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At least 811 records · Page 45

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NASA NTRS2020

Observing System Simulation Experiments Investigating Atmospheric Motion Vectors and Radiances from a Constellation of 4-5 μm Infrared Sounders

A set of Observing System Simulation Experiments (OSSEs) was performed to investigate the utility of a constellation of passive infrared spectrometers, strategically designed with the aim of deriving the three-dimensional retrievals of the horizontal wind via atmospheric motion vectors (AMVs) from instruments with the spectral resolution of an infrared sounder. The instrument and constellation designs were performed in the context of the Midwave Infrared Sounding of Temperature and humidity in a Constellation for Winds, or MISTiC Winds. The Global Modeling and Assimilation Office OSSE system, which includes a full suite of operational meteorological observations, served as the control. To illustrate the potential impact of this observing strategy, two experiments were performed by adding the new simulated observations to the control. First, perfect (error-free) simulated AMVs and radiances were assimilated. Second, the data were made imperfect by adding realistic modeled errors to the AMVs and radiances that were assimilated. The experimentation showed beneficial impacts on both the mass and wind fields, as based on analysis verification, forecast verification, and the assessment of the observations using the Forecast Sensitivity to Observation Impact (FSOI) metric. In all variables and metrics, the impacts of the imperfect observations were smaller than those of the perfect observations, though much of the positive benefit was retained. The FSOI metric illustrated two key points. First, the largest impacts were seen in the middle troposphere AMVs, which is a targeted capability of the constellation strategy. Second, the addition of modeled errors showed that the assimilation system was unable to fully exploit the 4.3 μm carbon dioxide absorption radiances.

Will McCarty↗

DOE OSTI2024

Seismic moment tensor classification using elliptical distribution functions on the hypersphere

Discrimination of underground explosions from naturally occurring earthquakes and other anthropogenic sources is one of the fundamental challenges of nuclear explosion monitoring. In an operational setting, the number of events that can be thoroughly investigated by analysts is limited by available resources. The capability to rapidly screen out events that can be robustly identified as not being explosions is, therefore, of great potential benefit. Nevertheless, possible mis-classification of explosions as earthquakes currently limits the use of screening methods for verification of test-ban treaties. Moment tensors provide a physics-based classification tool for the characterization of different seismic sources and have enabled the advent of new techniques for discriminating between earthquakes and explosions. Following normalization and projection of their six-degree vectors onto the hypersphere, existing screening approaches use spherically symmetric metrics to determine whether any new moment tensor may have been an explosion. Here, we show that populations of moment tensors for both earthquakes and explosions are anisotropically distributed on the hypersphere. Distributions possessing elliptical symmetry, such as the scaled von Mises–Fisher distribution, therefore provide a better description of these populations than the existing spherically symmetric models. We describe a method that uses these elliptical distributions in combination with a Bayesian classifier to achieve successful classification rates of 99 per cent for explosions and 98 per cent for earthquakes using existing catalogues of events from the western United States. The 1983 May 5 Crowdie underground nuclear test and 2018 July 20 DAG-1 deep-borehole chemical explosion are the only two explosions out of 140 that are incorrectly classified. Application of the method to the 2006–2017 nuclear tests in the Democratic People’s Republic of Korea yields 100 per cent identification rates and we provide a simple routine MTid for general usage. The approach provides a means to rapidly assess the likelihood of an event being an explosion and can be built into monitoring workflows that rely on simultaneously assessing multiple different discrimination metrics.

