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At least 991 records · Page 55

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NASA NTRS1981

Jupiter's magnetopause, bow shock, and 10-hour modulated magnetosheath Voyagers 1 and 2

This summary report discusses Jupiter's magnetopause, bow shock and magnetosheath, based on fine scale magnetic field data from the Voyager 1 and 2 encounters. Explicit models of the dawnside magnetopause and bow shock in Jupiter's orbital plane employ an axisymmetric parabola and hyperbola, respectively, and satisfy average boundary crossing positions, inbound and outbound; these models are determined separately for the two encounters. A new phenomenon has been discovered in Jupiter's magnetosheath. It is manifested as (5 or) 10 hour quasi-periodic modulation of the direction of the magnetic field in the outbound magnetosheath, predominantly in the northward (N) and southward (S) directions. It was seen to occur during both encounters and appears most evident in Voyager 2 outbound observations.

Lepping, R. P.↗

NASA NTRS2005

MTRAP: The Magnetic Transition Region Probe

The Magnetic Transition Region Probe is a space telescope designed to measure the magnetic field at several heights and temperatures in the solar atmosphere, providing observations spanning the chromospheric region where the field is expected to become force free. The primary goal is to provide an early warning system (hours to days) for solar energetic particle events that pose a serious hazard to astronauts in deep space and to understand the source regions of these particles. The required magnetic field data consist of simultaneous circular and linear polarization measurements in several spectral lines over the wavelength range from 150 to 855 nm. Because the observations are photon limited an optical telescope with a large (>18sq m) collecting area is required. To keep the heat dissipation problem manageable we have chosen to implement MTRAP with six separate Gregorian telescopes, each with approx. 3 sq m collecting area, that are brought to a common focus. The large field of view (5 x 5 arcmin(sup 2)) and angular resolution (0.025 arcsec pixels) require large detector arrays and, because of the requirements on signal to noise (10(exp 3)), pixels with large full well depths to reduce the readout time and improve the temporal resolution. The optical and engineering considerations that have gone into the development of a concept that meets MTRAP's requirements are described.

Davis, J. M.↗

NASA NTRS2022

L-Band Microwave Satellite Data and Model Simulations Over the Dry Chaco to Estimate Soil Moisture, Soil Temperature, Vegetation and Soil Salinity

The Dry Chaco in South America is a semi-arid ecoregion prone to dryland salinization. In this region, we investigated coarse-scale surface soil moisture (SM), soil temperature, soil salinity and vegetation, using L-band microwave brightness temperature (TB) observations and retrievals from the Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellite missions, Catchment Land Surface Model (CLSM) simulations, and in situ measurements within 26 sampled satellite pixels. Across these 26 sampled pixels, the satellite-based SM outperformed CLSM SM compared to field data, and forward L-band TB simulations derived from in situ SM and temperature performed better than those derived from CLSM estimates relative to SMOS TB observations. The surface salinity for the sampled pixels was on average only 4 mg/g and only locally influenced the TB simulations, when including salinity in the dielectric mixing model of the forward radiative transfer model (RTM) simulations. To explore the potential of retrieving salinity together with other RTM parameters to optimize TB simulations over the entire Dry Chaco, the RTM was inverted using 10 years of multi-angular SMOS TB data and constraints of CLSM SM and temperature. However, the latter modeled SM was not sufficiently accurate and factors such as open surface water were missing in the background constraints, so that the salinity retrievals effectively represented a bulk correction of the dielectric constant, rather than salinity per se. However, the retrieval of vegetation, scattering albedo and surface roughness resulted in realistic values.

L-Band↗

NASA NTRS2014

An Efficient, Multi-Layered Crown Delineation Algorithm for Mapping Individual Tree Structure Across Multiple Ecosystems

