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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2011

Nanocrystalline ferrites Ni{sub x}Zn{sub 1-x}Fe{sub 2}O{sub 4}: Influence of cation distribution on acidic and gas sensing properties

This work is devoted to a detailed analysis of the interconnection between composition, cation distribution and acidic properties of the surface of nanocrystalline ferrites Ni{sub x}Zn{sub 1-x}Fe{sub 2}O{sub 4} obtained by aerosol pyrolysis. The detailed analysis of the Moessbauer spectra allows us to determine the distribution of cations between tetrahedral and octahedral positions in spinel structure. Depending on samples composition, the tetrahedral positions can be occupied by only Fe{sup 3+} cations (inverse spinel, x{>=}0.4) or by Fe{sup 3+} and Zn{sup 2+} cations (mixed spinel, x=0, 0.2). Increasing the nickel concentration in the ferrite leads to decrease in the number of strong acid centers on the surface. It was found that the decrease in the contribution of strong surface acid sites leads to an increase in sensory sensitivity of the ferrite towards ammonia. For ethanol detection an inverse relationship between sensor signal and surface acidity was observed. - Graphical abstract: (a) n- and p-types change in electrical resistance and (b) sensor signal of Ni{sub x}Zn{sub 1-x}Fe{sub 2}O{sub 4} towards NH{sub 3} and ethanol vapor vs. the part of strong acid sites. Highlights: > Relationship between composition, cation distribution, and acidity in nanocrystalline Ni{sub x}Zn{sub 1-x}Fe{sub 2}O{sub 4}. > Degree of inversion of spinel is determined by Moessbauer spectroscopy. > Increase of nickel content leads to decrease in the number of strong acid centers. > This results in an increase in sensory sensitivity of the ferrite towards ammonia. > Relationship between sensor signal to ethanol and surface acidity is opposite.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Remote Sensing-Based, 5-m, Vegetation Distributions, Kougarok Study Site, Seward Peninsula, Alaska, ca. 2009 - 2016

A multi-sensor remote sensing-based deep learning approach was developed for generating high-resolution (5~m) vegetation maps for the western Alaskan Arctic on the Seward Peninsula, Alaska. This data was developed using the fusion of hyperspectral, multispectral, and terrain datasets. The current data is located in the Kougarok watershed but we plan to expand this over the Seward Peninsula. Additional metadata and vegetation maps included as two *.pdf, one *.txt, one *.nc, and three *.tif files.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Corrosion Sensors for Structural Health Monitoring of Oil and Natural Gas Infrastructure: A Review

Corrosion has been a great concern in the oil and natural gas industry costing billions of dollars annually in the U.S. The ability to monitor corrosion online before structural integrity is compromised can have a significant impact on preventing catastrophic events resulting from corrosion. This article critically reviews conventional corrosion sensors and emerging sensor technologies in terms of sensing principles, sensor designs, advantages, and limitations. Conventional corrosion sensors encompass corrosion coupons, electrical resistance probes, electrochemical sensors, ultrasonic testing sensors, magnetic flux leakage sensors, electromagnetic sensors, and in-line inspection tools. Emerging sensor technologies highlight optical fiber sensors (point, quasi-distributed, distributed) and passive wireless sensors such as passive radio-frequency identification sensors and surface acoustic wave sensors. Emerging sensors show great potential in continuous real-time in-situ monitoring of oil and natural gas infrastructure. Distributed chemical sensing is emphasized based on recent studies as a promising method to detect early corrosion onset and monitor corrosive environments for corrosion mitigation management. Additionally, challenges are discussed including durability and stability in extreme and harsh conditions such as high temperature high pressure in subsurface wellbores.

passive RFID sensors↗
DOE OSTI2012

GFOC Project results: High Temperature / High Pressure, Hydrogen Tolerant Optical Fiber

Tests results are given for exposure of multimode optical fiber to high temperatures (300 deg. C) and high partial pressure (15 bar) hydrogen. These results demonstrate that fluorine down doped optical fibers are much more hydrogen tolerant than traditional germanium doped multimode optical fibers. Also demonstrated is the similar hydrogen tolerance of carbon coated and non-carbon coated fibers. Model for reversible H2 impact in fiber versus T{sup o}C and H2 pressure is given. These results have significant impact for the longevity of use for distributed temperature sensing applications in harsh environments such as geothermal wells.

