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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 19 records

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NASA NTRSDate not supplied

Developing a Customizable Composite Drought Index for Pakistan

Pakistan has experienced intense agricultural droughts in recent years, with the southern provinces experiencing the most severe drought conditions. These prolonged dry conditions often result in failed crop production, impacting families and communities. To reduce the impacts a drought may have on a community by identifying dry conditions, drought indices are used. A customizable composite drought index (CDI) is developed to improve the spatial and temporal understanding of historical agricultural droughts that have affected Pakistan. An in depth analysis using 9 input variables will provide information regarding the most important variables for differing locations. This framework can enhance drought monitoring and forecasting systems. The performance of the CDI will be evaluated by using production data from different crops that have significant economic value in Pakistan. These crops include wheat, rice, maize, cotton and barley. The expected relationship between the CDI and crop production would be to see production decrease when the CDI decreases, an indication that crops failed due to drought. The CDI is made more precise by evaluating only agricultural areas using the months of the growing season for each specific crop. This will allow for a custom drought index to be developed based on crop type and geographic location that only uses the most important variables, while still capturing the full extent of historical drought.

Caily Schwartz↗

NASA NTRS2024

Vegetation Greening Mitigates the Impacts of Increasing Extreme Rainfall on Runoff Events

Future flood risk assessment has primarily focused on heavy rainfall as the main driver, with the assumption that projected increases in extreme rain events will lead to subsequent flooding. However, the presence of and changes in vegetation have long been known to influence the relationship between rainfall and runoff. Here, we extract historical (1850–1880) and projected (2070–2100) daily extreme rainfall events, the corresponding runoff, and antecedent conditions simulated in a prominent large Earth system model ensemble to examine the shifting extreme rainfall and runoff relationship. Even with widespread projected increases in the magnitude (78% of the land surface) and number (72%) of extreme rainfall events, we find projected declines in event-based runoff ratio (runoff/rainfall) for a majority (57%) of the Earth surface. Runoff ratio declines are linked with decreases in antecedent soil water driven by greater transpiration and canopy evaporation (both linked to vegetation greening) compared to areas with runoff ratio increases. Using a machine learning regression tree approach, we find that changes in canopy evaporation is the most important variable related to changes in antecedent soil water content in areas of decreased runoff ratios (with minimal changes in antecedent rainfall) while antecedent ground evaporation is the most important variable in areas of increased runoff ratios. Our results suggest that simulated interactions between vegetation greening, increasing evaporative demand, and antecedent soil drying are projected to diminish runoff associated with extreme rainfall events, with important implications for society.

soil moisture↗

NASA NTRS1982

Rapid solidification of metallic particulates

In order to maximize the heat transfer coefficient the most important variable in rapid solidification is the powder particle size. The finer the particle size, the higher the solidification rate. Efforts to decrease the particle size diameter offer the greatest payoff in attained quench rate. The velocity of the liquid droplet in the atmosphere is the second most important variable. Unfortunately the choices of gas atmospheres are sharply limited both because of conductivity and cost. Nitrogen and argon stand out as the preferred gases, nitrogen where reactions are unimportant and argon where reaction with nitrogen may be important. In gas atomization, helium offers up to an order of magnitude increase in solidification rate over argon and nitrogen. By contrast, atomization in vacuum drops the quench rate several orders of magnitude.

Grant, N. J.↗

NASA NTRS1982

Rapid solidification of metallic particulates

Grant, N. J.↗

NASA NTRS1998

Predicting Ecologically Important Vegetation Variables from Remotely Sensed Optical/Radar Data Using Neural Networks

A number of satellite sensor systems will collect large data sets of the Earth's surface during NASA's Earth Observing System (EOS) era. Efforts are being made to develop efficient algorithms that can incorporate a wide variety of spectral data and ancillary data in order to extract vegetation variables required for global and regional studies of ecosystem processes, biosphere-atmosphere interactions, and carbon dynamics. These variables are, for the most part, continuous (e.g. biomass, leaf area index, fraction of vegetation cover, vegetation height, vegetation age, spectral albedo, absorbed photosynthetic active radiation, photosynthetic efficiency, etc.) and estimates may be made using remotely sensed data (e.g. nadir and directional optical wavelengths, multifrequency radar backscatter) and any other readily available ancillary data (e.g., topography, sun angle, ground data, etc.). Using these types of data, neural networks can: 1) provide accurate initial models for extracting vegetation variables when an adequate amount of data is available; 2) provide a performance standard for evaluating existing physically-based models; 3) invert multivariate, physically based models; 4) in a variable selection process, identify those independent variables which best infer the vegetation variable(s) of interest; and 5) incorporate new data sources that would be difficult or impossible to use with conventional techniques. In addition, neural networks employ a more powerful and adaptive nonlinear equation form as compared to traditional linear, index transformations, and simple nonlinear analyses. These neural networks attributes are discussed in the context of the authors' investigations of extracting vegetation variables of ecological interest.

