Adapting quantile mapping to bias correct solar radiation data
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Bailey, Maggie D. (ORCID:0000000347017849)↗
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Results for “analysis of variance”
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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An Acoustic Charge Transport Imager for High Definition Television Applications: Reliability Modeling and Parametric Yield Prediction of GaAs Multiple Quantum Well Avalanche Photodiodes
Reliability modeling and parametric yield prediction of GaAs/AlGaAs multiple quantum well (MQW) avalanche photodiodes (APDs), which are of interest as an ultra-low noise image capture mechanism for high definition systems, have been investigated. First, the effect of various doping methods on the reliability of GaAs/AlGaAs multiple quantum well (MQW) avalanche photodiode (APD) structures fabricated by molecular beam epitaxy is investigated. Reliability is examined by accelerated life tests by monitoring dark current and breakdown voltage. Median device lifetime and the activation energy of the degradation mechanism are computed for undoped, doped-barrier, and doped-well APD structures. Lifetimes for each device structure are examined via a statistically designed experiment. Analysis of variance shows that dark-current is affected primarily by device diameter, temperature and stressing time, and breakdown voltage depends on the diameter, stressing time and APD type. It is concluded that the undoped APD has the highest reliability, followed by the doped well and doped barrier devices, respectively. To determine the source of the degradation mechanism for each device structure, failure analysis using the electron-beam induced current method is performed. This analysis reveals some degree of device degradation caused by ionic impurities in the passivation layer, and energy-dispersive spectrometry subsequently verified the presence of ionic sodium as the primary contaminant. However, since all device structures are similarly passivated, sodium contamination alone does not account for the observed variation between the differently doped APDs. This effect is explained by the dopant migration during stressing, which is verified by free carrier concentration measurements using the capacitance-voltage technique.
Hunt, W. D.↗
Spectra of normal and nutrient-deficient maize leaves
Reflectance, transmittance and absorptance spectra of normal and six types of nutrient-deficient (N, P, K, S, Mg, and Ca) maize (Zea mays L.) leaves were analyzed at 30 selected wavelengths from 500 to 2600 nm. The analysis of variance showed significant differences in reflectance, transmittance and absorptance in the visible wavelengths among leaf numbers 3, 4, and 5, among the seven treatments, and among the interactions of leaf number and treatments. In the infrared wavelengths only treatments produced significant differences. The chlorophyll content of leaves was reduced in all nutrient-deficient treatments. Percent moisture was increased in S-, Mg-, and N-deficiencies. Polynomial regression analysis of leaf thickness and leaf moisture content showed that these two variables were significantly and directly related. Leaves from the P- and Ca-deficient plants absorbed less energy in the near infrared than the normal plants; S-, Mg-, K-, and N-deficient leaves absorbed more than the normal. Both S- and N-deficient leaves had higher temperatues than normal maize leaves.
Al-Abbas, A. H.↗
HopBox: An image analysis pipeline to characterize hop cone morphology
Abstract Hop cone morphology can influence picking and drying ability, and color can impact consumer preference and may be indicative of quality. However, these characteristics are not generally evaluated in hop breeding programs due to the tedious nature of trait quantification and the extensive variation among cones within a genotype. We developed the HopBox, which is a simply constructed light box with a camera mount, and a publicly available image processing pipeline that identifies hop cones within color‐corrected images, reads a QR code within the image, and outputs data on hop cone length, width, area, perimeter, openness, weight, color, and density. The trained model was applied to images of 500 cones each from 15 replicated advanced hop genotypes from the USDA‐ARS breeding program in Prosser, Washington. Analysis of variance revealed significant ( p < 0.001) differences between genotypes for all traits measured, enabling breeders to discriminate between genotypes for selection purposes. Broad sense heritability for all traits ranged from 0.23 to 0.59. A random sampling of hop cones from the complete dataset revealed that imaging only 5–10 cones adequately captured genotypic variation and provided acceptable rank correlations ( r s > 0.75); however, increasing the sample size to 30 provided optimal precision. Instructions for constructing a HopBox and the code for the analysis pipeline are publicly available online and have wide applicability for hop breeding and research.
