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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 1,171 records · Page 65
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Mapping in the Northern Martian Plains: A Renewed Survey of the Acidalia Mensae and Colles Regions

Acidalia Mensae is an oblong field of tilted mesas roughly trending east-west, with Acidalia Colles encompassing a regime of knobby terrain trending northeast from the center of the mesa region. Each province extends for approximately 300km in their respective directions in the heart of the vast plains of Acidalia Planitia. As part of efforts in the 1980s targeting evidences for a putative northern Martian paleo-ocean – manifesting itself most notably in geologic maps of quadrangles in East Acidalia – the Acidalia Mensae region was selected and divided into three quadrangles ranging across 26.8 deg W to 36 deg W and 47.5 deg N to 52.5 deg N. The region was then mapped at a 1:1,000,000 scale using Canvas on a Viking base map. We present an updated draft of this document converted into ArcMap, including a completed fourth quadrangle that covers the bulk of the Acidalia Colles region. Previously mapped units have been updated according to the availability of new higher-resolution data – particularly, Thermal Emission Imaging System (THEMIS) Day Infrared (IR) data as a base map, paired with supporting Context Camera (CTX) and High Resolution Stereo Camera (HRSC) HRSC stamps extracted from the Mars Orbital Data Explorer repository. Geologic units and contacts for the mesa and knob regimes, interstitial alluvial units and the surrounding plains are comprehensively presented alongside lower-level features such as levels (hypothesized to be shorelines) and pitted cones (putative pingos). The project was mapped in a transverse Mercator projection. Upon minor revision, the map is to be submitted to the USGS for publication.

Siwabessy, Andrew G.↗
NASA NTRSDate not supplied

A Worldwide Celebration: International Observe the Moon Night

International Observe the Moon Night is a worldwide public engagement program that has been held annually since 2010. Every autumn, we ask people to observe the Moon in whatever way makes sense to them (via binoculars, telescopes, unaided eye, images, artwork, songs, stories, etc.). The event occurs when the Moon is in or near a first-quarter phase, which provides excellent viewing opportunities along the terminator (the line between night and day), as long shadows place lunar features into great relief. Hundreds of thousands of individuals all around the globe participate in the event as a collective whole, learning about lunar science and exploration, taking part in celestial observations, and honoring cultural and personal connections to the Moon. People participate in a variety of ways, including hosting or attending virtual or in-person events and observing the Moon from home. Participants also have the opportunity to connect with other lunar observers around the world through our Facebook page (facebook.com/observethemoon/), our Flickr group for images (flickr.com/groups/observethemoon2021/), and through the hashtag #ObserveTheMoon across social media platforms.

S L Tiedeken↗
DOE OSTI2022

The Geothermal Data Repository: Ten Years of Supporting the Geothermal Industry with Open Access to Geothermal Data: Preprint

The Department of Energy's (DOE) Geothermal Data Repository (GDR) is celebrating its tenth anniversary! Over the last decade it has grown from the simple idea of storing public data in a centralized location to a valuable tool at the center of the US geothermal scientific community and an integral part of the DOE Geothermal Technologies Office (DOE GTO) project management strategy. Researchers funded by the DOE GTO have contributed over 1,300 data submissions to the GDR. These data have been used to further advancements in geothermal science, economic analysis, exploration, research, development, and operational efficiency. The adoption of open data methodologies and a data management strategy that prioritizes universal open access and standardized, interoperable data have further increased the value of GDR data, making them available across a distributed network of data sharing partners and improving their utility to other industries and related fields, including material science and space exploration. Incorporating feedback from users has been critical to the GDRs success, allowing it to grow over the years to meet the evolving needs of the geothermal community. This paper will explore some of many changes that occurred throughout the GDRs tenure and the lessons learned along the way, as well as highlight some of the new features and recent improvements that been implemented to support innovation, reduce duplication of effort, and advance the geothermal industry as a whole.

