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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRS1996

Characterization and Evaluation of Acid Rain in East Central Florida from 1978 to 1995 and Evaluation of Some Chromatographic/Spectroscopic Results from Leachate Samples from CELSS

The results of monitoring the chemical composition of rain in east-central Florida have shown that the rain is moderately acid. The measured acidity of rain is less than that observed in other regions of the U. S., however, it does suggest that the level of acidity is substantial. The annual chemical composition of rain at UCF and at KSC has shown moderate variability. Extreme daily and monthly variations are observed, however these variations are not addressed here. The total ionic composition of rain collected at FL99 is greater than that for rain collected at UCF, however this can be accounted for by site proximity to the ocean with the accompanying marine influence. Difference in acidity data collected from the UCF and FL99 sites which are separated by 50 km may be due in part to the differences that have been observed between laboratory and field pH measurements. Trend assessment for precipitation composition requires evaluation of data that covers some minimum time period. In fact, the subdivision of the multi-year UCF record into individual 10 year records as described above can lead to the conclusion that a significant increase, a significant decrease or no trend exists for acidity depending upon the time period chosen for evaluation. Trend evaluation has also been accomplished by linear and nonlinear regression analysis using monthly volume weighted average concentrations and deposition using the UCF data set and some of the Florida NADP data set.

Madsen, Brooks C.↗
NASA NTRS2007

Physical Interpretation of the Correlation Between Multi-Angle Spectral Data and Canopy Height

Recent empirical studies have shown that multi-angle spectral data can be useful for predicting canopy height, but the physical reason for this correlation was not understood. We follow the concept of canopy spectral invariants, specifically escape probability, to gain insight into the observed correlation. Airborne Multi-Angle Imaging Spectrometer (AirMISR) and airborne Laser Vegetation Imaging Sensor (LVIS) data acquired during a NASA Terrestrial Ecology Program aircraft campaign underlie our analysis. Two multivariate linear regression models were developed to estimate LVIS height measures from 28 AirMISR multi-angle spectral reflectances and from the spectrally invariant escape probability at 7 AirMISR view angles. Both models achieved nearly the same accuracy, suggesting that canopy spectral invariant theory can explain the observed correlation. We hypothesize that the escape probability is sensitive to the aspect ratio (crown diameter to crown height). The multi-angle spectral data alone therefore may not provide enough information to retrieve canopy height globally

Schull, M. A.↗
NASA NTRS2013

Improved Understanding of the Modeled QBO Using MLS Observations and MERRA Reanalysis

The Quasi-Biennial Oscillation (QBO) dominates the variability of the tropical stratosphere on interannual time scales. The QBO has been shown to extend its influence into the chemical composition of this region through dynamical mechanisms. We have started our analysis using the realistic QBO internally generated by the Goddard Earth Observing System Version 5 (GEOS-5) general circulation model coupled to a comprehensive stratospheric and tropospheric chemical mechanism forced with observed sea surface temperatures over the past 33 years. We will show targeted comparisons with observations from NASAs Aura satellite Microwave Limb Sounder (MLS) and the Modern Era Retrospective-Analysis for Research and Applications (MERRA) reanalysis to provide insight into the simulation of the primary and secondary circulations associated with the QBO. Using frequency spectrum analysis and multiple linear regression we can illuminate the resulting circulations and deduce the strengths and weaknesses in their modeled representation. Inclusion of the QBO in our simulation improves the representation of the subtropical barriers and overall tropical variability. The QBO impact on tropical upwelling is important to quantify when calculating trends in sub-decadal scale datasets.

