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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS2000

Vertical Roughness of the Polar Regions of Mars from Mars Orbiter Laser Altimeter Pulse-Width Measurements

The sub-kilometer scale vertical roughness of the martian surface in the polar regions can be investigated using calibrated, optical pulse width data provided by the Mars Orbiter Laser Altimeter (MOLA). Garvin and others have previously discussed initial observations of what we have called "total vertical roughness" or TVR, as derived from MOLA optical pulse width observations acquired during the pre-mapping phases of the Mars Global Surveyor (MGS) mission. Here we present the first assessment of the Mars polar region properties of the TVR parameter from more than nine months of continuous mapping by MOLA as part of the MGS mapping mission. Other than meter-scale surface properties directly inferred from Mars Orbiter Camera (MOC) images, MOLA measurements of footprint-scale TVR represent the only direct measurements of the local vertical structure of the martian surface at approx. 150 m length scales. These types of data have previously been shown to correlate with geologic process histories for terrestrial desert surfaces on the basis of Shuttle Laser Altimeter (SLA) observations. Additional information is obtained in the original extended abstract.

Garvin, J. B.↗
DOE OSTI2024

Environmental Dependence of Type Ia Supernovae in Low-redshift Galaxy Clusters

Abstract We present an analysis of 102 Type Ia supernovae (SNe Ia) in nearby ( z < 0.1), x-ray-selected galaxy clusters. This is the largest such sample to date and is based on archival data primarily from ZTF and ATLAS. We divide our SNe Ia into an inner cluster sample projected within r 500 of the cluster center and an outer cluster sample projected between r 500 and 2 r 500 . We compare these to field samples of SNe Ia at similar redshifts in both quiescent and star-forming host galaxies. Based on SALT3 fits to the light curves, we find that the inner cluster SNe Ia have a higher fraction of fast-evolving objects (SALT3 x 1 < −1) than the outer cluster or field quiescent samples. This implies an intrinsically different population of SNe Ia occurs in inner cluster environments, beyond known correlations based on host galaxy alone. Our cluster samples show a strongly bimodal x 1 distribution with a fast-evolving component that dominates the inner cluster objects (≳75%) but is just a small fraction of SNe Ia in field star-forming galaxies (≲10%). We do not see strong evidence for variations in the color (SALT3 c ) distributions among the samples and find only minor differences in SN Ia standardization parameters and Hubble residuals. We suggest that the age of the stellar population drives the observed distributions, with the oldest populations nearly exclusively producing fast-evolving SNe Ia.

Larison, Conor (ORCID:0000000320374619)↗
DOE OSTI2021

Machine learning to identify geologic factors associated with production in geothermal fields: a case-study using 3D geologic data, Brady geothermal field, Nevada

Abstract In this paper, we present an analysis using unsupervised machine learning (ML) to identify the key geologic factors that contribute to the geothermal production in Brady geothermal field. Brady is a hydrothermal system in northwestern Nevada that supports both electricity production and direct use of hydrothermal fluids. Transmissive fluid-flow pathways are relatively rare in the subsurface, but are critical components of hydrothermal systems like Brady and many other types of fluid-flow systems in fractured rock. Here, we analyze geologic data with ML methods to unravel the local geologic controls on these pathways. The ML method, non-negative matrix factorization with k -means clustering (NMF k ), is applied to a library of 14 3D geologic characteristics hypothesized to control hydrothermal circulation in the Brady geothermal field. Our results indicate that macro-scale faults and a local step-over in the fault system preferentially occur along production wells when compared to injection wells and non-productive wells. We infer that these are the key geologic characteristics that control the through-going hydrothermal transmission pathways at Brady. Our results demonstrate: (1) the specific geologic controls on the Brady hydrothermal system and (2) the efficacy of pairing ML techniques with 3D geologic characterization to enhance the understanding of subsurface processes.

58 GEOSCIENCES↗
DOE OSTI2025

User-Defined Types as Task Parameters and Arguments in FleCSI

FleCSI is a C++ framework for writing multi-physics simulations. FleCSI abstracts away complicated backends, enabling high performance while eliminating the need for users to directly manage memory and execution. This is done via FleCSI’s data and execution model.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

DeepLearnMOR: a deep-learning framework for fluorescence image-based classification of organelle morphology

Abstract The proper biogenesis, morphogenesis, and dynamics of subcellular organelles are essential to their metabolic functions. Conventional techniques for identifying, classifying, and quantifying abnormalities in organelle morphology are largely manual and time-consuming, and require specific expertise. Deep learning has the potential to revolutionize image-based screens by greatly improving their scope, speed, and efficiency. Here, we used transfer learning and a convolutional neural network (CNN) to analyze over 47,000 confocal microscopy images from Arabidopsis wild-type and mutant plants with abnormal division of one of three essential energy organelles: chloroplasts, mitochondria, or peroxisomes. We have built a deep-learning framework, DeepLearnMOR (Deep Learning of the Morphology of Organelles), which can rapidly classify image categories and identify abnormalities in organelle morphology with over 97% accuracy. Feature visualization analysis identified important features used by the CNN to predict morphological abnormalities, and visual clues helped to better understand the decision-making process, thereby validating the reliability and interpretability of the neural network. This framework establishes a foundation for future larger-scale research with broader scopes and greater data set diversity and heterogeneity.

