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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

DEEPEN Global Standardized Categorical Exploration Datasets for Magmatic Plays

DEEPEN stands for DE-risking Exploration of geothermal Plays in magmatic ENvironments. As part of the development of the DEEPEN 3D play fairway analysis (PFA) methodology for magmatic plays (conventional hydrothermal, superhot EGS, and supercritical), weights needed to be developed for use in the weighted sum of the different favorability index models produced from geoscientific exploration datasets. This was done using two different approaches: one based on expert opinions, and one based on statistical learning. This GDR submission includes the datasets used to produce the statistical learning-based weights. While expert opinions allow us to include more nuanced information in the weights, expert opinions are subject to human bias. Data-centric or statistical approaches help to overcome these potential human biases by focusing on and drawing conclusions from the data alone. The drawback is that, to apply these types of approaches, a dataset is needed. Therefore, we attempted to build comprehensive standardized datasets mapping anomalies in each exploration dataset to each component of each play. This data was gathered through a literature review focused on magmatic hydrothermal plays along with well-characterized areas where superhot or supercritical conditions are thought to exist. Datasets were assembled for all three play types, but the hydrothermal dataset is the least complete due to its relatively low priority. For each known or assumed resource, the dataset states what anomaly in each exploration dataset is associated with each component of the system. The data is only a semi-quantitative, where values are either high, medium, or low, relative to background levels. In addition, the dataset has significant gaps, as not every possible exploration dataset has been collected and analyzed at every known or suspected geothermal resource area, in the context of all possible play types. The following training sites were used to assemble this dataset: - Conventional magmatic hydrothermal: Akutan (from AK PFA), Oregon Cascades PFA, Glass Buttes OR, Mauna Kea (from HI PFA), Lanai (from HI PFA), Mt St Helens Shear Zone (from WA PFA), Wind River Valley (From WA PFA), Mount Baker (from WA PFA). - Superhot EGS: Newberry (EGS demonstration project), Coso (EGS demonstration project), Geysers (EGS demonstration project), Eastern Snake River Plain (EGS demonstration project), Utah FORGE, Larderello, Kakkonda, Taupo Volcanic Zone, Acoculco, Krafla. - Supercritical: Coso, Geysers, Salton Sea, Larderello, Los Humeros, Taupo Volcanic Zone, Krafla, Reyjanes, Hengill. **Disclaimer: Treat the supercritical fluid anomalies with skepticism. They are based on assumptions due to the general lack of confirmed supercritical fluid encounters and samples at the sites included in this dataset, at the time of assembling the dataset. The main assumption was that the supercritical fluid in a given geothermal system has shared properties with the hydrothermal fluid, which may not be the case in reality. Once the datasets were assembled, principal component analysis (PCA) was applied to each. PCA is an unsupervised statistical learning technique, meaning that labels are not required on the data, that summarized the directions of variance in the data. This approach was chosen because our labels are not certain, i.e., we do not know with 100% confidence that superhot resources exist at all the assumed positive areas. We also do not have data for any known non-geothermal areas, meaning that it would be challenging to apply a supervised learning technique. In order to generate weights from the PCA, an analysis of the PCA loading values was conducted. PCA loading values represent how much a feature is contributing to each principal component, and therefore the overall variance in the data.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Machine Learning Approaches to Predicting Induced Seismicity and Imaging Geothermal Reservoir Properties