Kimes, Daniel S.↗

NASA NTRS2003

A Framework for Categorizing Important Project Variables

While substantial research has led to theories concerning the variables that affect project success, no universal set of such variables has been acknowledged as the standard. The identification of a specific set of controllable variables is needed to minimize project failure. Much has been hypothesized about the need to match project controls and management processes to individual projects in order to increase the chance for success. However, an accepted taxonomy for facilitating this matching process does not exist. This paper surveyed existing literature on classification of project variables. After an analysis of those proposals, a simplified categorization is offered to encourage further research.

Parsons, Vickie S.↗

NASA NTRS2015

A Researcher's Perspective on Function Allocation and Its Application to Air Traffic Management

Functional Allocation is an important research area for NASA going forward, and aims at exploring a range of potential options for future ATM (Air Traffic Management) operation concepts. The goal of this work is to gain some insight into the relative strengths and limitations of a range of different concepts, and to identify the important variables as well as any critical or threshold values for those variables for each of those concepts. This information will be captured in a form that can be easily referenced, and can then be used by decision makers to chose which concept or concepts best suit the needs of the US NAS (National Airspace System). The work presented here is one researcher's perspective on this process. A brief overview of Functional Allocation will be followed by more detailed discussion about two of the topics being currently investigated, as well as why and how this work differs from the way we have done ATM research in the past. More specifically, the goal of the analysis work is to understand the reasons for any differences in our metrics (such as delay) when adjusting input variables rather than trying to analyze the raw numbers themselves. In other words, finding which variables different concepts are sensitive to is important for this work, while finding a best or recommended operational concept is not. This will be followed by some examples of this process using results from our current studies. Finally, there will be a discussion on where we are in the process as well as our next steps.

Air-Ground Studies↗

NASA NTRSDate not supplied

Mapping National Forest Aboveground Biomass in Mexico by Integrating GEDI, Sentinel‐1 and Sentinel‐2 Data

Accurate mapping of forest aboveground biomass density (AGBD) is required to better understand the role of forests in the global carbon cycle and to support international policies for climate change mitigation and adaptation. Mexico is one of the countries having great potential for the United Nations Programme on Reducing Emissions from Deforestation and Forest Degradation (or UN-REDD program) and there is a growing demand for unbiased Monitoring Reporting Verification systems at a national level. As an effort under NASA’s Carbon Monitoring System (CMS) program, we developed a machine learning model using multi-stream remote sensing measurements as well as topographic data to create a high spatial resolution AGBD map (~100 m) over Mexico (circa 2020). The remote sensing data includes Global Ecosystem Dynamic Investigation (GEDI) lidar, Sentinel 1 Synthetic-Aperture Radar (SAR), and Sentinel-2 multispectral imagery (MSI). GEDI onboard the International Space Station provides unprecedented forest structure and AGBD sampling datasets for model training and validation practices. Our analysis indicates that the developed random forest model can capture 63 % of the spatial variation (RMSE = 33.7 Mg/ha) of AGBD of Mexican forests. We find that shortwave infrared bands of Sentinel-2 MSI and topographical variables from elevation data are the most important variables in the developed AGBD model. Our study highlights methodological opportunities in synergistic uses of multiple sensors for large-scale forest AGBD mapping and shows potential for retrospective analysis and operational monitoring of forest AGBD and its dynamics.

Taejin Park↗

NASA NTRS1986

Determining the influence and effects of manufacturing variables on sulfur dioxide cells

A survey of the Li/SO2 manufacturing community was conducted to determine where variability exists in processing. The upper and lower limits of these processing variables might, by themselves or by interacting with other variables, influence safety, performance, and reliability. A number of important variables were identified and a comprehensive design experiment is being proposed to make the proper determinations.

Zajac, W. V.↗

NASA NTRS1992

Global change data sets: Excerpts from the Master Directory, version 2.0

The recent awakening to the reality of human-induced changes to the environment has resulted in an organized effort to promote global change research. The goal of this research as outlined by NASA's Earth System Science Committee (Earth System Science: A closer View, 1988) is to understand the entire Earth system on a global scale by describing how its component parts and their interactions have evolved, how they function, and how they may be expected to evolve on all timescales. The practical result is the capacity to predict that evolution over the next decade to century. Key variables important for the study of global change include external forcing factors (solar radiance, UV flux), radiatively and chemically important trace species (CO2, CH4, N2O, etc.), atmospheric response variables (temperature, pressure, winds), landsurface properties (river run-off, snow cover, albedo, soil moisture, vegetation cover), and oceanic variables (sea surface temperature, sea ice extent, sea level ocean wind stress, currents, chlorophyll, biogeochemical fluxes). The purpose of this document is to identify existing data sets available (both remotely sensed and in situ data) covering some of these variables. This is not intended to be a complete list of global change data, but merely a highlight of what is available. The information was extracted from the Master Directory (MD), an on-line scientific data information service which may be used by any researcher. This report contains the coverage dates for the data sets, sources (satellites, instruments) of the data and where they are archived.

