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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Structure of Self-Generated Magnetic Fields in Laser-Solid Interaction from Proton Tomography

Self-generated magnetic fields in laser-solid interactions are experimentally characterized to reveal the 3D location and local field strength, rather than path-integrated quantities, using multi-view proton radiography and tomographic inversion. We infer magnetic fields that extend several millimeters off the target into the hot, rarefied corona, sufficient to strongly magnetize the plasma (Ω e τ e ≫ 1). The data are compared to MHD simulations incorporating recent improvements in modeling magnetic field generation and transport; the volume-averaged coronal field strength and magnetic flux agree to within 25% using a model with magnetic re-localization of transport, although the near-target morphology is not reproduced. This work demonstrates tomographic proton radiography as a valuable tool for investigating magnetic fields in laser-produced plasmas.

High-energy-density plasmas↗
NASA NTRS1990

Mid- and low-latitude model of thermospheric emissions. I - O(+) (2P) 7320 A and N2 (2P) 3371 A

This paper presents an interhemispherically coupled model of airglow emissions of the O(+) (2P) metastable state (7320 A) and the 0-0 band of the N2 second positive system (3371 A), covering mid- and low-latitudes. The model produces volume emission rates as a function of altitude, latitude, longitude, and local time for any selected data and solar and magnetic conditions, and, thus, can be used to provide three-dimensional maps of the selected emission for comparison with data obtained from orbiting vehicles. The semiglobal graphical maps of the 7320 A and 3371 A emissions illustrate the seasonal, diurnal, and solar cyclic variations on a scale that covers mid- and low-latitudes, as well as the effects of interhemispheric coupling (conjugate photoelectrons). It is shown that, in both cases, conjugate photoelectrons result in excitation at mid- to high- nighttime-latitudes in the winter hemisphere.

Torr, Marsha R.↗
NASA NTRS1991

Scattering behavior of Lunar Lake playa determined from parabola bidirectional reflectance data

Bidirectional reflectance data obtained with the Portable Apparatus for Rapid Acquisition of Bidirectional Observations of Land and Atmosphere instrument at Lunar Lake, Nevada, were analyzed to determine the scattering properties of playas. The data are approximated by a Lambertian function, except at high phase angles in the solar principal plane, where Fresnel reflectance appears to dominate. The data also depart slightly (15 percent) from a Lambertian function at high emission angles in all azimuthal planes. No published photometric model accounts for the observed data. It is hypothesized that the observed scattering behavior is the superposition of volume and surface (Fresnel) scattering mechanisms.

Shepard, Michael K.↗
NASA NTRS1992

Application of theoretical models to active and passive remote sensing of saline ice

The random medium model is used to interpret the polarimetric active and passive measurements of saline ice. The ice layer is described as a host ice medium embedded with randomly distributed inhomogeneities, and the underlying sea water is considered as a homogeneous half-space. The scatterers in the ice layer are modeled with an ellipsoidal correlation function. The orientation of the scatterers is vertically aligned and azimuthally random. The strong permittivity fluctuation theory is employed to calculate the effective permittivity and the distorted Born approximation is used to obtain the polarimetric scattering coefficients. We also calculate the thermal emissions based on the reciprocity and energy conservation principles. The effects of the random roughness at the air-ice, and ice-water interfaces are accounted for by adding the surface scattering to the volume scattering return incoherently. The above theoretical model, which has been successfully applied to analyze the radar backscatter data of the first-year sea ice near Point Barrow, AK, is used to interpret the measurements performed in the CRRELEX program.

Han, H. C.↗
NASA NTRS1993

Classification of simple vegetation types using POLSAR image data

Mapping basic vegetation or land cover types is a fairly common problem in remote sensing. Knowledge of the land cover type is a key input to algorithms which estimate geophysical parameters, such as soil moisture, surface roughness, leaf area index or biomass from remotely sensed data. In an earlier paper, an algorithm for fitting a simple three-component scattering model to POLSAR data was presented. The algorithm yielded estimates for surface scatter, double-bounce scatter and volume scatter for each pixel in a POLSAR image data set. In this paper, we show how the relative levels of each of the three components can be used as inputs to simple classifier for vegetation type. Vegetation classes include no vegetation cover (e.g. bare soil or desert), low vegetation cover (e.g. grassland), moderate vegetation cover (e.g. fully developed crops), forest and urban areas. Implementation of the approach requires estimates for the three components from all three frequencies available using the NASA/JPL AIRSAR, i.e. C-, L- and P-bands. The research described in this paper was carried out by the Jet Propulsion Laboratory, California Institute of Technology under a contract with the National Aeronautics and Space Administration.