Altendorf, Kayla R.↗
Spectral characteristics of normal and nutrient-deficient maize leaves
Reflectance, transmittance and absorbance spectra of normal and six types of mineral-deficient (N,P,K,S,Mg and Ca) maize (Zea mays L.) leaves were analyzed at 30 selected wavelengths along the electromagnetic spectrum from 500 to 2600 nm. Chlorophyll content and percent leaf moisture were also determined. Leaf thermograms were obtained for normal, N- and S- deficient leaves. The results of the analysis of variance showed significant differences in reflectance, transmittance and absorbance in the visible wavelengths among leaf numbers 3, 4, and 5, among the seven nutrient treatments, and among the interactions of leaves and treatments. In the reflective infrared wavelengths only treatments produced significant differences. The chlorophyll content of leaves was reduced in all deficiencies in comparison to controls. Percent moisture was increased in S-, Mg- and N- deficiencies. Positive correlation (r = 0.707) between moisture content and percent absorption at both 1450 and 1930 nm were obtained. Polynomial regression analysis of leaf thickness and leaf moisture content showed that these two variables were significantly and directly related (r = 0.894).
Al-Abbas, A. H.↗
Pulmonary blood flow redistribution by increased gravitational force
This study was undertaken to assess the influence of gravity on the distribution of pulmonary blood flow (PBF) using increased inertial force as a perturbation. PBF was studied in unanesthetized swine exposed to -Gx (dorsal-to-ventral direction, prone position), where G is the magnitude of the force of gravity at the surface of the Earth, on the Armstrong Laboratory Centrifuge at Brooks Air Force Base. PBF was measured using 15-micron fluorescent microspheres, a method with markedly enhanced spatial resolution. Each animal was exposed randomly to -1, -2, and -3 Gx. Pulmonary vascular pressures, cardiac output, heart rate, arterial blood gases, and PBF distribution were measured at each G level. Heterogeneity of PBF distribution as measured by the coefficient of variation of PBF distribution increased from 0.38 +/- 0.05 to 0.55 +/- 0.11 to 0.72 +/- 0.16 at -1, -2, and -3 Gx, respectively. At -1 Gx, PBF was greatest in the ventral and cranial and lowest in the dorsal and caudal regions of the lung. With increased -Gx, this gradient was augmented in both directions. Extrapolation of these values to 0 G predicts a slight dorsal (nondependent) region dominance of PBF and a coefficient of variation of 0.22 in microgravity. Analysis of variance revealed that a fixed component (vascular structure) accounted for 81% and nonstructure components (including gravity) accounted for the remaining 19% of the PBF variance across the entire experiment (all 3 gravitational levels). The results are inconsistent with the predictions of the zone model.
NASA Discipline Cardiopulmonary↗
Comparing gas composition from fast pyrolysis of live foliage measured in bench-scale and fire-scale experiments
Background: Fire models have used pyrolysis data from oxidising and non-oxidising environments for flaming combustion. In wildland fires pyrolysis, flaming and smouldering combustion typically occur in an oxidising environment (the atmosphere). Aims: Using compositional data analysis methods, determine if the composition of pyrolysis gases measured in non-oxidising and ambient (oxidising) atmospheric conditions were similar. Methods: Permanent gases and tars were measured in a fuel-rich (non-oxidising) environment in a flat flame burner (FFB). Permanent and light hydrocarbon gases were measured for the same fuels heated by a fire flame in ambient atmospheric conditions (oxidising environment). Log-ratio balances of the measured gases common to both environments (CO, CO 2 , CH 4 , H 2 , C 6 H 6 O (phenol), and other gases) were examined by principal components analysis (PCA), canonical discriminant analysis (CDA) and permutational multivariate analysis of variance (PERMANOVA). Key results: Mean composition changed between the non-oxidising and ambient atmosphere samples. PCA showed that flat flame burner (FFB) samples were tightly clustered and distinct from the ambient atmosphere samples. CDA found that the difference between environments was defined by the CO-CO 2 log-ratio balance. PERMANOVA and pairwise comparisons found FFB samples differed from the ambient atmosphere samples which did not differ from each other. Conclusion: Relative composition of these pyrolysis gases differed between the oxidising and non-oxidising environments. This comparison was one of the first comparisons made between bench-scale and field scale pyrolysis measurements using compositional data analysis. Implications: These results indicate the need for more fundamental research on the early time-dependent pyrolysis of vegetation in the presence of oxygen.