58 GEOSCIENCES↗

DOE OSTI2024

Noncoplanar magnetic order in the breathing kagome lattice compound Pb⁡(OF)⁢Cu 3 ⁢(SeO 3 ) 2⁢ (NO 3 )

We report a comprehensive study investigating the magnetic ordering properties of the quasi-two-dimensional breathing kagome lattice compound Pb⁡(OF)⁢Cu 3 ⁢(SeO 3 ) 2 ⁢(NO 3 ) using neutron diffraction and thermodynamic measurements. A long-range magnetic order emerges below the Néel temperature, T N = 18K, characterized by a propagating vector k = (0,0,1.5). Refinement analysis reveals that the copper moments display a noncoplanar arrangement, with the c components parallelly aligned within the breathing kagome plane and antiparallelly aligned between the layers. In-plane moment components form a chiral “tail-chase” structure. The magnetic order is quickly suppressed under moderate external fields applied both within and perpendicular to the kagome plane. Potential magnetic interactions that may be associated with the noncoplanar chiral magnetic structure are discussed.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2020

Predicting Future Disease Burden in a Rapidly Changing Climate

The interplay of a rapidly changing climate and infectious disease occurrence is emerging as a critical topic, requiring investigation of possible direct, as well as indirect, connections between disease processes and climate-related variation and phenomena. First, we introduce and overview three infectious disease exemplars (dengue, influenza, valley fever) representing different transmission classes (insect-vectored, human-to-human, environmentally-transmitted) to illuminate the complex and significant interplay between climate disease processes, as well as to motivate discussion of how Sandia can transform the field, and change our understanding of climate-driven infectious disease spread. We also review state-of-the-art epidemiological and climate modeling approaches, together with data analytics and machine learning methods, potentially relevant to climate and infectious disease studies. We synthesize the modeling and disease exemplars information, suggesting initial avenues for research and development (R&D) in this area, and propose potential sponsors for this work. Whether directly or indirectly, it is certain that a rapidly changing climate will alter global disease burden. The trajectory of climate change is an important control on this burden, from local, to regional and global scales. The efforts proposed herein respond to the National Research Councils call for the creation of a multidisciplinary institute that would address critical aspects of these interlocking, cascading crises.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1989

Spaceborne radar observations: A guide for Magellan radar-image analysis

Geologic analyses of spaceborne radar images of Earth are reviewed and summarized with respect to detecting, mapping, and interpreting impact craters, volcanic landforms, eolian and subsurface features, and tectonic landforms. Interpretations are illustrated mostly with Seasat synthetic aperture radar and shuttle-imaging-radar images. Analogies are drawn for the potential interpretation of radar images of Venus, with emphasis on the effects of variation in Magellan look angle with Venusian latitude. In each landform category, differences in feature perception and interpretive capability are related to variations in imaging geometry, spatial resolution, and wavelength of the imaging radar systems. Impact craters and other radially symmetrical features may show apparent bilateral symmetry parallel to the illumination vector at low look angles. The styles of eruption and the emplacement of major and minor volcanic constructs can be interpreted from morphological features observed in images. Radar responses that are governed by small-scale surface roughness may serve to distinguish flow types, but do not provide unambiguous information. Imaging of sand dunes is rigorously constrained by specific angular relations between the illumination vector and the orientation and angle of repose of the dune faces, but is independent of radar wavelength. With a single look angle, conditions that enable shallow subsurface imaging to occur do not provide the information necessary to determine whether the radar has recorded surface or subsurface features. The topographic linearity of many tectonic landforms is enhanced on images at regional and local scales, but the detection of structural detail is a strong function of illumination direction. Nontopographic tectonic lineaments may appear in response to contrasts in small-surface roughness or dielectric constant. The breakpoint for rough surfaces will vary by about 25 percent through the Magellan viewing geometries from low to high Venusian latitudes. Examples of anomalies and system artifacts that can affect image interpretation are described.

Ford, J. P.↗

NASA NTRS2004

A New, More Physically Based Algorithm, for Retrieving Aerosol Properties over Land from MODIS