Freeman, A.↗
NASA NTRS2003

Global Assessment of Volcanic Debris Hazards from Space

Hazard (slope stability) assessment for different sectors of volcano edifices was successfully obtained from volcanoes in North and South America. The assessment entailed Hyperion images to locate portions of the volcano that were hydrothermally altered to clay rich rocks with zones that were also rich in alunite and other minerals. The identified altered rock zones were field checked and sampled. The rock strength of these zones was calculated from the field and laboratory measurements. Volcano modeling utilizing the distinct element method and limit equilibrium technique, with the calculated strength data was used to assess stability and deformation of the edifice. Modeling results give indications of possible failure volumes, velocities and direction. The models show the crucial role hydrothermally weak rock plays in reducing the strength o the volcano edifice and the rapid identification of weak rock through remote sensing techniques. Volcanoes were assessed in the Cascade Range (USA), Mexico, and Chile (ongoing).

Watters, Robert J.↗
NASA NTRS2012

Modeling of Melt Growth During Carbothermal Processing of Lunar Regolith

The carbothermal processing of lunar regolith has been proposed as a means to produce carbon monoxide and ultimately oxygen to support human exploration of the moon. In this process, gaseous methane is pyrolyzed as it flows over the hot surface of a molten zone of lunar regolith and is converted to carbon and hydrogen. Carbon gets deposited on the surface of the melt, and mixes and reacts with the metal oxides in it to produce carbon monoxide that bubbles out of the melt. Carbon monoxide is further processed in other reactors downstream to ultimately produce oxygen. The amount of oxygen produced crucially depends on the amount of regolith that is molten. In this paper we develop a model of the heat transfer in carbothermal processing. Regolith in a suitable container is heated by a heat flux at its surface such as by continuously shining a beam of solar energy or a laser on it. The regolith on the surface absorbs the energy and its temperature rises until it attains the melting point. The energy from the heat flux is then used for the latent heat necessary to change phase from solid to liquid, after which the temperature continues to rise. Thus a small melt pool appears under the heated zone shortly after the heat flux is turned on. As time progresses, the pool absorbs more heat and supplies the energy required to melt more of the regolith, and the size of the molten zone increases. Ultimately, a steady-state is achieved when the heat flux absorbed by the melt is balanced by radiative losses from the surface. In this paper, we model the melting and the growth of the melt zone with time in a bed of regolith when a portion of its surface is subjected to a constant heat flux. The heat flux is assumed to impinge on a circular area. Our model is based on an axisymmetric three-dimensional variation of the temperature field in the domain. Heat transfer occurs only by conduction, and effects of convective heat transport are assumed negligible. Radiative heat loss from the surface of the melt and the regolith to the surroundings is permitted. We perform numerical computations to determine the shape and the mass of the melt at steady state and its time evolution. We first neglect the volume change upon melting, and subsequently perform calculations including it. Predictions from our model are compared to test data to determine the effective thermal conductivities of the regolith and the melt that are compatible with the data

Balasubramaniam, R.↗
NASA NTRS2014

USM3D Predictions of Supersonic Nozzle Flow

This study focused on the NASA Tetrahedral Unstructured Software System CFD code (USM3D) capability to predict supersonic plume flow. Previous studies, published in 2004 and 2009, investigated USM3D's results versus historical experimental data. This current study continued that comparison however focusing on the use of the volume souring to capture the shear layers and internal shock structure of the plume. This study was conducted using two benchmark axisymmetric supersonic jet experimental data sets. The study showed that with the use of volume sourcing, USM3D was able to capture and model a jet plume's shear layer and internal shock structure.

Carter, Melissa B.↗
NASA NTRS1993

Classification of Simple Vegetation Types Using POLSAR Image Data

Mapping basic vegetation or land cover types is a fairly common problem in remote sensing. Knowledge of the land cover type is a key input to algorithms which estimate geophysical parameters, such as soil moisture, surface roughness, leaf area index or biomass from remotely sensed data. In an earlier paper, an algorithm for fitting a simple three-component scattering model to POLSAR data was presented. The algorithm yielded estimates for surface scatter, double-bounce scatter and volume scatter for each pixel in a POLSAR image data set. In this paper, we show how the relative levels of each of the three components can be used as inputs to a simple classifier for vegetation type.

