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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Distributed fiber sensor

In an embodiment, a sensor comprises a waveguide comprising a photoacoustic generation element disposed on the waveguide, the photoacoustic generation element comprising a photoabsorptive material; and a sensing element comprising an optical acoustic wave detector. In another embodiment, a sensing system comprises the sensor and a laser. In yet another embodiment, a method of sensing comprises providing the sensing system; heating the photoabsorptive material with a laser to generate an acoustic signal; sensing an intensity of laser light reflected by the optical acoustic wave detector to detect the acoustic signal; and determining a time of flight of the acoustic signal between the generation and the detection to determine a change in a parameter change in a medium between the photoabsorptive material and the optical acoustic wave detector.

Wang, Xingwei↗
DOE OSTI2024

Quantum for Energy Systems and Technologies

Quantum information science (QIS) is creating a transformative opportunity to improve processes for energy production, distribution, and consumption. In this presentation, we will show the works that are currently pursued into two major directions, (i) quantum sensing and (ii) quantum computing is undergoing in order to find applications in the areas of carbon captures, critical minerals and gas sensing applications.

Paudel, Hari P.↗
NASA NTRS1986

Radiation physics and modelling for off-nadir satellite-sensing of non-Lambertian surfaces

The primary objective of this paper is to provide a deeper understanding of the physics of satellite remote-sensing when off-nadir observations are considered. Emphasis is placed on the analysis and modeling of atmospheric effects and the radiative transfer of non-Lambertian surface reflectance characteristics from ground-level to satellite locations. The relative importance of spectral, spatial, angular, and temporal reflectance characteristics for satellite-sensed identification of vegetation types in the visible and near-infrared wavelength regions is evaluated. The highest identification value is attributed to angular reflectance signatures. Using radiative transfer calculations to evaluate the atmospheric effects on angular reflectance distributions of vegetation surfaces, atmosphere-invariant angular reflectance features such as the 'hot spot' and the 'persistent valley' are identified. A new atmospheric correction formalism for complete angular reflectance distributions is described. A sample calculation demonstrates that a highly non-Lambertian measured surface reflectance distribution can be retrieved from simulated satellite data in the visible and near infrared to within about 20 percent accuracy for almost all view directions up to 60 deg off-nadir. Thus the high value of angular surface reflectance characteristics (the 'angular signature') for satellite-sensed feature identification is confirmed, which provides a scientific basis for future off-nadir satellite observations.

Gerstl, S. A.↗
DOE OSTI2020

Projecting spatiotemporally explicit effects of climate change on stream temperature: A model comparison and implications for coldwater fishes

Conservation planners and resource managers seek information about how the availability and locations of cold-water habitats will change in the future and how these predictions vary among models. In this work, we used a physical process-based model to demonstrate the implications of climate change for streamflow and water temperature in two watersheds with distinctive flow regimes: the Snoqualmie watershed (WA) and Siletz watershed (OR), USA. Our model incorporated a downscaled ensemble of global climate model outputs and was calibrated with in situ and remotely sensed water temperatures. Furthermore, we compared predictions from our processed-based model to those from a publicly available and widely used statistical model. The process-based model projected greater changes in summer maximum water temperatures for the mixed-rain-snow Snoqualmie watershed than for the rain-dominated Siletz watershed as a result of the near-complete loss of winter snowpack and significant reduction in summer flow in the Snoqualmie watershed expected by the 2080s. Both models projected generally similar future spatial patterns of maximum water temperature in the two rivers, with cool reaches distributed farther upstream and fewer in number. However, the process-based model projected higher spatial heterogeneity in water temperature due to our spatially explicit simulation of streamflow and because we calibrated the model with spatially continuous remotely sensed water temperature data. We used stream temperature projections to assess the vulnerability of Pacific salmon and trout to changes in the spatial distribution of cold-water habitats during August by the 2080s. Results suggest that salmonids may have fewer summertime cold-water habitats in both watersheds. Projected stream warming may further limit particular species and life stages, especially in the Snoqualmie watershed. Our comparison of models highlights the importance of considering what might be gained by using a process-based model for evaluating and prioritizing management actions that mitigate climate impacts on cold-water habitats for stream fishes.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2024

Applications of Remote Sensing for Land Use Planning Scenarios with Suitability Analysis

