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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 1,333 records · Page 74
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

MO-G-BRE-09: Validating FMEA Against Incident Learning Data: A Study in Stereotactic Body Radiation Therapy

Purpose: Though FMEA (Failure Mode and Effects Analysis) is becoming more widely adopted for risk assessment in radiation therapy, to our knowledge it has never been validated against actual incident learning data. The objective of this study was to perform an FMEA analysis of an SBRT (Stereotactic Body Radiation Therapy) treatment planning process and validate this against data recorded within an incident learning system. Methods: FMEA on the SBRT treatment planning process was carried out by a multidisciplinary group including radiation oncologists, medical physicists, and dosimetrists. Potential failure modes were identified through a systematic review of the workflow process. Failure modes were rated for severity, occurrence, and detectability on a scale of 1 to 10 and RPN (Risk Priority Number) was computed. Failure modes were then compared with historical reports identified as relevant to SBRT planning within a departmental incident learning system that had been active for two years. Differences were identified. Results: FMEA identified 63 failure modes. RPN values for the top 25% of failure modes ranged from 60 to 336. Analysis of the incident learning database identified 33 reported near-miss events related to SBRT planning. FMEA failed to anticipate 13 of these events, among which 3 were registered with severity ratings of severe or critical in the incident learning system. Combining both methods yielded a total of 76 failure modes, and when scored for RPN the 13 events missed by FMEA ranked within the middle half of all failure modes. Conclusion: FMEA, though valuable, is subject to certain limitations, among them the limited ability to anticipate all potential errors for a given process. This FMEA exercise failed to identify a significant number of possible errors (17%). Integration of FMEA with retrospective incident data may be able to render an improved overview of risks within a process.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Data for "Soil carbon dynamics during drying vs. rewetting: Importance of antecedent moisture conditions"

This dataset contains data used for the paper "Soil carbon dynamics during drying vs. rewetting: importance of antecedent moisture conditions". The Related References field will be updated with a full citation when available.Soil moisture influences soil carbon dynamics, including microbial growth and respiration. The response of such ‘soil respiration’ to moisture changes is generally assumed to be linear and reversible, i.e. to depend only on the current moisture state. Current models thus do not account for antecedent soil moisture conditions when determining soil respiration or the available substrate pool. We conducted a laboratory incubation to determine how the antecedent conditions of drought and flood influenced soil organic matter (SOM) chemistry, bioavailability, and respiration. We sampled soils from an upland coastal forest, Beaver Creek, WA USA, and subjected them to drying and rewetting treatments. For the drying treatment, field moist soils were saturated and then dried to 75, 50, 35, and 5 % saturation. In the rewetting treatment, field moist soils were air-dried and then rewet to 35, 50, 75, and 100 % saturation. We measured respiration and water extractable organic carbon (WEOC) concentrations and used 1H-NMR and FT-ICR-MS to characterize the WEOC pool across the treatments. The drying vs. wetting treatment strongly influenced SOM bioavailability, as rewet soils (with antecedent drought) had greater WEOC concentrations and respiration fluxes compared to the drying soils (with antecedent flood). In addition, air-dry soils had the highest WEOC concentrations, and the NMR-resolved peaks showed a strong contribution of protein groups in these soils. Both NMR and FT-ICR-MS analyses indicated increased contribution of complex aromatic groups/molecules in the rewet soils, compared to the drying soils. We suggest that drying introduced organic matter into the WEOC pool via desorption of aromatic molecules and/or by microbial cell lysis, and this stimulated microbial mineralization rates. Our work indicates that even short-term shifts in antecedent moisture conditions can strongly influence soil C dynamics at the core scale. The predictive uncertainties in current soil models may be reduced by a more accurate representation of soil water and C persistence that includes a mechanistic and quantitative understanding of the impact of antecedent moisture conditions.This dataset contains a compressed (.zip) archive of the data and R scripts used for this manuscript. The dataset includes files in .csv and .txt format, which can be accessed and processed using MS Excel or R. NMR data are provided as raw output data (accessed in Bruker TopSpin or MestreNova) as well as the MestreNova-processed files. This archive can also be accessed on GitHub at https://github.com/kaizadp/hysteresis_and_soil_carbon (DOI: 10.5281/zenodo.4432885).

