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Results for “Digital Gradient Sensing”

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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24 records · Page 2

The Origin of Star Formation in Early-type Galaxies Inferred from Spatially Resolved Spectroscopy

Abstract We investigate the origin of star formation activity in early-type galaxies with current star formation using spatially resolved spectroscopic data from the Mapping Nearby Galaxies at Apache Point Observatory in the Sloan Digital Sky Survey (SDSS). We first identify star-forming early-type galaxies from the SDSS sample, which are morphologically early-type but show current star formation activity in their optical spectra. We then construct comparison samples with different combinations of star formation activity and morphology, which include star-forming late-type galaxies, quiescent early-type galaxies, and quiescent late-type galaxies. Our analysis of the optical spectra reveals that the star-forming early-type galaxies have two distinctive episodes of star formation, which is similar to late-type galaxies but different from quiescent early-type galaxies with a single star formation episode. Star-forming early-type galaxies have properties in common with star-forming late-type galaxies, which include stellar population, gas and dust content, mass, and environment. However, the physical properties of star-forming early-type galaxies derived from spatially resolved spectroscopy differ from those of star-forming late-type galaxies in the sense that the gas in star-forming early-type galaxies is more concentrated than their stars, and is often kinematically misaligned with stars. The age gradient of star-forming early-type galaxies also differs from those of star-forming late-type galaxies. Our findings suggest that the current star formation in star-forming early-type galaxies has an external origin including galaxy mergers or accretion gas from the cosmic web.

Astronomy & Astrophysics↗

Miniaturized Magnetoelastic Sensor System

This article describes the design, assembly, and implementation of a hand-held, magnetic-field-based sensor system that can be adapted for a variety of sensing applications. The miniaturized system is based on Chemical Identification by Magneto-Elastic Sensing (ChIMES) technology, which uses three concentric solenoid coils to wirelessly interrogate a sensor body comprised of a response material coupled to a magnetoelastic wire. The response material expands when it encounters a target, imposing mechanical stress on the wire and altering its magnetic permeability. The sensor bodies are passive, requiring no external power source, and they are small, measuring about 15 mm in length and 3.0 mm in diameter. Up to four sensor bodies can be configured as an evenly-spaced linear array. The sensor system operates by applying a low-frequency, current-stabilized, filtered triangle wave to a uniform-density excitation coil to switch the magnetic domains within the wire. Further, the responses from the sensors are picked up by a detection coil as stress-induced changes in the Faraday voltage, and the strong magnetic field induced by the excitation coil in the detection coil is nullified by a cancellation coil reverse-wound in series with the detection coil. The responses of the sensors in an array are separated in time by a linear gradient dc biasing coil. The sensors can be interrogated through metallic and nonmetallic barriers. The signals from the detection coil and the excitation coil are digitized by a pair of bipolar analog-to-digital converters (ADCs). A Raspberry Pi single-board computer (SBC) and associated software perform data acquisition and control all aspects of the sensor system hardware. The program allows the user to select the number of sensors in the array, the type of signal that is being collected, and the number of samples to take. The program also allows for signal processing of the sensor data, such as baseline correction. The program can differentiate sensor peaks from each other and calculate the magnitude of each sensor response with less than 1% error. The data are then displayed along with a graph of the signal.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Snowex 2017 Community Snow Depth Measurements: A Quality-Controlled, Georeferenced Product

