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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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Quantum Leap: Evaluating the Feasibility of Quantum Machine Learning Using NASA Earth Observational Data

This study explores the feasibility of leveraging quantum machine learning (QML) to analyze NASA Earth Observational (EO) data for climate change research, with a particular focus on the phenomenon of ”crop frosting” which has become more prevalent due to climate change. We implemented and evaluated two QML models, the Variational Quantum Classifier (VQC) and Quantum Support Vector Classifier (QSVC), in both simulated and real quantum computing environments using a 127 qubit IBM quantum processor. Our study emphasizes the scientific rigor in comparing these quantum models with a classical Support Vector Machine (SVM) classifier, highlighting their performance in processing climate data. The results offer valuable insights into the potential scientific advantages, limitations, and scalability of QML for analyzing EO datasets, thus paving the way for more advanced climate modeling and predictive analytics using quantum computing. We showcased how Environmental Interaction Knowledge Graphs (EIKGs) and Digital Twins (DTs) can be integrated into this study. This research underscores the transformative potential of Classical and QML leveraging KGs and DT to address the multifaceted challenges posed by climate change.

Quantum Computing↗
DOE OSTI2013

Development of Metric for Measuring the Impact of RD&D Funding on GTO's Geothermal Exploration Goals

The Department of Energy's Geothermal Technology Office (GTO) provides RD&D funding for geothermal exploration technologies with the goal of lowering the risks and costs of geothermal development and exploration. The National Renewable Energy Laboratory (NREL) was tasked with developing a metric in 2012 to measure the impacts of this RD&D funding on the cost and time required for explorationactivities. The development of this cost and time metric included collecting cost and time data for exploration techniques, creating a baseline suite of exploration techniques to which future exploration cost and time improvements can be compared, and developing an online tool for graphically showing potential project impacts (all available at http://en.openei.org/wiki/Gateway: Geothermal).This paper describes the methodology used to define the baseline exploration suite of techniques (baseline), as well as the approach that was used to create the cost and time data set that populates the baseline. The resulting product, an online tool for measuring impact, and the aggregated cost and time data are available on the Open Energy Information website (OpenEI, http://en.openei.org) forpublic access.

15 GEOTHERMAL ENERGY↗
DOE OSTI2012

ACTIVE LEARNING TO OVERCOME SAMPLE SELECTION BIAS: APPLICATION TO PHOTOMETRIC VARIABLE STAR CLASSIFICATION

Despite the great promise of machine-learning algorithms to classify and predict astrophysical parameters for the vast numbers of astrophysical sources and transients observed in large-scale surveys, the peculiarities of the training data often manifest as strongly biased predictions on the data of interest. Typically, training sets are derived from historical surveys of brighter, more nearby objects than those from more extensive, deeper surveys (testing data). This sample selection bias can cause catastrophic errors in predictions on the testing data because (1) standard assumptions for machine-learned model selection procedures break down and (2) dense regions of testing space might be completely devoid of training data. We explore possible remedies to sample selection bias, including importance weighting, co-training, and active learning (AL). We argue that AL-where the data whose inclusion in the training set would most improve predictions on the testing set are queried for manual follow-up-is an effective approach and is appropriate for many astronomical applications. For a variable star classification problem on a well-studied set of stars from Hipparcos and Optical Gravitational Lensing Experiment, AL is the optimal method in terms of error rate on the testing data, beating the off-the-shelf classifier by 3.4% and the other proposed methods by at least 3.0%. To aid with manual labeling of variable stars, we developed a Web interface which allows for easy light curve visualization and querying of external databases. Finally, we apply AL to classify variable stars in the All Sky Automated Survey, finding dramatic improvement in our agreement with the ASAS Catalog of Variable Stars, from 65.5% to 79.5%, and a significant increase in the classifier's average confidence for the testing set, from 14.6% to 42.9%, after a few AL iterations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Brown dwarf census with the Dark Energy Survey year 3 data and the thin disc scale height of early L types