This project developed machine learning (ML) methods, lab data sets, and field data to advance geothermal exploration and geothermal energy production. The work had three focus areas. One involved the development of ML methods to use microearthquakes (MEQs) for imaging geothermal reservoir properties and improving subsurface characterization – most importantly the evolution of permeability within the evolving reservoir. This part of the work included development of ML approaches for automated MEQ location, focal mechanism determination and identification of earthquake precursors. The second area focused on using MEQ signals generated by geothermal exploration and production to predict the relationship between fluid injection and seismicity. Here, we extended to reservoir scale our success in using ML to predict laboratory earthquakes and fault zone stress state. The third focus area was on lab experiments. Here, we developed new ML models for lab earthquake prediction and identification of precursors to failure to improve earthquake forecasting and early warning in geothermal settings. Major outcomes of our work include ML models that learn from MEQ signals during geothermal exploration and production to predict induced seismicity. MEQs occur naturally in connection with drilling and energy production. We developed ML methods to use the seismic waves from these events to characterize the elastic, hydraulic and poromechanical properties of reservoirs. Our work illuminated fracture geometry and the evolution of fracture permeability by incorporating seismic coda wave analysis and ML methods to relate fluid injection and seismicity. We significantly expanded laboratory earthquake prediction to include methods that use both passive measurements of microearthquakes within the lab fault zones and also active source acoustic measurements of fault zone elastic properties. These methods can now predict fault zone stress state, time to failure and the magnitude of lab earthquakes. Our work showed that repetitive stick- slip failure events during frictional sliding (the lab equivalent of earthquakes) are preceded by a cascade of micro-failure events that radiate energy in a manner that foretells unstable failure – manifest as laboratory MEQs. We documented a mapping between fracture properties and statistical attributes of elastic radiation. We extended existing works to geothermal reservoir scale and developed ML methods to determine reservoir permeability, fracture properties, and their evolution during geothermal energy production. An attractive feature of ML algorithms is their ability to handle big datasets and reveal patterns and correlations that may remain invisible to conventional analyses. Our work connected data from field, laboratory and intermediate scales to study permeability, stress, strength, fracture stiffness and geometry. At the field scale we used data from the Newberry Volcano field site, UtahFORGE, EGS Collab, and also the Bedretto underground research lab in Switzerland. These data sets are bridging the gap between the lab scale, theory, and reservoir scale. Our work produced plain language summaries to improve public understanding of DOE research. We also developed openly distributed ML and seismicity datasets for use by all researchers and we published connections between induced seismicity in geothermal areas and reservoir properties including permeability, fracture properties, and stress state. Our models are designed for the large data sets of induced seismicity typically associated with geothermal sites. We produced labeled event catalogs and used them on geothermal data to assess how ML can facilitate geothermal production and exploration. All datasets are available on the GDR Productivity: The project produced 32 publications in peer reviewed journals (two are in review). It supported the work of 6 PhD students, 40 conference presentations, 6 keynote talks at national meetings, and mentoring and professional development for 4 postdoctoral fellows.

15 GEOTHERMAL ENERGY↗
NASA NTRS1973

The comparative evaluation of ERTS-1 imagery for resource inventory in land use planning

The author has identified the following significant results. Multidiscipline team interpretation and mapping of resources for Crook County is nearly complete on 1:250,000 scale enlargements of ERTS-1 imagery. Maps of geology, landforms, soils and vegetation-land use are being interpreted to show limitations, suitabilities and geologic hazards for land use planning. Mapping of lineaments and structures from ERTS-1 imagery has shown a number of features not previously mapped in Oregon. A timber inventory of Ochoco National Forest has been made. Inventory of forest clear-cutting practices has been successfully demonstrated with ERTS-1 color composites. Soil tonal differences in fallow fields shown on ERTS-1 correspond with major soil boundaries in loess-mantled terrain. A digital classification system used for discriminating natural vegetation and geologic materials classes has been successful in separation of most major classes around Newberry Cauldera, Mt. Washington and Big Summit Prairie. Computer routines are available for correction of scanner data variations; and for matching scales and coordinates between digital and photographic imagery. Methods of Diazo film color printing of computer classifications and elevation-slope perspective plots with computer are being developed.

Simonson, G. H.↗
NASA NTRS1974

The comparative evaluation of ERTS-1 imagery for resource inventory in land use planning

The author has identified the following significant results. Multidiscipline team interpretation and mapping of resources for Crook County is complete on 1:250,000 scale enlargements of ERTS imagery and 1:120,000 hi-flight photography. Maps of geology, soils, vegetation-land use and land resources units were interpreted to show limitations, suitabilities, and geologic hazards for land use planning. Mapping of lineaments and structures from ERTS imagery has shown a number of features not previously mapped in Oregon. A multistage timber inventory of Ochoco National Forest was made, using ERTS images as the first stage. Inventory of forest clear-cutting practices was successfully demonstrated with color composites. Soil tonal differences in fallow fields correspond with major soil boundaries in loess-mantled terrain. A digital classification system used for discriminating natural vegetation and geologic material classes was successful in separating most major classes around Newberry Caldera, Mt. Washington, and Big Summit Prairie.

