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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
All sourcesNASA NTRSDOE OSTI
NASA NTRS1981

A High Resolution Spectroscopic Study of the Nu2 Band of Hydrogen Sulfide and the 1-0 Band of Hydrogen Iodide

A tunable diode laser spectrometer was constructed and used to study: (1) the effects of centrifugal distortion on the transition frequencies and strengths of the nu sub 2 band of H2S, and (2) nuclear quadrupole hyperfine structure in the 1-0 band of HI. A total of 126 line frequencies and 94 line strengths in the nu sub 2 band of H2S were measured. The average accuracy of the line frequency measurements was + or - 0.0016 cm. The line strengths were measured to an average accuracy of about 3 percent. The effect of the finite spectral width of the diode laser on the measurement of line strengths is discussed. The observed H2S line frequencies were fit to Watson's AS and NS reduced Hamiltonian in both the Ir and IIIr coordinate representations in order to determine the best set of rotation distortion constants for the upper state of the nu sub 2 band. Comparisons of the observed line strengths in this band to rigid rotor line strengths are also presented. Nuclear quadrupole hyperfine structure in the low J lines of the 1-0 band of HI was observed. The upper vibrational state nuclear quadrupole coupling constant, determined from the observed splittings, was -1850 MHz + or - 12 MHz or 1.2 percent + or - 0.7 percent larger than the ground state coupling constant.

Strow, L. L.↗
NASA NTRS2000

The Impact of Sea Ice Concentration Accuracies on Climate Model Simulations with the GISS GCM

The Goddard Institute for Space Studies global climate model (GISS GCM) is used to examine the sensitivity of the simulated climate to sea ice concentration specifications in the type of simulation done in the Atmospheric Modeling Intercomparison Project (AMIP), with specified oceanic boundary conditions. Results show that sea ice concentration uncertainties of +/- 7% can affect simulated regional temperatures by more than 6 C, and biases in sea ice concentrations of +7% and -7% alter simulated annually averaged global surface air temperatures by -0.10 C and +0.17 C, respectively, over those in the control simulation. The resulting 0.27 C difference in simulated annual global surface air temperatures is reduced by a third, to 0.18 C, when considering instead biases of +4% and -4%. More broadly, least-squares fits through the temperature results of 17 simulations with ice concentration input changes ranging from increases of 50% versus the control simulation to decreases of 50% yield a yearly average global impact of 0.0107 C warming for every 1% ice concentration decrease, i.e., 1.07 C warming for the full +50% to -50% range. Regionally and on a monthly average basis, the differences can be far greater, especially in the polar regions, where wintertime contrasts between the +50% and -50% cases can exceed 30 C. However, few statistically significant effects are found outside the polar latitudes, and temperature effects over the non-polar oceans tend to be under 1 C, due in part to the specification of an unvarying annual cycle of sea surface temperatures. The +/- 7% and 14% results provide bounds on the impact (on GISS GCM simulations making use of satellite data) of satellite-derived ice concentration inaccuracies, +/- 7% being the current estimated average accuracy of satellite retrievals and +/- 4% being the anticipated improved average accuracy for upcoming satellite instruments. Results show that the impact on simulated temperatures of imposed ice concentration changes is least in summer, encouragingly the same season in which the satellite accuracies are thought to be worst. Hence the impact of satellite inaccuracies is probably less than the use of an annually averaged satellite inaccuracy would suggest.