Deriving individual tree information from discrete return, small footprint LiDAR data may improve forest above ground biomass estimates, and provide tree-level information that is important in many ecological studies. Several crown delineation algorithms have been developed to extract individual tree information from LiDAR point clouds or rasterized canopy height models (CHM), but many of these algorithms have difficulty discriminating between overlapping crowns, and also may fail to detect understory trees. Our approach uses a watershed based delineation of a CHM, which is subsequently refined using the LiDAR point cloud. Individual tree detection was validated with stem mapped field data from the Smithsonian Environmental Research Center (SERC), Maryland, and on a plot and stand level through comparisons of stem density and basal area to delineated metrics at both SERC and a study area in the Sierra Nevada, California. For individual tree detection, the algorithm correctly identified 70% of dominant trees, 58% of co-dominant trees, 35% of intermediate trees and 21% of suppressed trees at SERC. The algorithm had difficulty distinguishing between crowns of small, dense understory trees of approximately the same height. Delineated crown volume alone explained 53% and 84% of the variability in basal area at the SERC and Sierra Nevada sites, respectively. The algorithm produced crown area distributions comparable to diameter at breast height (DBH) size class distributions observed in the field in both study sites. The algorithm detected understory crowns better in the conifer-dominated Sierra Nevada site than in the closed-canopy deciduous site in Maryland. The ability for the algorithm to reproduce both accurate tree size distributions and individual crown geometries in two dissimilar and complex forests suggests great promise for applicability to a wide range of forest systems.

LiDAR↗

NASA NTRS1978

Plasma fluctuations in the solar wind

Using Ogo 5 plasma and magnetic field data, the power spectra of solar wind fluctuations are calculated over the 10 to the -3 to 10 to the -1 Hz range. It is found that the spectra calculated from flux measurements equal the density power spectra times the square of the average solar wind speed. The power spectrum relative density is of the same order of magnitude as the power spectrum of speed fluctuations relative to the Alfven speed. In cases where density and field fluctuations are inconsistent with magnetosonic waves, static inhomogeneities with a balance between electron thermal and magnetic pressures is assumed. It is also felt that a resonant proton cyclotron instability driven by the solar wind's proton thermal anisotropy may cause the observed power enhancements near 1 Hz.

Neugebauer, M.↗

NASA NTRS1987

Filamentary structures in the magnetotail lobes

The observations of plasmas which may be the source population for polar cap arcs are reported. This population consists of energetic plasma contained within filamentary structures aligned approximately parallel to the XZ plane. Using a combination of plasma, particle, and magnetic field data for several events, it is established that the filaments have their origin in the plasma sheet or its adjacent boundary layer, and that the structures protrude into the lobe region. In a number of cases, a correlation between the convection pattern and the IMF B(Z) can be inferred. This is in agreement with the observed characteristics of polar cap arcs, observations of which are correlated with a northward directed IMF. The presence of field-aligned currents suggests that field-aligned acceleration may occur between the observing satellite and earth.

Huang, C. Y.↗

DOE OSTI2022

Finding Hidden Patterns in High Resolution Wind Flow Model Simulations

Wind flow data is critical in terms of investment decisions and policy making. High resolution data from wind flow model simulations serve as a supplement to the limited resource of original wind flow data collection. Given the large size of data, finding hidden patterns in wind flow model simulations are critical for reducing the dimensionality of the analysis. In this work, we first perform dimension reduction with two autoencoder models: the CNN-based autoencoder (CNN-AE) [1], and hierarchical autoencoder (HIER-AE) [2], and compare their performance with the Principal Component Analysis (PCA). We then investigate the super-resolution of the wind flow data. By training a Generative Adversarial Network (GAN) with 300 epochs, we obtained a trained model with 2× resolution enhancement. We compare the results of GAN with Convolutional Neural Network (CNN), and GAN results show finer structure as expected in the data field images. Also, the kinetic energy spectra comparisons show that GAN outperforms CNN in terms of reproducing the physical properties for high wavenumbers and is critical for analysis where high-wavenumber kinetics play an important role.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Towards automated and real-time multi-object detection of anguilliform fishes from sonar data using YOLOv8 deep learning algorithm