54 ENVIRONMENTAL SCIENCES↗
Studies in Astronomical Time Series Analysis. VI. Bayesian Block Representations
This paper addresses the problem of detecting and characterizing local variability in time series and other forms of sequential data. The goal is to identify and characterize statistically significant variations, at the same time suppressing the inevitable corrupting observational errors. We present a simple nonparametric modeling technique and an algorithm implementing it-an improved and generalized version of Bayesian Blocks [Scargle 1998]-that finds the optimal segmentation of the data in the observation interval. The structure of the algorithm allows it to be used in either a real-time trigger mode, or a retrospective mode. Maximum likelihood or marginal posterior functions to measure model fitness are presented for events, binned counts, and measurements at arbitrary times with known error distributions. Problems addressed include those connected with data gaps, variable exposure, extension to piece- wise linear and piecewise exponential representations, multivariate time series data, analysis of variance, data on the circle, other data modes, and dispersed data. Simulations provide evidence that the detection efficiency for weak signals is close to a theoretical asymptotic limit derived by [Arias-Castro, Donoho and Huo 2003]. In the spirit of Reproducible Research [Donoho et al. (2008)] all of the code and data necessary to reproduce all of the figures in this paper are included as auxiliary material.
signal detection↗
Global Sensitivity Analysis of Large Distribution System with PVs using Deep Gaussian Process
Global sensitivity analysis (GSA) of the voltage to uncertain power injection variations plays an important role for appropriate Volt-VAR optimization. This paper proposes a data-driven GSA method for large-scale distribution systems with a large number of uncertain sources. Specifically, the deep Gaussian process is used to identify the mapping relationship between uncertain power injections and voltages. This allows us to resort to the analysis of variance framework to calculate the Sobol indices for GSA. Unlike the existing polynomial chaos expansion and Gaussian process-based approaches, our proposed method has much better scalability. Test results on the EPRI 1747-node K1 circuit with a different number and different probability distributions of uncertain sources demonstrate that the proposed method can achieve accurate GSA under various conditions.
14 SOLAR ENERGY↗
Single-base-pair discrimination of terminal mismatches by using oligonucleotide microarrays and neural network analyses
The effects of single-base-pair near-terminal and terminal mismatches on the dissociation temperature (T(d)) and signal intensity of short DNA duplexes were determined by using oligonucleotide microarrays and neural network (NN) analyses. Two perfect-match probes and 29 probes having a single-base-pair mismatch at positions 1 to 5 from the 5' terminus of the probe were designed to target one of two short sequences representing 16S rRNA. Nonequilibrium dissociation rates (i.e., melting profiles) of all probe-target duplexes were determined simultaneously. Analysis of variance revealed that position of the mismatch, type of mismatch, and formamide concentration significantly affected the T(d) and signal intensity. Increasing the concentration of formamide in the washing buffer decreased the T(d) and signal intensity, and it decreased the variability of the signal. Although T(d)s of probe-target duplexes with mismatches in the first or second position were not significantly different from one another, duplexes with mismatches in the third to fifth positions had significantly lower T(d)s than those with mismatches in the first or second position. The trained NNs predicted the T(d) with high accuracies (R(2) = 0.93). However, the NNs predicted the signal intensity only moderately accurately (R(2) = 0.67), presumably due to increased noise in the signal intensity at low formamide concentrations. Sensitivity analysis revealed that the concentration of formamide explained most (75%) of the variability in T(d)s, followed by position of the mismatch (19%) and type of mismatch (6%). The results suggest that position of the mismatch at or near the 5' terminus plays a greater role in determining the T(d) and signal intensity of duplexes than the type of mismatch.