Beier, Joy↗

NASA NTRS2020

Forest Potential Productivity Mapping by Linking Remote-Sensing-Derived Metrics to Site Variables

A fine-resolution region-wide map of forest site productivity is an essential need for effective large-scale forestry planning and management. In this study, we incorporated Sentinel-2 satellite data into an increment-based measure of forest productivity (biomass growth index (BGI)) derived from climate, lithology, soils, and topographic metrics to map improved BGI (iBGI) in parts of North American Acadian regions. Initially, several Sentinel-2 variables including nine single spectral bands and 12 spectral vegetation indices (SVIs) were used in combination with forest management variables to predict tree volume/ha and height using Random Forest. The results showed a 10–12 % increase in out of bag (OOB) r2when Sentinel-2 variables were included in the prediction of both volume and height together with BGI. Later, selected Sentinel-2 variables were used for biomass growth prediction in Maine, USA and New Brunswick, Canada using data from 7738 provincial permanent sample plots. The Sentinel-2 red-edge position (S2REP) index was identified as the most important variable over others to have known influence on site productivity. While a slight improvement in the iBGI accuracy occurred compared to the base BGI model (~2%), substantial changes to coefficients of other variables were evident and some site variables became less important when S2REP was included.

site productivity↗

NASA NTRS1980

Genesis of breath sounds-Preliminary verification of theory

Experimental results are presented which tend to validate a previously developed theory of sound production in the human lung over a particular Reynolds number range. In addition, a new, presently nonunderstood, phenomenon was observed at higher Reynolds number. These results, which show how sound generation in the lung depends upon the physiologically important variables of volume flow rate and bronchial diameter, have potentially important application in noninvasive lung examination and the diagnosis of lung disease.

Patterson, J. L.↗

NASA NTRS1976

The sensitivity of relative toxicity rankings by the USF/NASA test method to some test variables

Pyrolysis temperature and the distance between the source and sensor of effluents are two important variables in tests for relative toxicity. Modifications of the USF/NASA toxicity screening test method to increase the upper temperature limit of pyrolysis, reduce the distance between the sample and the test animals, and increase the chamber volume available for animal occupancy, did not significantly alter rankings of relative toxicity of four representative materials. The changes rendered some differences no longer significant, but did not reverse any rankings. The materials studied were cotton, wool, aromatic polyamide, and polybenzimidazole.

Hilado, C. J.↗

NASA NTRS2010

Toward Malaria Risk Prediction in Afghanistan Using Remote Sensing

Malaria causes more than one million deaths every year worldwide, with most of the mortality in Sub-Saharan Africa. It is also a significant public health concern in Afghanistan, with approximately 60% of the population, or nearly 14 million people, living in a malaria-endemic area. Malaria transmission has been shown to be dependent on a number of environmental and meteorological variables. For countries in the tropics and the subtropics, rainfall is normally the most important variable, except for regions with high altitude where temperature may also be important. Afghanistan s diverse landscape contributes to the heterogeneous malaria distribution. Understanding the environmental effects on malaria transmission is essential to the effective control of malaria in Afghanistan. Provincial malaria data gathered by Health Posts in 23 provinces during 2004-2007 are used in this study. Remotely sensed geophysical parameters, including precipitation from TRMM, and surface temperature and vegetation index from MODIS are used to derive the empirical relationship between malaria cases and these geophysical parameters. Both neural network methods and regression analyses are used to examine the environmental dependency of malaria transmission. And the trained models are used for predicting future transmission. While neural network methods are intrinsically more adaptive for nonlinear relationship, the regression approach lends itself in providing statistical significance measures. Our results indicate that NDVI is the strongest predictor. This reflects the role of irrigation, instead of precipitation, in Afghanistan for agricultural production. The second strongest prediction is surface temperature. Precipitation is not shown as a significant predictor, contrary to other malarious countries in the tropics or subtropics. With the regression approach, the malaria time series are modelled well, with average R2 of 0.845. For cumulative 6-month prediction of malaria cases, the average provincial accuracy reaches 91%. The developed predictive and early warning capabilities support the Third Strategic Approach of the WHO EMRO Malaria Control and Elimination Plan.