08 HYDROGEN↗
DOE OSTI2021

Predicting distribution of malaria vector larval habitats in Ethiopia by integrating distributed hydrologic modeling with remotely sensed data

Abstract Larval source management has gained renewed interest as a malaria control strategy in Africa but the widespread and transient nature of larval breeding sites poses a challenge to its implementation. To address this problem, we propose combining an integrated high resolution (50 m) distributed hydrological model and remotely sensed data to simulate potential malaria vector aquatic habitats. The novelty of our approach lies in its consideration of irrigation practices and its ability to resolve complex ponding processes that contribute to potential larval habitats. The simulation was performed for the year of 2018 using ParFlow-Common Land Model (CLM) in a sugarcane plantation in the Oromia region, Ethiopia to examine the effects of rainfall and irrigation. The model was calibrated using field observations of larval habitats to successfully predict ponding at all surveyed locations from the validation dataset. Results show that without irrigation, at least half of the area inside the farms had a 40% probability of potential larval habitat occurrence. With irrigation, the probability increased to 56%. Irrigation dampened the seasonality of the potential larval habitats such that the peak larval habitat occurrence window during the rainy season was extended into the dry season. Furthermore, the stability of the habitats was prolonged, with a significant shift from semi-permanent to permanent habitats. Our study provides a hydrological perspective on the impact of environmental modification on malaria vector ecology, which can potentially inform malaria control strategies through better water management.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

A multi-scale temperature-based strategy to map hydrologic exchange flows in highly dynamic systems

Mapping and quantifying hydrologic exchange flows (HEFs) is critical to environmental monitoring and remediation at contaminated sites; however, these objectives are challenging in highly dynamic systems, e.g., along dam-regulated rivers, where HEFs vary rapidly. Direct seepage measurements are labor-intensive and difficult to automate, whereas indirect (e.g., thermal) and remote sensing methods have potential to allow continuous monitoring with limited field effort. We present a preliminary assessment of a multi-scale temperature-based strategy for monitoring HEFs along the Hanford Reach of the Columbia River, in eastern WA, United States. Five thermal methods were assessed. First, a vertical temperature profile (VTP) was installed into the streambed. The VTP data were analyzed using a data assimilation algorithm designed for automated real-time estimation in dynamic systems. Second, a thermal infrared (TIR) camera was used in roving surveys to identify seeps. Third, a TIR camera was stationed at the VTP site to collect images at 1-h intervals. Together, the two TIR datasets provided a basis to assess the potential for drone-based TIR. Fourth, temperature was measured at the sediment/water interface to assess fiber-optic distributed temperature sensing. Fifth, imagery from the ECOSTRESS satellite mission was acquired to assess the potential of spaceborne thermal monitoring. Based on our preliminary assessment, VTP, TIR, and bed temperature measurements provide complementary spatial coverage, temporal sampling, and resolution; these methods have potential for long-term, automated monitoring of HEFs. The publicly available spaceborne imagery, however, proved inadequate because of insufficient spatial resolution and data gaps resulting from cloud cover and revisit frequency.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Utah FORGE: Well 58-32 Stimulation Conference Paper and Data