Plant Sciences↗
DOE OSTI2024

Resolving crystallographic geometrically necessary dislocations in three dimensions in a hexagonal close packed titanium alloy

Abstract Geometrically necessary dislocation (GND) content is measured from mm 3 -scaled Ti7Al three-dimensional (3D) microstructural data using a theory extended for hexagonal close packed crystals, which accounts for basal, prismatic and pyramidal ⟨ c + a ⟩ type dislocation content. The Ti7Al samples have been mechanically pre-strained to two different strain levels, and will then be strained along the same axis in uniaxial tension during simulation. Both inter- and intragranular GNDs across the microstructures have been characterized, with a large contribution of pyramidal ⟨ c + a ⟩ GNDs, consistent with the relative slip activity involved in pre-straining. The spatially resolved crystallographic GND distributions within the 3D microstructures are used to instantiate a microstructure model for forward modeling deformation simulations by a dislocation density hardening elasto-viscoplastic fast Fourier transform framework. Coarsening the voxel resolution during the initial microstructure construction procedure is shown to strongly impact both the magnitude and spatial distribution of the GNDs and in turn the forward deformation response of the pre-strained material. This study indicates that the voxel resolution desired when transferring from measured to model microstructures need not only be proportionally scaled with the microstructure but also sufficiently fine to capture the subgranular orientation gradients that may already be present in the material.

Materials Science↗
DOE OSTI2023

Nanoprobe Based Information Processing: Nanoprobe‐Electronics

Abstract With computational architectures becoming data‐centric and with the rise of in‐memory computing, the role of memory will be ever more crucial. In turn, storing massive amount of data demands for ultra‐low power and non‐volatile types necessitating new memory technologies. Here, a controllable nanoelectromechanical (NEM) spin memory is proposed for compact nonvolatile memory arrays. It combines the advantages of both nanomechanical and spin‐transfer torque (STT) magnetic memory devices to overcome fundamental scalability roadblocks of non‐hybrid alternatives. The hybrid system paves the way to a new memory technology in the regime of sub‐10‐nm lateral size with a sub‐1‐MA cm −2 switching energy.

Yi, Bao↗
DOE OSTI2021

PAO 1.0: A Python Library for Adversarial Optimization

PAO is a Python-based package for Adversarial Optimization. The goal of this package is to provide a general modeling and analysis capability for bilevel, trilevel and other multilevel optimization forms that express adversarial dynamics. PAO integrates two different modeling abstractions: 1. Algebraic models extend the modeling concepts in the Pyomo algebraic modeling language to express problems with an intuitive algebraic syntax. Thus, we expect that this modeling abstraction will commonly be used by PAO end-users. 2. Compact models express objective and constraints in a manner that is typically used to express the mathematical form of these problems (e.g. using vector and matrix data types). PAO denes custom Multilevel Problem Representations (MPRs) that simplify the implementation of solvers for bilevel, trilevel and other multilevel optimization problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Lessons learned from the two largest Galaxy morphological classification catalogues built by convolutional neural networks

ABSTRACT We compare the two largest galaxy morphology catalogues, which separate early- and late-type galaxies at intermediate redshift. The two catalogues were built by applying supervised deep learning (convolutional neural networks, CNNs) to the Dark Energy Survey data down to a magnitude limit of ∼21 mag. The methodologies used for the construction of the catalogues include differences such as the cutout sizes, the labels used for training, and the input to the CNN – monochromatic images versus gri-band normalized images. In addition, one catalogue is trained using bright galaxies observed with DES (i < 18), while the other is trained with bright galaxies (r < 17.5) and ‘emulated’ galaxies up to r-band magnitude 22.5. Despite the different approaches, the agreement between the two catalogues is excellent up to i < 19, demonstrating that CNN predictions are reliable for samples at least one magnitude fainter than the training sample limit. It also shows that morphological classifications based on monochromatic images are comparable to those based on gri-band images, at least in the bright regime. At fainter magnitudes, i > 19, the overall agreement is good (∼95 per cent), but is mostly driven by the large spiral fraction in the two catalogues. In contrast, the agreement within the elliptical population is not as good, especially at faint magnitudes. By studying the mismatched cases, we are able to identify lenticular galaxies (at least up to i < 19), which are difficult to distinguish using standard classification approaches. The synergy of both catalogues provides an unique opportunity to select a population of unusual galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2001