1H-NMR↗
DOE OSTI2023

Probing rutile solid-phase crystallization of atomically mixed Mn-alloyed TiO 2 coatings through XANES analysis

Phase transformation via dopant introduction is an emerging strategy for tuning the electronic and catalytic properties of oxides. However, developing a general theory of ternary alloys and their microstructure evolution remains challenging due to the knowledge gaps in how point defects such as dopants lead to the formation of complex or metastable phases. We investigate the influence of Mn concentration and annealing temperature on the microstructure evolution of amorphous Mn-alloyed TiO 2 synthesized via Atomic Layer Deposition. In conclusion, our findings reveal a new crystallization pathway resulting in a direct amorphous TiO 2 to rutile transformation without an intermediate anatase phase and our multi-parameter thermal processing conditions show potential for AI-assisted data acquisition and analysis for studying complex oxide coatings.

36 MATERIALS SCIENCE↗
DOE OSTI2016

High-fidelity plasma codes for burn physics

Accurate predictions of equation of state (EOS), ionic and electronic transport properties are of critical importance for high-energy-density plasma science. Transport coefficients inform radiation-hydrodynamic codes and impact diagnostic interpretation, which in turn impacts our understanding of the development of instabilities, the overall energy balance of burning plasmas, and the efficacy of self-heating from charged-particle stopping. Important processes include thermal and electrical conduction, electron-ion coupling, inter-diffusion, ion viscosity, and charged particle stopping. However, uncertainties in these coefficients are not well established. Fundamental plasma science codes, also called high-fidelity plasma codes, are a relatively recent computational tool that augments both experimental data and theoretical foundations of transport coefficients. This paper addresses the current status of HFPC codes and their future development, and the potential impact they play in improving the predictive capability of the multi-physics hydrodynamic codes used in HED design.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Characterizing Ocean Acidification and Atmospheric Emission caused by Methane Released from Gas Hydrate Systems along the US Atlantic Margin (Final Report)

To assess the environmental impact of decomposing gas hydrates, this project characterized the atmospheric emission and ocean acidification associated with seafloor-released methane along the U.S. Atlantic Margin. This project leveraged the newly discovered seafloor seeps in this region, many of which are located near the landward limit of gas hydrate stability, to investigate the fate and environmental impact of methane once released from the seafloor. Several research directions were pursued during these investigations. First, high-spatial resolution surface surveys were conducted to characterize the degree to which sea-to-air emission of methane is correlated with acoustically detected seafloor bubble emissions. Second, the natural isotopic signatures of both radiocarbon and δ 13 C were measured throughout the water column to observe if seafloor-released methane was detected in surface waters or if it was replaced by a different source. Third, the extent of methane oxidation in the water column was constrained to assess the ability of this process to mitigate atmospheric methane emissions. Fourth, since aerobic methane oxidation ultimately converts methane to carbon dioxide, the extent of ocean acidification relating to this process was assessed. Ultimately, this project was conducted in four stages. Stage one involved preparations for the research expedition along the U.S. Atlantic Margin. This involved calibrating, modifying, and validating equipment needed for sample and data collection in this unique environment. Stage two involved executing the at-sea science. This research was conducted from 25 August to 5 September 2017 on the R/V Hugh R. Sharp. This expedition left from Lewes, Delaware and investigated the continental slope from approximately this latitude down to Cape Hatteras, North Carolina. Third, any samples unanalyzed on the research vessel were returned to the laboratory and measured. Fourth, the data were interpreted, and publications and presentations were prepared.

03 NATURAL GAS↗
DOE OSTI2025

A Cloud-Tracking Data Set for the CSAPR2 Adaptive Scanning during TRACER

The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) User Facility (Mather and Voyles 2013) deployed the first ARM Mobile Facility (AMF1; Miller et al. 2016) near LaPorte, Texas to support the Tracking Aerosol Convection Interactions Experiment (TRACER) (Jensen et al. 2025) near Houston, Texas. From October 2021 to September 2022, AMF1 was deployed to 29.67° N, 95.06° W near LaPorte, Texas and the 2nd Generation C-band Scanning ARM Precipitation Radar (CSAPR2) was deployed to a supplementary site at 29.53° N, 95.28° W (Figure 1). During an intensive operational period (IOP) from 1 June to 30 September 2022, the CSAPR2 sampled precipitation echoes in an adaptive scanning mode following the Multisensor Agile Adaptive Scanning (MAAS) framework (Kollias et al. 2020). MAAS helped optimize the CSAPR2 scan strategy to perform frequent plan position indicator (PPI) and range height indicator (RHI) scans (Lamer et al. 2023). Details of the CSAPR2 scanning, data processing, and calibration procedures used by the principal investigator (PI), and the PI data files are described by Oue et al. (2023). Details of the CSAPR2 operational performance, ARM data processing and correction procedures, and data quality masks are described by Feng et al. (2024a).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