Schrumpf, B. J.↗
NASA NTRS1982

An investigation into the utilization of HCMM thermal data for the discrimination of volcanic and Eolian geological units

The nature of the HCMM data set and the general geologic application of thermal inertia imaging using HCMM data were studied. The CCT's of five sites of interest were obtained and displayed. The upgrading of the image display system was investigated. Fragment/block size distributions in various terrain types wre characterized with emphasis on volcanic terrain. The SEASAT L-band radar images of volcanic landforms at Newberry Volcano, Oregon, were analyzed and compared to imagery from LANDSAT band 7.

Head, J. W., III↗
NASA NTRS1986

On the relationship between age of lava flows and radar backscattering

The observation that older lava flows have lower backscatter in radar images is assessed with multiwavelength/polarization scatterometer data with incidence angles from 15 to 50 deg. Backscatter decreases over time because surface roughness decreases due to infilling with dust and mechanical weathering of the rocks. Pahoehoe lavas in the Snake River Plain with ages of 2.1, 7,4, and 12.0 K yr are best separated with 2.25 cm wavelength data. Blocky obsidian flows at Medicine Lake Highland and Newberry Volcano with ages of 0.9, 1.1 and 1.4 K yr are best separated with 6.3 cm wavelength data. Two Pleistocene flows at the Snake River Plain are best separated with 19.0 cm wavelength data. Incidence angles from 20 to 35 deg are best. These data indicate it may be possible to separate lava flows into eruptive periods using calibrated multiwavelength radar backscatter data.

Blom, R. G.↗
NASA NTRS1993

Design project: LONGBOW supersonic interceptor

A recent white paper entitled 'From the Sea' has spotlighted the need for Naval Aviation to provide overland support to joint operations. The base for this support, the Aircraft Carrier (CVN), will frequently be unable to operate within close range of the battleground because of littoral land-based air and subsurface threats. A high speed, long range, carrier capable aircraft would allow the CVN to provide timely support to distant battleground operations. Such an aircraft, operating as a Deck-Launched Interceptor (DLI), would also be an excellent counter to Next Generation Russian Naval Aviation (NGRNA) threats consisting of supersonic bombers, such as the Backfire, equipped with the next generation of high-speed, long-range missiles. Additionally, it would serve as an excellent high speed Reconnaissance airplane, capable of providing Battle Force commanders with timely, accurate pre-mission targeting information and post-mission Bomb Damage Assessment (BDA). Recent advances in computational hypersonic airflow modeling has produced a method of defining aircraft shapes that fit a conical shock flow model to maximize the efficiency of the vehicle. This 'Waverider' concept provides one means of achieving long ranges at high speeds. A Request for Proposal (RFP) was issued by Professor Conrad Newberry that contained design requirements for an aircraft to accomplish the above stated missions, utilizing Waverider technology.

Stoney, Robert↗
DOE OSTI2025

A New Method for Polishing Laminated Dissimilar Metals

Here, this paper presents a novel method for preparing metallographic samples of laminated dissimilar metals, specifically a layered structure of zirconium (Zr), vanadium (V), and tungsten (W), which pose a challenge due to their differing crystal structures and hardness levels. The work was motivated by the need to develop a cladding for tungsten targets in spallation neutron sources, where a zirconium face is applied over an interlayer of either vanadium or titanium to prevent the formation of brittle intermetallic compounds such as ZrW 2 . Traditional polishing techniques prove inadequate because each constituent metal requires a different polishing approach based on its unique material properties. The methodology outlined includes a sequential process involving conductive epoxy mounting, successive grinding and polishing with specific abrasives, and a final chemical-mechanical polish using a colloidal silica-based solution. Optical microscopy confirmed that this multi-step process successfully produced scratch-free surfaces and minimized surface relief across the different metal interfaces. Although developed for refractory metals, this approach shows promise for other layered structures comprised of transition metals, offering a meticulous yet effective technique for high-quality metallographic sample preparation.