MERRA↗
DOE OSTI2026

Mountain Basin Controls on the Snow-to-Streamflow Signal: An AIC-Weighted Multiple Linear Regression Framework

A regression-based analysis quantifies how basin characteristics modulate the snow-to-streamflow signal. First, we use the ERA5-Land reanalysis gridded product (European Centre for Medium Range Weather Forecasts reanalysis 5 -Land component) for 4,655 hydrologic unit code - 10 (HUC10) mountain basins across the western United States (US) for water years 1987–2024. Linear regressions are performed for peak snow water equivalent (SWE) and annual streamflow for each mountain basin. Models use ordinary least squares in Python’s statsmodels package. After which, an Akaike Information Criterion (AIC)–weighted ensemble multiple linear regression (MLR) framework with 47 watershed traits is used to predict the linear regression coefficient of determination (r-squared) defining the ability of peak SWE to predict annual streamflow across all mountain basin. Predictor sets are constrained to avoid multicollinearity by excluding models with variance inflation factors (VIF) greater than 5. Mountain basin traits included in the MLR include seasonal climate, topography, vegetation type and structure, and bedrock geology. Accepted models are considered if their AIC is within 2.0 of the model with the minimum AIC, or best model. To compare predictor influence across acceptable models, we computed standardized regression coefficients. To evaluate structural redundancy among models, we constructed binary inclusion vectors for each acceptable model, denoting whether a predictor was present (1) or absent (0). Core predictor variables are defined as occurring in at least 67% of the acceptable models. For this regional analysis, only one model was found acceptable, with higher snow-to-streamflow translation (higher r-squared) occurring in colder mountain basins with higher relative winter precipitation, more snow accumulation and a lower fraction of annual precipitation that falls in the spring and summer. The second component of the data package uses previously published, high-resolution output from an integrated hydrological model of the East River watershed using the U.S. Geological Survey Groundwater and Surface water Flow model (GSFLOW, doi:10.15485/1998576). East River MLR expands upon the approach described above to explore the response of five streamflow metrics—annual streamflow, runoff efficiency, 7-day minimum flow, low-flow duration, and non-perennial stream fraction to snow system indicators including peak SWE, snow-covered area, snow disappearance date, and the fraction of basin area characterized by low-to-no snow, as well as seasonal precipitation and temperature, and annual hydrologic variables representing soil moisture, evapotranspiration (ET), the partitioning of incoming precipitation to evapotranspiration (ET/P), groundwater storage, and groundwater inflow to streams. MLR was done on all water years (P0: 1987-2024) and for each period as determined in the split analysis using pooled regression techniques (P1: 1987-2011 and P2: 2012-2024) to evaluate shifting predictor variable emphasis on streamflow generation. Results indicate that since 2012, peak SWE has lost statistical strength in its prediction of annual streamflow and runoff efficiency, and the indirect influence of spring temperature has emerged as critically important. Low-flow metrics remain largely influenced by soil moisture, vegetation water use and groundwater inflows with summer precipitation becoming a direct influence on minimum summer flow. Together, these data and Python-based analysis tools provide a framework for identifying the key watershed characteristics that control how streamflow responds to snow from year to year. The package also helps quantify uncertainty in statistical models and assess how snow–streamflow relationships vary across regions and over time. This dataset contains comma-separated values files (.csv), text files (.txt), python code files (.py), figure files (.png), and shapefiles (.cpg, .dbf, .prj, .sbn, .sbx, .shp, .xml). Further details on file contents and MLR execution can be found in the readme file and the FLMD files. Work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Comparison of four learning-based methods for predicting groundwater redox status

Knowing the location where groundwater denitrification occurs, or by proxy the groundwater redox status (oxic, mixed, and anoxic), is valuable information for assessing and managing potential agricultural land-use impacts on freshwater quality. We compare the efficacy of supervised (Linear Discriminant Analysis LDA; Boosted Regression Trees, BRT; and Random Forest, RF) and unsupervised (Modified Self-Organizing Map, MSOM) learning-based methods to predict groundwater redox status in the agriculturally dominated Tasman, Waikato, and Wellington regions of New Zealand. Thresholds applied to regional groundwater-quality samples provide redox status variables and learn heuristics constrained by these variables and applied to spatial factors (climate, elevation, geologic, hydrology soils, and well depth) identify optimal sets of regional predictor variables. A split-sample approach is used to train and test the learning methods ability to predict redox status using the optimal predictor variables. Overall, the supervised methods demonstrate a prediction bias toward oxic conditions and inability to perform statistically well when using independent regional data; for example, consider kappa statistics for BRT (Tasman: 0.42, Waikato: 0.38, Wellington: 0.17), RF (Tasman: 0.42, Waikato: 0.47, Wellington: 0.17 and LDA (Tasman: 0.46, Waikato: 0.32, Wellington: 0.17). By contrast, the unsupervised method performs statistically well when predicting oxic, mixed, and anoxic conditions and corresponding depths when using independent regional data; for example, consider MSOM kappa statistics for Tasman: 0.78, Waikato: 0.80, Wellington: 0.76. The unsupervised learning method provides the added benefits of being (1) able to combine predictions into 3D regional anoxic probability plots for interpreting the spatial influence of paleosols and groundwater flowpaths on redox status, and (2) readily extended to map 3D redox status across New Zealand and other countries despite data bias and sparsity.