The U.S. Department of Energy’s (U.S. DOE) Frontier Observatory for Research in Geothermal Energy (FORGE) is a field laboratory that provides a unique opportunity to develop and test new technologies for characterizing, creating and sustaining Enhanced Geothermal Systems (EGS) in a controlled environment. In 2018, the U.S. DOE selected a site in south-central Utah for the FORGE laboratory. Numerous geoscientific studies have been conducted in the region since the 1970s in support of geothermal development at Roosevelt Hot Springs. A vertical scientific well, 58-32, was drilled and tested to a depth of 2290 m (7515 ft) GL in 2017 on the FORGE site to provide additional characterization of the reservoir rocks. The well encountered a conductive thermal regime and a bottom hole temperature of 199degC (390degF). More than 2000 natural fractures were identified, but measured permeabilities are low, less than 30 micro-darcies. Induced fractures indicate that the maximum horizontal stress trends NNE-SSW, consistent with geologic and well observations from the surrounding area. Approximately 45 m (147 ft) at the base of the well was left uncased. A maximum wellhead pressure of 27.6 MPa (4000 psig) at an injection rate of ~1431 L/min (~9 bpm) was measured during stimulation testing in September 2017. Conventional diagnostic evaluations of the data suggest that hydraulic fracturing and shearing occurred. Estimates of the stress gradient for delta_h_min range from of 16.7 to 17.6 kPa/m (0.74 to 0.78 psi/ft). A gradient of 25.6 kPa/m (1.13psi/ft) was calculated for delta_V. In 2019, the 2017 open-hole stimulation in well 58-32 was repeated with injection rates up to 2385 L/min (15 bpm). Two additional stimulations were conducted in the cased portion of the well; one to stimulate critically stressed fractures and the second to test noncritically stressed fractures. Breakdown of the zone spanning critically-stressed fractures occurred at a surface pressure of approximately 29.0 MPa (4200 psig). Although stimulation of the noncritically stressed fractures was interrupted by failure of the bridge plug beneath the perforated interval, micro-seismic data suggests stimulation of the fractures may have been initiated at a surface pressure of 45.5 MPa (6600 psig). These stimulation results support the conclusion the Mineral Mountains granitoid is an appropriate host for EGS development. Micro-seismicity was monitored during the stimulations using surface and downhole instrumentation. Five seismometers and a nodal array of 150 seismic sensors were deployed on the surface. A Distributed Acoustic Sensing (DAS) cable and a string of 12 geophones were deployed in well 78-32, drilled to a depth of 998 m (3274 ft) GL. A broadband sensor and a high-temperature geophone were deployed in well 68-32, drilled to a depth of 303 m (994 ft) GL. More than 420 micro-seismic events were detected by the geophone string. Other instruments detected fewer events.

15 GEOTHERMAL ENERGY↗
DOE OSTI2017

Brady's Geothermal Field DASH Resampled in Time

This submission includes a link to the DASH (Horizontal distributed acoustic sensing) data files that were resampled in time to 100 samples per second from the original 1000 samples per second files. Data is in 30 second Matlab files organized into directories by day. The README_DASH.pdf file includes details on the contents of the Matlab files. Metadata is the same as the original data files and can be found in GDR submission 980 (see "Original Data Files" link).

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

CODAS Data from Oliktok Point, Beaufort Sea, Alaska

Cryosphere/Ocean Distributed Acoustic Sensing (CODAS) data collected from the Beaufort Sea, Alaska, using ~37.4 km of dark telecommunications fiber located at Oliktok Point, Alaska. Data were collected with a Silixa iDAS, using 10 m gauge length, 2 m spatial resolution, and 1000 Hz sample rate. Provided here are the DAS-recorded time series for the rapid refreeze event described in Baker & Abbott (2022) (see link below). This covers a date range of 2021-11-10 15:00 UTC to 2021-11-11 17:00 UTC. Data have been decimated to 100 Hz and 20 m (i.e., every 10th channel for 1831 channels, total), as used in Baker & Abbott (2022). Data have been extracted from raw format into 1-hour long .sac* files and organized into directories by channel number, spanning channels 100 to 18400. Time series units are nano-strainrate (nm/m/s). For distribution, data have been compressed into .zip files containing all time series files for 100 channels. *For information on the Seismic Analysis Code (SAC) file format: https://seiscode.iris.washington.edu/projects/sac

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

Fostering Geothermal Machine Learning Success: Elevating Big Data Accessibility and Automated Data Standardization in the Geothermal Data Repository: Preprint