In this paper we present a catalogue of 11745 brown dwarfs with spectral types ranging from L0 to T9, photometrically classified using data from the Dark Energy Survey (DES) year 3 release matched to the Vista Hemisphere Survey (VHS) DR3 and Wide-field Infrared Survey Explorer (WISE) data, covering ≈2400 deg 2 up to i AB = 22. The classification method follows the same phototype method previously applied to SDSS-UKIDSS-WISE data. We note the most significant difference comes from the use of DES data instead of SDSS, which allow us to classify almost an order of magnitude more brown dwarfs than any previous search and reaching distances beyond 400 pc for the earliest types. Next, we also present and validate the GalmodBD simulation, which produces brown dwarf number counts as a function of structural parameters with realistic photometric properties of a given survey. We use this simulation to estimate the completeness and purity of our photometric LT catalogue down to i AB = 22, as well as to compare to the observed number of LT types. We put constraints on the thin disc scale height for the early L (L0–L3) population to be around 450 pc, in agreement with previous findings. For completeness, we also publish in a separate table a catalogue of 20863 M dwarfs that passed our colour cut with spectral types greater than M6. Both the LT and the late M catalogues are found at DES release page https://des.ncsa.illinois.edu/releases/other/y3-mlt.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Fingerprinting Anomalous Computation with RNN for GPU-accelerated HPC Machines

This paper presents a workload classification framework that discriminates illicit computation from authorized workloads on GPU-accelerated HPC systems. As such systems become more and more powerful, they are exploited by attackers to run malicious and for-profit programs that typically require extremely high computing ability to be successful. Our classification framework leverages the distinctive signatures between illicit and authorized workloads, and explore machine learning methods to learn the workloads and classify them. The framework uses lightweight, non-intrusive workload profiling to collect model input data, and explores multiple machine learning methods, particularly recurrent neural network (RNN) that is suitable for online anomalous workload detection. Evaluation results on four generations of GPU machines demonstrate that the workload classification framework can tell apart the illicit authorized workloads with a high accuracy of over 95%.

Zou, Pengfei↗
DOE OSTI2020

Detecting Anomalous Computation with RNNs on GPU-Accelerated HPC Machines

This paper presents a workload classification framework that discriminates illicit computation from authorized workloads on GPU-accelerated HPC systems. As such systems become more and more powerful, they are exploited by attackers to run malicious and for-profit programs that typically require extremely high computing ability to be successful. Our classification framework leverages the distinctive signatures between illicit and authorized workloads, and explore machine learning methods to learn the workloads and classify them. The framework uses lightweight, non-intrusive workload profiling to collect model input data, and explores multiple machine learning methods, particularly recurrent neural network (RNN) that is suitable for online anomalous workload detection. Evaluation results on three generations of GPU machines demonstrate that the workload classification framework can tell apart the illicit authorized workloads with a high accuracy of over 95%.

Pengfei, Zou↗
DOE OSTI2017

Human Factors in Streaming Data Analysis: Challenges and Opportunities for Information Visualization: Human Factors in Streaming Data Analysis

State-of-the-art visual analytics models and frameworks mostly assume a static snapshot of the data, while in many cases it is a stream with constant updates and changes. Exploration of streaming data poses unique challenges as machine-level computations and abstractions need to be synchronized with the visual representation of the data and the temporally evolving human insights. In the visual analytics literature, we lack a thorough characterization of streaming data and analysis of the challenges associated with task abstraction, visualization design, and adaptation of the role of human-in-the-loop for exploration of data streams. We aim to fill this gap by conducting a survey of the state-of-the-art in visual analytics of streaming data for systematically describing the contributions and shortcomings of current techniques and analyzing the research gaps that need to be addressed in the future. Our contributions are: i) problem characterization for identifying challenges that are unique to streaming data analysis tasks, ii) a survey and analysis of the state-of-the-art in streaming data visualization research with a focus on the visualization design space for dynamic data and the role of the human-in-the-loop, and iii) reflections on the design-trade-offs for streaming visual analytics techniques and their practical applicability in real-world application scenarios.