Eels (Anguilla spp.), including American eels (Anguilla rostrata), European eels (Anguilla anguilla), and Japanese eels (Anguilla japonica), are species of critical management and regulatory concern due to their vulnerability to various stressors during downstream migrations. Accurate and efficient detection of migrating eels can improve our understanding of fish behaviors and fish-hydraulic structure interactions, thereby facilitating the design, operation, and optimization of more effective downstream passage facilities from both biological and economic perspectives. However, a real-time, automated framework for detecting migrating eels in real-world applications is currently lacking. Leveraging imaging sonar as a reliable technology for fish passage monitoring, field data are acquired using imaging sonar and then converted to single sonar frames/images for subsequent analysis. In this study, a framework based on the You Only Look Once Version 8 (YOLOv8)-based convolutional neural network is proposed for multi-object detection of eels and non-eel fish using the sonar images after image subtraction and additional wavelet denoising. The results from both training and testing phases demonstrate that the framework's ability can successfully detect both eels and non-eel fish in preprocessed sonar images, achieving F1-scores and mAP@0.50 exceeding 0.84. Additionally, the incorporation of wavelet denoising during preprocessing slightly improve detection performance. Furthermore, the transferability of this framework from eel to lamprey detection is demonstrated to be feasible given the similar morphological characteristics of these two species. Overall, the proposed framework achieves accurate and efficient detection of migrating eels, providing reliable and real-time information that can help conserve vulnerable eel and eel-like populations.

Deep learning↗

NASA NTRS1984

Experimental evaluation of an advanced Space Shuttle Main Engine hot-gas manifold design concept

The Space Shuttle Main Engine's hot gas manifold (HGM) has been the subject of an experimental study aimed at the establishment of an aerodynamic data base to support the development of an advanced, three-dimensional, fluid dynamic analysis computer model. The advanced HGM design used in the study demonstrated improved flow uniformity in the fuel-side turbine exit and transfer duct exit regions. Major modifications were incorporated in the HGM flow test article model, using two large transfer ducts on the fuel turbine side in place of the three small transfer ducts of the present design. The HGM flow field data were found to be essentially independent of Reynolds number over the range examined.

Pelaccio, D. G.↗

NASA NTRS2006

Merging IceSAT GLAS and Terra MODIS Data in Order to Derive Forest Type Specific Tree Heights in the Central Siberian Boreal Forest

Mapping of boreal forest's type, biomass, and other structural parameters are critical for understanding of the boreal forest's significance in the carbon cycle, its response to and impact on global climate change. We believe the nature of the forest structure information available from MISR and GLAS can be used to help identify forest type, age class, and estimate above ground biomass levels beyond that now possible with MODIS alone. The ground measurements will be used to develop relationships between remote sensing observables and forest characteristics and provide new information for understanding forest changes with respect to environmental change. Lidar is a laser altimeter that determines the distance from the instrument to the physical surface by measuring the time elapsed between the pulse emission and the reflected return. Other studies have shown that the returned signal may identify multiple returns originating from trees, building and other objects and permits the calculation of their height. Studies using field data have shown that lidar data can provide estimates of structural parameters such as biomass, stand volume and leaf area index and allows remarkable differentiation between primary and secondary forest. NASA's IceSAT Geoscience Laser Altimeter System (GLAS) was launched in January 2003 and collected data during February and September of that year. This study used data acquired over our study sites in central Siberia to examine the GLAS signal as a source of forest height and other structural characteristics. The purpose of our Siberia project is to improve forest cover maps and produce above-ground biomass maps of the boreal forest in Northern Eurasia from MODIS by incorporating structural information inherent in the Terra MISR and ICESAT Geoscience Laser Altimeter System (GLAS) instruments. A number of forest cover classifications exist for the boreal forest. We believe the limiting factor in these products is the lack of structural information, particularly in the vertical dimension. The emphasis of this project is to improve upon satellite maps of boreal forest structure parameters (i.e. height and biomass) using temporal, multi-angle, and vertical profile information of GLAS data. The existing and near future lidar data is useful for demonstrating these techniques and pursuing current estimates. Future lidar missions may be several years in the future, so we will work other new data sets that may aide in biomass estimates such as ALOS PALSAR We will continue this work to produce an accurate map of current above ground forest phytomass/carbon storage possible for the study area. We plan to develop, test, and integrate remote sensing methods for extracting forest canopy structure measures. We are compiling our field measurements and will compare them with the remote sensing methods where possible. We also be able to produce a realistic error bound on the remotely sensed carbon estimates.