Parkinson, Claire L.↗
NASA NTRS2024

Presound: UAV Diagnostic System Enabled by Vibration-Based Machine Learning

A low-weight, inexpensive small unmanned aerial system (sUAS) that takes off, performs a mission, lands, and safely stows and recharges itself has myriad future applications ranging from agricultural imaging to last-mile package delivery. Likewise, Urban Air Mobility (UAM) systems will enable people to take air taxis from point to point in cities, rapidly moving commuters long distances without concern for road traffic and congestion. Fully electric aviation systems will be cleaner and quieter than ground transport. Cities could eliminate cars and buses, and convert roads to higher capacity bike and pedestrian throughways. Yet, for sUAS as well as UAM, system reliability and assurance is a limiting factor to deploying affordable autonomous flight systems. For this bright future of aviation to be realized, aircraft must be able to autonomously and accurately self-diagnose health issues both before takeoff and during flight. The GreenSight PreSound system is designed to identify defects on aircraft through intelligent analysis of vibration. It accomplishes this by measuring structural vibrations induced by the vehicle’s own propellers, and analyzing that data using a machine learning model that determines whether a defect is present. The PreSound system is designed to require no human oversight, and to operate across a wide array of vehicles through re-training of the model for each target aircraft. PreSound has been developed and seen limited early success using data collected from the GreenSight Dreamer sUAS, a 5lb quadrotor vehicle designed for aerial imaging applications. The final detection model, trained on data with props spinning at 50% throttle, achieves excellent performance with over 99% average accuracy in detecting blade damage using a single FFT vector input. It demonstrates the ability to generalize to new types of blade damage, correctly classifying a different type of blade damage with 98% accuracy. Full test pulses were classified with 100% accuracy, and in live testing, all sets of data during blade movement were classified accurately with over 95% confidence. When trained on in-flight data, the same model achieves an average accuracy of 85% in distinguishing between undamaged and blade-damaged states in flight. The authors believe that these accuracies show significant potential of this approach to expand unmanned flight safety, with significant potential benefits in accelerating Advanced Aerial Mobility (AAM) and UAM aviation applications.

UAS↗
NASA NTRS2014

An Automated Approach to Map the History of Forest Disturbance from Insect Mortality and Harvest with Landsat Time-Series Data

Forests contain a majority of the aboveground carbon (C) found in ecosystems, and understanding biomass lost from disturbance is essential to improve our C-cycle knowledge. Our study region in the Wisconsin and Minnesota Laurentian Forest had a strong decline in Normalized Difference Vegetation Index (NDVI) from 1982 to 2007, observed with the National Ocean and Atmospheric Administration's (NOAA) series of Advanced Very High Resolution Radiometer (AVHRR). To understand the potential role of disturbances in the terrestrial C-cycle, we developed an algorithm to map forest disturbances from either harvest or insect outbreak for Landsat time-series stacks. We merged two image analysis approaches into one algorithm to monitor forest change that included: (1) multiple disturbance index thresholds to capture clear-cut harvest; and (2) a spectral trajectory-based image analysis with multiple confidence interval thresholds to map insect outbreak. We produced 20 maps and evaluated classification accuracy with air-photos and insect air-survey data to understand the performance of our algorithm. We achieved overall accuracies ranging from 65% to 75%, with an average accuracy of 72%. The producer's and user's accuracy ranged from a maximum of 32% to 70% for insect disturbance, 60% to 76% for insect mortality and 82% to 88% for harvested forest, which was the dominant disturbance agent. Forest disturbances accounted for 22% of total forested area (7349 km2). Our algorithm provides a basic approach to map disturbance history where large impacts to forest stands have occurred and highlights the limited spectral sensitivity of Landsat time-series to outbreaks of defoliating insects. We found that only harvest and insect mortality events can be mapped with adequate accuracy with a non-annual Landsat time-series. This limited our land cover understanding of NDVI decline drivers. We demonstrate that to capture more subtle disturbances with spectral trajectories, future observations must be temporally dense to distinguish between type and frequency in heterogeneous landscapes.

Forest↗
NASA NTRS1993

On the accuracy of ERS-1 orbit predictions

Since the launch of ERS-1, the D-PAF (German Processing and Archiving Facility) provides regularly orbit predictions for the worldwide SLR (Satellite Laser Ranging) tracking network. The weekly distributed orbital elements are so called tuned IRV's and tuned SAO-elements. The tuning procedure, designed to improve the accuracy of the recovery of the orbit at the stations, is discussed based on numerical results. This shows that tuning of elements is essential for ERS-1 with the currently applied tracking procedures. The orbital elements are updated by daily distributed time bias functions. The generation of the time bias function is explained. Problems and numerical results are presented. The time bias function increases the prediction accuracy considerably. Finally, the quality assessment of ERS-1 orbit predictions is described. The accuracy is compiled for about 250 days since launch. The average accuracy lies in the range of 50-100 ms and has considerably improved.

Koenig, Rolf↗
NASA NTRS1995

Determining GPS average performance metrics

Analytic and semi-analytic methods are used to show that users of the GPS constellation can expect performance variations based on their location. Specifically, performance is shown to be a function of both altitude and latitude. These results stem from the fact that the GPS constellation is itself non-uniform. For example, GPS satellites are over four times as likely to be directly over Tierra del Fuego than over Hawaii or Singapore. Inevitable performance variations due to user location occur for ground, sea, air and space GPS users. These performance variations can be studied in an average relative sense. A semi-analytic tool which symmetrically allocates GPS satellite latitude belt dwell times among longitude points is used to compute average performance metrics. These metrics include average number of GPS vehicles visible, relative average accuracies in the radial, intrack and crosstrack (or radial, north/south, east/west) directions, and relative average PDOP or GDOP. The tool can be quickly changed to incorporate various user antenna obscuration models and various GPS constellation designs. Among other applications, tool results can be used in studies to: predict locations and geometries of best/worst case performance, design GPS constellations, determine optimal user antenna location and understand performance trends among various users.