streaming data↗
DOE OSTI2017

Planck intermediate results: L. Evidence of spatial variation of the polarized thermal dust spectral energy distribution and implications for CMB B -mode analysis

The characterization of the Galactic foregrounds has been shown to be the main obstacle in thechallenging quest to detect primordial B-modes in the polarized microwave sky. In this paper, we make use of the Planck-HFI 2015 data release at high frequencies to place new constraints on the properties of the polarized thermal dust emission at high Galactic latitudes. Here, we specifically study the spatial variability of the dust polarized spectral energy distribution (SED), and its potential impact on the determination of the tensor-to-scalar ratio, r. We use the correlation ratio of the C BB ℓ angular power spectra between the 217 and 353 GHz channels as a tracer of these potential variations, computed on different high Galactic latitude regions, ranging from 80% to 20% of the sky. The new insight from Planck data is a departure of the correlation ratio from unity that cannot be attributed to a spurious decorrelation due to the cosmic microwave background, instrumental noise, or instrumental systematics. The effect is marginally detected on each region, but the statistical combination of all the regions gives more than 99% confidence for this variation in polarized dust properties. In addition, we show that the decorrelation increases when there is a decrease in the mean column density of the region of the sky being considered, and we propose a simple power-law empirical model for this dependence, which matches what is seen in the Planck data. We explore the effect that this measured decorrelation has on simulations of the BICEP2-Keck Array/Planck analysis and show that the 2015 constraints from these data still allow a decorrelation between the dust at 150 and 353 GHz that is compatible with our measured value. In conclusion, using simplified models, we show that either spatial variation of the dust SED or of the dust polarization angle are able to produce decorrelations between 217 and 353 GHz data similar to the values we observe in the data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Assessing the Dynamics and Complexity of Disease Pathogenicity Using 4-Dimensional Immunological Data

Investigating disease pathogenesis and personalized prognostics are major biomedical needs. Because patients sharing the same diagnosis can experience different outcomes, such as survival or death, physicians need new personalized tools, including those that rapidly differentiate several inflammatory phases. To address these topics, a pattern recognition-based method (PRM) that follows an inverse problem approach was designed to assess, in <10 min, eight concepts: synergy , pleiotropy , complexity , dynamics , ambiguity , circularity , personalized outcomes , and explanatory prognostics (pathogenesis). By creating thousands of secondary combinations derived from blood leukocyte data, the PRM measures synergic, pleiotropic, complex and dynamic data interactions, which provide personalized prognostics while some undesirable features—such as false results and the ambiguity associated with data circularity-are prevented. Here, this method is compared to Principal Component Analysis (PCA) and evaluated with data collected from hantavirus-infected humans and birds that appeared to be healthy. When human data were examined, the PRM predicted 96.9 % of all surviving patients while PCA did not distinguish outcomes. Demonstrating applications in personalized prognosis, eight PRM data structures sufficed to identify all but one of the survivors. Dynamic data patterns also distinguished survivors from non-survivors, as well as one subset of non-survivors, which exhibited chronic inflammation. When the PRM explored avian data, it differentiated immune profiles consistent with no, early, or late inflammation. Yet, PCA did not recognize patterns in avian data. Findings support the notion that immune responses, while variable, are rather deterministic: a low number of complex and dynamic data combinations may be enough to, rapidly, unmask conditions that are neither directly observable nor reliably forecasted.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1983

Spatial relationship of field-aligned currents, electron precipitation, and plasma convection in the auroral oval

Observations reported by Winningham et al. (1975) have established that the auroral oval mapped to the magnetosphere along closed field lines divided the oval into two distinct regions of particle precipitation. In order to determine relationships between field-aligned current, convection, and particle precipitation, simultaneous measurements of all quantities are needed. The studies of Bythrow et al. (1980, 1981) have utilized Atmosphere Explorer C data for sunlit passes of the high-latitude ionosphere. The addition of magnetometer information for the eclipsed high-latitude passes of the Atmospheric Explorer C spacecraft makes it possible to make simultaneous measurements of Birkeland currents, plasma convection, and electron precipitation in the nightside auroral oval and polar cap. The present investigation provides the results of such observations, discusses the observed relationships, and attempts to correlate boundaries.

