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At least 703 records · Page 39

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NASA NTRS2017

Physical Properties of the Stone Meteorites: Implications for the Properties of Their Parent Bodies

The physical properties of the stone meteorites provide important clues to understanding the formation and physical evolution of material in the Solar protoplanetary disk as well providing indications of the properties of their asteroidal parent bodies. Knowledge of these properties is essential for modeling a number of Solar System processes, such as bolides in planetary atmospheres, the thermal inertia of atmosphereless solid body surfaces, and the internal physical and thermal evolution of asteroids and rock-rich icy bodies. In addition, insight into the physical properties of the asteroids is important for the design of robotic and crewed reconnaissance, lander, and sample return spacecraft missions to the asteroids. One key property is meteorite porosity, which ranges from 0% to more than 40%, similar to the range of porosities seen in asteroids. Porosity affects many of the other physical properties including thermal conductivity, speed of sound, deformation under stress, strength, and response to impact. As a result of the porosity, the properties of most stone meteorites differ significantly from those of compact terrestrial rocks, whose physical properties have been used in many models of asteroid behavior. A few physical properties, such as grain density, magnetic susceptibility, and heat capacity are not functions of porosity. Taken together, the grain density and the magnetic susceptibility can be used to classify unweathered or minimally weathered ordinary chondrites. This provides a rapid screening technique to identify heterogeneous samples, classify new samples, and identify misclassified meteorites or interlopers in strewn fields.

Chondritic meteorites↗

NASA NTRS1995

Telescience operations with the solar array module plasma interaction experiment

The Solar Array Module Plasma Interactions Experiment (SAMPIE) is a flight experiment that flew on the Space Shuttle Columbia (STS-62) in March 1994, as part of the OAST-2 mission. The overall objective of SAMPIE was to determine the adverse environmental interactions within the space plasma of low earth orbit (LEO) on modern solar cells and space power system materials which are artificially biased to high positive and negative direct current (DC) voltages. The two environmental interactions of interest included high voltage arcing from the samples to the space plasma and parasitic current losses. High voltage arcing can cause physical damage to power system materials and shorten expected hardware life. parasitic current losses can reduce power system efficiency because electric currents generated in a power system drain into the surrounding plasma via parasitic resistance. The flight electronics included two programmable high voltage DC power supplies to bias the experiment samples, instruments to measure the surrounding plasma environment in the STS cargo bay, and the on-board data acquisition system (DAS). The DAS provided in-flight experiment control, data storage, and communications through the Goddard Space Flight Center (GSFC) Hitchhiker flight avionics to the GSFC Payload Operations Control Center (POCC). The DAS and the SAMPIE POCC computer systems were designed for telescience operations; this paper will focus on the experiences of the SAMPIE team regarding telescience development and operations from the GSFC POCC during STS-62. The SAMPIE conceptual development, hardware design, and system verification testing were accomplished at the NASA Lewis Research Center (LeRC). SAMPIE was developed under the In-Space Technology Experiment Program (IN-STEP), which sponsors NASA, industry, and university flight experiments designed to enable and enhance space flight technology. The IN-STEP Program is sponsored by the Office of Space Access and Technology (OSAT).

Wald, Lawrence W.↗

DOE OSTI2023

Physical and technical basis of Materials Plasma Exposure eXperiment from modeling and Proto-MPEX results *

Abstract The Materials Plasma Exposure eXperiment (MPEX) is a steady-state linear plasma device that will address plasma-material interaction (PMI) science and enable testing of fusion reactor-relevant divertor plasma-facing materials. The MPEX source concept consists of a helicon plasma source to generate the plasma, electron cyclotron heating (ECH) for electron heating, and ion cyclotron heating (ICH) for ion heating. The MPEX source plasma is then transported axially to the PMI material target region to test material samples in fusion reactor-relevant divertor conditions. This paper will summarize the physical and technical basis of MPEX. The paper will first define the MPEX parameters and scenarios at the target relevant to PMI science for various fusion reactor-relevant divertor conditions and show plasma transport modeling results to set the MPEX source parameters. Recent experimental and modeling results from Proto-MPEX, a short-pulse experiment to develop the plasma production, heating, and transport physics for MPEX, will be shown. From these results, it will be shown that MPEX can reach its desired scenarios. The MPEX physical and technical basis will also determine important functional requirements for magnetic field, radiofrequency (RF) power, RF frequency, and neutral pressure in the helicon, ECH, ICH, and PMI regions that are required to achieve the desired MPEX scenarios. The necessity for key in-vessel components such as skimmers, limiters, and microwave absorbers will also be highlighted.