Moore, G. V.↗
NASA NTRS1977

The application of LANDSAT-1 imagery for monitoring strip mines in the new river watershed in northeast Tennessee, part 2

The author has identified the following significant results. LANDSAT imagery and supplementary aircraft photography of the New River drainage basin were subjected to a multilevel analysis using conventional photointerpretation methods, densitometric techniques, multispectral analysis, and statistical tests to determine the accuracy of LANDSAT-1 imagery for measuring strip mines of common size. The LANDSAT areas were compared with low altitude measurements. The average accuracy over all the mined land sample areas mapped from LANDSAT-1 was 90%. The discrimination of strip mine subcategories is somewhat limited on LANDSAT imagery. A mine site, whether active or inactive, can be inferred by lack of vegetation, by shape, or image texture. Mine ponds are difficult or impossible to detect because of their small size and turbidity. Unless bordered and contrasted with vegetation, haulage roads are impossible to delineate. Preparation plants and refuge areas are not detectable. Density slicing of LANDSAT band 7 proved most useful in the detection of reclamation progress within the mined areas. For most state requirements for year-round monitoring of surface mined land, LANDSAT is of limited value. However, for periodic updating of regional surface maps, LANDSAT may provide sufficient accuracies for some users.

Shahrokhi, F.↗
NASA NTRS1973

Signature extension: An approach to operational multispectral surveys

Two data processing techniques were suggested as applicable to the large area survey problem. One approach was to use unsupervised classification (clustering) techniques. Investigation of this method showed that since the method did nothing to reduce the signal variability, the use of this method would be very time consuming and possibly inaccurate as well. The conclusion is that unsupervised classification techniques of themselves are not a solution to the large area survey problem. The other method investigated was the use of signature extension techniques. Such techniques function by normalizing the data to some reference condition. Thus signatures from an isolated area could be used to process large quantities of data. In this manner, ground information requirements and computer training are minimized. Several signature extension techniques were tested. The best of these allowed signatures to be extended between data sets collected four days and 80 miles apart with an average accuracy of better than 90%.

Nalepka, R. F.↗
NASA NTRS1974

The reliability of the daily quick look solar wind velocities as indicators of interplanetary activity

It is shown that the daily quick look solar wind velocities based on the Ames Research Center solar wind plasma data obtained on the Pioneer spacecraft are surprisingly reliable indicators of interplanetary activity. These velocities can appropriately be used in correlation studies with an average accuracy of plus or minus 5%. This accuracy reflects the 'jitter' in the solar wind velocity during quiescent intervals and across high-speed streams.

Intriligator, D. S.↗
NASA NTRS1983

Line strength measurements using diode lasers - The nu2 band of H2S

The strengths of 94 lines in the nu2 band of H2S have been measured with an average accuracy of 3 percent using a tunable diode laser. The line strengths are determined from the peak absorption of nearly Doppler-shaped lines. A detailed error analysis of this measurement method is given. Ratios of the measured line strengths to strengths calculated assuming no vibration-rotation interactions are shown to range from as low as 0.057 to as high as 4.71.

Strow, L. L.↗
NASA NTRS1992

Ozone Measurements with the US EPA UV-DIAL: Preliminary Results

A compact airborne down-looking lidar system was developed at the Environmental Protection Agency in Las Vegas. This differential absorption lidar (DIAL) was designed to simultaneously measure range-resolved concentrations of ozone (O3) and sulfur dioxide (SO2) in the lower troposphere, together with an indication of the aerosol distribution. The five laser wavelengths (i.e., lambda(sub 1) = 277 nm, lambda(sub 2) = 292 nm, lambda(sub 3) = 313 nm, lambda(sub4) = 319 nm, lambda(sub 5) = 369 nm) were generated via Raman conversion of a focused KrF excimer laser. The system is currently installed in a truck-based mobile laboratory. For the ground testing, an opening in the truck floor together with a folding mirror under the truck makes a horizontal, or upwardly inclined direction of measurement possible. Initial ground testing has been performed in the vicinity of a Desert Research Institute (DRI) ambient air monitoring site, located at Cottonwood Cove approximately 85 km south east of Las Vegas, Nevada. At this site O3 and SO2 concentrations are continuously monitored with an average accuracy better than +/- 10 percent. A temporary ozone measurement station with identical accuracy was set up at a distance to get a second point of comparison for the range-resolved DIAL measurements.

