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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

GeoThermalCloud: Machine Learning for Geothermal Resource Exploration

Geothermal is a renewable energy source that can provide reliable and flexible electricity generation for the world. In the past decade, the U.S. Geological Survey's resource assessments, Play Fairway Analyses (PFA), and GeoVision report by the U.S. Department of Energy's Geothermal Technologies Office provided insights on enormous untapped potential for geothermal energy to contribute to the U.S. domestic energy needs. The past studies identified that geothermal resources without surface expression (e.g., blind/hidden hydrothermal systems) comprise a huge potential. These blind systems can significantly increase power generation. But a primary challenge is locating and quantifying these hidden resources, which do not have any thermal manifestations on the surface. PFA has successfully identified some blind systems in the western USA (e.g., specific locations in the Great Basin region within Nevada). However, a comprehensive search for these blind systems can be time-consuming, expensive, and resource-intensive with a low probability of success. Accelerated discovery of these blind resources is needed with growing energy needs and higher chances of exploration success. Recent advances in machine learning (ML) have shown promise in shortening the timeline for this discovery. This paper presents a novel ML-based methodology for geothermal exploration towards PFA applications. Our methodology is provided through our open-source ML framework called GeoThermalCloud \url{https://github.com/SmartTensors/GeoThermalCloud.jl}. GeoThermalCloud uses a series of unsupervised, supervised, and physics-informed ML methods available in SmartTensors AI platform \url{https://github.com/SmartTensors}. Here, the presented analyses are performed using our unsupervised ML algorithm called NMF$k$, which is available in the SmartTensors AI platform. Our ML algorithm facilitates the discovery of new phenomena, hidden patterns, and mechanisms that helps us to make informed decisions. Moreover, the GeoThermalCloud enhances the collected PFA data and discovers signatures representative of geothermal resources. Through GeoThermalCloud, we were able to identify hidden patterns in the geothermal field data needed for the efficient discovery of blind systems. Crucial geothermal signatures often overlooked in traditional PFA are extracted using GeoThermalCloud and analyzed by the subject matter experts to provide ML-enhanced PFA, which is informative for efficient exploration. We applied our ML methodology on various open-source geothermal datasets within the U.S. (some of these are collected by past PFA work), and the results provide valuable insights on resource types within those explored regions. This ML-enhanced workflow makes GeoThermalCloud attractive for the geothermal community to improve existing datasets and extract valuable information often unnoticed during geothermal exploration.

machine learning (ML), geothermal energy↗
DOE OSTI2025

Probabilistic Predictions for Fastener Failure in the Sandia Mechanics Challenge Using the Discrete-Direct Uncertainty Quantification Approach

This paper documents the blind and post-blind analysis predictions for the 2023 Sandia Mechanics Challenge (SMC), which involved predicting the behavior of a threaded fastener joint structure subjected to shock loading. Utilizing repeat sets of fastener calibration data from various experimental configurations including tension, double shear, and joint tension, we developed a library of calibrated models which were propagated through the application model using the Discrete-Direct (DD) uncertainty quantification (UQ) approach. Although the initial blind predictions did not incorporate spare-sample processing to quantify fastener failure probabilities, the analyses yielded reasonable conclusions aligned with experimental results. In the post-blind analysis phase, we focused on enhancing the fidelity of the aluminum constitutive model and innovating the DD approach to obtain probabilistic predictions for fastener failure, particularly when quantities of interest (QoIs) approach their bounds. The improved aluminum model captures the behavior of the cantilever under shock loading more accurately, predicting both partial and complete cracks, although it tends to underpredict failure propagation. The enhanced DD approach facilitates probabilistic predictions that reflect the interdependent failure mechanisms of the fasteners and the cantilever, revealing that while certain fasteners are more likely to fail, the failure does not necessarily follow a progressive pattern. Overall, the post-blind analyses significantly improved the predictive capabilities of the model, providing valuable insights into the SMC application and establishing a robust foundation for informed engineering decisions. The methodology demonstrates a cost-effective and extensible approach suitable for a wide range of applications, highlighting the importance of uncertainty quantification to provide context for engineering decision making.