accessibility↗
DOE OSTI2022

The Geothermal Data Repository: Ten Years of Supporting the Geothermal Industry with Open Access to Geothermal Data

The Department of Energy's (DOE) Geothermal Data Repository (GDR) is celebrating its tenth anniversary! Over the last decade it has grown from the simple idea of storing public data in a centralized location to a valuable tool at the center of the US geothermal scientific community and an integral part of the DOE Geothermal Technologies Office (DOE GTO) project management strategy. Researchers funded by the DOE GTO have contributed over 1,300 data submissions to the GDR. These data have been used to further advancements in geothermal science, economic analysis, exploration, research, development, and operational efficiency. The adoption of open data methodologies and a data management strategy that prioritizes universal open access and standardized, interoperable data have further increased the value of GDR data, making them available across a distributed network of data sharing partners and improving their utility to other industries and related fields, including material science and space exploration. Incorporating feedback from users has been critical to the GDR's success, allowing it to grow over the years to meet the evolving needs of the geothermal community. This paper will explore some of many changes that occurred throughout the GDRs tenure and the lessons learned along the way, as well as highlight some of the new features and recent improvements that been implemented to support innovation, reduce duplication of effort, and advance the geothermal industry as a whole.

access↗
DOE OSTI2023

LDRD 22A1059-068FP Tailoring the Properties of Multi-Phase Materials Through the Use of Correlative Microscopy and Machine Learning - Poster

High strength alloys with good ductility, hardness, and toughness are needed to meet stringent design requirements for extreme environments. One complication in this pursuit is the evidence that metals rarely exhibit both high strength and good fracture toughness as the underlying mechanisms work in opposition. An exception to this behavior is found in multiphase alloys that form complex microstructures of mixed phases with variable grain sizes and shapes that provide increased fracture toughness by the arrangement of their constituent elements. We propose to explore this phenomenon using state-of-the-art machine learning (ML) techniques in a new and novel manner to identify and correlate the critical microstructural features in a Titanium-10Vanadium-2Iron-3Aluminum (Ti-10V-2Fe-3Al) alloy that is reported to exhibit high strength and fracture toughness. Additionally, we will employ multiple, complementary characterization techniques such as optical microscopy, electron backscatter diffraction (EBSD), energy dispersive spectroscopy (EDS) and scanning electron microscopy to provide multi-layer, quantitative ground truth measures of the microstructures. This data will be used to train a Convolutional Neural Network (CNN) in a semi-supervised environment to identify key microstructural features such as ? platelet dimensions and locations and ?/? phase boundaries and correlate those features with the strength and toughness. Here the ? and ? nomenclature refers to hexagonal close pack (hcp) and body center cubic (bcc) crystal structures, respectively. Previous work has focused on popular alloys and typically used one characterization technique. This research is focused on a promising titanium alloy, uses multiple complimentary characterization tools to provide precise microstructural information and correlates to improved fracture toughness. The resulting ML tool can be trained for additional microstructural features, different alloy(s), and or target mechanical properties.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Measurement of J/psi Production near Threshold in J/psi -> µ+µ-

This dissertation presents a detailed analysis of J/psi photoproduction near the kinematic threshold in the J/psi -> µ+µ- decay channel, based on data collected by the GlueX experiment at Jefferson Lab. The study aims to probe the structure of the proton and the underlying mechanisms of heavy vector quarkonium photoproduction, contributing to a deeper understanding of Quantum Chromodynamics (QCD) in the non-perturbative regime. The measurement focuses on the total and differential cross-sections of J/psi photoproduction and explores various theoretical models, including two-gluon and three-gluon exchange, open-charm contributions, and potential exotic states like pentaquarks. The experimental setup, featuring a high-precision tagged photon beam and advanced particle identification techniques, allowed for the separation of J/psi events from background processes. The analysis of the J/psi -> µ+µ- channel yields cross-sections that complement previous measurements in the J/psi -> e+e- decay mode, providing new insights into gluon dynamics at low momentum transfer. This work also examines the systematic uncertainties and provides a comprehensive comparison of results with theoretical predictions, highlighting the role of gluon exchange mechanisms in the photoproduction process. The results presented in this dissertation help refine our understanding of proton structure and QCD dynamics, offering a robust dataset for future theoretical and experimental studies in hadronic physics.