Moosmueller, H.↗
NASA NTRS1992

Photometric calibration of NGS/POSS and ESO/SRC plates using the NOAO PDS measuring engine. I - Stellar photometry

The PDS/Monet measuring engine at the National Optical Astronomy Observatory was used to obtain photometry of nearly 10,000 stars on the NGS/POSS and 2000 stars on the ESO/SRC Survey glass plates. These measurements have been used to show that global transformation functions exist that allow calibration of stellar photometry from any blue or red plate to equivalent Johnson B and Cousins R photoelectric magnitudes. The four transformation functions appropriate for the POSS O and E and ESO/SRC J and R plates were characterized, and it was found that, within the measurement uncertainties, they vary from plate to plate only by photometric zero-point offsets. A method is described to correct for the zero-point shifts and to obtain calibrated B and R photometry of stellar sources to an average accuracy of 0.3-0.4 mag within the range R between values of 8 and 19.5 for red plates in both surveys, B between values of 9 and 20.5 on POSS blue plates, and B between values of 10 and 20.5 on ESO/SRC blue plates. This calibration procedure makes it possible to obtain rapid photometry of very large numbers of stellar sources.

Cutri, Roc M.↗
NASA NTRS1995

Rapid calibration of seven-hole probes

This paper summarizes the major conclusions and some of the key supporting analyses resulting from the calibration and application of two small seven hole probes at NASA Lewis Research Center. These probes can produce reasonably accurate and rapid surveys of unknown steady flow fields which may include flow angles up to 70 degrees and Mach numbers up to 0.8. The probes were calibrated with both 'complete' and 'reduced' test matrices. Both types of test matrices produced similar results suggesting the the reduced matrices are adequate for most purposes. The average accuracy fo the calibration was about the same as that achieved in previous seven hole probe calibrations. At the higher Mach numbers, the calibration was sensitive to the diameter of the free jet in the calibration facility. Over a narrow angular range at the higher Mach numbers, the system had serious repeatability problems. This lack or repeatability apparently results from aliasing of high frequency (20 to 40 Hz) noise with the data acquisition system sampling frequency of 10 Hz. Analyses show that these noise frequencies are probably not related to airflow dynamics in the connecting tubing.

Ericksen, A. L.↗
NASA NTRS2004

AQUARIUS: A Passive/Active Microwave Sensor to Monitor Sea Surface Salinity Globally from Space

Salinity is important for understanding ocean dynamics, energy exchange with the atmosphere and the global water cycle. Existing data is limited and much of the ocean has never even been sampled. Sea surface salinity can be measured remotely by satellite and a three year mission for this purpose called AquariudSAC-D has recently been selected by NASA's Earth System Science Pathfinder (ESSP) program. The objective is to map the salinity field globally with a spatial resolution of 100 km and a monthly average accuracy of 0.2 psu. The mission, scheduled for launch in 2008, is a partnership of the United States National Aeronautics and Space Agency (NASA) and the Argentine Comision National de Actividades Epaciales (CONAE).

LeVine, David↗
NASA NTRS2005

Sub-Microsecond Temperature Measurement in Liquid Water Using Laser Induced Thermal Acoustics

Using laser-induced thermal acoustics, we demonstrate non-intrusive and remote sound speed and temperature measurements over the range 10 - 45 C in liquid water. Averaged accuracy of sound speed and temperature measurements (10 s) are 0.64 m/s and 0.45 C respectively. Single-shot precisions based on one standard deviation of 100 or greater samples range from 1 m/s to 16.5 m/s and 0.3 C to 9.5 C for sound speed and temperature measurements respectively. The time resolution of each single-shot measurement was 300 nsec.