machine learning algorithms, Random Forest, booste↗
NASA NTRS2020

Statistical Analysis of Factors Riving Surface Ozone Variability over Continental South Africa

Statistical relationships between surface ozone (O3) concentration, precursor species and meteorological conditions in continental South Africa were examined from data obtained from measurement stations in north-eastern South Africa. Three multivariate statistical methods were applied in the investigation, i.e. multiple linear regression (MLR), principal component analysis (PCA) and –regression (PCR), and generalised additive model (GAM) analysis. The daily maximum 8-h moving average O3 concentrations were considered in these statistical models (dependent variable). MLR models indicated that meteorology and precursor species concentrations are able to explain ~50% of the variability in daily maximum O3 levels. MLR analysis revealed that atmospheric carbon monoxide (CO), temperature and relative humidity were the strongest factors affecting the daily O3 variability. In summer, daily O3 variances were mostly associated with relative humidity, while winter O3 levels were mostly linked to temperature and CO. PCA indicated that CO, temperature and relative humidity were not strongly collinear. GAM also identified CO, temperature and relative humidity as the strongest factors affecting the daily variation of O3. Partial residual plots found that temperature, radiation and nitrogen oxides most likely have a non-linear relationship with O3,while the relationship with relative humidity and CO is probably linear. An inter-comparison between O3 levels modelled with the three statistical models compared to measured O3 concentrations showed that the GAM model offered a slight improvement over the MLR model. These findings emphasise the critical role of regional-scale O3 precursors coupled with meteorological conditions in daily variances of O3 levels in continental South Africa.

multiple linear regression (MLR)↗
NASA NTRS2001

Analysis of fractal dimensions of rat bones from film and digital images

OBJECTIVES: (1) To compare the effect of two different intra-oral image receptors on estimates of fractal dimension; and (2) to determine the variations in fractal dimensions between the femur, tibia and humerus of the rat and between their proximal, middle and distal regions. METHODS: The left femur, tibia and humerus from 24 4-6-month-old Sprague-Dawley rats were radiographed using intra-oral film and a charge-coupled device (CCD). Films were digitized at a pixel density comparable to the CCD using a flat-bed scanner. Square regions of interest were selected from proximal, middle, and distal regions of each bone. Fractal dimensions were estimated from the slope of regression lines fitted to plots of log power against log spatial frequency. RESULTS: The fractal dimensions estimates from digitized films were significantly greater than those produced from the CCD (P=0.0008). Estimated fractal dimensions of three types of bone were not significantly different (P=0.0544); however, the three regions of bones were significantly different (P=0.0239). The fractal dimensions estimated from radiographs of the proximal and distal regions of the bones were lower than comparable estimates obtained from the middle region. CONCLUSIONS: Different types of image receptors significantly affect estimates of fractal dimension. There was no difference in the fractal dimensions of the different bones but the three regions differed significantly.