SPRUCE Large-Collar In Situ CO2 and CH4 Flux Data for the SPRUCE Experimental Plots: Whole-Ecosystem-Warming

This data set reports community-level CO2 and CH4 flux measurements for the SPRUCE experimental study plots located in the S1-Bog. Surface flux measurements of CO2 and CH4 were made using open-path analyzers over an area of 1.13 m2 within each of 16 plots. A custom-designed chamber encloses the hummock-hollow topography and allows point in time measurements of the shrub, forb, Sphagnum spp. and the complex microbial community. These observations were made with ambient light and imposed dark conditions to allow estimates of community daytime and night respiratory processes. This data set is all inclusive – beginning in 2011 and continuing through the Whole-Ecosystem-Warming (WEW) phase of the experiment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Robotic Planning under Uncertainty in Spatiotemporal Environments in Expeditionary Science

In the expeditionary sciences, spatiotemporally varying environments -- hydrothermal plumes, algal blooms, lava flows, or animal migrations -- are ubiquitous. Mobile robots are uniquely well-suited to study these dynamic, mesoscale natural environments. We formalize expeditionary science as a sequential decision-making problem, modeled using the language of partially-observable Markov decision processes (POMDPs). Solving the expeditionary science POMDP under real-world constraints requires efficient probabilistic modeling and decision-making in problems with complex dynamics and observational models. Previous work in informative path planning, adaptive sampling, and experimental design have shown compelling results, largely in static environments, using data-driven models and information-based rewards. However, these methodologies do not trivially extend to expeditionary science in spatiotemporal environments: they generally do not make use of scientific knowledge such as equations of state dynamics, they focus on information gathering as opposed to scientific task execution, and they make use of decision-making approaches that scale poorly to large, continuous problems with long planning horizons and real-time operational constraints. In this work, we discuss these and other challenges related to probabilistic modeling and decision-making in expeditionary science, and present some of our preliminary work that addresses these gaps. We ground our results in a real expeditionary science deployment of an autonomous underwater vehicle (AUV) in the deep ocean for hydrothermal vent discovery and characterization. Our concluding thoughts highlight remaining work to be done, and the challenges that merit consideration by the reinforcement learning and decision-making community.

Preston, Victoria↗
DOE OSTI2019

csapr2cfrmrhi (a1)

The C-Band Scanning ARM Precipitation Radar is a dual-polarization Doppler weather radar that operates in a simultaneous transmit and receive (STAR) mode, splitting the transmit signal so that power is transmitted on both horizontal and vertical polarizations at the same time. The CSAPR can also transmit on a single polarization (horizontal or vertical) and measure linear depolarization ratio (LDR). In addition to the first three Doppler moments (reflectivity, radial velocity, and spectra width), the CSAPR also provides differential reflectivity, correlation coefficient, and specific differential phase. The dual-polarization variables enable the provision of rainfall rate estimates and identification of precipitation types. For the 2018–2019 Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in Argentina, ARM deployed a second-generation CSAPR, known as CSAPR2. CACTI data from the CSAPR2 are now available as b1-level products. Building on the original CACTI operational data, the b1-level products feature improved data quality resulting from extensive analyses and corrections. The data are cross-calibrated to a common point, datastreams are corrected for operational issues that occurred during the campaign, and several data quality masks and basic derived products are incorporated. For more information, read the CACTI radar b1-level processing report.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

csapr2cfrrhi (a1)