Non-NASA Center↗
An Analysis of Future Wind Energy Resources and Cost Uncertainties Across the United States
Wind power is a growing source of energy generation that relies on complex global atmospheric and earth system processes. There has been evidence of reductions in average wind speeds over land in North America since the 1980s, and several models project that average wind speeds will continue to decrease. Concurrently, the cost of wind energy systems in the United States has been decreasing since around 2010, a trend also projected to continue. There is considerable uncertainty in these future projections, with quantitative estimates of future wind resource and system costs varying widely. To study this, we run wind energy models with possible future system costs, turbine designs, and meteorological inputs from multiple downscaled earth system models over the contiguous United States. Changes in mean annual energy production from 2000-2019 to 2040-2059 can be as high as +10% in South Texas or as low as -20% in Iowa. Larger turbines and moderate reductions in system costs can offset the largest projected decreases in wind resource, but much uncertainty remains in the extent to which wind resources will change and to what extent system costs can be reduced. An analysis of variance shows, in several states in the Midwest, uncertainty in future wind resources can influence the cost of wind energy nearly as much as uncertainty in future system costs.
17 WIND ENERGY↗
Predicting the viscoplastic response of a crystallizing fluoropolymer using transient network theory
We employ a molecular theory of dynamic polymer networks to describe the viscoplastic response of rubbery FK-800, a thermoplastic copolymer of chlorotrifluoroethylene and vinylidene fluoride, over a broad range of thermal histories. The kinetics of crystallization at different annealing temperatures was modeled using a modified Avrami equation, whose parameters were found to evolve through simple relationships over the full temperature range of the rubbery state. By fitting experimental compression data, we discovered predictable trends for the physical parameters in our mechanical model over its full range of crystallinities (up to ≈20%) and provided insights based on molecular-level physics to justify them. Using this, an end-to-end model was developed to predict the yielding and post-yield behavior of rubbery FK-800 for arbitrary thermal histories. The model successfully predicted the highly nonlinear evolution of characteristic mechanical signatures (stiffness, yield point, post-yield drop) throughout the crystallization process. A statistical analysis of variance test was employed to determine that the measured variations in the mechanical behavior of rubbery FK-800 are primarily dictated by its fractional crystallinity, regardless of its exact thermal history.
36 MATERIALS SCIENCE↗
Optimizing aluminum oxide passivation layers—Laser-induced plasmas for layer formation, real-time diagnostics, and layer evaluation
Molten salts have beneficial thermophysical and electrolytic properties, but the aggressive nature of molten salts requires corrosion mitigation strategies, such as structural material surface treatment. In this study, a laser-induced breakdown spectroscopy (LIBS) system was used to simultaneously ablate and monitor de-excitation spectra to form and identify dense alumina passivation layers. Additionally, high-frequency (kHz) LIBS imaging allowed rapid elemental mapping and depth profiling for surface O/Al ratios via minimally destructive analysis. The paired t-test rejected the null hypothesis that air and Ar cover gases were equivalent (p = 0.00012), and the analysis of variance (ANOVA) showed that overlap between shots (p = 0.03112) and cover gas flow rate (p = 0.02729) were statistically significant. At 75% overlap, the O/Al ratio increased to 10% ± 2%, and 90% overlap rose further to 31% ± 2%. Regardless of overlap, tuning the gas flow from 0.5 to 2.5 L min −1 enhanced the O/Al ratio by 24% ± 4%. LIBS depth profiling showed a 3× increase in layer thickness from 75% to 90% overlap. Molecular band peaks of AlO, a precursor to Al 2 O 3 found during treatment, suggest that band intensity could be used for real-time Al 2 O 3 optimization. Ultimately, by adjusting laser spot overlap and cover gas flow rate, a 266 nm laser was shown to form Al 2 O 3 layers on an Al6061 sample, with cover gas flow rate primarily affecting the O/Al ratio and layer thickness correlating with laser spot overlap. This combination of molecular spectra collection, imaging, and depth profiling demonstrated the robust layer analysis capabilities of LIBS.