The MOD Imaging Spectrometer (MODIS) has been successfully retrieving aerosol properties, beginning in early 2000 from Terra and from mid 2002 from Aqua. Over land, the retrieval algorithm makes use of three MODIS channels, in the blue, red and infrared wavelengths. As part of the validation exercises, retrieved spectral aerosol optical thickness (AOT) has been compared via scatterplots against spectral AOT measured by the global Aerosol Robotic NETwork (AERONET). On one hand, global and long term validation looks promising, with two-thirds (average plus and minus one standard deviation) of all points falling between published expected error bars. On the other hand, regression of these points shows a positive y-offset and a slope less than 1.0. For individual regions, such as along the U.S. East Coast, the offset and slope are even worse. Here, we introduce an overhaul of the algorithm for retrieving aerosol properties over land. Some well-known weaknesses in the current aerosol retrieval from MODIS include: a) rigid assumptions about the underlying surface reflectance, b) limited aerosol models to choose from, c) simplified (scalar) radiative transfer (RT) calculations used to simulate satellite observations, and d) assumption that aerosol is transparent in the infrared channel. The new algorithm attempts to address all four problems: a) The new algorithm will include surface type information, instead of fixed ratios of the reflectance in the visible channels to the mid-IR reflectance. b) It will include updated aerosol optical properties to reflect the growing aerosol retrieved from eight-plus years of AERONE". operation. c) The effects of polarization will be including using vector RT calculations. d) Most importantly, the new algorithm does not assume that aerosol is transparent in the infrared channel. It will be an inversion of reflectance observed in the three channels (blue, red, and infrared), rather than iterative single channel retrievals. Thus, this new formulation of the MODIS aerosol retrieval over land includes more physically based surface, aerosol and radiative transfer with fewer potentially erroneous assumptions.

Levy, Robert C.↗

NASA NTRS2006

Impact of Solar Array Position on ISS Vehicle Charging

The International Space Station (ISS), because of its large structure and high voltage solar arrays, has a complex plasma interaction with the ionosphere in low Earth orbit (LEO). This interaction of the ISS US Segment photovoltaic (PV) power system with the LEO ionospheric plasma produces floating potentials on conducting elements of the ISS structure relative to the local plasma environment. To control the ISS floating potentials, two Plasma Contactor Units (PCUs) are installed on the Z1 truss. Each PCU discharges accumulated electrons from the Space Station structure, thus reducing the potential difference between the ISS structure and the surrounding charged plasma environment. Operations of the PCUs were intended to keep the ISS floating potential to 40 Volts (Reference 1). Exposed dielectric surfaces overlying conducting structure on the Space Station will collect an opposite charge from the ionosphere as the ISS charges. In theory, when an Extravehicular Activity (EVA) crewmember is tethered to structure via the crew safety tether or when metallic surfaces of the Extravehicular Mobility Unit (EMU) come in contact with conducting metallic surfaces of the ISS, the EMU conducting components, including the perspiration-soaked crewmember inside, can become charged to the Space Station floating potential. The concern is the potential dielectric breakdown of anodized aluminum surfaces on the EMU producing an arc from the EMU to the ambient plasma, or nearby ISS structure. If the EMU arcs, an electrical current of an unknown magnitude and duration may conduct through the EVA crewmember, producing an unacceptable condition. This electrical current may be sufficient to startle or fatally shock the EVA crewmember (Reference 2). Hence, as currently defined by the EVA community, the ISS floating potential for all nominal and contingency EVA worksites and translation paths must have a magnitude less than 40 volts relative to the local ionosphere at all times during EVA. Arcing from the EMU is classified as a catastrophic hazard, which requires two-failure tolerant controls, i.e., three hazard controls. Each PCU is capable of maintaining the ISS floating potential below the requirement during EVA. The two PCUs provide a single failure tolerant control of ISS floating potential. In the event of the failure of one or two PCUs, a combination of solar array shunting and turning the solar arrays into their own wakes will be used to supply control of the plasma hazard (Reference 3). The purpose of this paper is to present on-orbit information that shows that ISS solar array placement with respect to the ISS velocity vector can control solar array plasma charging, and hence, provide an operational control for the plasma hazard. Also, this paper will present on-orbit information that shows that shunting of the ISS solar arrays can control solar array plasma charging, and hence, provide an additional operational control for the plasma hazard.