42 ENGINEERING↗
NASA NTRS1998

A Novel Approach for Adaptive Signal Processing

Adaptive linear predictors have been used extensively in practice in a wide variety of forms. In the main, their theoretical development is based upon the assumption of stationarity of the signals involved, particularly with respect to the second order statistics. On this basis, the well-known normal equations can be formulated. If high- order statistical stationarity is assumed, then the equivalent normal equations involve high-order signal moments. In either case, the cross moments (second or higher) are needed. This renders the adaptive prediction procedure non-blind. A novel procedure for blind adaptive prediction has been proposed and considerable implementation has been made in our contributions in the past year. The approach is based upon a suitable interpretation of blind equalization methods that satisfy the constant modulus property and offers significant deviations from the standard prediction methods. These blind adaptive algorithms are derived by formulating Lagrange equivalents from mechanisms of constrained optimization. In this report, other new update algorithms are derived from the fundamental concepts of advanced system identification to carry out the proposed blind adaptive prediction. The results of the work can be extended to a number of control-related problems, such as disturbance identification. The basic principles are outlined in this report and differences from other existing methods are discussed. The applications implemented are speech processing, such as coding and synthesis. Simulations are included to verify the novel modelling method.

Chen, Ya-Chin↗
NASA NTRS1998

Nonphotic entrainment of the human circadian pacemaker

In organisms as diverse as single-celled algae and humans, light is the primary stimulus mediating entrainment of the circadian biological clock. Reports that some totally blind individuals appear entrained to the 24-h day have suggested that nonphotic stimuli may also be effective circadian synchronizers in humans, although the nonphotic stimuli are probably comparatively weak synchronizers, because the circadian rhythms of many totally blind individuals "free run" even when they maintain a 24-h activity-rest schedule. To investigate entrainment by nonphotic synchronizers, we studied the endogenous circadian melatonin and core body temperature rhythms of 15 totally blind subjects who lacked conscious light perception and exhibited no suppression of plasma melatonin in response to ocular bright-light exposure. Nine of these fifteen blind individuals were able to maintain synchronization to the 24-h day, albeit often at an atypical phase angle of entrainment. Nonphotic stimuli also synchronized the endogenous circadian rhythms of a totally blind individual to a non-24-h schedule while living in constant near darkness. We conclude that nonphotic stimuli can entrain the human circadian pacemaker in some individuals lacking ocular circadian photoreception.

NASA Discipline Regulatory Physiology↗
DOE OSTI2012

3D characterization of a Great Basin geothermal system: Astor Pass, NV

The Great Basin exhibits both anomalously high heat flow (~75±5 mWm-2) and active faulting and extension resulting in robust geothermal activity. There are ~430 known geothermal systems in the Great Basin, with evidence suggesting that undiscovered blind geothermal systems may actually represent the majority of geothermal activity. These systems employ discrete fault intersection/interaction areas as conduits for geothermal circulation. Recent studies show that steeply dipping normal faults with step-overs, fault intersections, accommodation zones, horse-tailing fault terminations and transtensional pull-aparts are the most prominent structural controls of Great Basin geothermal systems. These fault geometries produce sub-vertical zones of high fault and fracture density that act as fluid flow conduits. Structurally controlled fluid flow conduits are further enhanced when critically stressed with respect to the ambient stress conditions. The Astor Pass blind geothermal system, northwestern Nevada, lies along the boundary between the Basin and Range to the east and the Walker Lane to the west. Along this boundary, strain is transferred from dextral shear in the Walker Lane to west-northwest directed extension in the Basin and Range. As such, the Astor Pass area lies in a transtensional setting consisting of both northwest-striking, left-stepping dextral faults and more northerly striking normal faults. The Astor Pass tufa tower implies the presence of a blind geothermal system. Previous studies suggest that deposition of the Astor Pass tufa was controlled by the intersection of a northwest-striking dextral normal fault and north-northwest striking normal fault. Subsequent drilling (to ~1200 m) has revealed fluid temperatures of ~94°C, confirming the presence of a blind geothermal system at Astor Pass. Expanding upon previous work and employing additional detailed geologic mapping, interpretation of 2D seismic reflection data and analysis of well cuttings, a 3-dimensional geologic model of the Astor Pass blind geothermal system was constructed. The 3D structural framework indicates that the Pleistocene tufa is associated with three discrete fault zones whose intersections plunge moderately to steeply NW-NNW. These critically stressed fault intersections act as conduits for upwelling geothermal fluids.

