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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Implementation and Performance Evaluation of a Community-Scale Adobe Evaporative Cooling Chamber for Vegetable Preservation

The construction of evaporative coolers in remote areas can increase the longevity of vegetables, improving food security and the local economy of small farmers in remote, impoverished communities without access to electricity. This work presents a 1:1 scale prototype of an 8 m 3 (2.1 × 2.1 × 2.3 m) stabilized adobe evaporative cooler, with a design based on the appropriate technology framework, and it was built as a chamber using double adobe walls, filled with wet sand, to induce evaporative cooling. Furthermore, the paper presents the prototype’s performance evaluation. The tests were carried out in the dry and wet states, with different volumes of water. The results show good performance compared with other prototypes, although the optimum watering volume could not be determined because of the high climate variance (outside temperature and humidity) that prevented the repetition of the experiments in identical operating conditions. Stabilized adobe proved to be a good choice for use in the cooler, even when subject to moisture accumulation, indicating an estimated long lifetime for the cooler. The data obtained about the efficiency of evaporative cooling show that the cooler, as expected, has its best performance on the hottest and driest days, reducing the internal temperature (up to 13.24 °C) and managing to keep the internal humidity. The cost, efficiency, durability, and replicability make the proposed evaporative cooler a feasible solution for food preservation.

42 ENGINEERING↗
NASA NTRS1972

Studies of images of short-lived events using ERTS data

The author has identified the following significant results. It has been possible to identify old fires in the Alaskan tundra and to monitor the development of active fires. The area burned can be quickly determined by a number of methods. The ERTS-1 satellite provides a convenient way to moniter fire damage in remote areas.

Deutschman, W. A.↗
NASA NTRS1985

Multisensor Data Integration Techniques

The availability of data from sensors operating in several different wavelength regions had led to the development of new techniques and strategies for both data management and image analysis. Work is ongoing to develop computer techniques for analysis of integrated data sets. These techniques include coregistration of multisensor images, rectification of radar images in areas of topographic relief to ensure pixel to pixel registration with planimetric data sets, calibration of data so that signatures can be applied to remote areas, normalization of data acquired with disparate sensors and determination of extended spectral signatures of surface units. In addition, software is being developed to analyze coregistrated digital terrain and image data so that automated stratigraphic and structural analyses can be performed. These software procedures include: strike and dip determination, terrain profile generation, stratigraphic column generation, stratigraphic thickness measurements, structural cross-section generation, and creation of 3-D block diagrams. These techniques were applied to coregistered LANDSAT 4 Thematic Mapper (TM), Thermal Infrared Multispectral Scanner (TIMS), and multipolarization synthetic aperture radar (SAR) data of the Wind River Basin in Wyoming.

Evans, D. L.↗
NASA NTRS1994

Detection of Cracks at Welds in Steel Tubing Using Flux Focusing Electromagnetic Probe

The inspection of weldments in critical pressure vessel joints is a major concern in the nuclear power industry. Corrosive environments can speed the fatigue process and access to the critical area is often limited. Eddy current techniques have begun to be used to help overcome these obstacles [1]. As direct contact and couplants are not required, remote areas can be inspected by simply snaking an eddy current coil into the intake tube of the vessel. The drawback of the eddy current method has been the high sensitivity to small changes in the conductivity and permeability of the test piece which are known to vary at weldments [1]. The flaw detection mechanism of the flux focusing electromagnetic probe can help alleviate these difficulties and provide a unique capability for detecting longitudinal fatigue cracks in critical tube structures. The Flux Focusing Electromagnetic Flaw Detector, originally invented for the detection of fatigue and corrosion damage in aluminum plates [2-3], has been adapted for use in testing steel tubing for longitudinal fatigue cracks. The modified design allows for the probe to be placed axisymmetrically into the tubing, inducing eddy currents in the tube wall. The pickup coil of the probe is fixed slightly below the primary windings and is rotated 90 so that its axis is normal to the tube wall. The magnetic flux of the primary coil is focused through the use of ferromagnetic material so that in the absence of fatigue damage there will be no flux linkage with the pickup coil. The presence of a longitudinal fatigue crack will cause the eddy currents induced in the tube wall to flow around the flaw and directly under the pickup coil. The magnetic field associated with these currents will then link the pickup coil and an unambiguous increase in the output voltage of the probe will be measured. The use of the flux focusing electromagnetic probe is especially suited for the detection of flaws originating at or near tube welds. The probe is shown to discriminate against signals due solely to the weld joint so that flaw signals are not hidden in the background in these locations. Experimental and finite element modeling results are presented for the flaw detection capabilities of the probe in stainless steel tubes.