Ebersole, Donavan [Florida State Univ., Tallahasse↗
DOE OSTI2026

Infrared-enhanced Photometric Redshifts for the Dark Energy Survey Y6 Gold catalogue

The Dark Energy Survey (DES) provides optical data across 5000 square degrees of the southern sky, enabling a broad range of extragalactic and cosmological studies. Combining DES data with infrared surveys offers the opportunity to improve its photometric redshift (photo-z) estimates. We aim to investigate improvements in photometric redshift estimation achieved by combining DES optical data with infrared measurements from the VISTA Hemisphere Survey (VHS) and the Wide-field Infrared Survey Explorer (WISE), and release an updated version of the catalogue. We performed a positional sky cross-match between the DES Y6 Gold catalogue matched to a spectroscopic dataset, the 2013 AllWISE Data Release, and VHS Data Release 5, in order to test these improvements using the Directional Neighbourhood Fitting (DNF) algorithm (Y6 Gold catalogue reference estimator). We additionally matched it to the unWISE catalogue to verify the performance against this deeper dataset. Adding infrared data reduces all the metrics (scatter, bias and outlier fraction) in photo-z estimates, particularly at higher redshifts in comparison with only using optical data from DES. The obtained results are globally better for the DES+WISE sample, with improvements that are statistically significant. On the other hand, the addition of the VHS bands to available depth is only marginal. The combined use of DES and WISE W1 and W2 data improves the photometric redshift metrics analysed here. The addition of VHS data at the DES and VHS depths explored here does not provide any further improvement at z less than 1.5, indicating that, under these constraints, WISE data may already capture the key infrared features and depth needed for accurate photo-z estimation. In addition, low signal-to-noise (less than 10) infrared data does not contribute to any improvement beyond the DES optical dataset.

Puebla, M. M. [Madrid U.; Madrid, CIEMAT]↗
NASA NTRS2011

The 2006-2007 Active Phase of Anomalous X-Ray Pulsar 4U 0142+61: Radiative and Timing Changes, Bursts,and Burst Spectral Features

After at least 6 years of quiescence, Anomalous X-ray Pulsar (AXP) 4U 0142+61 entered an active phase in 2006 March that lasted several months and included six X-ray bursts as well as many changes in the persistent X-ray emission. The bursts, the first seen from this AXP in > 11 years of Rossi X-ray Timing Explorer monitoring, all occurred in the interval between 2006 April 6 and 2007 February 7. The burst durations ranged from 0.4 - 1.8 x 10(exp 3) s. The first five burst spectra are well modeled by blackbodies, with temperatures kT approx 2 - 9 keV. However, the sixth burst had a complicated spectrum that is well characterized by a blackbody plus two emission features whose amplitude varied throughout the burst. The most prominent feature was at 14.0 keV. Upon entry into the active phase the pulsar showed a significant change in pulse morphology and a likely timing glitch. The glitch had a total frequency jump of (1.9+/-0.4) x 10(exp -7) Hz, which recovered with a decay time of 17+/-2 days by more than the initial jump, implying a net spin-down of the pulsar. Within the framework of the magnetar model, the net spin-down of the star could be explained by regions of the superfluid that rotate. slower than the rest. The bursts, flux enhancements, and pulse morphology changes can be explained as arising from crustal deformations due to stresses imposed by the highly twisted internal magnetic field. However, unlike other AXP outbursts, we cannot account for a major twist being implanted in the magnetosphere.