The Department of Energy's (DOE) Geothermal Data Repository (GDR) has implemented improvements to both its data lakes and its data standards and automated data pipelines. The GDR data lakes have reduced storage and compute-related barriers to using large geothermal datasets, enabling these large datasets to be accessed by anyone with a modern computer and internet access. More recently, the GDR has been working to further reduce barriers through streamlining the data intake process, educating users on the process and requirements, and aiding users in accessing data from the data lakes. These improvements have augmented the quantity of datasets the GDR is able to accept into its data lakes and have enabled users who are new to cloud tools to access these datasets more easily, overall increasing the accessibility of big geothermal data for use in machine learning and other projects. In addition, the GDR now has built-in data standards and pipelines for drilling data, geospatial data, and distributed acoustic sensing (DAS) data. These standardization efforts aim to enhance the real-world applicability of geothermal machine learning outcomes by improving the quality of training data. Specifically, through standardizing high-value datasets, the GDR is reducing project-specific data curation requirements, thus allowing more time for actual research. By automating this process, the burden of standardization is lifted from the user, ultimately increasing the availability of standardized data.

accessibility↗
DOE OSTI2021

Utah FORGE: Seismic Velocity Models, February 2021

This dataset contains a map, showing the Utah FORGE seismic stations, and seismic velocity model data. There are 61 1-D velocity models which are in a compressed TAR file. A paper is referenced at the end of this description which discusses the use of these data in 3D modelling. The paper summary follows: We expand the application of spatial autocorrelation (SPAC) from typical 1-D Vs profiles to quasi-3-D imaging via Bayesian Monte Carlo inversion (BMCI) using a dense nodal array (49 nodes) located at the Utah Frontier Observatory for Research in Geothermal Energy (FORGE) site. Combinations of 4 and 9 geophones in subarrays provide for 36 and 25 1-D Vs profiles, respectively. Profiles with error bars are determined by calculating coherency functions that fit observations in a frequency range of 0.2-5 Hz. Thus, a high-resolution quasi-3-D Vs model from the surface to 2.0 km depth is derived and shows that surface-parallel sedimentary strata deepen to the west, consistent with a 3-D seismic reflection survey. Moreover, the resulting Vs profile is consistent with a Vs profile derived from distributed acoustic sensing (DAS) data located in a borehole at the FORGE site. The quasi-3-D velocity model shows that the base of the basin dips ~22 degrees to the west and topography on the basement interface coincident with the Mag Lee Wash suggests that the bedrock interface is an unconformity. Reference: Zhang, H. and K. L. Pankow (2021). High-resolution Bayesian spatial auto-correlation (SPAC) pseudo-3D Vs model of Utah FORGE site with a dense geophone array, Geophys. Res. Int, https://doi.org/10.1093/gji/ggab049

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Evidence of Nonlinear Seismic Effects in the Earth from Downhole Distributed Acoustic Sensors

Seismic velocities and elastic moduli of rocks are known to vary significantly with applied stress, which indicates that these materials exhibit nonlinear elasticity. Monochromatic waves in nonlinear elastic media are known to generate higher harmonics and combinational frequencies. Such effects have the potential to be used for broadening the frequency band of seismic sources, characterization of the subsurface, and safety monitoring of civil engineering infrastructure. However, knowledge on nonlinear seismic effects is still scarce, which impedes the development of their practical applications. To explore the potential of nonlinear seismology, we performed three experiments: two in the field and one in the laboratory. The first field experiment used two vibroseis sources generating signals with two different monochromatic frequencies. The second field experiment used a surface orbital vibrator with two eccentric motors working at different frequencies. In both experiments, the generated wavefield was recorded in a borehole using a fiber-optic distributed acoustic sensing cable. Both experiments showed combinational frequencies, harmonics, and other intermodulation products of the fundamental frequencies both on the surface and at depth. Laboratory experiments replicated the setup of the field test with vibroseis sources and showed similar nonlinear combinations of fundamental frequencies. Amplitudes of the nonlinear signals observed in the laboratory showed variation with the saturating fluid. These results confirm that nonlinear components of the wavefield propagate as body waves, are likely to generate within rock formations, and can be potentially used for reservoir fluid characterization.