Ranson, K. Jon↗

NASA NTRS1991

Automated pattern eduction from turbulent flow diagnostics

The development of an automated technique for the eduction of 3-D spatial patterns in vector or scalar diagnostics was completed. The method is based on an iterative convolution between a trial pattern and the data field. It was applied to the analysis of low Reynolds number turbulent channel flow and homogeneous shear flow. The results yielded new information on the dominant flow structures in these flows, particularly with respect to the spatial relationships between various forms of organized motion. A particular application of the pattern eduction method, which is tentatively referred to as an 'adaptive wavelet transformation', is proposed with the objective of investigating the way turbulence structure changes with scale. Preliminary results using data from homogeneous turbulent shear flow simulations are presented. At the low Reynolds numbers of the simulations, there is no evidence of scale similarity. The small scales appear to be associated with the edges of the larger scale vortical structures.

Stretch, Derek D.↗

NASA NTRS2019

Turbulence Measurements on a Wing-Fuselage Junction Model for CFD Validation

Current turbulence models, such as those employed in Reynolds-averaged Navier-Stokes CFD, are unable to reliably predict the onset and extent of the three-dimensional separated flow that typically occurs in wing fuselage junctions. To critically assess, as well as to improve upon, existing turbulence models, experimental validation-quality flow field data in the junction region is needed. To that end, NASA has developed a CFD validation experiment for a generic full-span wing-fuselage junction model at subsonic conditions. In this paper, we present and discuss sample results from this effort, with a focus on turbulence measurements in the separated corner flow of the wing-fuselage junction that were obtained with an internally mounted laser Doppler velocimetry (LDV) system. In addition, sample comparisons between the flow field measurements and representative CFD of our test case are presented and here, we highlight some of the key discrepancies.

Kegerise, Michael↗

DOE OSTI2021

Multi-spatio-temporal scales PIV in a turbulent buoyant jet discharging in a linearly stratified environment

Time-resolved particle image velocimetry is implemented with a camera array and several laser sheets; this results in a multi-spatio-temporal scale system that is modular and flexible. The setup is optimized to capture global flow features, while locally resolving in space and time near the Kolmogorov scale. The apparatus relies extensively on machine vision CMOS cameras; they are inexpensive and stream to computer hard drive with virtually continuous time-resolved records (up to one hour for the current system). This leads to statistically converged data and also helps in minimizing down time between experimental runs. Details of the implementation and design of experiment are reported. The system instruments a vertical buoyant jet discharging in a linearly stratified environment. Refractive index matched aqueous solutions enable precise optical deployment. The density difference is 3% and the fluids have similar dynamic viscosity. The jet Reynolds number is 2.00 x 10 4 and is above the mixing transition. Such flows are typically challenging to instrument and few velocity field data are available to date. The velocity statistics are reported as well as first insights gained from the campaign.

42 ENGINEERING↗

DOE OSTI2020

Predicting Emission Source Terms in a Reduced-Order Fire Spread Model—Part 1: Particulate Emissions

A simple, easy-to-evaluate, surrogate model was developed for predicting the particle emission source term in wildfire simulations. In creating this model, we conceptualized wildfire as a series of flamelets, and using this concept of flamelets, we developed a one-dimensional model to represent the structure of these flamelets which then could be used to simulate the evolution of a single flamelet. A previously developed soot model was executed within this flamelet simulation which could produce a particle size distribution. Executing this flamelet simulation 1200 times with varying conditions created a data set of emitted particle size distributions to which simple rational equations could be tuned to predict a particle emission factor, mean particle size, and standard deviation of particle sizes. These surrogate models (the rational equation) were implemented into a reduced-order fire spread model, QUIC-Fire. Using QUIC-Fire, an ensemble of simulations were executed for grassland fires, southeast U.S. conifer forests, and western mountain conifer forests. Resulting emission factors from this ensemble were compared against field data for these fire classes with promising results. Also shown is a predicted averaged resulting particle size distribution with the bulk of particles produced to be on the order of 1 μm in size.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1983

Solar maximum mission: Ground support programs at the Harvard Radio Astronomy Station

Observations of the spectral characteristics of solar radio bursts were made with new dynamic spectrum analyzers of high sensitivity and high reliability, over the frequency range 25-580 MHz. The observations also covered the maximum period of the current solar cycle and the period of international cooperative programs designated as the Solar Maximum Year. Radio data on shock waves generated by solar flares were combined with optical data on coronal transients, taken with equipment on the SMM and other satellites, and then incorporated into computer models for the outward passage of fast-mode MHD shocks through the solar corona. The MHD models are non-linear, time-dependent and for the most recent models, quasi-three-dimensional. They examine the global response of the corona for different types of input pulses (thermal, magnetic, etc.) and for different magnetic topologies (for example, open and closed fields). Data on coronal shocks and high-velocity material ejected from solar flares have been interpreted in terms of a model consisting of three main velocity regimes.