In regions undergoing rapid urbanization, such as West Africa, land use planning (LUP) is vital to accommodate growing population and manage natural resources. Suitability analysis modeling is a widely used tool in LUP to determine the extent to which a land area is suitable for a designated purpose, but there is a gap in the integration of remote sensing time series data into land use decisions. The goal of this study was to incorporate remote sensing time series information with suitability analyses to inform LUP decisions in urban areas. In the study area of Kumasi, Ghana, land cover trends and land surface temperature (LST) from 2000 to 2019 were used to understand climate change trends. Suitability analyses determined the fitness of land areas for predetermined uses. These background processes informed a genetic algorithm to project plausible futures for three land use scenarios. One scenario represented current land use planning practices for addressing population growth, another scenario prioritized minimizing climate change impacts while also accommodating population growth, and the final scenario focused on both of these climate and population goals in addition to high density urban development. Each of these scenarios was successful in achieving population accommodation and respective climate change mitigation goals. The results for these scenarios provide insight into plausible land use distributions in 2050 based on different planning approaches. The genetic algorithm was able to effectively develop results for each scenario through the integration of remotely sensed trends and suitability models, providing a novel approach to land use decision-making.

remote sensing time series↗
NASA NTRS2009

Sensor Requirements and Roadmaps

The chapter starts with a brief overview of the state-of-the-art of the sensing technologies. Principles of detection and the stage of integration in conjunction with the need are discussed. Based on the projected sensor requirements to accomplish propulsion systems with distributed active control, the existent gaps and selected types of sensor technologies are addressed, which are foreseen to deliver the required sensitivity, the resolution, the range, and the bandwidth in classes of sensors and which will allow operation under harsh environment conditions. By changing the packaging and/or design of the current sensors, operation environments of 750°C may be possible, which would meet requirements for sensors located towards the engine intake, compressor and in some cases low-pressure turbine. However, the majority of sensors for locations close to the engine combustion chamber or afterburner (with operation temperatures up to 1700°C) do not exist. There is also a need for smart sensors, which would enable future distributed control architecture. In addition a number of sensors, which are of interest for more intelligent gas turbine engines, are being explored or do not yet exist, for example turbine emission species sensors, burning patter factor sensors, fuel property sensors, and exhaust gas composition sensors. Meanwhile, rapid emerging technologies were made available during the three years duration of the Task Group. These include tip clearance measurement technologies such as eddy current, and microwave methods. To meet future sensor requirements, new fabrication and material technologies (MEMS, other), advanced sensing principles (spectroscopy, other), and their potential applications to new sensor types need to be explored. The status of current R&D for new sensors and future expectations are summarized in a comprehensive table. Sensors and associated electronics that have to operate in high temperature are foreseen to be largely available by 2015 (SiC) and 2020 (SiCN). Both of these technologies come with the potential of embedding such high temperature sensors within the structure. The information presented in the chapter represents the state-of-the-art of the sensing technology of the years 2005-2007 to the best knowledge of the authors. The authors are also fully aware of the fact that there may be many ongoing research programs as well as individual research that are not publicly supported and public information is not available. Therefore, the report might miss that section of knowledge. Also, progress in research and development is made every day such that often, the recent information may be fast outdated. The reader should bear in mind these facts.

Ion Stiharu↗
DOE OSTI2026

Wildfire-Power Grid Interactions: Feedback, Impacts, Monitoring, Modeling, and Mitigation Strategies

Wildfires are increasingly interacting with electric power systems through a two-way hazard chain: fires damage grid assets and trigger cascading outages, while grid faults can ignite new fires under hot, dry, and windy conditions. This review synthesizes the state of knowledge across five domains: (i) physical impacts of flames, heat, and smoke on lines, towers, insulators, and substations; (ii) power-infrastructure-initiated ignitions via conductor clash, high-impedance faults, and corona discharge; (iii) widespread blackouts and disproportionate societal impacts; (iv) multi-scale monitoring spanning laboratory tests, in-situ and grid-integrated sensors, and Earth observation; (v) coupled modeling that links fire behavior with grid operations; and (vi) technological and strategic mitigation pathways spanning prevention, response, and recovery. We integrate these domains into a novel 'feedback-aware' socio-technical framework. Through a longitudinal analysis (2005-2025) of global incidents, we identify that while vegetation contact remains the most frequent ignition source, aging infrastructure failure has emerged as a critical driver of catastrophic 'mega-fires'. We further identify persistent gaps, including limited interoperability of high-frequency grid and environmental data, scarce real-time data assimilation, and under-developed equity metrics for outage management. We conclude by outlining a research agenda to (1) deploy interoperable sensing architectures, (2) advance feedback-coupled fire-grid simulations, and (3) evaluate mitigation portfolios through techno-economic and fairness lenses. Recognizing wildfire-grid interactions as coupled socio-technical systems is essential for protecting infrastructure and communities and for ensuring reliable, sustainable electricity in a changing world.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2000