15 GEOTHERMAL ENERGY↗
DOE OSTI2017

Progress Report on the Nevada Play Fairway Project: Integrated Geological, Geochemical, and Geophysical Analyses of Possible New Geothermal Systems in the Great Basin Region

The Great Basin region of the western USA is capable of producing much greater amounts of geothermal energy than the current ~670 MW from ~25 power plants. Most geothermal resources in this region are blind, and thus the favorable characteristics for geothermal activity must be synthesized and methodologies need to be developed to discover new commercial-grade systems. In the Nevada play fairway project, nine geologic, geochemical, and geophysical parameters were synthesized to produce a new detailed geothermal potential map of 96,000 km2 from west-central to eastern Nevada. These parameters were grouped into subsets and individually weighted to delineate rankings for local permeability, regional permeability, and heat, which collectively defined geothermal play fairways (i.e., most likely locations for significant geothermal fluid flow). This work is aimed at reducing the risks in geothermal exploration and therefore facilitating discovery of new commercial-grade systems in blind settings, as well as in areas with surface expressions of geothermal activity. Twenty-four highly prospective areas, including both known undeveloped systems and previously undiscovered potential blind systems, were identified across the region for further analysis. Five of the most promising sites have been selected for detailed studies: Granite Springs Valley, Sou Hills, southern Gabbs Valley, Crescent Valley, and Steptoe Valley. Multiple techniques, including detailed geologic mapping, shallow temperature surveys, detailed gravity surveys, LiDAR, geochemical studies, seismic reflection analysis, and 3D modeling, are now being employed in these areas. The ultimate goal of these detailed studies is to determine the most likely areas for high permeability thermal fluids such that potential drill sites can be targeted. Potential new blind systems discovered in this project include southern Gabbs Valley and Granite Springs Valley currently undergoing detailed study, as well as step-overs in the Dun Glen area and along the west flank of the Humboldt Range.

15 GEOTHERMAL ENERGY↗
DOE OSTI2015

Measured daylighting potential of a static optical louver system under real sun and sky conditions

Side-by-side comparisons were made over solstice-to-solstice changes in sun and sky conditions between an optical louver system (OLS) and a conventional Venetian blind set at a horizontal slat angle and located inboard of a south-facing, small-area, clerestory window in a full-scale office testbed. Daylight autonomy (DA), window luminance, and ceiling luminance uniformity were used to assess performance. The performance of both systems was found to have significant seasonal variation, where performance under clear sky conditions improved as maximum solar altitude angles transitioned from solstice to equinox. Although the OLS produced fewer hours per day of DA on average than the Venetian blind, the OLS never exceeded the designated 2000 cd/m2 threshold for window glare. In contrast, the Venetian blind was found to exceed the visual discomfort threshold over a large fraction of the day during equinox conditions. Notably, these peak periods of visual discomfort occurred during the best periods of daylighting performance. Luminance uniformity was analyzed using calibrated high dynamic range luminance images. Under clear sky conditions, the OLS was found to increase the luminance of the ceiling as well as produce a more uniform distribution. Furthermore, compared to conventional venetian blinds, the static optical sunlight redirecting system studied has the potential to significantly reduce the annual electrical lighting energy demand of a daylit space and improve the quality from the perspective of building occupants by consistently transmitting useful daylight while eliminating window glare.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2013