Snow depth was one of the core ground measurements required to validate remotely-sensed data collected during SnowEx Year 1, which occurred in Colorado. The use of a single, common protocol was fundamental to produce a community reference dataset of high quality. Most of the nearly 100 Grand Mesa and Senator Beck Basin SnowEx ground crew participants contributed to this crucial dataset during 6-25 February 2017. Snow depths were measured along ~300 m transects, whose locations were determined according to a random-stratified approach using snowfall and tree-density gradients. Two-person teams used snowmobiles, skis, or snowshoes to travel to staked transect locations and to conduct measurements. Depths were measured with a 1-cm incremented probe every 3 meters along transects. In shallow areas of Grand Mesa, depth measurements were also collected with GPS snow-depth probes (a.k.a. MagnaProbes) at ~1-m intervals. During summer 2017, all reference stake positions were surveyed with <10 cm accuracy to improve overall snow depth location accuracy. During the campaign, 193 transects were measured over three weeks at Grand Mesa and 40 were collected over two weeks in Senator Beck Basin, representing more than 27,000 depth values. Each day of the campaign depth measurements were written in waterproof field books and photographed by National Snow and Ice Data Center (NSIDC) participants. The data were later transcribed and prepared for extensive quality assessment and control. Common issues such as protocol errors (e.g., survey in reverse direction), notebook image issues (e.g., halo in the center of digitized picture), and data-entry errors (sloppy writing and transcription errors) were identified and fixed on a point-by-point basis. In addition, we strove to produce a georeferenced product of fine quality, so we calculated and interpolated coordinates for every depth measurement based on surveyed stakes and the number of measurements made per transect. The product has been submitted to NSIDC in csv format. To educate data users, we present the study design and processing steps that have improved the quality and usability of this product. Also, we will address measurement and design uncertainties, which are different in open vs. forest areas.

Brucker, L.↗

Performance seeking control excitation mode

Flight testing of the performance seeking control (PSC) excitation mode was successfully completed at NASA Dryden on the F-15 highly integrated digital electronic control (HIDEC) aircraft. Although the excitation mode was not one of the original objectives of the PSC program, it was rapidly prototyped and implemented into the architecture of the PSC algorithm, allowing valuable and timely research data to be gathered. The primary flight test objective was to investigate the feasibility of a future measurement-based performance optimization algorithm. This future algorithm, called AdAPT, which stands for adaptive aircraft performance technology, generates and applies excitation inputs to selected control effectors. Fourier transformations are used to convert measured response and control effector data into frequency domain models which are mapped into state space models using multiterm frequency matching. Formal optimization principles are applied to produce an integrated, performance optimal effector suite. The key technical challenge of the measurement-based approach is the identification of the gradient of the performance index to the selected control effector. This concern was addressed by the excitation mode flight test. The AdAPT feasibility study utilized the PSC excitation mode to apply separate sinusoidal excitation trims to the controls - one aircraft, inlet first ramp (cowl), and one engine, throat area. Aircraft control and response data were recorded using on-board instrumentation and analyzed post-flight. Sensor noise characteristics, axial acceleration performance gradients, and repeatability were determined. Results were compared to pilot comments to assess the ride quality. Flight test results indicate that performance gradients were identified at all flight conditions, sensor noise levels were acceptable at the frequencies of interest, and excitations were generally not sensed by the pilot.

Schkolnik, Gerard↗

(Project 14-6770) An Investigation to Establish Multiphysical Property Dataset of Nuclear Materials Based on in-situ Observations and Measurements