Coley, W. R.↗
DOE OSTI2012

Estimate of the Geothermal Energy Resource in the Major Sedimentary Basins in the United States (Presentation)

Because most sedimentary basins have been explored for oil and gas, well logs, temperatures at depth, and reservoir properties such as depth to basement and formation thickness are well known. The availability of this data reduces exploration risk and allows development of geologic exploration models for each basin. This study estimates the magnitude of recoverable geothermal energy from 15 major known U.S. sedimentary basins and ranks these basins relative to their potential. The total available thermal resource for each basin was estimated using the volumetric heat-in-place method originally proposed by (Muffler, 1979). A qualitative recovery factor was determined for each basin based on data on flow volume, hydrothermal recharge, and vertical and horizontal permeability. Total sedimentary thickness maps, stratigraphic columns, cross sections, and temperature gradient information was gathered for each basin from published articles, USGS reports, and state geological survey reports. When published data were insufficient, thermal gradients and reservoir properties were derived from oil and gas well logs obtained on oil and gas commission databases. Basin stratigraphy, structural history, and groundwater circulation patterns were studied in order to develop a model that estimates resource size, temperature distribution, and a probable quantitative recovery factor.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Improved Earthquake Source Parameters with 3D Wavespeed Models in California and Nevada

Seismic tomography harnesses earthquake data to explore the inaccessible structure of the Earth. Adjoint waveform tomography (AWT), a method of seismic tomography, updates the tomographic model by optimizing the fit between observed earthquake data and synthetic waveforms. The synthetic data are calculated by solving the wave equation through a given 3D model. An important requirement to calculating synthetics is the source information (location, centroid time, depth, and moment tensor). Errors in source information affect the quality of the synthetics produced, which in turn can limit how structure can be inferred in the AWT workflow. Here, to test the effect of updating source information, we used MTTime (Chiang, 2020), a time-domain full-waveform moment tensor inversion code, to calculate the moment tensors and depths of 118 earthquakes that occurred in California and Nevada over a 20-yr period. We calculated 3D Green’s functions using a 3D seismic wavespeed model of California and Nevada (Doody et al., 2023b). We show that the inverted solutions provide better waveform fits than the Global Centroid Moment Tensor catalog and increase usable, well-correlated data by up to 7%. Therefore, we argue that recalculating source parameters should be considered in AWT workflows, particularly for smaller magnitude events (⁠M w > 5.0).

58 GEOSCIENCES↗
NASA NTRS2021

Diving below the Spin-down Limit: Constraints on Gravitational Waves from the Energetic Young Pulsar PSR J0537-6910

We present a search for quasi-monochromatic gravitational-wave signals from the young, energetic X-ray pulsar PSR J0537−6910 using data from the second and third observing runs of LIGO and Virgo. The search is enabled by a contemporaneous timing ephemeris obtained using Neutron star Interior Composition Explorer (NICER) data. The NICER ephemeris has also been extended through 2020 October and includes three new glitches. PSR J0537−6910 has the largest spin-down luminosity of any pulsar and exhibits fRequent and strong glitches. Analyses of its long-term and interglitch braking indices provide intriguing evidence that its spin-down energy budget may include gravitational-wave emission from a time-varying mass quadrupole moment. Its 62 Hz rotation frequency also puts its possible gravitational-wave emission in the most sensitive band of the LIGO/Virgo detectors. Motivated by these considerations, we search for gravitational-wave emission at both once and twice the rotation frequency from PSR J0537−6910. We find no signal, however, and report upper limits. Assuming a rigidly rotating triaxial star, our constraints reach below the gravitational-wave spin-down limit for this star for the first time by more than a factor of 2 and limit gravitational waves from the l = m = 2 mode to account for less than 14% of the spin-down energy budget. The fiducial equatorial ellipticity is constrained to less than about 3 ×10^(−5), which is the third best constraint for any young pulsar.