Wincheski, Buzz↗
NASA NTRS2007

Using MODIS and GLAS Data to Develop Timber Volume Estimates in Central Siberia

The boreal forest is the Earth's largest terrestrial biome, covering some 12 million km2 and accounting for about one third of this planet's total forest area. Mapping of boreal forest's type, structure parameters and biomass are critical for understanding the boreal forest's significance in the carbon cycle, its response to and impact on global climate change. Ground based forest inventories, have much uncertainty in the inventory data, particularly in remote areas of Siberia where sampling is sparse and/or lacking. In addition, many of the forest inventories that do exist for Siberia are now a decade or more old. Thus, available forest inventories fail to capture the current conditions. Changes in forest structure in a particular forest-type and region can change significantly due to changing environment conditions, and natural and anthropogenic disturbance. Remote sensing methods can potentially overcome these problems. Multispectral sensors can be used to provide vegetation cover maps that show a timely and accurate geographic distribution of vegetation types rather than decade old ground based maps. Lidar sensors can be used to directly obtain measurements that can be used to derive critical forest structure information (e.g., height, density, and volume). These in turn can used to estimate biomass components using allometric equations without having to use out dated forest inventory. Finally, remote sensing data is ideally suited to provide a sampling basis for a rigorous statistical estimate of the variance and error bound on forest structure measures. In this study, new remote sensing methods were applied to develop estimates timber volume using NASA's MODerate resolution Imaging Spectroradiometer (MODIS) and unique waveform data of the geoscience laser altimeter system (GLAS) for a 10 deg x 10 deg area in central Siberia. Using MODIS and GLAS data, maps were produced for cover type and timber volume for 2003, and a realistic variance (error bound) for timber volume was calculated for the study area. In this 'study we used only GLAS footprints that had a slope value of less than 10 deg. This was done to avoid large errors due to the effect of slope on the GLAS models. The method requires the integration of new remote sensing methods with available ground studies of forest timber volume conducted in Russian forests. The results were compared to traditional ground forest inventory methods reported in the literature and to ground truth collected in the study area.

Ranson, K. Jon↗
NASA NTRS1973

Research for applications of remote sensing to state and local governments (ARSIG)

Remote sensing and its application to problems confronted by local and state planners are reported. The added dimension of remote sensing as a data gathering tool has been explored identifying pertinent land use factors associated with urban growth such as soil associations, soil capability, vegetation distribution, and flood prone areas. Remote sensing within rural agricultural setting has also been utilized to determine irrigation runoff volumes, cropping patterns, and land use. A variety of data sources including U-2 70 mm multispectral black and white photography, RB-57 9-inch color IR, HyAC panoramic color IR and ERTS-1 imagery have been used over selected areas of Arizona including Tucson, Arizona (NASA Test Site #30) and the Sulphur Springs Valley.

Foster, K. E.↗
NASA NTRS1977

LTA - Recent developments

NASA-sponsored studies of existing and new LTA missions showed that airships looked very promising for some two dozen civil and military applications. These include surveillance of rural and urban areas, in the form of forest and police patrols; transport of very heavy large-volume maritime, industrial, and military payloads; coastal patrol and sea control; seismographic surveys; air pollution monitoring; and moving goods to remote areas; along with a number of less important but still attractive missions. A figure of merit of productivity (payload weight, ton moles per hour) was used to compare airships of various type and size. In each case, this criterion established an index of efficiency for evaluating not only conceptual approaches but also modes of flight. Some, in part unexpected, results of these studies are described.

Mayer, N. J.↗
NASA NTRS2016

Ocean-Atmosphere Interactions Modulate Irrigation's Climate Impacts

Numerous studies have focused on the local and regional climate effects of irrigated agriculture and other land cover and land use change (LCLUC) phenomena, but there are few studies on the role of ocean- atmosphere interaction in modulating irrigation climate impacts. Here, we compare simulations with and without interactive sea surface temperatures of the equilibrium effect on climate of contemporary (year 2000) irrigation geographic extent and intensity. We find that ocean-atmosphere interaction does impact the magnitude of global-mean and spatially varying climate impacts, greatly increasing their global reach. Local climate effects in the irrigated regions remain broadly similar, while non-local effects, particularly over the oceans, tend to be larger. The interaction amplifies irrigation-driven standing wave patterns in the tropics and mid-latitudes in our simulations, approximately doubling the global-mean amplitude of surface temperature changes due to irrigation. The fractions of global area experiencing significant annual-mean surface air temperature and precipitation change also approximately double with ocean-atmosphere interaction. Subject to confirmation with other models, these findings imply that LCLUC is an important contributor to climate change even in remote areas such as the Southern Ocean, and that attribution studies should include interactive oceans and need to consider LCLUC, including irrigation, as a truly global forcing that affects climate and the water cycle over ocean as well as land areas.