Field Model: An Object-Oriented Data Model for Fields

We present an extensible, object-oriented data model designed for field data entitled Field Model (FM). FM objects can represent a wide variety of fields, including fields of arbitrary dimension and node type. FM can also handle time-series data. FM achieves generality through carefully selected topological primitives and through an implementation that leverages the potential of templated C++. FM supports fields where the nodes values are paired with any cell type. Thus FM can represent data where the field nodes are paired with the vertices ("vertex-centered" data), fields where the nodes are paired with the D-dimensional cells in R(sup D) (often called "cell-centered" data), as well as fields where nodes are paired with edges or other cell types. FM is designed to effectively handle very large data sets; in particular FM employs a demand-driven evaluation strategy that works especially well with large field data. Finally, the interfaces developed for FM have the potential to effectively abstract field data based on adaptive meshes. We present initial results with a triangular adaptive grid in R(sup 2) and discuss how the same design abstractions would work equally well with other adaptive-grid variations, including meshes in R(sup 3).

Moran, Patrick J.↗
NASA NTRS1993

Cellular responses to low-gravity: Pilot studies on suborbital rockets and orbiting spacecraft

The allocated funding supported, in part, experiments conducted on two Consort sounding rockets and five Shuttle flights. The primary parameters investigated were signal transduction in response to various mediators, cellular differentiation and metabolism in microgravity, and effect of microgravity on cytoskeletal morphology. Achievements include: demonstration of effect of spaceflight on the actin cytoskeleton in mouse osteoblasts and frog cells; confirmation that the T cell receptor-mediated signal transduction pathway in T lymphocytes is not affected by low-gravity compared to non-TCR-mediated stimulation (Con-A) which classically does not promote proliferative response; indication that microgravity may allow separation of proliferative signaling and secretory function in lymphocytes; demonstration that T lymphocytes and bone cells utilized less glucose indicating a shift in metabolism and confirming Spacelab results with WI-38 cells which used significantly less glucose, during spaceflight; confirmation that activation of human splenic B cells with a number of different mediators is not affected during spaceflight; demonstration of increased prostaglandin synthesis during reduced bone cell growth suggesting an effect of microgravity on prostaglandin-induced mitogenesis. The funding contributed significantly to the database described above and resulted in submission of six collaborative abstracts in 1993 (five to the ASGSB Annual Meeting and one to the ASCB Annual Meeting). Two abstracts were presented at the 1992 ASGSB Annual Meeting in Tucson. In addition, several peer reviewed papers are being generated and data will be included as background in preparation of future proposals, which hopefully will allow us to continue this type of extremely productive collaborative research.

Lewis, Marian L.↗
NASA NTRS2006

Chemical Diversity along the Traverse of the Rover Spirit at Gusev Crater

The Alpha-Particle-X-ray Spectrometer (APXS) is part of the in situ payload of the Mars Exploration Rovers. It has determined the chemical composition of soils and rocks along the nearly 6 km long traverse of the rover Spirit. The measuring method a combination of PIXE and XRF using Cm244 sources - allowed the unambiguous identification of elemental compositions with high precision. Besides sample triage and quantification of saltforming elements as indicators for aqueous alteration, the APXS also delivered important constraints to mineralogy intruments (i.e., Mossbauer (MB), MiniTES, Pancam) on minerals and rock types. The mineralogy instruments on the other hand provided constraints on minerals used for APXS normative calculations and, e.g. allowed the attribution of S to sulfate, instead of sulfide or elemental sulfur. This abstract gives an updated overview of the data obtained up to our current rover position on sol 720 at the eastern base of the Columbia Hills. We will emphasize elemental correlations that imply the presence of certain minerals that can not be identified by the MER mineralogy instruments.

Gellert, R.↗
DOE OSTI2022

Learning Coagulation Processes With Combinatorial Neural Networks

Abstract Simulating the evolution of a coagulating aerosol or cloud of droplets in a key problem in atmospheric science. We present a proof of concept for modeling coagulation processes using a novel combinatorial neural network (CombNN) architecture. Using two types of data from a high‐detail particle‐resolved aerosol simulation, we show that CombNN models outperform standard neural networks and are competitive in accuracy with traditional state‐of‐the‐art sectional models. These CombNN models could have application in learning coarse‐grained coagulation models for multi‐species aerosols and for learning coagulation models from observed size‐distribution data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Evidence for sub-Chandrasekhar Type Ia supernovae from the last major merger