Corrosion↗
Future Wind Energy Resources and Cost Uncertainties Across the United States
This dataset contains results estimating projections of change of annual capacity factors and levelized cost of energy for several turbine technologies in the 2024 Annual Technology Baseline (ATB). Projections of change are based on downscaled earth system model (ESM) data from Sup3rCC. There has been evidence of reductions in average wind speeds over land in North America since the 1980s, and several models project that average wind speeds will continue to decrease. Concurrently, the cost of wind energy systems in the United States has been decreasing since around 2010, a trend also projected to continue. There is considerable uncertainty in these future projections, with quantitative estimates of future wind resource and system costs varying widely. To study this, we run land-based wind energy models with a range of possible future system costs, turbine designs, and meteorological inputs from multiple downscaled earth system models over the contiguous United States to estimate critical system performance metrics such as annual energy production (AEP) and levelized cost of energy. Where multiple earth system models agree, changes in mean AEP from the time period 2000-2019 to 2040-2059 can be as high as +10% in South Texas or as low as -20% in Iowa. Several additional states in the Midwest that currently have considerable wind generation capacity show the possibility of substantial decreases in AEP by mid-century. Larger turbines and moderate reductions in system costs can offset even the largest projected decreases in wind resource, but much uncertainty remains in the extent to which wind resources will actually change into the future and to what extent wind energy systems can drive down future costs. An analysis of variance shows, in several states in the Midwest, the uncertainty in future wind resource can be almost as important for future changes in the cost of wind energy as the uncertainty in future system costs.
17 WIND ENERGY↗
Stress, and pathogen response gene expression in modeled microgravity
Purpose: Immune suppression in microgravity has been well documented. With the advent of human exploration and long-term space travel, the immune system of the astronaut must be optimally maintained. It is important to investigate the expression patterns of cytokine genes, because they are directly related to immune response. Heat shock proteins (HSPs), also called stress proteins, are a group of proteins that are present in the cells of every life form. These proteins are induced when a cell responds to stressors such as heat, cold and oxygen deprivation. Microgravity is another stressor that may regulate HSPs. Heat shock proteins trigger immune response through activities that occur both inside the cell (intracellular) and outside the cell (extracellular). Knowledge about these two gene groups could lead to establishment of a blueprint of the immune response and adaptation-related genes in the microgravity environment. Methods: Human peripheral blood cells were cultured in 1g (T flask) and modeled microgravity (MMG, rotating-wall vessel) for 24 and 72 hours. Cell samples were collected and subjected to gene array analysis using the Affymetrix HG_U95 array. Data was collected and subjected to a two-way analysis of variance. The genes related to immune and stress responses were analyzed. Results and Conclusions: HSP70 was up-regulated by more than two fold in microgravity culture, while HSP90 was significantly down-regulated. HSP70 is not typically expressed in all kinds of cells, but it is expressed at high levels in stress conditions. HSP70 participates in translation, protein translocation, proteolysis and protein folding, suppressing aggregation and reactivating denatured proteins. Increased serum HSP70 levels correlate with a better outcome for heat-stroke or severe trauma patients. At the same time, elevated serum levels of HSP70 have been detected in patients with peripheral or renal vascular disease. HSP90 has been identified in the cytosol, nucleus and endoplasmic reticulum, and exists in many tissue types. HSP90 associates with actin filaments in certain conditions and aids cell motility. The down-regulation of HSP90 could lead to deleterious effects in the lymphocytes, thereby contributing to suppressed immune function in microgravity. Interleukins such as IL 1 alpha, IL11 receptor chain alpha, IL7R, and IL4R were significantly down regulated in modeled microgravity. Further analysis of the genes involved in immune response at the protein level may provide a basis for prophylactic and countermeasure strategies to augment the human immune system for space exploration.