Air water interactioins↗
NASA NTRS2013

Earth Observation from Space - The Issue of Environmental Sustainability

Remote sensing scientists work under assumptions that should not be taken for granted and should, therefore, be challenged. These assumptions include the following: 1. Space, especially Low Earth Orbit (LEO), will always be available to governmental and commercial space entities that launch Earth remote sensing missions. 2. Space launches are benign with respect to environmental impacts. 3. Minimization of Type 1 error, which provides increased confidence in the experimental outcome, is the best way to assess the significance of environmental change. 4. Large-area remote sensing investigations, i.e. national, continental, global studies, are best done from space. 5. National space missions should trump international, cooperative space missions to ensure national control and distribution of the data products. At best, all of these points are arguable, and in some cases, they're wrong. Development of observational space systems that are compatible with sustainability principles should be a primary concern when Earth remote sensing space systems are envisioned, designed, and launched. The discussion is based on the hypothesis that reducing the environmental impacts of thedata acquisition step,which is at the very beginning of the information streamleading to decision and action, will enhance coherence in the information streamand strengthen the capacity of measurement processes to meet their stated functional goal, i.e. sustainable management of Earth resources. We suggest that unconventional points of view should be adopted and when appropriate, remedial measures considered that could help to reduce the environmental footprint of space remote sensing and of Earth observation and monitoring systems in general. This article discusses these five assumptions inthe contextof sustainablemanagementof Earth's resources. Takingeachassumptioninturn,we find the following: (1) Space debris may limit access to Low Earth Orbit over the next decades. (2) Relatively speaking, given that they're rare event, space launches may be benign, but study is merited on upper stratospheric and exospheric layers given the chemical activity associated with rocket combustion by-products. (3) Minimization of Type II error should be considered in situations where minimization of Type I error greatly hampers or precludes our ability to correct the environmental condition being studied. (4) In certain situations, airborne collects may be less expensive and more environmentally benign, and comparative studies should be done to determine which path is wisest. (5) International cooperation and data sharing will reduce instrument and launch costs and mission redundancy. Given fiscal concerns of most of the major space agencies e e.g. NASA, ESA, CNES e it seems prudent to combine resources.

Earth observation systems↗
NASA NTRS2026

PERSIANN-Unet: A Global Deep Learning Framework for Near-Real-Time Precipitation Estimation Using Infrared Data

Access to high-quality, high-resolution, near-real-time precipitation data is essential for hydrological and meteorological research and disaster mitigation. Traditional tools such as rain gauges and radar networks, though effective, have limitations, including sparse coverage in remote areas and high operational costs. Satellite data, with its global coverage and high spatial and temporal resolutions, mitigates limitations in coverage. Satellite precipitation products like Hydro Estimator (HE), Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) utilize both geosynchronous thermal infrared (IR) and passive microwave (PMW) data in their operation. PMW sensors offer detailed atmospheric profiles but suffer from higher latency, whereas IR sensors provide lower latency but only capture cloud-top information. Despite this constraint, IR data remains attractive for low-latency precipitation estimation. Recent advances in deep learning, particularly convolutional neural networks (CNNs), have further improved satellite precipitation retrievals. This study introduces PERSIANN-Unet (PUnet or PERSIANN V3), a quasi-global algorithm covering 60°N–60°S that combines IR data, monthly climatology, and the UNet architecture to produce half-hourly precipitation estimates at 0.04° resolution. The product is evaluated against HE, IMERG, and PDIR-Now for 2022–2023. Results show that PUnet closely matches its training target, IMERG V07 Final, at the global scale, and performance is further evaluated against Stage IV as a reference over CONUS. Training PUnet on IMERG (2016–2021) leverages a high-quality, integrated PMW IR-gauge precipitation product while developing an IR-based framework not reliant on PMW availability. By operating on a single global image, PUnet avoids tile partitioning and blending steps, reducing edge discontinuities, and produces more spatially consistent precipitation fields across hemispheres.