Lau, C. (ORCID:0000000285765867)↗

NASA NTRS1988

Sampling strategies on Mars: Remote and not-so-remote observations from a surface rover

The mobility and speed of a semi-autonomous Mars rover are of necessity limited by the need to think and stay out of trouble. This consideration makes it essential that the rover's travels be carefully directed to likely targets of interest for sampling and in situ study. Short range remote sensing conducted from the rover, based on existing technology, can provide significant information about the chemistry and mineralogy of surrounding rocks and soils in support of sampling efforts. These observations are of course of direct scientific importance as well. Because of the small number of samples actually to be returned to Earth, it is also important that candidate samples be analyzed aboard the rover so that diversity can be maximized. It is essential to perform certain types of analyses, such as those involving volatiles, prior to the thermal and physical shocks of the return trip to Earth. In addition, whatever measurements can be made of nonreturned samples will be important to enlarge the context of the detailed analyses to be performed later on the few returned samples. Some considerations related to these objectives are discussed.

Singer, R. B.↗

DOE OSTI2024

Stochastic economic dispatch of wind power under uncertainty using clustering-based extreme scenarios

Operation of power systems with high penetrations of renewable energy sources requires tools for robust decision making under uncertainty. Stochastic economic dispatch and stochastic unit commitment are effective techniques for planning and operation under uncertainty, whose effectiveness depends on the cardinality and quality of the scenario set. Here, this article proposes a machine learning method using -means clustering for capturing relevant physical information from a large population of analog scenarios. Extreme scenario samples drawn from the clusters are used in a two-stage stochastic economic dispatch computation. The effectiveness of the proposed approach is assessed on a synthetic 200-bus system with a geographic footprint over Illinois, USA for four months from each season of WIND Toolkit data. The combination of -means clustering with importance sampling is shown to reduce the total operational cost by over 43% compared to sampling from populations based on heuristic clustering-based methods. Additionally, the variability in the mean cost is about 56% lower than the variability using Monte Carlo sampling. Moreover, the operational cost with the presented approach is shown to be close to the cost calculated based on a hindsight exact wind profile, signifying a highly accurate quantification of wind uncertainty by the presented -means clustering based sampling method.

17 WIND ENERGY↗

DOE OSTI2020

A small-volume, high-throughput approach for surface tension and viscosity measurements of liquid fuels

An ongoing effort looking at bioblendstocks to function as drop-in replacements or blending components for gasoline has identified a large number of candidate fuels. This work documents an approach for rapid screening of candidate fuels using relatively small sample sizes ($\mathcal{O}(\mu\,{\rm l})$) that targets two key physical properties of liquid fuels—surface tension and viscosity. The approach utilizes shape oscillation dynamics of single droplets generated by a piezo-electric device and their decay over time. Strobed imaging of the oscillation process is used along with image processing, edge detection, and data analysis to capture the decay of the oscillation over time. The time constant of the decay process along with oscillation frequency are then used to estimate viscosity and surface tension using a theory for small amplitude droplet oscillations. Measurements are obtained for primary reference fuels (isooctane and n-heptane) as well as candidate fuels from four bio-derived functional groups of interest. Measurement results for surface tension and viscosity are correlated with literature data as well as measurements from standard reference instruments. The measurement results show that the droplet oscillation based approach is capable of reproducing surface tension and viscosity values for the tested fuels within deviations of 7% and 13% respectively from literature data. Results are obtained using an average of 5 µl per fuel within about 20 s, thus demonstrating a small-volume, high-throughput approach that can be used for screening of candidate bioblendstock fuels.