Gavriil, Fotis P.↗
NASA NTRS1977

Skylab explores the Earth

Data from visual observations are integrated with results of analyses of approxmately 600 of the nearly 2000 photographs taken of Earth during the 84-day Skylab 4 mission to provide additional information on (1) Earth features and processes; (2) operational procedures and constraints in observing and photographing the planet; and (3) the use of man in real-time analysis of oceanic and atmospheric phenomena.

Source record↗
NASA NTRS2015

Testing Dissipative Magnetosphere Model Light Curves and Spectra with Fermi Pulsars

We explore the emission properties of a dissipative pulsar magnetosphere model introduced by Kalapotharakos et al. comparing its high-energy light curves and spectra, due to curvature radiation, with data collected by the Fermi LAT. The magnetosphere structure is assumed to be near the force-free solution. The accelerating electric field, inside the light cylinder (LC), is assumed to be negligible, while outside the LC it rescales with a finite conductivity (sigma). In our approach we calculate the corresponding high-energy emission by integrating the trajectories of test particles that originate from the stellar surface, taking into account both the accelerating electric field components and the radiation reaction forces. First, we explore the parameter space assuming different value sets for the stellar magnetic field, stellar period, and conductivity. We show that the general properties of the model are in a good agreement with observed emission characteristics of young gamma-ray pulsars, including features of the phase-resolved spectra. Second, we find model parameters that fit each pulsar belonging to a group of eight bright pulsars that have a published phase-resolved spectrum. The sigma values that best describe each of the pulsars in this group show an increase with the spin-down rate (E˙ ) and a decrease with the pulsar age, expected if pair cascades are providing the magnetospheric conductivity. Finally, we explore the limits of our analysis and suggest future directions for improving such models.

acceleration of particles↗
DOE OSTI2025

Efficient analysis of small-angle scattering curves for large biomolecular assemblies using Monte Carlo methods

Structure elucidation from small-angle scattering curves of large biomolecular assemblies is notoriously challenging. This is because the simulation of high-resolution features in the structure of large macromolecular assemblies, such as de novo protein assemblies, is computationally demanding when it needs to cover a broad range of length scales. Conventional methods, such as the numerical approximation to the Debye equation or the use of spherical harmonics, do not scale well as the size of the assembly increases, which limits their application to small structures (e.g. individual proteins). This work explores the effectiveness of a Monte Carlo method to simulate and fit scattering curves for large biomolecular assemblies spanning over ranges covering atomic and molecular detail (e.g. spacing and orientation of proteins in an assembly) as well as large-scale (hundreds of nanometres) features. Owing to its speed and scalability, it can be combined with a fitting algorithm to extract structural features from experimental small-angle scattering curves in biomolecular assemblies that are otherwise intractable for interpretation. This work first demonstrates the effectiveness of the tool using experimental small-angle X-ray scattering (SAXS) data from tile-like proteins that assemble into 1D tube-like macromolecular structures. Here, the diameter distribution of tubes is extracted from SAXS fits, and this is quantitatively compared with distributions from electron microscopy. SAXS data are also obtained from 2D sheet-like protein assemblies, and the proposed method is used to quantify structural features such as the separation distance between protein building blocks and the flexing of the sheet. An open-source implementation of the methodology is provided for use in a broad range of biological systems involving multi-scale scattering analysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

The phase diagram of quantum chromodynamics in one dimension on a quantum computer

The quantum chromodynamics (QCD) phase diagram, which reveals the state of strongly interacting matter at different temperatures and densities, is key to answering open questions in physics, ranging from the behaviour of particles in neutron stars to the conditions of the early universe. However, classical simulations of QCD face significant computational barriers, such as the sign problem at finite matter densities. Quantum computing offers a promising solution to overcome these challenges. Here, we take an important step toward exploring the QCD phase diagram with quantum devices by preparing thermal states in one-dimensional non-Abelian gauge theories. We experimentally simulate the thermal states of SU(2) and SU(3) gauge theories at finite densities on a trapped-ion quantum computer using a variational method. This is achieved by introducing two features: Firstly, we add motional ancillae to the existing qubit register to efficiently prepare thermal probability distributions. Secondly, we introduce charge-singlet measurements to enforce colour-neutrality constraints. This work pioneers the quantum simulation of QCD at finite density and temperature for two and three colours, laying the foundation to explore QCD phenomena on quantum platforms.