Alred, John↗

DOE OSTI2023

Machine Learning for Advanced Building Construction: Preprint

High-efficiency retrofits can play a key role in reducing carbon emissions associated with buildings if processes can be scaled-up to reduce cost, time, and disruption. Here we demonstrate an artificial intelligence/computer vision (AI/CV)- enabled framework for converting exterior build scans and dimensional data directly into manufacturing and installation specifications for overclad panels. In our workflow point clouds associated with LiDAR-scanned buildings are segmented into a facade feature space, vectorized features are extracted using an iterative random-sampling consensus algorithm, and from this representation an optimal panel design plan satisfying manufacturing constraints is generated. This system and the corresponding construction process is demonstrated on a test facade structure constructed at the National Renewable Energy Laboratory (NREL). We also include a brief summary of a techno-economic study designed to estimate the potential energy and cost impact of this new system.

building retrofits↗

DOE OSTI2023

Machine Learning for Advanced Building Construction

High-efficiency retrofits can play a key role in reducing carbon emissions associated with buildings if processes can be scaled-up to reduce cost, time, and disruption. Here we demonstrate an artificial intelligence/computer vision (AI/CV)-enabled framework for converting exterior build scans and dimensional data directly into manufacturing and installation specifications for overclad panels. In our workflow point clouds associated with LiDAR-scanned buildings are segmented into a facade feature space, vectorized features are extracted using an iterative random-sampling consensus algorithm, and from this representation an optimal panel design plan satisfying manufacturing constraints is generated. This system and the corresponding construction process is demonstrated on a test facade structure constructed at the National Renewable Energy Laboratory (NREL). We also include a brief summary of a techno-economic study designed to estimate the potential energy and cost impact of this new system.

build scans↗

DOE OSTI2025

Developing predictive models for µ opioid receptor binding using machine learning and deep learning techniques

Opioids exert their analgesic effect by binding to the µ opioid receptor (MOR), which initiates a downstream signaling pathway, eventually inhibiting pain transmission in the spinal cord. However, current opioids are addictive, often leading to overdose contributing to the opioid crisis in the United States. Therefore, understanding the structure-activity relationship between MOR and its ligands is essential for predicting MOR binding of chemicals, which could assist in the development of non-addictive or less-addictive opioid analgesics. This study aimed to develop machine learning and deep learning models for predicting MOR binding activity of chemicals. Chemicals with MOR binding activity data were first curated from public databases and the literature. Molecular descriptors of the curated chemicals were calculated using software Mold2. The chemicals were then split into training and external validation datasets. Random forest, k-nearest neighbors, support vector machine, multi-layer perceptron, and long short-term memory models were developed and evaluated using 5-fold cross-validations and external validations, resulting in Matthews correlation coefficients of 0.528–0.654 and 0.408, respectively. Furthermore, prediction confidence and applicability domain analyses highlighted their importance to the models’ applicability. Our results suggest that the developed models could be useful for identifying MOR binders, potentially aiding in the development of non-addictive or less-addictive drugs targeting MOR.

Research & Experimental Medicine↗

NASA NTRS1978

Canard configured aircraft with 2-D nozzle

A closely-coupled canard fighter with vectorable two-dimensional nozzle was designed for enhanced transonic maneuvering. The HiMAT maneuver goal of a sustained 8g turn at a free-stream Mach number of 0.9 and 30,000 feet was the primary design consideration. The aerodynamic design process was initiated with a linear theory optimization minimizing the zero percent suction drag including jet effects and refined with three-dimensional nonlinear potential flow techniques. Allowances were made for mutual interference and viscous effects. The design process to arrive at the resultant configuration is described, and the design of a powered 2-D nozzle model to be tested in the LRC 16-foot Propulsion Wind Tunnel is shown.