The C-Band Scanning ARM Precipitation Radar is a dual-polarization Doppler weather radar that operates in a simultaneous transmit and receive (STAR) mode, splitting the transmit signal so that power is transmitted on both horizontal and vertical polarizations at the same time. The CSAPR can also transmit on a single polarization (horizontal or vertical) and measure linear depolarization ratio (LDR). In addition to the first three Doppler moments (reflectivity, radial velocity, and spectra width), the CSAPR also provides differential reflectivity, correlation coefficient, and specific differential phase. The dual-polarization variables enable the provision of rainfall rate estimates and identification of precipitation types. For the 2018–2019 Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in Argentina, ARM deployed a second-generation CSAPR, known as CSAPR2. CACTI data from the CSAPR2 are now available as b1-level products. Building on the original CACTI operational data, the b1-level products feature improved data quality resulting from extensive analyses and corrections. The data are cross-calibrated to a common point, datastreams are corrected for operational issues that occurred during the campaign, and several data quality masks and basic derived products are incorporated. For more information, read the CACTI radar b1-level processing report.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

csapr2cfrcrc (a1)

The C-Band Scanning ARM Precipitation Radar is a dual-polarization Doppler weather radar that operates in a simultaneous transmit and receive (STAR) mode, splitting the transmit signal so that power is transmitted on both horizontal and vertical polarizations at the same time. The CSAPR can also transmit on a single polarization (horizontal or vertical) and measure linear depolarization ratio (LDR). In addition to the first three Doppler moments (reflectivity, radial velocity, and spectra width), the CSAPR also provides differential reflectivity, correlation coefficient, and specific differential phase. The dual-polarization variables enable the provision of rainfall rate estimates and identification of precipitation types. For the 2018–2019 Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in Argentina, ARM deployed a second-generation CSAPR, known as CSAPR2. CACTI data from the CSAPR2 are now available as b1-level products. Building on the original CACTI operational data, the b1-level products feature improved data quality resulting from extensive analyses and corrections. The data are cross-calibrated to a common point, datastreams are corrected for operational issues that occurred during the campaign, and several data quality masks and basic derived products are incorporated. For more information, read the CACTI radar b1-level processing report.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

csapr2cfrppi (a1)

The C-Band Scanning ARM Precipitation Radar is a dual-polarization Doppler weather radar that operates in a simultaneous transmit and receive (STAR) mode, splitting the transmit signal so that power is transmitted on both horizontal and vertical polarizations at the same time. The CSAPR can also transmit on a single polarization (horizontal or vertical) and measure linear depolarization ratio (LDR). In addition to the first three Doppler moments (reflectivity, radial velocity, and spectra width), the CSAPR also provides differential reflectivity, correlation coefficient, and specific differential phase. The dual-polarization variables enable the provision of rainfall rate estimates and identification of precipitation types. For the 2018–2019 Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in Argentina, ARM deployed a second-generation CSAPR, known as CSAPR2. CACTI data from the CSAPR2 are now available as b1-level products. Building on the original CACTI operational data, the b1-level products feature improved data quality resulting from extensive analyses and corrections. The data are cross-calibrated to a common point, datastreams are corrected for operational issues that occurred during the campaign, and several data quality masks and basic derived products are incorporated. For more information, read the CACTI radar b1-level processing report.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

csapr2cfrzppi (a1)

The C-Band Scanning ARM Precipitation Radar is a dual-polarization Doppler weather radar that operates in a simultaneous transmit and receive (STAR) mode, splitting the transmit signal so that power is transmitted on both horizontal and vertical polarizations at the same time. The CSAPR can also transmit on a single polarization (horizontal or vertical) and measure linear depolarization ratio (LDR). In addition to the first three Doppler moments (reflectivity, radial velocity, and spectra width), the CSAPR also provides differential reflectivity, correlation coefficient, and specific differential phase. The dual-polarization variables enable the provision of rainfall rate estimates and identification of precipitation types. For the 2018–2019 Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in Argentina, ARM deployed a second-generation CSAPR, known as CSAPR2. CACTI data from the CSAPR2 are now available as b1-level products. Building on the original CACTI operational data, the b1-level products feature improved data quality resulting from extensive analyses and corrections. The data are cross-calibrated to a common point, datastreams are corrected for operational issues that occurred during the campaign, and several data quality masks and basic derived products are incorporated. For more information, read the CACTI radar b1-level processing report.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

csapr2cfrmmisc (a1)

