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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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112 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS1978

Analysis of vector wind change with respect to time for Cape Kennedy, Florida

Multivariate analysis was used to determine the joint distribution of the four variables represented by the components of the wind vector at an initial time and after a specified elapsed time is hypothesized to be quadravariate normal; the fourteen statistics of this distribution, calculated from 15 years of twice-daily rawinsonde data are presented by monthly reference periods for each month from 0 to 27 km. The hypotheses that the wind component changes with respect to time is univariate normal, that the joint distribution of wind component change with respect to time is univariate normal, that the joint distribution of wind component changes is bivariate normal, and that the modulus of vector wind change is Rayleigh are tested by comparison with observed distributions. Statistics of the conditional bivariate normal distributions of vector wind at a future time given the vector wind at an initial time are derived. Wind changes over time periods from 1 to 5 hours, calculated from Jimsphere data, are presented. Extension of the theoretical prediction (based on rawinsonde data) of wind component change standard deviation to time periods of 1 to 5 hours falls (with a few exceptions) within the 95 percentile confidence band of the population estimate obtained from the Jimsphere sample data. The joint distributions of wind change components, conditional wind components, and 1 km vector wind shear change components are illustrated by probability ellipses at the 95 percentile level.

Adelfang, S. I.↗
NASA NTRS2003

Investigation of electrolyte measurement in diluted whole blood using spectroscopic and chemometric methods

The feasibility of using near-infrared (NIR) spectroscopy in combination with partial least-squares (PLS) regression was explored to measure electrolyte concentration in whole blood samples. Spectra were collected from diluted blood samples containing randomized, clinically relevant concentrations of Na+, K+, and Ca2+. Sodium was also studied in lysed blood. Reference measurements were made from the same samples using a standard clinical chemistry instrument. Partial least squares (PLS) was used to develop calibration models for each ion with acceptable results (Na+, R2 = 0.86, CVSEP = 9.5 mmol/L; K+, R2 = 0.54, CVSEP = 1.4 mmol/L; Ca2+, R2 = 0.56, CVSEP = 0.18 mmol/L). Slightly improved results were obtained using a narrower wavelength region (470-925 nm) where hemoglobin, but not water, absorbed indicating that ionic interaction with hemoglobin is as effective as water in causing measurable spectral variation. Good models were also achieved for sodium in lysed blood, illustrating that cell swelling, which is correlated with sodium concentration, is not required for calibration model development.

Non-NASA Center↗
NASA NTRS2003

Vector-Ordering Filter Procedure for Data Reduction

The vector-ordering filter (VOF) technique involves a procedure for sampling a large population of data vectors to select a subset of data vectors that fully characterize the state space of the large population. The VOF technique enables a large reduction of the volume of data that must be handled in the automated monitoring system and method discussed in the two immediately preceding articles. In so doing, the VOF technique enables the development of data-driven mathematical models of a monitored asset from sets of data that would otherwise exceed the memory capacities of conventional engineering computers. Data-driven mathematical models have been shown to offer high fidelity for purposes of control and monitoring of assets. In practice, a collection of asset-operating observations is acquired with the intention that the collection contain observations characteristic of the full dynamic range of operation of the asset. Often, such a collection contains an extremely large number of observations, many of which are redundant. The VOF technique fills the need for a means to extract, from the original collection of observational data, a reduced data matrix that excludes redundant data while maintaining the full statistical character and dynamic range of the original data. The reduced data matrix can then be used as the input data for development of a mathematical model of the monitored asset, or as training data for a neural-network substitute for an explicit mathematical model of the asset. Alternatively, the reduced data matrix can, itself, be used directly as a mathematical model of the monitored asset, as is commonly done in multivariate state-estimation techniques. The original data are collected from the asset over a range of operating states and are put in matrix form. Each column vector in the original data matrix represents the signal values acquired at a particular operational state of the asset. Thus, the number of columns of the original data matrix equals the number of observed states and the number of rows in this matrix equals the number of signals acquired at each observation. In the VOF technique, one extracts the reduced data matrix from the original data matrix through the selection of a representative subset of the column (state) vectors.

Bickford, Randall L.↗
NASA NTRS2012

The Validity Chlorophyll-a Estimation by Sun Induced Fluorescence in Estuarine Waters: An Analysis of Long-term (2003-2011) Water Quality Data from Tampa Bay, Florida (USA)

Satellite observation of phytoplankton concentration or chlorophyll-a is an important characteristic, critically integral to monitoring coastal water quality. However, the optical properties of estuarine and coastal waters are highly variable and complex and pose a great challenge for accurate analysis. Constituents such as suspended solids and dissolved organic matter and the overlapping and uncorrelated absorptions in the blue region of the spectrum renders the blue-green ratio algorithms for estimating chlorophyll-a inaccurate. Measurement of sun-induced chlorophyll fluorescence, on the other hand, which utilizes the near infrared portion of the electromagnetic spectrum, may provide a better estimate of phytoplankton concentrations. While modelling and laboratory studies have illustrated both the utility and limitations of satellite baseline algorithms based on the sun induced chlorophyll fluorescence signal, few have examined the empirical validity of these algorithms using a comprehensive long term in situ data set. In an unprecedented analysis of a long term (2003-2011) in situ monitoring data from Tampa Bay, Florida (USA), we assess the validity of the FLH product from the Moderate Resolution Imaging Spectrometer (MODIS) against chlorophyll ]a and a suite of water quality parameters taken in a variety of conditions throughout a large optically complex estuarine system. A systematic analysis of sampling sites throughout the bay is undertaken to understand how the relationship between FLH and in situ chlorophyll-a responds to varying conditions within the estuary including water depth, distance from shore and structures and eight water quality parameters. From the 39 station for which data was derived, 22 stations showed significant correlations when the FLH product was matched with in situ chlorophyll-alpha data. The correlations (r2) for individual stations within Tampa Bay ranged between 0.67 (n=28, pless than 0.01) and-0.457 (n=12, p=.016), indicating that for some areas within the Bay, FLH can be a good predictor of chlorophyll-alpha concentration and hence a useful tool for the analysis of water quality. Overall, the results show a 106% increase in the validity of chlorophyll -a concentration estimates using FLH over the standard the blue-green OC3M algorithm. This analysis also illustrates that the correlations between FLH and in situ chlorophyll -a measurements increases with increasing water depth and distance of the monitoring sites from both the shore and structures. However, due to confounding factors related to the complexity of the estuarine system, a linear improvement in the FLH to chlorophyll ]a relationship was not clearly noted with increasing depth and distance from shore alone. Correlations of FLH with turbidity, nutrients (total nitrogen and total phosphorous) biological oxygen demand, salinity, sea surface temperature correlated positively with FLH concentrations, while dissolved oxygen and pH showed negative correlations. Principle component analyses are employed to further describe the relationships between the multivariate water quality parameters and the FLH product. The majority of sites with higher and very significant correlations (pless than 0.01) also showed high correlation values for nutrients, turbidity and biological oxygen demand. These sites were on average in greater than seven meters of water and over five kilometers from shore. A thorough understanding of the relationship between the MODIS FLH product and in situ water quality parameters will enhance our understanding of the accuracy MODIS fs global FLH algorithm and assist in optimizing its calibration for use in monitoring the quality of estuarine and coastal waters worldwide.

Moreno-Madrinan, Max Jacobo↗