Child, R. D.↗

NASA NTRS2004

Evaluation of the Harmful Algal Bloom Mapping System (HABMapS) and Bulletin

The National Oceanic and Atmospheric Administration (NOAA) Harmful Algal Bloom (HAB) Mapping System and Bulletin provide a Web-based geographic information system (GIS) and an e-mail alert system that allow the detection, monitoring, and tracking of HABs in the Gulf of Mexico. NASA Earth Science data that potentially support HABMapS/Bulletin requirements include ocean color, sea surface temperature (SST), salinity, wind fields, precipitation, water surface elevation, and ocean currents. Modeling contributions include ocean circulation, wave/currents, along-shore current regimes, and chlorophyll modeling (coupled to imagery). The most immediately useful NASA contributions appear to be the 1-km Moderate Resolution Imaging Spectrometer (MODIS) chlorophyll and SST products and the (presently used) SeaWinds wind vector data. MODIS pigment concentration and SST data are sufficiently mature to replace imagery currently used in NOAA HAB applications. The large file size of MODIS data is an impediment to NOAA use and modified processing schemes would aid in NOAA adoption of these products for operational HAB forecasting.

Hall, Callie↗

NASA NTRS2012

Comparison of PARASOL Observations with Polarized Reflectances Simulated Using Different Ice Habit Mixtures

Insufficient knowledge of the habit distribution and the degree of surface roughness of ice crystals within ice clouds is a source of uncertainty in the forward light scattering and radiative transfer simulations required in downstream applications involving these clouds. The widely used MODerate Resolution Imaging Spectroradiometer (MODIS) Collection 5 ice microphysical model assumes a mixture of various ice crystal shapes with smooth-facets except aggregates of columns for which a moderately rough condition is assumed. When compared with PARASOL (Polarization and Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar) polarized reflection data, simulations of polarized reflectance using smooth particles show a poor fit to the measurements, whereas very rough-faceted particles provide an improved fit to the polarized reflectance. In this study a new microphysical model based on a mixture of 9 different ice crystal habits with severely roughened facets is developed. Simulated polarized reflectance using the new ice habit distribution is calculated using a vector adding-doubling radiative transfer model, and the simulations closely agree with the polarized reflectance observed by PARASOL. The new general habit mixture is also tested using a spherical albedo differences analysis, and surface roughening is found to improve the consistency of multi-angular observations. It is suggested that an ice model incorporating an ensemble of different habits with severely roughened surfaces would potentially be an adequate choice for global ice cloud retrievals.

Cole, Benjamin H.↗

NASA NTRS2023

SelenITA: A Dual Point Lunar Mission to Characterize the Near Surface Dust and Electromagnetic Plasma Environment

SelenITA is a dual CubeSat mission that will provide the first multi-point dust, plasma, and magnetic field measurements in lunar orbit. This mission will advance the understanding of the electromagnetic space environment at the Moon in support of the Artemis program, exploration, and the geosciences. Here we present the science rationale motivating the mission. The candidate mission science objectives include: (1) constrain the origins of crustal magnetic fields; (2) determine the nature of plasma interactions with crustal magnetic fields; (3) characterize plasma waves and turbulence at the Moon; (4) characterize the lunar surface electric potential in varying plasma environments; (5) constrain the composition, thermal state, and structure of the lunar upper mantle and crust; (6) characterize the ionizing radiation in lunar orbit; and lastly, (7) determine the density of the dust exosphere as a function of latitude, longitude, and altitude, including the lunar polar space environment. The measurement requirements include simultaneous two-point observations of the 3-component vector magnetic field, plasma distribution (flux, energy, density, temperature), and single-point observations of energetic particles (protons, electrons, gamma rays), and dust. These measurements are important because it helps us understand how future astronauts, robots, and space hardware will live and work on the lunar surface.

space weather↗

NASA NTRS2023

SelenITA: A Dual Point Lunar Mission to Characterize the Near Surface Dust and Electromagnetic Plasma Environment

space weather↗

DOE OSTI2023

Reining in Radium for Nuclear Medicine: Extra-Large Chelator Development for an Extra-Large Ion