NASA Discipline Musculoskeletal↗
DOE OSTI2024

GP-BayesOpInf

SAND2025-01851O GP-BayesOpInf is a software tool that uses algorithms to combine Gaussian process regression, principal component analysis, and linear Bayesian inference to produce a probabilistic reduced-order model for time-dependent systems. Numerical examples include the compressible Euler equations for an ideal gas, a heat diffusion process with a nonlinear reaction term, and a set of ordinary differential equations describing a compartmental model in epidemiology. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

SciDAC↗
DOE OSTI2024

Multi-Parameter Optical Fiber for Distributed Sensing of Humidity, CH4, CO2, and Corrosion

This work describes the use of the optical fiber sensor (OFS) for successful monitoring of humidity, CH4, and CO2 based on the strain produced along the single-mode fiber (SMF) sensor. This is enabled by absorption of H2O/gases onto the commercially available polyacrylate coated jacketed portion of the fiber resulting in a change in strain. Under equilibrium, a differential microstrain was observed along the jacketed portion of the SMF with N2, CH4, and CO2 at different relative humidity (RH) conditions. The strain response of the SMF under various mixed gas composition and different RH conditions were also measured and made calibration curves accordingly. Linear regression and principal component analysis of the strain datasets provided deconvolution of the impact of strain from H2O, N2, CH4, and CO2. Additionally, modified OFS comprised of the Fe coated fiber section was employed to monitor corrosion based on the increase in backscattered intensity amplitude of the light being passed once corrosion of Fe occurs. Also, corrosion of Fe was studied under soil by installing the fiber in soil with protective measures which would prevent the sensor being mechanically disturbed or broken during installation. The corrosion rates were studied by monitoring the rate at which the intensity of backscattered light amplitude attains a steady state value when complete corrosion of Fe with a specific coating thickness occurs.

Mainali, Badri↗
DOE OSTI2022

Machine learning models for rat multigeneration reproductive toxicity prediction

Reproductive toxicity is one of the prominent endpoints in the risk assessment of environmental and industrial chemicals. Due to the complexity of the reproductive system, traditional reproductive toxicity testing in animals, especially guideline multigeneration reproductive toxicity studies, take a long time and are expensive. Therefore, machine learning, as a promising alternative approach, should be considered when evaluating the reproductive toxicity of chemicals. We curated rat multigeneration reproductive toxicity testing data of 275 chemicals from ToxRefDB (Toxicity Reference Database) and developed predictive models using seven machine learning algorithms (decision tree, decision forest, random forest, k-nearest neighbors, support vector machine, linear discriminant analysis, and logistic regression). A consensus model was built based on the seven individual models. An external validation set was curated from the COSMOS database and the literature. The performances of individual and consensus models were evaluated using 500 iterations of 5-fold cross-validations and the external validation data set. The balanced accuracy of the models ranged from 58% to 65% in the 5-fold cross-validations and 45%–61% in the external validations. Prediction confidence analysis was conducted to provide additional information for more appropriate applications of the developed models. The impact of our findings is in increasing confidence in machine learning models. We demonstrate the importance of using consensus models for harnessing the benefits of multiple machine learning models (i.e., using redundant systems to check validity of outcomes). While we continue to build upon the models to better characterize weak toxicants, there is current utility in saving resources by being able to screen out strong reproductive toxicants before investing in vivo testing. The modeling approach (machine learning models) is offered for assessing the rat multigeneration reproductive toxicity of chemicals. Our results suggest that machine learning may be a promising alternative approach to evaluate the potential reproductive toxicity of chemicals.

consensus model↗
NASA NTRS1987

Vegetation spatial variability and its effect on vegetation indices

Landsat MSS data were used to simulate low resolution satellite data, such as NOAA AVHRR, to quantify the fractional vegetation cover within a pixel and relate the fractional cover to the normalized difference vegetation index (NDVI) and the simple ratio (SR). The MSS data were converted to radiances from which the NDVI and SR values for the simulated pixels were determined. Each simulated pixel was divided into clusters using an unsupervised classification program. Spatial and spectral analysis provided a means of combining clusters representing similar surface characteristics into vegetated and non-vegetated areas. Analysis showed an average error of 12.7 per cent in determining these areas. NDVI values less than 0.3 represented fractional vegetated areas of 5 per cent or less, while a value of 0.7 or higher represented fractional vegetated areas greater than 80 per cent. Regression analysis showed a strong linear relation between fractional vegetation area and the NDVI and SR values; correlation values were 0.89 and 0.95 respectively. The range of NDVI values calculated from the MSS data agrees well with field studies.