Alderfer, David W.↗
NASA NTRS2013

Application and Evaluation of a Snowmelt Runoff Model in the Tamor River Basin, Eastern Himalaya Using a Markov Chain Monte Carlo (MCMC) Data Assimilation Approach

Previous studies have drawn attention to substantial hydrological changes taking place in mountainous watersheds where hydrology is dominated by cryospheric processes. Modelling is an important tool for understanding these changes but is particularly challenging in mountainous terrain owing to scarcity of ground observations and uncertainty of model parameters across space and time. This study utilizes a Markov Chain Monte Carlo data assimilation approach to examine and evaluate the performance of a conceptual, degree-day snowmelt runoff model applied in the Tamor River basin in the eastern Nepalese Himalaya. The snowmelt runoff model is calibrated using daily streamflow from 2002 to 2006 with fairly high accuracy (average Nash-Sutcliffe metric approx. 0.84, annual volume bias <3%). The Markov Chain Monte Carlo approach constrains the parameters to which the model is most sensitive (e.g. lapse rate and recession coefficient) and maximizes model fit and performance. Model simulated streamflow using an interpolated precipitation data set decreases the fractional contribution from rainfall compared with simulations using observed station precipitation. The average snowmelt contribution to total runoff in the Tamor River basin for the 2002-2006 period is estimated to be 29.7+/-2.9% (which includes 4.2+/-0.9% from snowfall that promptly melts), whereas 70.3+/-2.6% is attributed to contributions from rainfall. On average, the elevation zone in the 4000-5500m range contributes the most to basin runoff, averaging 56.9+/-3.6% of all snowmelt input and 28.9+/-1.1% of all rainfall input to runoff. Model simulated streamflow using an interpolated precipitation data set decreases the fractional contribution from rainfall versus snowmelt compared with simulations using observed station precipitation. Model experiments indicate that the hydrograph itself does not constrain estimates of snowmelt versus rainfall contributions to total outflow but that this derives from the degree-day melting model. Lastly, we demonstrate that the data assimilation approach is useful for quantifying and reducing uncertainty related to model parameters and thus provides uncertainty bounds on snowmelt and rainfall contributions in such mountainous watersheds.

runoff↗
NASA NTRS2017

Prediction of Cognitive States During Flight Simulation Using Multimodal Psychophysiological Sensing

The Commercial Aviation Safety Team found the majority of recent international commercial aviation accidents attributable to loss of control inflight involved flight crew loss of airplane state awareness (ASA), and distraction was involved in all of them. Research on attention-related human performance limiting states (AHPLS) such as channelized attention, diverted attention, startle/surprise, and confirmation bias, has been recommended in a Safety Enhancement (SE) entitled "Training for Attention Management." To accomplish the detection of such cognitive and psychophysiological states, a broad suite of sensors was implemented to simultaneously measure their physiological markers during a high fidelity flight simulation human subject study. Twenty-four pilot participants were asked to wear the sensors while they performed benchmark tasks and motion-based flight scenarios designed to induce AHPLS. Pattern classification was employed to predict the occurrence of AHPLS during flight simulation also designed to induce those states. Classifier training data were collected during performance of the benchmark tasks. Multimodal classification was performed, using pre-processed electroencephalography, galvanic skin response, electrocardiogram, and respiration signals as input features. A combination of one, some or all modalities were used. Extreme gradient boosting, random forest and two support vector machine classifiers were implemented. The best accuracy for each modality-classifier combination is reported. Results using a select set of features and using the full set of available features are presented. Further, results are presented for training one classifier with the combined features and for training multiple classifiers with features from each modality separately. Using the select set of features and combined training, multistate prediction accuracy averaged 0.64 +/- 0.14 across thirteen participants and was significantly higher than that for the separate training case. These results support the goal of demonstrating simultaneous real-time classification of multiple states using multiple sensing modalities in high fidelity flight simulators. This detection is intended to support and inform training methods under development to mitigate the loss of ASA and thus reduce accidents and incidents.

Harrivel, Angela R.↗
NASA NTRS1988

Multisensor classification of sedimentary rocks

A comparison is made between linear discriminant analysis and supervised classification results based on signatures from the Landsat TM, the Thermal Infrared Multispectral Scanner (TIMS), and airborne SAR, alone and combined into extended spectral signatures for seven sedimentary rock units exposed on the margin of the Wind River Basin, Wyoming. Results from a linear discriminant analysis showed that training-area classification accuracies based on the multisensor data were improved an average of 15 percent over TM alone, 24 percent over TIMS alone, and 46 percent over SAR alone, with similar improvement resulting when supervised multisensor classification maps were compared to supervised, individual sensor classification maps. When training area signatures were used to map spectrally similar materials in an adjacent area, the average classification accuracy improved 19 percent using the multisensor data over TM alone, 2 percent over TIMS alone, and 11 percent over SAR alone. It is concluded that certain sedimentary lithologies may be accurately mapped using a single sensor, but classification of a variety of rock types can be improved using multisensor data sets that are sensitive to different characteristics such as mineralogy and surface roughness.

Evans, Diane↗