HIGH-n HYDROGEN RECOMBINATION LINES FROM THE FIRST GALAXIES

We investigate the prospects of blind and targeted searches in the radio domain (10 MHz to 1 THz) for high-n hydrogen recombination lines from the first generation of galaxies, at z ∼< 10. The expected optically thin spontaneous α-line luminosities are calculated as a function of the absolute AB magnitude of a galaxy at 1500 Å. For a blind search, semi-empirical luminosity functions are used to calculate the number of galaxies whose expected flux densities exceed an assumed detectability threshold. Plots of the minimum sky area, within which at least one detectable galaxy is expected at a given observing frequency, in the fiducial instantaneous passband of 10{sup 4} km s{sup –1}, allow us to assess the blind search time necessary for detection by a given facility. We show that the chances for detection are the highest in the millimeter and submillimeter domains, but finding spontaneous emission in a blind search, especially from redshifts z >> 1, is a challenge even with powerful facilities, such as the Actama Large Millimeter/Submillimeter Array and Square Kilometre Array. The probability of success is higher for a targeted search of lines with principal quantum number n ∼ 10 in Lyman-break galaxies amplified by gravitational lensing. Detection of more than one hydrogen line in such a galaxy will allow for line identification and a precise determination of the galaxy's redshift.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

EINSTEIN@HOME DISCOVERY OF FOUR YOUNG GAMMA-RAY PULSARS IN FERMI LAT DATA

We report the discovery of four gamma-ray pulsars, detected in computing-intensive blind searches of data from the Fermi Large Area Telescope (LAT). The pulsars were found using a novel search approach, combining volunteer distributed computing via Einstein@Home and methods originally developed in gravitational-wave astronomy. The pulsars PSRs J0554+3107, J1422–6138, J1522–5735, and J1932+1916 are young and energetic, with characteristic ages between 35 and 56 kyr and spin-down powers in the range 6 × 10{sup 34}—10{sup 36} erg s{sup –1}. They are located in the Galactic plane and have rotation rates of less than 10 Hz, among which the 2.1 Hz spin frequency of PSR J0554+3107 is the slowest of any known gamma-ray pulsar. For two of the new pulsars, we find supernova remnants coincident on the sky and discuss the plausibility of such associations. Deep radio follow-up observations found no pulsations, suggesting that all four pulsars are radio-quiet as viewed from Earth. These discoveries, the first gamma-ray pulsars found by volunteer computing, motivate continued blind pulsar searches of the many other unidentified LAT gamma-ray sources.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

The completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: N -body mock challenge for the quasar sample

ABSTRACT The growth rate and expansion history of the Universe can be measured from large galaxy redshift surveys using the Alcock–Paczynski effect. We validate the Redshift Space Distortion models used in the final analysis of the Sloan Digital Sky Survey (SDSS) extended Baryon Oscillation Spectroscopic Survey (eBOSS) Data Release 16 quasar clustering sample, in configuration and Fourier space, using a series of halo occupation distribution mock catalogues generated using the OuterRim N-body simulation. We test three models on a series of non-blind mocks, in the OuterRim cosmology, and blind mocks, which have been rescaled to new cosmologies, and investigate the effects of redshift smearing and catastrophic redshifts. We find that for the non-blind mocks, the models are able to recover fσ8 to within 3 per cent and α∥ and α⊥ to within 1 per cent. The scatter in the measurements is larger for the blind mocks, due to the assumption of an incorrect fiducial cosmology. From this mock challenge, we find that all three models perform well, with similar systematic errors on fσ8, α∥, and α⊥ at the level of $\sigma _{f\sigma _8}=0.013$, $\sigma _{\alpha _\parallel }=0.012$, and $\sigma _{\alpha _\bot }=0.008$. The systematic error on the combined consensus is $\sigma _{f\sigma _8}=0.011$, $\sigma _{\alpha _\parallel }=0.008$, and $\sigma _{\alpha _\bot }=0.005$, which is used in the final DR16 analysis. For baryon acoustic oscillation fits in configuration and Fourier space, we take conservative systematic errors of $\sigma _{\alpha _\parallel }=0.010$ and $\sigma _{\alpha _\bot }=0.007$.

79 ASTRONOMY AND ASTROPHYSICS↗