Quantum information↗
DOE OSTI2023

Improving the accuracy of freight mode choice models: A case study using the 2017 CFS PUF data set and ensemble learning techniques

Here, the US Census Bureau has collected two rounds of experimental data from the Commodity Flow Survey, providing shipment-level characteristics of nationwide commodity movements, published in 2012 (i.e., Public Use Microdata) and in 2017 (i.e., Public Use File). With this information, data-driven methods have become increasingly valuable for understanding detailed patterns in freight logistics. In this study, we used the 2017 Commodity Flow Survey Public Use File data set to explore building a high-performance freight mode choice model, considering three main improvements: (1) constructing local models for each separate commodity/industry category; (2) extracting useful geographical features, particularly the derived distance of each freight mode between origin/destination zones; and (3) applying additional ensemble learning methods such as stacking or voting to combine results from local and unified models for improved performance. The proposed method achieved over 92% accuracy without incorporating external information, an over 19% increase compared to directly fitting Random Forests models over 10,000 samples. Furthermore, SHAP (Shapely Additive Explanations) values were computed to explain the outputs and major patterns obtained from the proposed model. The model framework could enhance the performance and interpretability of existing freight mode choice models.

42 ENGINEERING↗
DOE OSTI2025

Denoising Autoencoder for Reconstructing Sensor Observation Data and Predicting Evapotranspiration: Noisy and Missing Values Repair and Uncertainty Quantification

Abstract Machine learning (ML) methods applied in scientific research often deal with interrelated features in high‐dimensional data. Reducing data noise and redundancy is needed to increase prediction accuracy and efficiency especially when dealing with data from field sensors. We explored an unsupervised learning method, the denoising autoencoder (DAE), to extract the underlying data structure from noisy raw data in the context of predicting hydrologic quantities from multiple field sensors. These sensors have intrinsic instrumental noise and occasional malfunctions that cause missing values. Our DAE neural network reconstructed meteorological sensor data containing noise and missing values to predict evapotranspiration in a mountainous watershed. The DAE reconstructed the sensor variables with a mean coefficient of determination value of 0.77 across 15 dimensions representing individual sensors. It reduced variance and bias uncertainties compared to a classical autoencoder model. The reconstruction quality varied across dimensions depending on their cross‐correlation and alignment with the underlying data structure. Uncertainties arising from the model structure were overall higher than those resulting from data corruption. We attached the DAE structure to a downstream ET‐prediction neural network in three formats and achieved reasonably accurate ET predictions . The use of the DAE notably reduced variance uncertainty in ET prediction. However, excessive variance reduction may be accompanied by an increase in bias due to the intrinsic bias‐variance tradeoff. Our method of evaluating and reducing uncertainties in aggregated data from different sources can be used to improve predictive models, process understanding, and uncertainty quantification for better water resource management. Plain Language Summary We present a machine learning method, namely the denoising autoencoder, which reduces the effects of data noise and missing values typically present in scientific data sets collected through sensor measurements. This method selects the most relevant information from noisy raw data collected by the instruments and fills in missing values. To demonstrate the effectiveness of our method, we applied it to predict evapotranspiration, a hydrologic variable that represents the water moved from the land surface to the atmosphere through a combination of evaporation and plant water use (transpiration). We also used a random sampling technique (the Monte Carlo method) to compare the uncertainty in the predictions when using the raw and noisy data versus the reconstructed data. The denoising process produced more accurate predictions of evapotranspiration with less uncertainty. Improved predictions of evapotranspiration can lead to a better understanding and accounting of water budgets. This ML approach is broadly suitable for a wide variety of applications that involve noisy sensor data with missing values. Key Points We used a denoising autoencoder (DAE) neural network to reduce noise in meteorological and soil sensor observations by on average We used Monte Carlo sampling to estimate the bias and variance of all model outputs, including uncertainty sources from data and the model We attached the DAE component to a downstream neural network to predict ET with the variance reduced by , compared to that without the DAE

denoising autoencoder↗
DOE OSTI2020

In-situ spectroscopic ellipsometry for real-time characterization of the effects of high-flux helium plasmas on tungsten surfaces