Freeman, A.↗
NASA NTRSDate not supplied

Linear Regression Model for Predictive Service Provider Selection

The increasing number of satellites in orbit has led to a growing reliance on third-party service providers for data transfer between Earth and space. Traditional approaches to managing satellite communications require human intervention, which becomes more burdensome with the escalating number of satellites. This research addresses the need for an efficient and automated system to optimize service provider selection for NASA space communication. Previous research has utilized human-operated approaches for service provider management. Our study fills a gap by developing a cognitive algorithm that automates and optimizes the selection process based on various parameters, such as data volume, priority, quality of service and cost. This novel solution reduces user burden, facilitates service management, and contributes to the development of cognitive spaceflight missions, ultimately supporting NASA’s research into Cognitive Communications technology. The algorithm design consists of three major steps: modeling data, developing a Link Selection Algorithm (LSA) based on a grading system, and applying machine learning using linear regression. The LSA evaluates providers based on user-defined constraints, considering factors such as delivery time, cost, and quality of service. We define a suitability metric which allows our algorithm to make a recommendation to a user regarding which commercial service providers to select. The addition of Linear Regression predicts the future suitability value. Our main findings demonstrate that the resulting algorithm can autonomously manage connections between satellites and providers, maximizing communication channel efficiency. This research has significant implications, as it not only addresses a pressing issue in satellite communication management but also advances the field of cognitive spaceflight missions.

Linear regression↗
NASA NTRSDate not supplied

AERACEPT (AErosol Rapid Analysis Combined Entry Probe/sonde Technology) for the Nephele Venus Cloud Mission Concept

Nephele is a small atmospheric probe mission concept to determine whether the Venus clouds contain organic matter. Nephele complements larger missions that seek to conduct Venus atmospheric analyses, such as DAVINCI, Venera-D, and the Morning Star missions, by directly sampling and analyzing cloud and haze particles. AERACEPT is an aerosol sampling technology designed to minimize the mass, volume, and complexity required for in situ planetary atmosphere characterization. A single aeroshell functions as a combined entry vehicle, descent probe, and instrumented sonde, combining recent advances in thermal protection materials with heritage technology from planetary and airborne science. The velocity of descent drives aerosol through inlets embedded in the aeroshell’s thermal protection material; inertial separation isolates the particles from the gas stream; and the particle capture surface doubles as the analysis substrate. This method can work without heat shield separation, descent control, or active sample handling; these advantages trade against a smaller sample volume, higher risk of sample heating, and shorter time for analysis and data return. AERACEPT further benefits from modeling of the internal and external flow to understand the particle capture efficiency and bias for size and concentration distributions. AERACEPT is well suited for a Venus mission such as Nephele, where a passive descent trajectory is both subsonic over the altitudes of interest and provides sufficient velocity and pressure differential for the particle sizes of interest. A thermal and flow toolchain developed in-house (Traj→ DPLR→ PATO→ openFOAM) was used to model an 80-cm aeroshell with science operations 63–39 km. These models indicate that AERACEPT will provide ~24 µL accumulated sample of particles ≥ 0.2 µm, substantially above the 2 µL threshold requirement. The worst-case particle heating will be ~14 K above ambient, for the smallest particles at the lowest altitude; most particles will experience ≤ 1 K rise, at which almost all analytes of interest will be unaffected. Plasma jet testing of a small-scale aeroshell-material inlet was recently conducted to validate the thermal model parameters; wind tunnel testing to validate the flow model parameters is underway.

AERACEPT↗
DOE OSTI2026

Dominant Controls on Preferential Flow and Their Implications for Future Soil Water Fluxes