Maxwell, A.↗

NASA NTRS2019

Thin Current Sheets of Sub-Ion Scales Observed by MAVEN in the Martian Magnetotail

Current sheets (CSs) play a crucial role in the storage and conversion of magnetic energy in planetary magnetotails. Using high‐resolution magnetic field data from MAVEN spacecraft, we report the existence of super thin current sheets (STCSs) in the Martian magnetotail. The typical half‐thickness of the STCSs is ~5 km, and it is much less than the gyroradius of thermal protons (ρp). The STCSs are embedded into a thicker sheet with L ≥ ρp forming a multiscale current configuration. The formation of STCS does not depend on ion composition, but it is controlled by the small value of the normal component of the magnetic field at the neutral plane (BN). A number of the observed multiscale CSs are located in the parametric map close to the tearing‐unstable domain, and thus, the inner STCS can provide an additional free energy to excite ion tearing mode in the Martian magnetotail.

Martian↗

DOE OSTI2024

Local-scale heterogeneity of soil thermal dynamics and controlling factors in a discontinuous permafrost region

In permafrost regions, the strong spatial and temporal variability in soil temperature cannot be explained by the weather forcing only. Understanding the local heterogeneity of soil thermal dynamics and their controls is essential to understand how permafrost systems respond to climate change and to develop process-based models or remote sensing products for predicting soil temperature. In this study, we analyzed soil temperature dynamics and their controls in a discontinuous permafrost region on the Seward Peninsula, Alaska. We acquired one-year temperature time series at multiple depths (at 5 or 10 cm intervals up to 85 cm depth) at 45 discrete locations across a 2.3 km 2 watershed. We observed a larger spatial variability in winter temperatures than that in summer temperatures at all depths, with the former controlling most of the spatial variability in mean annual temperatures. We also observed a strong correlation between mean annual ground temperature at a depth of 85 cm and mean annual or winter season ground surface temperature across the 45 locations. We demonstrate that soils classified as cold, intermediate, or warm using hierarchical clustering of full-year temperature data closely match their co-located vegetation (graminoid tundra, dwarf shrub tundra, and tall shrub tundra, respectively). We show that the spatial heterogeneity in soil temperature is primarily driven by spatial heterogeneity in snow cover, which induces variable winter insulation and soil thermal diffusivity. These effects further extend to the subsequent summer by causing variable latent heat exchanges. Finally, we discuss the challenges of predicting soil temperatures from snow depth and vegetation height alone by considering the complexity observed in the field data and reproduced in a model sensitivity analysis.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Low-Power, Flexible Sensor Arrays with Solderless Board-to-Board Connectors for Monitoring Soil Deformation and Temperature: Supporting Data

This dataset was used to assess the potential of a Soil Deformation and Temperature Monitoring System developed and presented in the article named "Low-Power, Flexible Sensor Arrays with Solderless Board-to-Board Connectors for Monitoring Soil Deformation and Temperature" and published in Sensors. There are 21 comma-delimited data files (.csv). 14 files contain current measurements performed in a lab setting, enabling the electrical evaluation of the sensor probe. These files are generated by the Keithley DMM6500 multimeter, and list the measured supply current (first column) as a function of time (3rd column). Another set of 6 files is also acquired in lab experiments, but contain soil temperature an deformation measurements, enabling an assessment of the developed device's accuracy. In these files, the first column contains the time (UTC), followed by the sensor's battery voltage and temperature and acceleration values (X, Y, Z) in subsequent columns. The measurements were acquired every 5 seconds. Another .csv file contains field data in a similar format (time, temperature, acceleration) collected at the Teller road (mile 27) site near Nome, Alaska with one probe. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