's Regional Integrated Transportation Information System, transportation energy consumption will be modeled at a macro level. The application aims to provide highly accurate data while still maintaining the flexibility needed to adapt the model when new technologies arise. The methodology presented through this effort is expected to provide insights into how light-duty vehicles use energy on a large scale based on the road network they use. The application will also provide data exports to enable sharing and collaboration. " need for agility in turn calls for sophisticated, intuitive, and reliable means to visualize and share transportation data. Travel is inherently spatial, so methods of visualizing transportation information should also be rooted in spatial analysis. Geographic Information Systems (GIS) are the premier technological framework to analyze and display spatial data. GIS tools have been used in the past to depict flow of vehicles, and display of network conditions (speed, congestion, etc.). However, in an ever-changing technological landscape, spanning advancements in vehicle as well as information systems, depicting vehicle flows falls short of providing a comprehensive picture of the impact these advancements have on travel related energy consumption. To address this issue, this research effort presents a web-based mapping application to visualize how travel related energy flows across a city. The application is being developed by National Renewable Energy Laboratory researchers to integrate various transportation energy consumption models within a GIS schema. This has the goal of enabling rapid analysis of energy impacts of the dramatically evolving transportation environment. The application is being developed using a Python framework for ArcGIS Online. Using road network data from the City of Columbus, Ohio and traffic data from CATT Laboratory's Regional Integrated Transportation Information System, transportation energy consumption will be modeled at a macro level. The application aims to provide highly accurate data while still maintaining the flexibility needed to adapt the model when new technologies arise. The methodology presented through this effort is expected to provide insights into how light-duty vehicles use energy on a large scale based on the road network they use. The application will also provide data exports to enable sharing and collaboration.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Weak shock compaction on granular salt

This study conducted integrated experiments and computational modeling to investigate the speeds of a developing shock within granular salt and analyzed the effect of various impact velocities up to 245 m/s. Experiments were conducted on table salt utilizing a novel setup with a considerable bore length for the sample, enabling visualization of a moving shock wave. Experimental analysis using particle image velocimetry enabled the characterization of shock velocity and particle velocity histories. Mesoscale simulations further enabled advanced analysis of the shock wave’s substructure. In simulations, the shock front’s precursor was shown to have a heterogeneous nature, which is usually modeled as uniform in continuum analyses. The presence of force chains results in a spread out of the shock precursor over a greater ramp distance. With increasing impact velocity, the shock front thickness reduces, and the precursor of the shock front becomes less heterogeneous. Furthermore, mesoscale modeling suggests the formation of force chains behind the shock front, even under the conditions of weak shock. This study presents novel mesoscale simulation results on salt corroborated with data from experiments, thereby characterizing the compaction front speeds in the weak shock regime.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

AFUE Analysis Software Tool

The Annual Fuel Utilization Efficiency (AFUE) analysis of residential and light commercial furnaces follows ANSI/ASHRAE Standard 103-2017 (i.e., Method of Testing for Annual Fuel Utilization Efficiency of Residential Central Furnaces and Boilers) . The analysis is a complex, comprehensive method based on furnace configuration and specific components equipped, requiring detailed furnace testing and measurement data. For this reason, an AFUE Analysis Tool using Microsoft Excel enabled with Visual Basic for Applications (VBA) was developed with a user-friendly interface and comprehensive coverage. The tool consists of three worksheets: unit and configuration selection, geometry and measurement data input, and AFUE plus key results. This tool can be used to estimate the AFUE of both condensing and noncondensing furnaces with single-stage, two-stage, and step-modulating functions. The tool was validated with experimental data from Oak Ridge National Laboratory’s natural gas furnace projects that are commercially available. The results indicate the tool is reasonably accurate in the evaluation of a new R&D modified furnace unit.

Gao, Zhiming [Oak Ridge National Laboratory (ORNL)↗
DOE OSTI2026

Model Assessment Wizard (MAW)

SAND2026-18710O The Model Assessment Wizard (MAW) is a tool for evaluating ontologies and provides users with a comprehensive workbench for analysis. MAW features sub-modules for visualization, alignment, Shapes Constraint Language (SHACL) and Web Ontology Language (OWL) constraints, and simplification. Users can upload data, identify missing information, visualize ontologies, and update constraints. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.