ABSTRACT We investigate the contribution of sub-Chandrasekhar mass Type Ia supernovae to the chemical enrichment of the Gaia Sausage galaxy, the progenitor of a significant merger event in the early life of the Milky Way. Using a combination of data from Nissen & Schuster, the GALactic Archaeology with HERMES (GALAH) Data Release 3 [with 1D non-local thermal equilibrium (NLTE) abundance corrections], and the Apache Point Observatory Galactic Evolution Experiment (APOGEE) Data Release 16, we fit analytic chemical evolution models to a nine-dimensional chemical abundance space (Fe, Mg, Si, Ca, Cr, Mn, Ni, Cu, and Zn) in particular focusing on the iron-peak elements, Mn and Ni. We find that low [Mn/Fe] $\sim -0.15\, \mathrm{dex}$ and low [Ni/Fe] $\sim -0.3\, \mathrm{dex}$ Type Ia yields are required to explain the observed trends beyond the [α/Fe] knee of the Gaia Sausage (approximately at [Fe/H] $=-1.4\, \mathrm{dex}$). Comparison to theoretical yield calculations indicates a significant contribution from sub-Chandrasekhar mass Type Ia supernovae in this system (from ${\sim} 60\, \mathrm{per\, cent}$ to $100\, \mathrm{per\, cent}$ depending on the theoretical model with an additional ${\pm} 10\, \mathrm{per\, cent}$ systematic from NLTE corrections). We compare to results from other Local Group environments including dwarf spheroidal galaxies, the Magellanic Clouds, and the Milky Way’s bulge, finding the Type Ia [Mn/Fe] yield must be metallicity dependent. Our results suggest that sub-Chandrasekhar mass channels are a significant, perhaps even dominant, contribution to Type Ia supernovae in metal-poor systems, whilst more metal-rich systems could be explained by metallicity-dependent sub-Chandrasekhar mass yields, possibly with additional progenitor mass variation related to star formation history, or an increased contribution from Chandrasekhar mass channels at higher metallicity.

Sanders, Jason L.↗
DOE OSTI2024

Evaluating the Consistency of Cosmological Distances Using Supernova Siblings in the Near-infrared

Abstract The study of supernova (SN) siblings, supernovae with the same host galaxy, is an important avenue for understanding and measuring the properties of Type Ia SN Ia light curves (LCs). Thus far, sibling analyses have mainly focused on optical LC data. Considering that LCs in the near-infrared (NIR) are expected to be better standard candles than those in the optical, we carry out the first analysis compiling SN siblings with only NIR data. We perform an extensive literature search of all SN siblings and find six sets of siblings with published NIR photometry. We calibrate each set of siblings ensuring they are on homogeneous photometric systems, fit the LCs with the SALT3-NIR and SNooPy models, and find median absolute differences inμvalues between siblings of 0.248 and 0.186 mag, respectively. To evaluate the significance of these differences beyond measurement noise, we run simulations that mimic these LCs and provide an estimate for uncertainty on these median absolute differences of ∼0.052 mag, and we find that, statistically, our analysis rules out the nonexistence of intrinsic scatter in the NIR at the 99% level. When comparing the same sets of SN siblings, we observe a median absolute difference inμvalues between siblings of 0.177 mag when using optical data alone as compared to 0.186 mag when using NIR data alone. It is unclear if these results may be due to limited statistics or poor quality NIR data, all of which will be improved with the Nancy Grace Roman Space Telescope.

Astronomy & Astrophysics↗
NASA NTRS1980

A methodology for long range prediction of air transportation

The paper describes the methodology for long-time projection of aircraft fuel requirements. A new concept of social and economic factors for future aviation industry which provides an estimate of predicted fuel usage is presented; it includes air traffic forecasts and lead times for producing new engines and aircraft types. An air transportation model is then developed in terms of an abstracted set of variables which represent the entire aircraft industry on a macroscale. This model was evaluated by testing the required output variables from a model based on historical data over the past decades.

Ayati, M. B.↗
DOE OSTI2023

Climate Change Will Increase Biomass Proportion of Global Forest Carbon Stocks Under an SSP5–8.5 Climate Trajectory

Abstract A large amount of carbon is stored in global forests. However, the fraction of carbon stored as plant biomass versus soil organic carbon (SOC) varies among forest types, and potential changes over the 21st century are uncertain. Here, we used extensive data derived from inventories and remote sensing and Coupled Model Intercomparison Project Phase 6 (CMIP6) models to examine the current and 21st century dynamics in the proportion of biomass and SOC across global forests. We found that precipitation, elevation, soil, and wildfire were the primary controls of these differences in carbon pools. Under the SSP5–8.5 climate scenario, CMIP6 models project that the ratio of biomass to ecosystem carbon in global forests will increase across the 21st century, with the largest increases in boreal forests (95 ± 37%) compared to moist tropical forests (16 ± 15%). Changes in forest carbon pools resulting in greater biomass fraction will affect disturbance, and ecosystem carbon and energy balances, all of which interact with the climate system.

54 ENVIRONMENTAL SCIENCES↗