09 BIOMASS FUELS↗

DOE OSTI2022

Towards automating structural discovery in scanning transmission electron microscopy *

Abstract Scanning transmission electron microscopy is now the primary tool for exploring functional materials on the atomic level. Often, features of interest are highly localized in specific regions in the material, such as ferroelectric domain walls, extended defects, or second phase inclusions. Selecting regions to image for structural and chemical discovery via atomically resolved imaging has traditionally proceeded via human operators making semi-informed judgements on sampling locations and parameters. Recent efforts at automation for structural and physical discovery have pointed towards the use of ‘active learning’ methods that utilize Bayesian optimization with surrogate models to quickly find relevant regions of interest. Yet despite the potential importance of this direction, there is a general lack of certainty in selecting relevant control algorithms and how to balance a priori knowledge of the material system with knowledge derived during experimentation. Here we address this gap by developing the automated experiment workflows with several combinations to both illustrate the effects of these choices and demonstrate the tradeoffs associated with each in terms of accuracy, robustness, and susceptibility to hyperparameters for structural discovery. We discuss possible methods to build descriptors using the raw image data and deep learning based semantic segmentation, as well as the implementation of variational autoencoder based representation. Furthermore, each workflow is applied to a range of feature sizes including NiO pillars within a La:SrMnO 3 matrix, ferroelectric domains in BiFeO 3 , and topological defects in graphene. The code developed in this manuscript is open sourced and will be released at github.com/nccreang/AE_Workflows .

47 OTHER INSTRUMENTATION↗

DOE OSTI2025

External Magnetic Field Suppression of Carbon Diffusion in Iron

External magnetic fields reduce diffusion of carbon in BCC iron, but the physical mechanism is not understood. Using DFT calculations with magnetic moments sampled from a Heisenberg model, we calculate diffusivities of carbon in iron at high temperatures and with field. Our model reproduces the measured suppression of diffusivity from field. Here, we find that increasing magnetic disorder flattens the electron density of states compared with the ferromagnetic case, which distorts the octahedral cages around carbon, lowering the activation barrier to diffusion; an applied field reverses these trends.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Monte Carlo Event Generation with Continuous Normalizing Flows

We apply continuous normalizing flows trained with the flow matching method to the problem of phase-space sampling in Monte Carlo event generation for high-energy collider physics. Focusing on lepton-pair and top-quark pair production with multiple jets, the two computationally most expensive processes at the Large Hadron Collider, we train helicity-conditioned continuous normalizing flows to remap the random numbers used in matrix element evaluation. Compared to standard methods, we achieve unweighting efficiency improvements by factors of up to 184 and 25 for the two processes at their respective highest jet number, at the cost of an increased evaluation time. When combining the advantages of continuous normalizing flows with the fast evaluation times of coupling-layer-based flows, using the RegFlow approach, we find parton-level unweighted event generation walltime gains of about a factor of 10 at the highest jet numbers. These substantial gains highlight the promise of samplers based on machine learning for next-generation collider experiments.

Bothmann, Enrico [CERN; Gottingen U.] (ORCID:00000↗

DOE OSTI2025

Missing baryons recovered: A measurement of the gas fraction in galaxies and groups with the kinematic Sunyaev-Zel’dovich effect and CMB lensing