Murdock, Jaimie [Sandia National Lab. (SNL-CA), Li↗
DOE OSTI2022

Analysis of DWPF Sludge Batch 6 (Macrobatch 7): Pour Stream Glass Samples

The Defense Waste Processing Facility (DWPF) began processing Sludge Batch 6 (SB6), also referred to as Macrobatch 7 (MB7), in June 2010. SB6 is a blend of the heel of Tank 40 from Sludge Batch 5 (SB5), H-Canyon Np transfers and SB6 that was transferred to Tank 40 from Tank 51.1 SB6 was processed using Frit 418. Sludge is received into the DWPF Chemical Processing Cell (CPC) and is processed through the Sludge Receipt and Adjustment Tank (SRAT) and Slurry Mix Evaporator Tank (SME). The treated sludge slurry is then transferred to the Melter Feed Tank (MFT) and fed to the melter. During processing of each sludge batch, the DWPF is required to take at least one glass sample to meet the objectives of the Glass Product Control Program (GPCP) and to complete the necessary Production Records so that the final glass product may be disposed of at a Federal Repository. The DWPF requested various analyses of radioactive glass samples obtained from the melter pour stream during processing of SB6 as well as reduction/oxidation (REDOX) analysis of MFT samples to determine the impact of Argon bubbling. Sample analysis followed the Task Technical and Quality Assurance Plan (TTQAP) and an Analytical Study Plan (ASP). Four Pour Stream (PS) glass samples and two MFT slurry samples were delivered to the Savannah River National Laboratory (SRNL) from the DWPF. Table 1-1 lists the sample information for each pour stream glass sample. SB6 PS3 (S03472) was selected as the official pour stream sample for SB6 and full analysis was requested. This report details the visual observations of the as-received SB6 PS No.3 glass sample as well as results for the chemical composition, Product Consistency Test (PCT), radionuclide content, noble metals, and glass density. REDOX results will be provided for all four pour stream samples and vitrified samples of MFT-558 and MFT-568A. Where appropriate, data from other pour stream samples will be provided.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Water Network Tool for Resilience (WNTR). User Manual, Version 0.2.3

The Water Network Tool for Resilience (WNTR, pronounced winter) is a Python package designed to simulate and analyze resilience of water distribution networks. Here, a network refers to the collection of pipes, pumps, valves, junctions, tanks, and reservoirs that make up a water distribution system. WNTR has an application programming interface (API) that is flexible and allows for changes to the network structure and operations, along with simulation of disruptive incidents and recovery actions. WNTR is based upon EPANET, which is a tool to simulate the movement and fate of drinking water constituents within distribution systems. Users are encouraged to be familiar with the use of EPANET and/or should have background knowledge in hydraulics and pressurized pipe network modeling before using WNTR. EPANET has a graphical user interface that might be a useful tool to facilitate the visualization of the network and the associated analysis results. Information on EPANET can be found at https://www.epa.gov/water-research/epanet. WNTR is compatible with EPANET 2.00.12 [Ross00]. In addition, users should have experience using Python, including the installation of additional Python packages. General information on Python can be found at https://www.python.org/.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Lambda-PFLOTRAN 1.0: a workflow for incorporating organic matter chemistry informed by ultra high resolution mass spectrometry into biogeochemical modeling

Abstract. Organic matter (OM) composition plays a central role in microbial respiration of dissolved organic matter and subsequent biogeochemical reactions. Here, a direct connection of organic matter chemistry and thermodynamics to reactive transport simulators has been achieved through the newly developed Lambda-PFLOTRAN workflow tool that succinctly incorporates carbon chemistry data generated from Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) into reaction networks to simulate organic matter degradation and the resulting biogeochemistry. Lambda-PFLOTRAN is a Python-based workflow, executed through a Jupyter notebook interface, that digests raw FTICR-MS data, develops a representative reaction network based on substrate-explicit thermodynamic modeling (also termed lambda modeling due to its key thermodynamic parameter λ used therein), and completes a biogeochemical simulation with the open source, reactive flow and transport code PFLOTRAN. The workflow consists of the following five steps: configuration, thermodynamic (lambda) analysis, sensitivity analysis, parameter estimation, and simulation output and visualization. Two test cases are provided to demonstrate the functionality of the Lambda-PFLOTRAN workflow. The first test case uses laboratory incubation data of temporal oxygen depletion to fit lambda parameters (i.e., maximum utilization rate and microbial carrying capacity). A slightly more complex second test case fits multiple lambda formulation and soil organic matter release parameters to temporal greenhouse gas generation measured during a soil incubation. Overall, the Lambda-PFLOTRAN workflow facilitates upscaling by using molecular-scale characterization to inform biogeochemical processes occurring at larger scales.

58 GEOSCIENCES↗