Abstract Soil water flow, particularly preferential flow (PF), is a critical control on hydrological and biogeochemical processes, including groundwater recharge, contaminant transport, and carbon cycling. However, it remains challenging to predict PF occurrence across large environmental gradients. Here, we developed a deep learning (DL) model to estimate event‐scale soil water flow velocity and the probability of PF occurrence using high‐frequency soil moisture and precipitation data from 33 sites across the National Ecological Observatory Network. The model demonstrated high skill in predicting the binary occurrence of PF (91% F1‐score; 85% accuracy) but the performance was limited in predicting soil water velocity ( R 2 = 0.31). We found that precipitation characteristics (duration, volume, and intensity) were the most important predictors for soil water velocity. Among the non‐precipitation event variables, sand content showed relatively high predictive skill, though differences among non‐event climate variables were generally modest. Lower sand content was associated with increased predicted soil water velocity, a finding that highlights the role of soil structure in producing more non‐uniform flow, which contrasts with traditional uniform flow models. Projecting a reduced DL model under both moderate and high‐emissions future climate scenarios (2060–2099 Representative Concentration Pathways 4.5 and 8.5), we found ∼7.3% increase under RCP4.5 and ∼15% under RCP8.5 of soil water velocities compared to the historical simulation, while modeled likelihood of PF changed little. These findings suggest climate change is not making PF more frequent, but it is making existing PF pathways more efficient with important consequences for associated nutrient and contaminant transport under climate change. Plain Language Summary Water movement in soil is critical for water quality. While often modeled as a uniform flow process, in reality water moves rapidly through cracks and burrows in what is called “preferential flow” (PF), which limits natural filtration and can transport pollutants. We developed a deep learning model, trained on data from 33 U.S. sites, to predict when and how fast this PF occurs based on precipitation, soil, and climate data. The model showed that precipitation characteristics (duration, intensity, volume) were the most important predictors of PF. Lower soil sand content/higher clay content was associated with faster water flow, likely due to clay soils forming aggregates and cracks that water moves through rather than infiltrating uniformly. Further analyses based on climate projections suggest that the speed at which PF occurs will become more rapid under future climate scenarios compared to historical simulation. This highlights the need to represent PF in soil water models when assessing future water quality. Key Points The effect of precipitation peak intensity on soil water velocities declined with increasing precipitation intensity Antecedent soil moisture failed to predict preferential flow (PF), contrasting the high predictive power of sand content Climate predictions suggest that soil water velocities through PF paths will increase ∼15% by 2099

Li, Bonan↗
NASA NTRS2021

Evaluation, Analysis, and Application of Internal Strain-Gage Balance Data

Experimental processes, analytical methods, and numerical algorithms are described that may be used to predict the forces and moments of an internal strain-gage balance during a wind tunnel test. First, the control volume model of a strain-gage balance and the concepts of load state, load space, and output space are introduced. These important abstractions provide a better understanding of fundamental characteristics of different balance load prediction approaches. Then, the description of strain-gage balance data and the definition of the primary gage sensitivity is discussed. Afterwards, basic elements of the calibration of a typical six-component balance are reviewed. Two fundamentally different balance load prediction methods, the processing of check loads, and related topics are also discussed. Three real-world balance data examples are reviewed in great detail to illustrate typical analysis results for a variety of strain-gage balance designs. Finally, important observations are summarized and recommendations are provided. Additional information and detailed mathematical derivations can be found in the appendices of the document. They include the following topics: balance terminology, definitions of important statistical metrics, balance axis system conventions, balance load transformations, the combined load diagram, electrical output format options, bi-directional gage output characteristics, determination of the natural zeros, derivation of two balance load prediction methods, description of two tare load iteration algorithms, modeling of balance temperature effects, basics of three-component moment balances, definition of the percent contribution, detection of linear and near-linear dependencies in balance calibration data, a regression model term selection algorithm, and other related topics.

wind tunnel test↗
DOE OSTI2025

The Artificial Scientist: in-Transit Machine Learning of Plasma Simulations

Large-scale simulations or scientific experiments produce petabytes of data per run. This poses massive challenges for I/O and storage when scientific analysis workflows are run manually offline. Unsupervised deep learning-based techniques to extract patterns and non-linear relations from these large amounts of data provide a way to build scientific understanding from raw data, reducing the need for manual pre-selection of analysis steps, but require exascale compute and memory to process the full dataset available. In this paper, we demonstrate a heterogeneous streaming workflow in which plasma simulation data is streamed directly to a Machine Learning (ML) application training a model on the simulation data in-transit, completely circumventing the capacity-constrained filesystem bottleneck. This workflow employs openPMD to provide a high level interface to describe scientific data and also uses ADIOS2, to transfer volumes of data that exceed the capabilities of the filesystem. We employ experience replay to avoid catastrophic forgetting in learning from this non-steady state process in a continual manner and adapt it to improve model convergence while learning in-transit. As a proof-of-concept, we approach the ill-posed inverse problem of predicting particle dynamics from radiation in a particle-incell (PIConGPU) simulation of the Kelvin-Helmholtz instability (KHI). We detail hardware-software co-design challenges as we scale PIConGPU to full Frontier, the Top-1 system as of June 2024 Top500 list.