Ormsby, J. P.↗
NASA NTRS1991

Ordinary chondrites - Multivariate statistical analysis of trace element contents

The contents of mobile trace elements (Co, Au, Sb, Ga, Se, Rb, Cs, Te, Bi, Ag, In, Tl, Zn, and Cd) in Antarctic and non-Antarctic populations of H4-6 and L4-6 chondrites, were compared using standard multivariate discriminant functions borrowed from linear discriminant analysis and logistic regression. A nonstandard randomization-simulation method was developed, making it possible to carry out probability assignments on a distribution-free basis. Compositional differences were found both between the Antarctic and non-Antarctic H4-6 chondrite populations and between two L4-6 chondrite populations. It is shown that, for various types of meteorites (in particular, for the H4-6 chondrites), the Antarctic/non-Antarctic compositional difference is due to preterrestrial differences in the genesis of their parent materials.

Lipschutz, Michael E.↗
NASA NTRS1993

Multivariate statistical analysis: Principles and applications to coorbital streams of meteorite falls

Multivariate statistical analysis techniques (linear discriminant analysis and logistic regression) can provide powerful discrimination tools which are generally unfamiliar to the planetary science community. Fall parameters were used to identify a group of 17 H chondrites (Cluster 1) that were part of a coorbital stream which intersected Earth's orbit in May, from 1855 - 1895, and can be distinguished from all other H chondrite falls. Using multivariate statistical techniques, it was demonstrated that a totally different criterion, labile trace element contents - hence thermal histories - or 13 Cluster 1 meteorites are distinguishable from those of 45 non-Cluster 1 H chondrites. Here, we focus upon the principles of multivariate statistical techniques and illustrate their application using non-meteoritic and meteoritic examples.

Wolf, S. F.↗
NASA NTRS1995

Chemical studies of H chondrites. 4: New data and comparison of Antarctic suites

We report data for the trace elements Au, Co, Sb, Ga, Rb, Ag, Se, Cs, Te, Zn, Cd, Bi, Ti, and In (ordered by putative volatility during nebular condensation and accretion) determined by neutron activation analysis in 13 H5 chondrites from Victoria Land and 20 H4-6 chondrites from Queen Maud Land, Antarctica. These and earlier results provide Antarctic sample suites of 34 chondrites from Victoria Land and 25 from Queen Maud Land. Treatment of data for the most volatile 10 elements (Rb to In) in these studies by multivariate statistical techniques more robust, as well as more conservative, than conventional linear discriminant analysis and logistic regression demonstrates that compositions differ at marginally significant levels. This difference cannot be explained by trivial (terrestrial) causes and becomes more significant, despite the smaller size of the database, when comparisons are limited to data from a single analyst and when all upper limits are eliminated from consideration. The Victoria Land and Queen Maud Land suites have different mean terrestrial ages (approximately 300 kyr and approximately 100 kyr, respectively) and age distributions, suggesting that a time-dependent variation of chondritic sources with different thermal histories is responsible. As a result, these two Antarctic suites are, on average, chemically distinguishable from each other. Since H chondrites serve as a paradigm for other meteorite classes, these results indicate that the near-Earth populations of planetary materials varied with time on the 10(exp 5)-year timescale.

Wolf, Stephen F.↗
NASA NTRS1995

Chemical studies of H chondrites. 6: Antarctic/non-Antarctic compositional differences revisited