Sundaresan, Alamelu↗
Uncertainty Analysis for a Jet Flap Airfoil
An analysis of variance (ANOVA) study was performed to quantify the potential uncertainties of lift and pitching moment coefficient calculations from a computational fluid dynamics code, relative to an experiment, for a jet flap airfoil configuration. Uncertainties due to a number of factors including grid density, angle of attack and jet flap blowing coefficient were examined. The ANOVA software produced a numerical model of the input coefficient data, as functions of the selected factors, to a user-specified order (linear, 2-factor interference, quadratic, or cubic). Residuals between the model and actual data were also produced at each of the input conditions, and uncertainty confidence intervals (in the form of Least Significant Differences or LSD) for experimental, computational, and combined experimental / computational data sets were computed. The LSD bars indicate the smallest resolvable differences in the functional values (lift or pitching moment coefficient) attributable solely to changes in independent variable, given just the input data points from selected data sets. The software also provided a collection of diagnostics which evaluate the suitability of the input data set for use within the ANOVA process, and which examine the behavior of the resultant data, possibly suggesting transformations which should be applied to the data to reduce the LSD. The results illustrate some of the key features of, and results from, the uncertainty analysis studies, including the use of both numerical (continuous) and categorical (discrete) factors, the effects of the number and range of the input data points, and the effects of the number of factors considered simultaneously.
Green, Lawrence L.↗
Variability of earth-emitted radiation from one year of Nimbus-6 ERB data
Outgoing longwave radiation (OLR) measurements from the Nimbus-6 ERB wide field-of-view instrument are used to study daytime and nighttime radiation variability on a 15 deg regional, zonal, and global scale. An analysis of components of variance is used to determine how much of the total variability is due to between-region and within-region variance. Most of the analysis is on July and January data from one year of Nimbus-6 ERB. Different geographical scales are considered: regions within latitude zones and latitude zones within hemispheres. Results show that much of the variability is spatial, peaks in the tropics and subtropics, and is concentrated in the Northern Hemisphere. Daytime variability is generally larger than nighttime variability for July but not for January. Variance in OLR in the tropics and subtropics is largely a function of cloud variability.
Bess, T. D.↗
Designing clay-polymer nanocomposite sorbents for water treatment: A review and meta-analysis of the past decade
Clay-polymer nanocomposites (CPNs) have been studied for two decades as sorbents for water pollutants, but their applicability remains limited. Our aim in this review is to present the latest progress in CPN research using a meta-analysis approach and identify key steps necessary to bridge the gap between basic research and CPN application. Based on results extracted from 99 research articles on CPNs and 8 review articles on other widely studies sorbents, CPNs had higher adsorption capacities for several inorganic and organic pollutant classes (including heavy metals, oxyanions, and dyes, n = 308 observations). We applied principal component analysis, analysis of variance, and multiple linear regressions to test how CPN and pollutant properties correlated with Langmuir adsorption model coefficients. While adsorption was, surprisingly, not influenced by mineral properties, it was influenced by CPN fabrication method, polymer functional groups, and pollutant properties. For example, among the pollutant classes, heavy metals had the highest adsorption capacity but the lowest adsorption affinity. Conversely, dyes had high adsorption affinities, as reflected by the linear correlation between adsorption affinity and pollutant molecular weight. Scaling from ‘basic research’ to ‘technological application’ requires testing CPN performance in real water, application in columns, comparison to commercial sorbents, regeneration, and cost evaluation. However, our survey indicates that of the 158 observations, only 20 compared the CPN's performance to that of a commercial sorbent. We anticipate that this review will promote the design of smart and functional CPNs, which can then evolve into an effective water treatment technology.
42 ENGINEERING↗