Galactic Noise and Passive Microwave Remote Sensing from Space at L-Band

The spectral window at L-band (1.4 GHz) is important for passive remote sensing of parameters such as soil moisture and ocean salinity best measured at long wavelengths. At L-band, radiation from extraterrestrial (galactic) sources is strong enough to warrant inclusion in calibration and retrieval algorithms and unlike the constant cosmic background is spatially variable. Previous estimates of the magnitude and distribution of this background radiation have been rather coarse, However, recent surveys of the radio sky at 1.4 GHz have made it possible to produce maps with sufficient spatial and radiometric accuracy to be relevant to remote sensing applications. This is of particular concern for remote sensing of sea surface salinity because the surface (water) is a good reflector and the salinity signal is relatively small. This paper presents a modem map of the radiometric sky at L-band and a solution to the problem of determining the portion of the sky seen by a radiometer in orbit. The data is derived from recent radio astronomy surveys and is presented as equivalent brightness temperature suitable for remote sensing applications. Examples using orbits and antennas representative of those contemplated for remote sensing of soil moisture and sea surface salinity from space are presented to illustrate the signal levels to be expected. Radiation near the galactic plane can exceed several kelvin.

LeVine, D. M.↗
DOE OSTI2016

Predicting the offshore distribution and abundance of marine birds with a hierarchical community distance sampling model

Abstract Proposed offshore wind energy development on the Atlantic Outer Continental Shelf has brought attention to the need for baseline studies of the distribution and abundance of marine birds. We compiled line transect data from 15 shipboard surveys (June 2012–April 2014), along with associated remotely sensed habitat data, in the lower Mid‐Atlantic Bight off the coast of Delaware, Maryland, and Virginia, USA . We implemented a recently developed hierarchical community distance sampling model to estimate the seasonal abundance of 40 observed marine bird species. Treating each season separately, we included six oceanographic parameters to estimate seabird abundance: three static (distance to shore, slope, sediment grain size) and three dynamic covariates (sea surface temperature [ SST ], salinity, primary productivity). We expected that avian bottom‐feeders would respond primarily to static covariates that characterize seafloor variability, and that surface‐feeders would respond more to dynamic covariates that quantify surface productivity. We compared the variation in species‐specific and community‐level responses to these habitat features, including for rare species, and we predicted species abundance across the study area. While several protected species used the study area in summer during their breeding season, estimated abundance and observed diversity were highest for nonbreeding species in winter. Distance to shore was the most common significant predictor of abundance, and thus useful in estimating the potential exposure of marine birds to offshore development. In many cases, our expectations based on feeding ecology were confirmed, such as in the first winter season, when bottom‐feeders associated significantly with the three static covariates (distance to shore, slope, and sediment grain size), and surface‐feeders associated significantly with two dynamic covariates ( SST , primary productivity). However, other cases revealed significant relationships between static covariates and surface‐feeders (e.g., distance to shore) and between dynamic covariates and bottom‐feeders (e.g., primary productivity during that same winter). More generally, we found wide interannual, seasonal, and interspecies variation in habitat relationships with abundance. These results show the importance of quantifying detection and determining the ecological drivers of a community's distribution and abundance, within and among species, for evaluating the potential exposure of marine birds to offshore development.