R. Abbott↗
DOE OSTI2012

Archuleta County CO Lineaments

This layer traces apparent topographic and air-photo lineaments in the area around Pagosa springs in Archuleta County, Colorado. It was made in order to identify possible fault and fracture systems that might be conduits for geothermal fluids. Geothermal fluids commonly utilize fault and fractures in competent rocks as conduits for fluid flow. Geothermal exploration involves finding areas of high near-surface temperature gradients, along with a suitable plumbing system that can provide the necessary permeability. Geothermal power plants can sometimes be built where temperature and flow rates are high. To do this, georeferenced topographic maps and aerial photographs were utilized in an existing GIS, using ESRI ArcMap 10.0 software. The USA_Topo_Maps and World_Imagery map layers were chosen from the GIS Server at server.arcgisonline.com, using a UTM Zone 13 NAD27 projection. This line shapefile was then constructed over that which appeared to be through-going structural lineaments in both the aerial photographs and topographic layers, taking care to avoid manmade features such as roads, fence lines, and right-of-ways. These lineaments may be displaced somewhat from their actual location, due to such factors as shadow effects with low sun angles in the aerial photographs. Note: This shape file was constructed as an aid to geothermal exploration in preparation for a site visit for field checking. We make no claims as to the existence of the lineaments, their location, orientation, and nature.

15 GEOTHERMAL ENERGY↗
NASA NTRS2011

HIFIRE Flight 2 Overview and Status Update 2011

A collaborative international effort, the Hypersonic International Flight Research Experimentation (HIFiRE) Program aims to study basic hypersonic phenomena through flight experimentation. HIFiRE Flight 2 teams the United States Air Force Research Lab (AFRL), NASA, and the Australian Defence Science and Technology Organisation (DSTO). Flight 2 will develop an alternative test technique for acquiring high enthalpy scramjet flight test data, allowing exploration of accelerating hydrocarbon-fueled scramjet performance and dual-to-scram mode transition up to and beyond Mach 8 flight. The generic scramjet flowpath is research quality and the test fuel is a simple surrogate for an endothermically cracked liquid hydrocarbon fuel. HIFiRE Flight 2 will be a first of its kind in contribution to scramjets. The HIFiRE program builds upon the HyShot and HYCAUSE programs and aims to leverage the low-cost flight test technique developed in those programs. It will explore suppressed trajectories of a sounding rocket propelled test article and their utility in studying ramjet-scramjet mode transition and flame extinction limits research. This paper describes the overall scramjet flight test experiment mission goals and objectives, flight test approach and strategy, ground test and analysis summary, development status and project schedule. A successful launch and operation will present to the scramjet community valuable flight test data in addition to a new tool, and vehicle, with which to explore high enthalpy scramjet technologies.

Jackson, Kevin R.↗
NASA NTRS1982

Recent advances in remote sensing; Proceedings of the First International Geoscience and Remote Sensing Symposium, Washington, DC, June 8-10, 1981

The state of the art in remote sensing of the earth and the planets was discussed in terms of sensor performance, signal processing, and data interpretation. Particular attention was given to lidar for characterizing atmospheric particulates, the modulation of short waves by long ocean gravity waves, and runoff modeling for snow-covered areas. The use of NOAA-6 spacecraft AVHRR data to explore hydrologic land surface features, the effects of soil moisture and vegetation canopies on microwave and thermal microwave emissions, and regional scale evapotranspiration rate determination through satellite IR data are examined. A Shuttle experiment to demonstrate high accuracy global time and frequency transfer is described, along with features of the proposed Gravsat, radar image processing for rock-type discrimination, and passive microwave sensing of temperature and salinity in coastal zones.

Mcintosh, R.↗