Phu Nguyen↗
NASA NTRS1973

Monitoring of streamflow in the Verde River by ERTS-1 Data Collection System (DCS)

The Verde River watershed in central Arizona furnishes municipal, industrial, and agricultural water to the Salt River Valley --an area that contains more than half of Arizona's population and about one-fourth of the State's irrigated land. Water-management decisions related to the operation of large multiple-use reservoirs require accurate and continuous monitoring of moisture conditions over large remote areas. The U.S. Geological Survey in cooperation with the Salt River Valley Water Users' Association installed a specially designed gaging station on the Verde River near the town of Camp Verde to evaluate near-real time streamflow data furnished by the ERTS-1 Data Collection System (DCS). On Nov. 3, 1972, the installation was equipped with a Stevens digital water-level recorder, modified for telemetry, and an ERTS-1 data collection platform operating in the digital-parallel mode.

Schumann, H. H.↗
NASA NTRS1975

The Screwworm Eradication Data System (SEDS)

One of the objectives of the SEDS was to build a versatile, yet economical, interactive system on the existing digital PDP 11/45 computers used in the Skylab production processing system. Previous experience on various interactive systems gave some background in the best methods of handling image data, registering and correcting the data, and extracting useful information from the images. The screwworm was eliminated in many parts of the United States by air-dropping sterile screwworms in areas where screwworms breed. The reproduction of the screwworm can be more or less predicted based on weather conditions -- cold weather slows the breeding, warm weather fosters it, and some moisture appears to enhance breeding conditions which, in turn, improves the chances of screwworm population growth. To obtain temperature and moisture conditions from remote areas, the NOAA improved TIROS operational satellite was selected to give data from the very high resolution radiometer (VHRR). The VHRR is a two-channel scanning instrument sensitive to energy in the visible spectrum from 0.6 to 0.7 micrometers and the infrared spectrum from 10.5 to 12.5 micrometers.

Quinn, M. J.↗
DOE OSTI2022

Automated identification of local contamination in remote atmospheric composition time series

Abstract. Atmospheric observations in remote locations offer a possibility of exploring trace gas and particle concentrations in pristine environments. However, data from remote areas are often contaminated by pollution from local sources. Detecting this contamination is thus a central and frequently encountered issue. Consequently, many different methods exist today to identify local contamination in atmospheric composition measurement time series, but no single method has been widely accepted. In this study, we present a new method to identify primary pollution in remote atmospheric datasets, e.g., from ship campaigns or stations with a low background signal compared to the contaminated signal. The pollution detection algorithm (PDA) identifies and flags periods of polluted data in five steps. The first and most important step identifies polluted periods based on the derivative (time derivative) of a concentration over time. If this derivative exceeds a given threshold, data are flagged as polluted. Further pollution identification steps are a simple concentration threshold filter, a neighboring points filter (optional), a median, and a sparse data filter (optional). The PDA only relies on the target dataset itself and is independent of ancillary datasets such as meteorological variables. All parameters of each step are adjustable so that the PDA can be “tuned” to be more or less stringent (e.g., flag more or fewer data points as contaminated). The PDA was developed and tested with a particle number concentration dataset collected during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in the central Arctic. Using strict settings, we identified 62 % of the data as influenced by local contamination. Using a second independent particle number concentration dataset also collected during MOSAiC, we evaluated the performance of the PDA against the same dataset cleaned by visual inspection. The two methods agreed in 94 % of the cases. Additionally, the PDA was successfully applied to a trace gas dataset (CO2), also collected during MOSAiC, and to another particle number concentration dataset, collected at the high-altitude background station Jungfraujoch, Switzerland. Thus, the PDA proves to be a useful and flexible tool to identify periods affected by local contamination in atmospheric composition datasets without the need for ancillary measurements. It is best applied to data representing primary pollution. The user-friendly and open-access code enables reproducible application to a wide suite of different datasets. It is available at https://doi.org/10.5281/zenodo.5761101 (Beck et al., 2021).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2012

Using Remotely Sensed Data and Hydrologic Models to Evaluate the Effects of Climate Change on Shallow Aquatic Ecosystems in the Mobile Bay, AL Estuary

Coastal systems in the northern Gulf of Mexico, including the Mobile Bay, AL estuary, are subject to increasing pressure from a variety of activities including climate change. Climate changes have a direct effect on the discharge of rivers that drain into Mobile Bay and adjacent coastal water bodies. The outflows change water quality (temperature, salinity, and sediment concentrations) in the shallow aquatic areas and affect ecosystem functioning. Mobile Bay is a vital ecosystem that provides habitat for many species of fauna and flora. Historically, submerged aquatic vegetation (SAV) and seagrasses were found in this area of the northern Gulf of Mexico; however the extent of vegetation has significantly decreased over the last 60 years. The objectives of this research are to determine: how climate changes affect runoff and water quality in the estuary and how these changes will affect habitat suitability for SAV and seagrasses. Our approach is to use watershed and hydrodynamic modeling to evaluate the impact of climate change on shallow water aquatic ecosystems in Mobile Bay and adjacent coastal areas. Remotely sensed Landsat data were used for current land cover land use (LCLU) model input and the data provided by Intergovernmental Panel on Climate Change (IPCC) of the future changes in temperature, precipitation, and sea level rise were used to create the climate scenarios for the 2025 and 2050 model simulations. Project results are being shared with Gulf coast stakeholders through the Gulf of Mexico Data Atlas to benefit coastal policy and climate change adaptation strategies.