Kelling, Jeffrey [Helmholtz-Zentrum Dresden Rossen↗
DOE OSTI2025

Simulated effect of defect volume and location on very high cycle fatigue of laser beam powder bed fused AlSi10Mg

This study quantifies the interaction between volumetric defect location and size on the very high cycle fatigue (VHCF) of laser beam powder bed fused (LB-PBF) AlSi10Mg. Crystal plasticity finite element method (CPFEM) simulations were used to investigate the effects of defect location and size on the driving force for crack initiation. The CPFEM model was calibrated against uniaxial and cyclic experimental data of LB-PBF AlSi10Mg. Defect characteristics were informed by experimental data from the specimens produced in various geometries to create realistic representative volume elements (RVEs) with equivalent volume fractions of defects. By embedding defects of varying sizes and locations within the RVEs, fatigue indicator parameters (FIPs) were calculated to analyze the impact of defects’ characteristics on fatigue performance. Different combinations of defect volume and locations were generated for various microstructure instantiations, providing insight into extreme value fatigue responses. Larger defect volumes located on free surfaces consistently generated the highest FIPs, suggesting defect size and boundary proximity intensify stress concentration effects. RVEs with multiple smaller defects produced lower FIPs than those with single large critical defects. These findings underscore the critical role of defect characteristics on fatigue life, providing a foundation for future predictive modeling in fatigue-sensitive AM applications.

AlSi10Mg↗
NASA NTRS1998

First results from the aerosol lidar and backscatter sonde intercomparison campaign STRAIT'1997 at table mountain facility during February-March 1997

First results of an intercomparison measurement campaign between three aerosol lidar instruments and in-situ backscatter sondes performed at Table Mountain Facility (34.4 deg N, 117.7 deg E, 2280 m asl) in February-March 1997 are presented. During the campaign a total of 414 hours of lidar data were acquired by the Aerosol-Temperature-Lidar (ATL, Goddard Space Flight Center) the Mobile-aerosol-Raman-Lidar (MARL, Alfred Wegener Institute), and the TMF-Aerosol-Lidar (TAL, Jet Propulsion Laboratory), and four backscatter sondes were launched. From the data set altitude profiles of backscatter ratio and volume depolarization of stratospheric background aerosols at altitudes between 15 and 25 km and optically thin high-altitude cirrus clouds at altitudes below 13 km are derived. On the basis of a sulfuric acid aerosol model color ratio profiles obtained from two wavelength lidar data are compared to the corresponding profiles derived from the sonde observations. We find an excellent agreement between the in-situ and ATL lidar data with respect to backscatter and color ratio. Cirrus clouds were present on 16 of 26 nights during the campaign. Lidar observations with 17 minute temporal and 120-300 m spatial resolution indicate high spatial and temporal variability of the cirrus layers. Qualitative agreement is found between concurrent lidar measurements of backscatter ratio and volume depolarization.

Beyerle, G.↗
DOE OSTI2021

Comprehensive Physical Activity Assessment During U.S. Army Basic Combat Training

Abstract Alemany, JA, Pierce, JR, Bornstein, DB, Grier, TL, Jones, BH, and Glover, SH. Comprehensive physical activity assessment during U.S. Army Basic Combat Training. J Strength Cond Res 36(12): 3505–3512, 2022—Physical activity (PA) volume, intensity, and qualitative contextual information regarding activity type and loads carried are limited during U.S. Army Basic Combat Training (BCT). The purpose of this study was to characterize daily (05:00–20:00 hours) PA during BCT using a comprehensive approach. During 2 10-week BCT cycles ( n = 40 trainees per cycle), pedometers, accelerometers, and direct observation were used to estimate daily step count, PA volume, and intensity. Physical activity intensity was categorized by metabolic equivalents (METs) such as “sedentary” (1–2 METs), “light” (2–3 METs), “moderate” (3–6 METs), or “vigorous” (≥6 METs). Daily PA data were analyzed longitudinally using linear mixed models, with significance set at p ≤ 0.05. The mean daily step count was 13,459 ± 4,376 steps, and the mean daily accelerometer-assessed PA volume and intensity were as follows: sedentary: 505 ± 98 minutes, light: 190 ± 78 minutes, moderate: 168 ± 51 minutes, and vigorous: 14 ± 14 minutes, with no differences between cycles for all measures ( p > 0.50). Cumulative time on feet (∼50%) and sitting (20–25%) accounted for most daily activity types during both cycles. Trainees, on average, carried between 3 and 9 kg, and ≥9 kg, for 60% and 10% of the monitored day, respectively. Basic Combat Training's physical demands are high, where trainees achieved 1.7 to 2.7 times greater daily ambulation and 6 times the recommended weekly moderate-to-vigorous PA compared with civilian counterparts and performed weight-bearing load carriage for nearly half of the day. Basic Combat Training-associated PA may increase injury risk among trainees unaccustomed to arduous PA and exercise. Implementing national PA policies to improve physical fitness and facilitate acclimatization to BCT's high physical demands could reduce public health burdens and military nonreadiness.

Sport Sciences↗