We present new constraints on the halo masses and matter density profiles of DESI galaxy groups by cross-correlating samples of luminous red galaxies (LRGs) and bright galaxy survey (BGS) galaxies with the publicly available CMB lensing convergence map from ACT DR6. This provides an independent, lensing-based calibration of halo masses, complementary to methods relying on clustering or dynamics. We derive constraints on the mean halo mass for three DESI-selected samples, finding log⁡(𝑀 halo /(𝑀 ⊙ /ℎ)) ≈ 13.18, 13.03 and 13.02 for the main LRG, extended LRG, and BGS samples, respectively. Using a halo model approach, we also compare the projected galaxy-matter density profiles with previously reported gas profiles inferred from measurements of the kinematic Sunyaev-Zel’dovich (kSZ) effect. This work addresses one of the key uncertainties in interpreting kSZ signals—the unknown host halo mass distribution—by providing an independent and consistent mass calibration. The agreement between the gas and total mass profiles at large aperture suggests that sufficiently far from the group center (2–3 virial radii), we recover all the baryons, offering a resolution to the missing baryon problem. We further study the cumulative gas fractions for all galaxies as well as for the most massive galaxy groups in the sample [log⁡(𝑀 halo /(𝑀 ⊙ /ℎ)) ≈ 13.5], finding values that are physically sensible and in agreement with previous findings using kSZ and x-ray data: compared to the TNG300 simulation, the observed gas fractions are systematically lower at fixed radius by ≳ 4⁢𝜎, providing compelling, independent evidence for stronger baryonic feedback in the real Universe. These findings highlight the power of combining CMB lensing with galaxy surveys to probe the interplay between baryons and dark matter in group-sized halos.

Hadzhiyska, Boryana [Lawrence Berkeley National La↗

DOE OSTI2026

Toward an AI-Powered Software Pipeline for Real-Time Tracking and Analysis of Wildfire and Smoke

Real-time tracking of wildfires and smoke is crucial for effective response, minimizing damage, protecting lives, and efficiently managing resources during fire emergencies. We develop a web-based AI-powered pipeline that detects wildfires in aerial video and estimates deployment-relevant behavior metrics, including cumulative burned area, burned-area growth rate, fire spread direction, and smoke dispersion. The system combines a YOLO-based detector with YCbCr-based fire segmentation, HSV-based smoke segmentation, Farneback optical flow, and centroid-based spatiotemporal tracking. Using ground sampling distance (GSD), pixel-level fire masks are converted to physical burned-area measurements by correlating fire pixel counts with camera altitude and tilt angle. We benchmark YOLO variants and non-YOLO baselines (GoogLeNet, CNN, DBN, Autoencoder, U-Net, and AlexNet) on the IEEE FLAME dataset and a newly created aerial frame dataset, Wildfire-DB. Cross-dataset evaluation uses a strict threshold-transfer protocol: decision thresholds are selected on FLAME validation and transferred unchanged to Wildfire-DB to quantify generalization under domain shift. YOLOv6 achieves the strongest cross-dataset frame-level fire detection on Wildfire-DB (ROC-AUC 0.8200, PR-AUC 0.8044, and transferred-threshold F1 0.7596). For tracking-oriented deployment requiring oriented localization, YOLO11-OBB provides the most reliable cross-dataset behavior among OBB-capable models while remaining computationally feasible. To analyze the feasibility of UAV deployment, we further measure inference efficiency using synchronized GPU and CPU power logs on a fixed workload of 1569 frames. YOLO-family models process the video in 5.73–12.47 seconds with net energy of 1247.28–1775.39 J, substantially lower latency and energy than heavier classification and reconstruction baselines. Overall, model optimality depends on operational objectives: YOLOv6 is best for cross-dataset detection robustness, whereas YOL...

Color segmentation↗

DOE OSTI2025

Overview of leakage scenarios in supervised machine learning

Machine learning (ML) provides powerful tools for predictive modeling. ML’s popularity stems from the promise of sample-level prediction with applications across a variety of fields from physics and marketing to healthcare. However, if not properly implemented and evaluated, ML pipelines may contain leakage typically resulting in overoptimistic performance estimates and failure to generalize to new data. This can have severe negative financial and societal implications. Our aim is to expand understanding associated with causes leading to leakage when designing, implementing, and evaluating ML pipelines. Illustrated by concrete examples, we provide a comprehensive overview and discussion of various types of leakage that may arise in ML pipelines.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1972

Development of chip passivated monolithic complementary MISFET circuits with beam leads

The results are presented of a program to demonstrate the processes for fabricating complementary MISFET beam-leaded circuits, which, potentially, are comparable in quality to available bipolar beam-lead chips that use silicon nitride passivation in conjunction with a platinum-titanium-gold metal system. Materials and techniques, different from the bipolar case, were used in order to be more compatible with the special requirements of fully passivated complementary MISFET devices. Two types of circuits were designed and fabricated, a D-flip-flop and a three-input NOR/NAND gate. Fifty beam-leaded chips of each type were constructed. A quality and reliability assurance program was performed to identify failure mechanisms. Sample tests and inspections (including destructive) were developed to measure the physical characteristics of the circuits.