Estes, M. G.↗
DOE OSTI2022

Molecular Characterization of Water-Soluble Brown Carbon Chromophores in Snowpack from Northern Xinjiang, China

This study reports molecular-level characterization of brown carbon (BrC) attributed to water-soluble organic carbon in six snowpack samples collected from northern Xinjiang, China. Here, the molecular composition and light-absorbing properties of BrC chromophores were unraveled by application of high-performance liquid chromatography (HPLC) coupled to a photodiode array (PDA) detector and high-resolution mass spectrometry. The chromophores were classified into five major types, that is, (1) phenolic/lignin-derived compounds, (2) flavonoids, (3) nitroaromatics, (4) oxygenated aromatics, and (5) other chromophores. Identified chromophores account for ~23–64% of the total light absorption measured by the PDA detector in the wavelength range of 300–370 nm. In the representative samples from urban and remote areas, oxygenated aromatics and nitroaromatics dominate the absorption in the wavelengths below and above 320 nm, respectively. The highly polluted urban sample shows the most complex HPLC-PDA chromatogram, and more other chromophores contribute to the bulk absorption. Phenolic/ lignin-derived compounds are the most light-absorbing species in the soil-influenced sample. Chromophores in two remote samples exhibit ultraviolet–visible features distinct from other samples, which are attributed to flavonoids. Identification of individual chromophores and quantitative analysis of their optical properties are helpful for elucidating the roles of BrC in snow radiative balance and photochemistry.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2007

Ikhana: A NASA UAS Supporting Long Duration Earth Science Missions

The NASA Ikhana unmanned aerial vehicle (UAV) is a General Atomics Aeronautical Systems Inc. (San Diego, California) MQ-9 Predator-B modified to support the conduct of Earth science missions for the NASA Science Mission Directorate and, through partnerships, other government agencies and universities. It can carry over 2000 lb of experiment payloads in the avionics bay and external pods and is capable of mission durations in excess of 24 hours at altitudes above 40,000 ft. The aircraft is remotely piloted from a mobile ground control station (GCS) that is designed to be deployable by air, land, or sea. On-board support capabilities include an instrumentation system and an Airborne Research Test System (ARTS). The Ikhana project will complete GCS development, science support systems integration, external pod integration and flight clearance, and operations crew training in early 2007. A large-area remote sensing mission is currently scheduled for Summer 2007.

Cobleigh, Brent R.↗
NASA NTRS2007

Ikhana: A NASA UAS Supporting Long Duration Earth Science Missions

The NASA Ikhana unmanned aerial vehicle (UAV) is a General Atomics Ae ronautical Systems Inc. (San Diego, California) MQ-9 Predator-B modif ied to support the conduct of Earth science missions for the NASA Sci ence Mission Directorate and, through partnerships, other government agencies and universities. It can carry over 2000 lb of experiment p ayloads in the avionics bay and external pods and is capable of missi on durations in excess of 24 hours at altitudes above 40,000 ft. The aircraft is remotely piloted from a mobile ground control station (GC S) that is designed to be deployable by air, land, or sea. On-board s upport capabilities include an instrumentation system and an Airborne Research Test System (ARTS). The Ikhana project will complete GCS d evelopment, science support systems integration, external pod integra tion and flight clearance, and operations crew training in early 2007 . A large-area remote sensing mission is currently scheduled for Summ er 2007.

Cobleigh, B.↗
DOE OSTI2023

2022 Component Innovation Awardee: Carter Wind Turbines

Carter Wind Turbines will develop a competitively priced, portable, ultra-lightweight, self-erecting turbine that will expand the viability of wind energy systems for commercial and residential customers in areas far from population centers. The new turbine is a taller version of the Carter Model 300, which was developed with funding from previous Competitiveness Improvement Project (CIP) awards. Compared to conventional turbines, the new assembly is cheaper to ship, can be erected in one day without any cranes or heavy excavation equipment, and is compatible with the wide range of grid configurations and site conditions found in remote areas.

CIP↗