We report data for the trace elements Au, Co, Sb, Ga, Rb, Ag, Se, Cs, Te, Zn, Cd, Bi, T1, and In (ordered by putative volatility during nebular condensation and accretion) determined by radiochemical neutron activation analysis of 14 additional H5 and H6 chondrite falls. Data for the 10 most volatile elements (Rb to In) treated by the multivariate techniques of linear discriminant analysis and logistic regression in these and 44 other falls are compared with those of 59 H4-6 chondrites from Antarctica. Various populations are tested by the multivariate techniques, using the previously developed method of randomization-simulation to assess significance levels. An earlier conclusion, based on fewer examples, that H4-6 chondrite falls are compositionally distinguishable from the Antarctic suite is verified by the additional data. This distinctiveness is highly significant because of the presence of samples from Victoria Land in the Antarctic population, which differ compositionally from falls beyond any reasonable doubt. However, it cannot be proven unequivocally that falls and Antarctic samples from Queen Maud Land are compositionally distinguishable. Trivial causes (e.g., analyst bias, weathering) cannot explain the Victoria Land (Antarctic)/non-Antarctic compositional difference for paradigmatic H4-6 chondrites. This seems to reflect a time-dependent variation of near-Earth meteoroid source regions differing in average thermal history.

Wolf, Stephen F.↗
NASA NTRS1999

Time-on-task decrements in "steer clear" performance of patients with sleep apnea and narcolepsy

Loss of attention with time-on-task reflects the increasing instability of the waking state during performance in experimentally induced sleepiness. To determine whether patients with disorders of excessive sleepiness also displayed time-on-task decrements indicative of wake state instability, visual sustained attention performance on "Steer Clear," a computerized simple RT driving simulation task, was compared among 31 patients with untreated sleep apnea, 16 patients with narcolepsy, and 14 healthy control subjects. Vigilance decrement functions were generated by analyzing the number of collisions in each of six four-minute periods of Steer Clear task performance in a mixed-model analysis of variance and linear regression equations. As expected, patients had more Steer Clear collisions than control subjects (p=0.006). However, the inter-subject variability in errors among the narcoleptic patients was four-fold that of the apnea patients, and 100-fold that of the controls volunteers; the variance in errors among untreated apnea patients was 27-times that of controls. The results of transformed collision data revealed main effects for group (p=0.006), time-on-task (p=0.001), and a significant interaction (p=0.022). Control subjects showed no clear evidence of increasing collision errors with time-on-task (adjusted R2=0.22), while apnea patients showed a trend toward vigilance decrement (adjusted R2=0.42, p=0.097), and narcolepsy patients evidenced a robust linear vigilance decrement (adjusted R2=0.87, p=0.004). The association of disorders of excessive somnolence with escalating time-on-task decrements makes it imperative that when assessment of neurobehavioral performance is conducted in patients, it involves task durations and analyses that will evaluate the underlying vulnerability of potentially sleepy patients to decrements over time in tasks that require sustained attention and timely responses, both of which are key components in safe driving performance.

NASA Discipline Space Human Factors↗
DOE OSTI2024

Multi-Parameter Optical Fiber Sensing of Humidity, CH4, CO2, and Corrosion

In this work, the previously demonstrated capability of the optical fiber sensor (OFS) for successful monitoring of humidity has been extended to monitor the humidity, CH4, and CO2 with different gas composition based on the strain produced along the single mode fiber (SMF) sensor. This is enabled by absorption of H2O/gases on to the commercially available polyacrylate coated jacketed portion of the fiber resulting a change in strain. Under equilibrium, a differential microstrain was observed along the jacketed portion of the SMF with N2, CH4, and CO2 at different humidity conditions, while the unjacketed portion of the fiber was used only for sensing pressure/temperature induced strain. In case of N2 at 800 psig pressure, the observed microstrain was approximately 80, 65, 50 and 35 µε at 100, 75.0, 46.8, and 23.4 RH% respectively. Comparatively, a microstrain of approximately 95 µε was observed with 100% RH CH4 which demonstrates that SMF produces a measurable CH4 response alongside water. Similarly, the observed microstrain with CO2 was approximately 105, 90, 85, 80, and 70 µε at 100, 75.0, 46.8, 23.4, and 0 RH% respectively. Linear regression and principal component analysis of these dataset provided deconvolution of the impact of strain from H2O, CH4, and CO2 enabling good cross sensitivity. Additionally, modified OFS comprising of Fe coated fiber section was employed to monitor corrosion, using Fe as corrosion proxy, under harsh corrosive environment.

Mainali, Badri↗