58 GEOSCIENCES↗
DOE OSTI2024

Utah FORGE: GES Well 16A(78)-32 and Well 16B(78)-32 Stimulation Seismic Event Catalogs

This dataset contains seismic event catalogs from the hydraulic stimulation of wells 16A(78)-32 and 16B(78)-32 at the Utah FORGE site in April 2024. The data was collected by Geo Energy Suisse (GES) using a variety of seismic monitoring technologies, including 3-component (3C) geophones and distributed acoustic sensing (DAS) systems. These technologies were deployed across several locations, including wells 16A, 16B, and Delano-1, with sensor arrays at multiple depths to capture microseismic activity during the stimulations. The catalogs provide both real-time and manually checked seismic event locations, with detailed parameters such as trigger conditions, velocity models, and data acquisition settings. The dataset includes information on the stimulation stages, event rates, and hydraulic injection conditions for each well, with a report detailing the data acquisition configuration and seismic event location methodologies. Users will need to reference the included report for a complete understanding of the sensor network, data processing techniques, and accuracy considerations.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Imperial Valley Dark Fiber Project Continuous DAS Data

The Imperial Valley Dark Fiber Project acquired Distributed Acoustic Sensing (DAS) seismic data on a ~28 km segment of dark fiber between the cities of Calipatria and Imperial in the Imperial Valley, Southern California. Dark fiber refers to unused optical fiber cables in telecommunications networks and is repurposed in this project for DAS applications. The objective, which is further detailed in the attached journal article from Ajo-Franklin et al., is to demonstrate dark fiber DAS as a tool for basin-scale geothermal exploration and monitoring. The included DAS data were recorded during two days at the beginning the project. Data is stored in the .h5 (HDF5) file format, readable using various software tools, including the 'h5read' and 'h5info' functions in Matlab. Provided here are examples of methods to read and use the data with the 'h5py' package in Python.

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

Fostering Geothermal Machine Learning Success: Elevating Big Data Accessibility and Automated Data Standardization in the Geothermal Data Repository

The Department of Energy's (DOE) Geothermal Data Repository (GDR) has implemented improvements to both its data lakes and its data standards and automated data pipelines. The GDR data lakes have reduced storage and compute-related barriers to using large geothermal datasets, enabling these large datasets to be accessed by anyone with a modern computer and internet access. More recently, the GDR has been working to further reduce barriers through streamlining the data intake process, educating users on the process and requirements, and aiding users in accessing data from the data lakes. These improvements have augmented the quantity of datasets the GDR is able to accept into its data lakes and have enabled users who are new to cloud tools to access these datasets more easily, overall increasing the accessibility of big geothermal data for use in machine learning and other projects. In addition, the GDR now has built-in data standards and pipelines for drilling data, geospatial data, and distributed acoustic sensing (DAS) data. These standardization efforts aim to enhance the real-world applicability of geothermal machine learning outcomes by improving the quality of training data. Specifically, through standardizing high-value datasets, the GDR is reducing project-specific data curation requirements, thus allowing more time for actual research. By automating this process, the burden of standardization is lifted from the user, ultimately increasing the availability of standardized data.

15 GEOTHERMAL ENERGY↗
DOE OSTI2015

Groundwater flow characterization in a fractured bedrock aquifer using active DTS tests in sealed boreholes

In recent years, wireline temperature profiling methods have evolved to offer new insight into fractured rock hydrogeology. Important advances in wireline temperature logging in boreholes make use of active line source heating alone and then in combination with temporary borehole sealing with flexible impervious fabric liners to eliminate the effects of borehole cross-connection and recreate natural flow conditions. Here, a characterization technique was developed based on combining fiber optic distributed temperature sensing (DTS) with active heating within boreholes sealed with flexible borehole liners. DTS systems provide a temperature profiling method that offers significantly enhanced temporal resolution when compared with conventional wireline trolling-based techniques that obtain a temperature–depth profile every few hours. The ability to rapidly and continuously collect temperature profiles can better our understanding of transient processes, allowing for improved identification of hydraulically active fractures and determination of relative rates of groundwater flow. Here, the advantage of a sealed borehole environment for DTS-based investigations is demonstrated through a comparison of DTS data from open and lined conditions for the same borehole. Evidence for many depth-discrete active groundwater flow features under natural gradient conditions using active DTS heat pulse testing is presented along with high resolution geologic and geophysical logging and hydraulic datasets. Implications for field implementation are discussed.

15 GEOTHERMAL ENERGY↗