Targeted α therapy (TAT) of soft-tissue cancers using the α particle-emitting radionuclide 223 Ra holds great potential because of its favorable nuclear properties, adequate availability, and established clinical use for treating metastatic prostate cancer of the bone. Despite these advantages, the use of 223 Ra has been largely overshadowed by other α emitters due to its challenging chelation chemistry. A key criterion that needs to be met for a radionuclide to be used in TAT is its stable attachment to a targeting vector via a bifunctional chelator. The low charge density of Ra 2+ arising from its large ionic radius weakens its electrostatic binding interactions with chelators, leading to insufficient complex stability in vivo. In this study, we synthesized and evaluated macropa-XL as a novel chelator for 223 Ra. It bears a large 21-crown-7 macrocyclic core and two picolinate pendent groups, which we hypothesized would effectively saturate the large coordination sphere of the Ra 2+ ion. The structural chemistry of macropa-XL was first established with the nonradioactive Ba 2+ ion using X-ray diffraction and X-ray absorption spectroscopy, which revealed the formation of an 11-coordinate complex in a rare anti pendent-arm configuration. Subsequently, the stability constant of the [Ra(macropa-XL)] complex was determined via competitive cation exchange with 223 Ra and 224 Ra radiotracers and compared with that of macropa, the current state-of-the-art chelator for Ra 2+ . A moderate log K ML value of 8.12 was measured for [Ra(macropa-XL)], which is approximately 1.5 log K units lower than the stability constant of [Ra(macropa)]. This relative decrease in Ra 2+ complex stability for macropa-XL versus macropa was further probed using density functional theory calculations. Additionally, macropa-XL was radiolabeled with 223 Ra, and the kinetic stability of the resulting complex was evaluated in human serum. Furthermore, although macropa-XL could effectively bind 223 Ra under mild conditions, the complex appeared to be unstable to transchelation. Collectively, this study sheds additional light on the chelation chemistry of the exotic Ra 2+ ion and contributes to the small, but growing, number of chelator development efforts for 223 Ra-based TAT.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

HD-Bind: Encoding of Molecular Structure with Low Precision, Hyperdimensional Binary Representations

Publicly available collections of drug-like molecules have grown to comprise tens of billions of compounds due to advances in combinatorial chemistry. Traditional methods for identifying "hit" molecules from a large collection of potential drug-like candidates have relied on biophysical theory to compute approximations to the Gibbs free energy of the binding interaction between the drug and its protein target. These approaches have the major drawback that they require exceptional computing capabilities for even relatively small collections of molecules. Hyperdimensional Computing (HDC) is a recently-proposed learning paradigm that represents data with high-dimension binary vectors; this allows the use of low-precision binary vector arithmetic to create models of the data that can be learned without the need for the gradient-based optimization required in many conventional machine learning and deep learning methods. This algorithmic simplicity allows for acceleration in hardware that has been previously demonstrated in a range of application areas. We consider existing HDC approaches for molecular property classification and introduce two novel encodings of a commonly-used molecular representation, the extended connectivity fingerprint (ECFP). We show that HDC-based inference methods are as much as 91 times more efficient than traditional machine learning methods, and achieve an acceleration of nearly nine orders of magnitude compared to molecular docking. Our results show that HDC accelerated methods retain competitive accuracy on a number of well-studied tasks such as molecular property predictions using the MoleculeNet dataset, and bind/no-bind activity classification using the DUD-E and LIT-PCBA datasets. Our work thus motivates further investigation into molecular representation learning to develop ultraefficient pre-screening tools.

Jones, William↗

NASA NTRS1975

A finite element analysis of the exact nonlinear formulation of a lifting surface in steady incompressible flow, with the evaluation of the correct wake geometry

The problem of steady incompressible flow for lifting surfaces is considered. An integral equation is solved relating the values of the potential discontinuity on the lifting surface and its wake to the values of the normal derivative of the potential which are known from the boundary conditions. The lifting surface and the wake are divided into small quadrilateral surface elements. The values of the potential discontinuity and the normal derivative of the potential are assumed to be constant within each lifting surface element and equal to their values at the centroids of the lifting surface elements. This yields a set of linear algebraic equations. An iteration procedure is used to obtain the wake geometry: the velocities at the corner points of the wake elements are calculated and the wake streamlines are aligned to be parallel to the velocity vector. The procedure is repeated until convergence is attained.

Suciu, E. O.↗