Ragonese, L. J.↗

NASA NTRS1972

Trace elements profiles, notably Hg, from a preliminary study of the Apollo 15 deep-drill core.

The possible thermal gradient near the surface during a lunation is considered together with the heat flow from the interior, the physical process of Hg migration, the results from core and trench samples from previous missions, and other temperature sensitive phenomena that may help understand the processes. U, Os, and Ru concentrations in the deep drill core samples are of potential interest and are summarized in a table. The Os tends to parallel the Hg profile with depth.

Jovanovic, S.↗

NASA NTRS1976

Synthesis and analysis of jet fuels from shale oil and coal syncrudes

The technical problems involved in converting a significant portion of a barrel of either a shale oil or coal syncrude into a suitable aviation turbine fuel were studied. TOSCO shale oil, H-Coal and COED coal syncrudes were the starting materials. They were processed by distillation and hydrocracking to produce two levels of yield (20 and 40 weight percent) of material having a distillation range of approximately 422 to 561 K (300 F to 550 F). The full distillation range 311 to 616 K (100 F to 650 F) materials were hydrotreated to meet two sets of specifications (20 and 40 volume percent aromatics, 13.5 and 12.75 weight percent H, 0.2 and 0.5 weight percent S, and 0.1 and 0.2 weight percent N). The hydrotreated materials were distilled to meet given end point and volatility requirements. The syntheses were carried out in laboratory and pilot plant equipment scaled to produce thirty-two 0.0757 cu m (2-gal)samples of jet fuel of varying defined specifications. Detailed analyses for physical and chemical properties were made on the crude starting materials and on the products.

Antoine, A. C.↗

NASA NTRS1976

Development of fire resistant, nontoxic aircraft interior materials

All available newly developed nonmetallic polymers were examined for possible usage in developing fire resistant, nontoxic nonmetallic parts or assemblies for aircraft interiors. Specifically, feasibility for the development of clear films for new decorative laminates, compression moldings, injection molded parts, thermoformed plastic parts, and flexible foams were given primary considerations. Preliminary data on the flame resistant characteristics of the materials were obtained. Preliminary toxicity data were generated from samples of materials submitted from the contractor. Preliminary data on the physical characteristics of various thermoplastic materials to be considered for either compression molded, injection molded, or thermoformed parts were obtained.

Haley, G.↗

NASA NTRS1984

Weathered stony meteorites from Victoria Land, Antarctica, as possible guides to rock weathering on Mars

Parallel studies of Martian geomorphic features and their analogs on Earth continue to be fruitful in deciphering the geologic history of Mars. In the context of rock weathering, the Earth-analog approach is admirably served by the study of meteorites recovered from ice sheets in Antarctica. The weathering environment of Victoria Land possesses several Mars-like attributes. Four of the five Antarctic meteorites being studied contain rust and EETA79005 further possesses a conspicuous, dark, weathering rind on one side. Secondary minerals (rust and salts) occur both on the surfaces and interiors of some of the samples and textural evidence indicates that such secondary mineralization contributed to physical weathering (by salt riving) of the rocks. Several different rust morphologies occur and emphasis is being placed on identifying the phase compositions of the various rust occurrances. A thorough understanding of terrestrial weathering features of the meteorites is a prerequisite for identifying possible Martian weathering features (if such features exist) that might be postulated to occur in some meteorites.

Gooding, J. L.↗

NASA NTRS1990

Containerless processing projects

The primary research objective was the examination of fundamental acoustics of the Drop Physics Module. An advanced chamber system was developed for acoustics and sample characterization. Sample interaction with an acoustic field was studied. The shaping of liquid drops by acoustic forces was also examined. The acoustic torque as a function of sample position and shape was studied along with levitated sample stability and the effects of feedback systems.

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