The C-Band Scanning ARM Precipitation Radar is a dual-polarization Doppler weather radar that operates in a simultaneous transmit and receive (STAR) mode, splitting the transmit signal so that power is transmitted on both horizontal and vertical polarizations at the same time. The CSAPR can also transmit on a single polarization (horizontal or vertical) and measure linear depolarization ratio (LDR). In addition to the first three Doppler moments (reflectivity, radial velocity, and spectra width), the CSAPR also provides differential reflectivity, correlation coefficient, and specific differential phase. The dual-polarization variables enable the provision of rainfall rate estimates and identification of precipitation types. For the 2018–2019 Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in Argentina, ARM deployed a second-generation CSAPR, known as CSAPR2. CACTI data from the CSAPR2 are now available as b1-level products. Building on the original CACTI operational data, the b1-level products feature improved data quality resulting from extensive analyses and corrections. The data are cross-calibrated to a common point, datastreams are corrected for operational issues that occurred during the campaign, and several data quality masks and basic derived products are incorporated. For more information, read the CACTI radar b1-level processing report.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

csapr2cfrhsrhi (a1)

The C-Band Scanning ARM Precipitation Radar is a dual-polarization Doppler weather radar that operates in a simultaneous transmit and receive (STAR) mode, splitting the transmit signal so that power is transmitted on both horizontal and vertical polarizations at the same time. The CSAPR can also transmit on a single polarization (horizontal or vertical) and measure linear depolarization ratio (LDR). In addition to the first three Doppler moments (reflectivity, radial velocity, and spectra width), the CSAPR also provides differential reflectivity, correlation coefficient, and specific differential phase. The dual-polarization variables enable the provision of rainfall rate estimates and identification of precipitation types. For the 2018–2019 Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in Argentina, ARM deployed a second-generation CSAPR, known as CSAPR2. CACTI data from the CSAPR2 are now available as b1-level products. Building on the original CACTI operational data, the b1-level products feature improved data quality resulting from extensive analyses and corrections. The data are cross-calibrated to a common point, datastreams are corrected for operational issues that occurred during the campaign, and several data quality masks and basic derived products are incorporated. For more information, read the CACTI radar b1-level processing report.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Sky Residual Correction

The near-infrared is populated by numerous emission lines radiated from the Earth’s atmosphere, and these emission lines are often several orders of magnitude brighter in intensity than the typical astrophysical science target. The subtraction of these emission lines, sky subtraction, can create large systematic errors in ground-based astronomical spectra effectively limiting the number of usable resolution elements. A more effective sky subtraction, and the reduction of the systematic errors due to the sky subtraction process, is a major hurdle which ground-based astronomy must overcome to increase the amount of usable spectrum, and to be able to observe fainter scientific targets. Large high-quality data sets such as the Sloan Digital Sky Survey (SDSS) in themselves present opportunities for a reduction of the systematic sky subtraction errors through self-calibration. The sky residual correction method of Wild and Hewett is one such self-calibration technique which uses principal component analysis to reduce the systematic sky residual errors present in SDSS spectra. We apply sky residual corrections to the SDSS, Baryon Oscillation Spectroscopic Survey, and Apache Point Observatory Galactic Evolution Experiment data sets to optimize the number of subtraction components, and to quantify the reduction of the systematic errors in the science spectra. Finally as a proof of concept we use the sky-residual-corrected SDSS luminous red galaxy spectra to search for gravitationally lensed emission line Galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

WHONDRS 48 Hour Diel Cycling Study at HJ Andrews Experimental Forest Watershed 1 (WS1)

This dataset is from a 48 hour sampling campaign at H.J. Andrews Experimental Forest, Oregon, USA. Filtered surface water and pore water samples were collected every three hours from the Watershed 1 (WS1) stream. Samples were delivered to the Environmental Molecular Sciences Laboratory (EMSL) for high resolution carbon characterization via Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS). In addition, basic geochemistry analyses (e.g., dissolved organic carbon concentration, cations, anions, SpC, pH) were conducted, standardized photos were taken, surface water hydrographs were collated from existing instrumentation, and extensive metadata were captured. All data types are provided in a standard format. In addition, the data package contains instructions to process the FTICR data based on the user’s specific needs. All data are free to be used for any purpose, such as for manuscripts, presentations, and grant proposals. Please use the data package’s DOI to cite the data package. Note that the included hydrograph has a separate DOI that should also be cited. We ask that you email us at WHONDRS@pnnl.gov to let us know that you're using the data and acknowledge WHONDRS and the U.S. Department of Energy's Subsurface Biogeochemical Research program—which generously provides funding to WHONDRS—in your documents, presentations, etc. There is no obligation to include WHONDRS members as co-authors.

54 ENVIRONMENTAL SCIENCES↗