Goyert, Holly F.↗
DOE OSTI2016

Forecasting climate change impacts on plant populations over large spatial extents

Plant population models are powerful tools for predicting climate change impacts in one location, but are difficult to apply at landscape scales. Here, we overcome this limitation by taking advantage of two recent advances: remotely sensed, species-specific estimates of plant cover and statistical models developed for spatiotemporal dynamics of animal populations. Using computationally efficient model reparameterizations, we fit a spatiotemporal population model to a 28-year time series of sagebrush (Artemisia spp.) percent cover over a 2.5 × 5 km landscape in southwestern Wyoming while formally accounting for spatial autocorrelation. We include interannual variation in precipitation and temperature as covariates in the model to investigate how climate affects the cover of sagebrush. We then use the model to forecast the future abundance of sagebrush at the landscape scale under projected climate change, generating spatially explicit estimates of sagebrush population trajectories that have, until now, been impossible to produce at this scale. Our broadscale and long-term predictions are rooted in small-scale and short-term population dynamics and provide an alternative to predictions offered by species distribution models that do not include population dynamics. Finally, our approach, which combines several existing techniques in a novel way, demonstrates the use of remote sensing data to model population responses to environmental change that play out at spatial scales far greater than the traditional field study plot.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1992

Lunar Prospector: a Preliminary Surface Remote Sensing Resource Assessment for the Moon

The potential existence of lunar volatiles is a scientific discovery that could distinctly change the direction of pathways of inner solar system human expansion. With a dedicated germanium gamma ray spectrometer launched in the early 1990's, surface water concentrations of 0.7 percent could be detected immediately upon full lunar polar orbit operations. The expense of lunar base construction and operation would be dramatically reduced over a scenario with no lunar volatile resources. Global surface mineral distribution could be mapped out and integrated into a GIS database for lunar base site selection. Extensive surface lunar mapping would also result in the utilization of archived Apollo images. A variety of remote sensing systems and their parameters have been proposed for use in the detection of these lunar ice masses. The detection or nondetection of subsurface and surface ice masses in lunar polar crater floors could dramatically direct the development pathways that the human race might follow in its radiation from the Earth to habitable locales in the inner terran solar system. Potential sources of lunar volatiles are described. The use of remote sensing to detect lunar volatiles is addressed.

Mardon, A. A.↗
NASA NTRS2002

An Optical Model for Estimating the Underwater Light Field from Remote Sensing

A model of the wavelength-integrated scalar irradiance for a vertically homogeneous water column is developed. It runs twenty thousand times faster than simulations obtained using full Hydrolight code and limits the percentage error to less than 3.7%. Both the distribution of incident sky radiance and a wind-roughened surface are integrated in the model. Our model removes common limitations of earlier models and can be applied to waters with any composition of the inherent optical properties. Implementation of this new model, as well as the ancillary information required for processing global-scale satellite data, is discussed. This new model is fast, accurate, and flexible and therefore provides important information of the underwater light field from remote sensing.

Liu, Cheng-Chien↗
NASA NTRSDate not supplied

Tunable Mid-wave Infrared Fabry-Perot Bandpass Filters Using Phase-Change GeSbTe

Tunable mid-wave infrared (MWIR) filters are highly desirable in a wide variety of imaging and sensing, as the MWIR contains numerous spectral signatures. We fabricated spectrally-tunable Fabry-Perot bandpass filters operating across the MWIR by utilizing the phase-change material GeSbTe (GST) as a tunable cavity medium between distributed Bragg reflectors. The induced refractive index modulation of the GST increases the cavity’s optical path length, tuning the passband. Our filters have 300-nm spectral tunability, 60-75% transmission efficiencies, and 50-65 nm narrowband FWHMs (Q-factor 70-90). We demonstrate multispectral thermal imaging and dynamic gas sensing by matching the filter’s passband (~ 4.25μm) to a CO2 vibrational-absorption mode.