Newberry, Daniel [Oak Ridge National Laboratory (O↗
DOE OSTI2024

Genetic and Functional Diversity Help Explain Pathogenic, Weakly Pathogenic, and Commensal Lifestyles in the Genus Xanthomonas

The genus Xanthomonas has been primarily studied for pathogenic interactions with plants. However, besides host and tissue-specific pathogenic strains, this genus also comprises nonpathogenic strains isolated from a broad range of hosts, sometimes in association with pathogenic strains, and other environments, including rainwater. Based on their incapacity or limited capacity to cause symptoms on the host of isolation, nonpathogenic xanthomonads can be further characterized as commensal and weakly pathogenic. This study aimed to understand the diversity and evolution of nonpathogenic xanthomonads compared to their pathogenic counterparts based on their cooccurrence and phylogenetic relationship and to identify genomic traits that form the basis of a life history framework that groups xanthomonads by ecological strategies. We sequenced genomes of 83 strains spanning the genus phylogeny and identified eight novel species, indicating unexplored diversity. While some nonpathogenic species have experienced a recent loss of a type III secretion system, specifically the hrp2 cluster, we observed an apparent lack of association of the hrp2 cluster with lifestyles of diverse species. We performed association analysis on a large data set of 337 Xanthomonas strains to explain how xanthomonads may have established association with the plants across the continuum of lifestyles from commensals to weak pathogens to pathogens. Presence of distinct transcriptional regulators, distinct nutrient utilization and assimilation genes, transcriptional regulators, and chemotaxis genes may explain lifestyle-specific adaptations of xanthomonads.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Seeing through noise in power laws

Despite widespread claims of power laws across the natural and social sciences, evidence in data is often equivocal. Modern data and statistical methods reject even classic power laws such as Pareto’s law of wealth and the Gutenberg–Richter law for earthquake magnitudes. We show that the maximum-likelihood estimators and Kolmogorov–Smirnov (K-S) statistics in widespread use are unexpectedly sensitive to ubiquitous errors in data such as measurement noise, quantization noise, heaping and censorship of small values. This sensitivity causes spurious rejection of power laws and biases parameter estimates even in arbitrarily large samples, which explains inconsistencies between theory and data. We show that logarithmic binning by powers of λ > 1 attenuates these errors in a manner analogous to noise averaging in normal statistics and that λ thereby tunes a trade-off between accuracy and precision in estimation. Binning also removes potentially misleading within-scale information while preserving information about the shape of a distribution over powers of λ, and we show that some amount of binning can improve sensitivity and specificity of K-S tests without any cost, while more extreme binning tunes a trade-off between sensitivity and specificity. We therefore advocate logarithmic binning as a simple essential step in power-law inference.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Seeing through noise in power laws

Despite widespread claims of power laws across the natural and social sciences, evidence in data is often equivocal. Modern data and statistical methods reject even classic power laws such as Pareto’s law of wealth and the Gutenberg–Richter law for earthquake magnitudes. We show that the maximum-likelihood estimators and Kolmogorov–Smirnov (K-S) statistics in widespread use are unexpectedly sensitive to ubiquitous errors in data such as measurement noise, quantization noise, heaping and censorship of small values. This sensitivity causes spurious rejection of power laws and biases parameter estimates even in arbitrarily large samples, which explains inconsistencies between theory and data. We show that logarithmic binning by powers of λ > 1 attenuates these errors in a manner analogous to noise averaging in normal statistics and that λ thereby tunes a trade-off between accuracy and precision in estimation. Binning also removes potentially misleading within-scale information while preserving information about the shape of a distribution over powers of λ, and we show that some amount of binning can improve sensitivity and specificity of K-S tests without any cost, while more extreme binning tunes a trade-off between sensitivity and specificity. We therefore advocate logarithmic binning as a simple essential step in power-law inference.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1988

Preliminary statistical survey of plasmaspheric ion properties from observations by DE 1/RIMS

The paper presents mean temperatures and densities of plasmaspheric H(+) ions obtained from observations by the retarding ion mass spectrometer on Dynamics Explorer 1. Composition is shown in terms of ratios of densities of the various ion species to those of H(+). The spatial variations are indicated in terms of L shell profiles, with separate profiles for local morning and evening sides.

Comfort, R. H.↗
NASA NTRS1989

Thermal He(+) in the plasmasphere - Comparison of observations with numerical calculations

Observations made by the retarding ion mass spectrometer on the Dynamics Explorer-1 satellite are used to study the relationship between He(+) and H(+). The observations are compared to theoretical values obtained using the field line interhemispheric plasma model (Richards and Torr, 1988). The observations are presented in the form of statistical L shell distributions and density versus L shell profiles. To obtain agreement between the measured and predicted ion temperature, it must be assumed that approximaterly 55 pct of the energy of the ionospheric photoelectron escape flux is deposited in the plasmasphere.

Newberry, I. T.↗