Tungsten samples were exposed to He plasmas generated by an RF source (Γ i = 3.5 x 10 16 He cm -2 s -1 , ion energy = 92 eV.) The range of exposure conditions selected here is conducive to the growth of nearsurface He bubbles, and at higher fluence, the formation of W nanotendrils ranging between 50 — 100 nm in diameter. The evolution of these surface features was probed using a fixed-angle ellipsometer (280 — 1000 nm wavelength range) with direct line-of-sight to the sample. Over the parameter space explored here, changes in the two angles (p, S) that define the polarization of the reflected light followed a distinct trajectory with increasing plasma fluence. Ex-situ ellipsometry of 22 additional tungsten specimens tested at a wide range of plasma fluences and temperatures mapped onto these in-situ results well. We used helium ion microscopy and focused ion beam profiling to provide a direct calibration of the ellipsometry measurements. Our results indicate that for a reproducible process such as the growth helium-induced surface morphologies, ellipsometry is a practical in-situ diagnostic to study how fusion plasmas modify materials. To study more general effects of plasmas on surfaces, including co-deposition and sputtering, different approaches to modelling the optical properties of the exposed surfaces are also considered.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Emergent Quantum Magnetism and Cryogenic Spin-Memory in Twisted Bilayer Graphene

Recently developed techniques for assembling moiré heterostructures have enabled unprecedented control of the interlayer interactions within the heterostructure, allowing for the realization of strongly correlated electronic states. The experimentally realized states, to date, include a variety of topological states, superconductivity, and strange metal behavior. However, the phenomenology varies significantly from sample to sample. Here we will present microscopy techniques for further characterization of both the progenitor exfoliated materials and the composite moiré heterostructure. The application of these techniques has allowed for an unprecidented degree of characterization in ambient conditions. Additionally we will explore the use of resistively detected electron spin resonance (ESR) as a novel probe for spin order in moirés. While our results do not show clearly dispersive features that would unambiguously evince spin excitations within the sample, the technique is still promising.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

What’s the Difference? The Potential for Convolutional Neural Networks for Transient Detection without Template Subtraction

Abstract We present a study of the potential for convolutional neural networks (CNNs) to enable separation of astrophysical transients from image artifacts, a task known as “real–bogus” classification, without requiring a template-subtracted (or difference) image, which requires a computationally expensive process to generate, involving image matching on small spatial scales in large volumes of data. Using data from the Dark Energy Survey, we explore the use of CNNs to (1) automate the real–bogus classification and (2) reduce the computational costs of transient discovery. We compare the efficiency of two CNNs with similar architectures, one that uses “image triplets” (templates, search, and difference image) and one that takes as input the template and search only. We measure the decrease in efficiency associated with the loss of information in input, finding that the testing accuracy is reduced from ∼96% to ∼91.1%. We further investigate how the latter model learns the required information from the template and search by exploring the saliency maps. Our work (1) confirms that CNNs are excellent models for real–bogus classification that rely exclusively on the imaging data and require no feature engineering task and (2) demonstrates that high-accuracy (>90%) models can be built without the need to construct difference images, but some accuracy is lost. Because, once trained, neural networks can generate predictions at minimal computational costs, we argue that future implementations of this methodology could dramatically reduce the computational costs in the detection of transients in synoptic surveys like Rubin Observatory's Legacy Survey of Space and Time by bypassing the difference image analysis entirely.

79 ASTRONOMY AND ASTROPHYSICS↗