Tunable filter↗
DOE OSTI2012

Arc-based smoothing of ion beam intensity on targets

Manipulating a set of ion beams upstream of a target, makes it possible to arrange a smoother deposition pattern, so as to achieve more uniform illumination of the target. A uniform energy deposition pattern is important for applications including ion-beam-driven high energy density physics and heavy-ion beam-driven inertial fusion energy (“heavy-ion fusion”). Here, we consider an approach to such smoothing that is based on rapidly “wobbling” each of the beams back and forth along a short arc-shaped path, via oscillating fields applied upstream of the final pulse compression. In this technique, uniformity is achieved in the time-averaged sense; this is sufficient provided the beam oscillation timescale is short relative to the hydrodynamic timescale of the target implosion. This work builds on two earlier concepts: elliptical beams applied to a distributed-radiator target [D. A. Callahan and M. Tabak, Phys. Plasmas 7, 2083 (2000)] and beams that are wobbled so as to trace a number of full rotations around a circular or elliptical path [R. C. Arnold et al., Nucl. Instrum. Methods 199, 557 (1982)]. Here, we describe the arc-based smoothing approach and compare it to results obtainable using an elliptical-beam prescription. In particular, we assess the potential of these approaches for minimization of azimuthal asymmetry, for the case of a ring of beams arranged on a cone. We also found that, for small numbers of beams on the ring, the arc-based smoothing approach offers superior uniformity. In contrast with the full-rotation approach, arc-based smoothing remains usable when the geometry precludes wobbling the beams around a full circle, e.g., for the X-target [E. Henestroza, B. G. Logan, and L. J. Perkins, Phys. Plasmas 18, 032702 (2011)] and some classes of distributed-radiator targets.

43 PARTICLE ACCELERATORS↗
NASA NTRS1982

Application of future remote sensing systems to irrigation

Area estimates of irrigated crops and knowledge of crop type are required for modeling water consumption to assist farmers, rangers, and agricultural consultants in scheduling irrigation for distributed management of crop yields. Information on canopy physiology and soil moisture status on a spatial basis is potentially available from remote sensors, so the questions to be addressed relate to: (1) timing (data frequency, instantaneous and integrated measurement); and scheduling (widely distributed spatial demands); (2) spatial resolution; (3) radiometric and geometric accuracy and geoencoding; and (4) information/data distribution. This latter should be overnight, with no central storage, onsite capture, and low cost.

Miller, L. D.↗
NASA NTRS1981

Remote sensing and uranium exploration at Lisbon Valley, Utah

As part of the joint NASA-Geosat uranium test case program, aircraft-acquired multispectral scanner data are used to investigate the distribution of bleaching in Windgate sandstone exposed in Lisbon Valley anticline, Utah. It is noted that all of the large ore bodies contained in lower Chinle Triassic age or Cutler Permian age strata in this area lie beneath or closely adjacent to such bleached outcrops. The geographic coincidences reported here are seen as inviting renewed interest in speculation of a causal relation between occurrences of Mississippian-Pennsylvanian oil and gas in this area and of Triassic uranium accumulation and rock bleaching.

Conel, J. E.↗
NASA NTRS2020

Improving Quantitative Laboratory Analysis of Phycobiliproteins to Provide High Quality Validation Data for Ocean Color Remote Sensing Algorithm

Identification and characterization of phytoplankton communities and their physiology is a primary aim of NASA's PACE satellite mission. The concentration and composition of phytoplankton pigments modulate the spectral distribution of light emanating from the ocean, which is measured by ocean color satellites, and thus provide critical information on phytoplankton community composition and physiological parameters. One diagnostic class of pigments not routinely well-characterized is the phycobiliproteins (PBPs), and NASA has a requirement to collect and distribute high quality in situ data in support of data product validation activities for ocean color missions. Phycobiliproteins are light-harvesting proteins that are the predominant photosynthetic pigments in some classes of phytoplankton including cyanobacteria, such as Synechococcus, Trichodesmium, and Microcystis. With the advance of hyperspectral ocean color sensors such as on PACE (expected to launch in late 2022), it is essential that we implement routine analysis of PBPs that satisfies several considerations: reproducible, high extraction efficiency for a variety of environments, and Suitable for large scale analysis. Published techniques for PBP analysis vary in recommendations for: collection, extraction, disruption mode, and analysis; evidence suggests the variation in results may depend at least in part on the species and even strain(s) of interest. Experiments that tested variations in these parameters have drawn very different conclusions regarding extraction efficiency and reproducibility. Cyanobacteria are more difficult to extract than other PBP-containing algae such as cryptophytes, but can be important primary producers. We used a cryptophyte (Rhodomonas salina) and cyanobacterium (Synechococcus sp.) to compare extraction efficiencies of water samples concentrated via centrifugation to filtered samples using two different extraction buffers (phosphate and asolectin-CHAPS). Samples were analyzed on a fluorometer configured for PC and PE detection. The results have important implications for collection and storage of samples for routine analysis; some previous studies (although not all) have suggested that filtered samples have a much lower extraction efficiency than whole water samples.

Kenemer, Christopher↗