In-core nuclear materials including fuel pins and cladding materials fail due to issues including corrosion, mechanical wear, and pellet cladding interaction. In most such scenario microstructure dependent and corrosioninduced chemistry dependent property changes significantly affect performance of cladding, pellet, and housing. Emphasis of this work was on replace conventional pellet-cladding material models with a new straingradient viscoplasticity model that is informed by transmission electron microscopy (TEM) based measurements and by nanomechanical Raman spectroscopy (NMRS) based measurements. The TEM measurements are quantitative in nature and therefore reveal stress-strain relations with simultaneous insights into mechanisms of deformation at nanoscale. The NMRS measurements reveal the similar information at mesoscale along with additional information on relating local microstructural stresses with applied stresses. The resulting information is used to fit constants in the strain gradient viscoplasticity model as well as to validate one. During TEM measurements, a micro-electro-mechanical system based setup was developed with mechanical actuation, sensing, heating, and electrical loading. Contrary to post-mortem analysis or qualitative visualization, this setup combines direct visualization of the mechanisms behind deformation with measurement of stress, strain, thermal and electrical properties. The unique research philosophy of visualizing the microstructure at high resolution while measuring the properties led to fundamental understanding in grain size and temperature effects on measured mechanical properties such as fracture toughness. A key contribution is the role of mechanical loading boundary conditions to deconvolute the insitu TEM based nanoscale and NMRS based mesoscale data to bulk behavior. First the literature based pellet cladding mechanical interaction model based on the work of Retel’s and Williamson’s in literature work to predict tempurature and stress distribution in cladding and pellet at normal operating condition was analyzed. Later the data was fitted to find constants for a viscoplastic strain gradient model. The developed model still needs to be refined and calibrated using various experimental results. That remains the focus of future work. Overall, a major thrust of the work was therefore on active control of the microstructure (grain size, defect density and types) exploiting the multi-physics coupling in materials. In particular, using experiments the synergy of current density, mechanical stress and temperature were studied to annihilate defects and recrystallize grains. The developed model is being examined for implementation in BISON. Multiple invited talks, international journal publications, and conference publications were performed by students supported on this work. Another output is support multiple PhD and masters thesis students who will be an important asset for future basic nuclear research. Future Work Recommendations: A nuclear reactor operates under significant variations of thermal loads due to energy cycling and mechanical loads due to constraint effects. Significant thermal and chemical diffusion takes place at the pallet-cladding level. While the proposed work established new experimental approach and new dataset for Zircaloy-4, the irradiation level was in the range of 1-2 dpa. Samples with higher dpa need to be examined. Therefore, a continual of support of the performed work is essential. Currently, these are the only experiments that can measure the produced data. The work also needs to be extended to different fuel types and cladding types such as SiC and FeCrAl based claddings. A combination of datasets for these materials can then be used to analyze accurately predict behavior of critical pellet cladding systems in accident scenario with high heat flux and high thermal loads. This is a BIG unknown as if now.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Vegetation classification map and covariates associated with NEON AOP survey, East River, CO 2018

This package includes geospatial data layers developed to investigate how environmental gradients—specifically topography and near-surface soil properties—drive the spatial arrangement of dominant plant communities in mountainous watersheds. The geospatial products, which support the analysis of these ecological relationships, are derived from airborne hyperspectral and LiDAR datasets acquired by the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP), in conjunction with an extensive ground field campaign conducted in summer 2018. This work is part of the DOE Watershed Function Science Focus Area (SFA) and features geospatial datasets developed based on observations and ground data collected at East River, Colorado, in collaboration with the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) survey in June 2018. Classification Map: - Classification Map (PNG, GeoTIFF): Derived from hyperspectral and LiDAR airborne data using a machine learning approach. - Class Code Mapper (CSV): Associates pixel values with corresponding vegetation/non-vegetation classes. - Classification Reference Data (CSV): Reference data used in the machine learning procedure. LiDAR-Derived Products: - Topographical Metrics (GeoTIFFs): Elevation, slope, curvature, TWI, TPI, solar insolation, and canopy height model (CHM), smoothed with a 5x5 pixel window. Vegetation Indices: - GeoTIFFs of NDVI, NDNI, NDWI: Vegetation indices derived from hyperspectral data. Urban Masks: - Urban Mask (GeoTIFF): Applied to the mapping to convert bare soil classes to urban classes. Software Compatibility: GeoTIFFs: Can be visualized with GIS software or libraries that support GeoTIFF images. CSV Files: Can be opened with any software that handles comma-separated values. The FLMD file provides details and links to the source datasets used to derive the products. The manuscript (in the Method session) provides details on how each product was derived. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231. Update on 2026-03-25: Since the original dataset publication date of 02/28/2020, this package has a new classification map derived by an improved methodology. This update also includes additional ground data that improved the representation of some of the communities. See the methods for further details on what has changed between versions.

2018 NEON and 2025 CHESS Campaigns↗