Safi, N.↗

NASA NTRS1934

Tank tests of a family of flying-boat hulls

This report presents towing tests made in the N.A.C.A. tank of a parent form and five variations of a flying-boat hull. The beams of two of the derived forms were made the same as that of the parent and the lengths changed by increasing and decreasing the spacing of stations. The lengths of the two others of the derived forms were made the same as that of the parent while the beams were changed by increasing and decreasing the spacing of buttocks, all other widths being changed in proportion. The remaining derived form has the same length and beam as the parent, but the lines of the forebody were altered to give a planing bottom with no longitudinal curvature forward of the step. The test data were analyzed to determine the minimum resistance and the angle at which it occurs for all speeds and loads. The results of this analysis are given in the form of non dimensional curves for each model. The effect of variation in over-all size, as indicated by a "complete" test on any given hull, is pointed out. The effect of changing length alone by the spacing of buttocks, as well as the effects of the changes in length-beam ratio and longitudinal curvature that result from these operations are discussed. The difficulties encountered in interpreting test results of systematic families derived by the method used are emphasized. Further studies are suggested in which changes in the variable under consideration would not be obscured by secondary changes in other important variables.

Shoemaker, James M↗

NASA NTRS1991

Analysis of Fluctuating Static Pressure Measurements in a Large High Reynolds Number Transonic Cryogenic Wind Tunnel

Dynamic measurements of fluctuating static pressure levels were made using flush mounted high frequency response pressure transducers at eleven locations in the circuit of the National Transonic Facility (NTF) over the complete operating range of this wind tunnel. Measurements were made at test section Mach numbers from 0.2 to 1.2, at pressure from 1 to 8.6 atmospheres and at temperatures from ambient to -250 F, resulting in dynamic flow disturbance measurements at the highest Reynolds numbers available in a transonic ground test facility. Tests were also made independently at variable Mach number, variable Reynolds number, and variable drivepower, each time keeping the other two variables constant thus allowing for the first time, a distinct separation of these three important variables. A description of the NTF emphasizing its flow quality features, details on the calibration of the instrumentation, results of measurements with the test section slots covered, downstream choke, effects of liquid nitrogen injection and gaseous nitrogen venting, comparisons between air and nitrogen, isolation of the effects of Mach number, Reynolds number, and fan drive power, and identification of the sources of significant flow disturbances is included. The results indicate that primary sources of flow disturbance in the NTF may be edge-tones generated by test section sidewall re-entry flaps and the venting of nitrogen gas from the return leg of the tunnel circuit between turns 3 and 4 in the cryogenic mode of operation. The tests to isolate the effects of Mach number, Reynolds number, and drive power indicate that Mach number effects predominate. A comparison with other transonic wind tunnels shows that the NTF has low levels of test section fluctuating static pressure especially in the high subsonic Mach number range from 0.7 to 0.9.

Igoe, William B.↗

NASA NTRS1996

Strategies for GPS-Based Estimates of Troposphere Delay

Traditionally used for high precision geodesy, the GPS system has recently emerged as an equally powerful tool in atmospheric studies, in particular, climatology and meteorology. There are several products of GPS-based systems that are of interest to climatologists and meteorologists. One of the most important is the GPS-based estimate of the amount of Precipitable Water Vapor (PWV) in the troposphere. Water vapor is an important variable in the study of climate changes and atmospheric convection, and is of crucial importance for severe weather forecasting and operational numerical weather prediction. In this paper we discuss various aspects of the process by which Zenith Wet Delay (ZWD) are estimated from GPS data and we describe a very simple estimation stratagy for near real time applications.

GPS↗

NASA NTRS1994

Towards an MHD theory for the standoff distance of Earth's bow shock

An MHD theory is developed for the stand-off distance a(sub s) of the bow shock and the thickness delta(sub ms) of the magnetosheath, using the empirical Spreiter et al. relation delta(sub ms) = kX and the MHD density ratio X across the shock. The theory includes as special cases the well-known gasdynamic theory and associated phenomenological MHD-like models for delta(sub ms) and a(sub s). In general, however, MHD effects produce major differences from previous models, especially at low Alfven (M(sub A)) and sonic (M(sub S)) Mach numbers. The magnetic field orientation, M(sub A), M(sub S) and the ratio of specific heats gamma are all important variables of the theory. Three principal conclusions are reached. First, the gasdynamic and phenomenological models miss important dependances on field orientation and M(sub S) and generally provide poor approximations to the MHD results. Second, changes in field orientation and M(sub S) are predicted to cause factor of approximately 4 changes in delta(sub ms) at low M(sub A). Third, using Spreiter et al.'s value for k in the MHD theory leads to maximum a(sub s) values at low M(sub A) and nominal M(sub S) that are much smaller than observations and MHD simulations require. Resolving this problem requires either the modified Spreiter-like relation and larger k found in recent MHD simulations and/or breakdown in the Spreiter-like relation at very low M(sub A).

Cairns, Iver H.↗