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Results for “Analysis of Variance”

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At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRS1998

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↗
DOE OSTI2024

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↗
DOE OSTI2017

Functional vs. Traditional Analysis in Biomechanical Gait Data: An Alternative Statistical Approach

In human motion studies, discrete points such as peak or average kinematic values are commonly selected to test hypotheses. The purpose of this study was to describe a functional data analysis and describe the advantages of using functional data analyses when compared with a traditional analysis of variance (ANOVA) approach. Nineteen healthy participants (age: 22 ± 2 yrs, body height: 1.7 ± 0.1 m, body mass: 73 ± 16 kg) walked under two different conditions: control and pain+effusion. Pain+effusion was induced by injection of sterile saline into the joint capsule and hypertonic saline into the infrapatellar fat pad. Sagittal-plane ankle, knee, and hip joint kinematics were recorded and compared following injections using 2×2 mixed model ANOVAs and FANOVAs. The results of ANOVAs detected a condition × time interaction for the peak ankle (F1,18 = 8.56, p = 0.01) and hip joint angle (F1,18 = 5.77, p = 0.03), but did not for the knee joint angle (F1,18 = 0.36, p = 0.56). The functional data analysis, however, found several differences at initial contact (ankle and knee joint), in the mid-stance (each joint) and at toe off (ankle). Although a traditional ANOVA is often appropriate for discrete or summary data, in biomechanical applications, the functional data analysis could be a beneficial alternative. Thus when using the functional data analysis approach, a researcher can (1) evaluate the entire data as a function, and (2) detect the location and magnitude of differences within the evaluated function.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Evaluating Composite Sampling Methods of Bacillus Spores at Low Concentrations

Restoring facility operations after the 2001 Amerithrax attacks took over three months to complete, highlighting the need to reduce remediation time. The most time intensive tasks were environmental sampling and sample analyses. Composite sampling allows disparate samples to be combined, with only a single analysis needed, making it a promising method to reduce response times. We developed a statistical experimental design to test three different composite sampling methods: 1) single medium single pass composite: a single cellulose sponge samples multiple coupons; 2) single medium multi-pass composite: a single cellulose sponge is used to sample multiple coupons; and 3) multi-medium post-sample composite: a single cellulose sponge samples a single surface, and then multiple sponges are combined during sample extraction. Five spore concentrations of Bacillus atrophaeus Nakamura spores were tested; concentrations ranged from 5 to 100 CFU/coupon (0.00775 to 0.155CFU/cm 2 , respectively). Study variables included four clean surface materials (stainless steel, vinyl tile, ceramic tile, and painted wallboard) and three grime coated/dirty materials (stainless steel, vinyl tile, and ceramic tile). Analysis of variance for the clean study showed two significant factors: composite method (p-value < 0.0001) and coupon material (p-value = 0.0008). Recovery efficiency (RE) was higher overall using the post-sample composite (PSC) method compared to single medium composite from both clean and grime coated materials. RE with the PSC method for concentrations tested (10 to 100 CFU/coupon) was similar for ceramic tile, painted wall board, and stainless steel for clean materials. RE was lowest for vinyl tile with both composite methods. Statistical tests for the dirty study showed RE was significantly higher for vinyl and stainless steel materials, but significantly lower for ceramic tile. These results suggest post-sample compositing can be used to reduce sample analysis time when responding to a Bacillus anthracis contamination event of clean or dirty surfaces.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2013

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↗
DOE OSTI2021

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↗
NASA NTRS2002

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↗
DOE OSTI2016

SU-F-J-102: Lower Esophagus Margin Implications Based On Rapid Computational Algorithm for SBRT

Purpose: To quantify inter-fraction esophagus-variation. Methods: Computed tomography and daily on-treatment 0.3-T MRI data sets for 7 patients were analyzed using a novel Matlab-based (Mathworks, Natick, MA) rapid computational method. Rigid registration was performed from the cricoid to the gastro-esophageal junction. CT and MR-based contours were compared at slice intervals of 3mm. Variation was quantified by “expansion,” defined as additional length in any radial direction from CT contour to MR contour. Expansion computations were performed with 360° of freedom in each axial slice. We partitioned expansions into left anterior, right anterior, right posterior, and left posterior quadrants (LA, RA, RP, and LP, respectively). Sample means were compared by analysis of variance (ANOVA) and Fisher’s Protected Least Significant Difference test. Results: Fifteen fractions and 1121 axial slices from 7 patients undergoing SBRT for primary lung cancer (3) and metastatic lung disease (4) were analyzed, generating 41,970 measurements. Mean LA, RA, RP, and LP expansions were 4.30±0.05 mm, 3.71±0.05mm, 3.17±0.07, and 3.98±0.06mm, respectively. 50.13% of all axial slices showed variation > 5 mm in one or more directions. Variation was greatest in lower esophagus with mean LA, RA, RP, and LP expansion (5.98±0.09 mm, 4.59±0.09 mm, 4.04±0.16 mm, and 5.41±0.16 mm, respectively). The difference was significant compared to mid and upper esophagus (p<.0001). The 95th percentiles of expansion for LA, RA, RP, LP were 13.36 mm, 9.97 mm, 11.29 mm, and 12.19 mm, respectively. Conclusion: Analysis of on-treatment MR imaging of the lower esophagus during thoracic SBRT suggests margin expansions of 13.36 mm LA, 9.97 mm RA, 11.29 mm RP, 12.19 mm LP would account for 95% of measurements. Our novel algorithm for rapid assessment of margin expansion for critical structures with 360° of freedom in each axial slice enables continuously adaptive patient-specific margins which may reduce overlap with high-dose regions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

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↗
DOE OSTI2025

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↗
DOE OSTI2019

Combining simulations and data with deep learning and uncertainty quantification for advanced energy modeling

A novel and modern framework for energy modeling is developed in this paper with a focus on nuclear energy modeling and simulation. The framework combines multiphysics simulations and real data, with validation by uncertainty quantification tasks and facilitation by machine and deep learning methods. The hybrid framework is built on the basis of a wide range of physical models, real data, mathematical and statistical methods, and artificial intelligence techniques. The framework is demonstrated in different applications, including quantifying uncertainties in computer simulations, multiphysics coupling, analysis of variance using machine learning surrogate models, deep learning of time series phenomena, and propagating parametric uncertainties of nuclear data. The applications demonstrated are oriented to nuclear engineering simulations, even though majority of the methods are applicable to other energy sources (eg, renewable). Efficient utilization of this framework is expected to yield a much better understanding of the physical phenomena analyzed as well as an improvement in the performance of the energy design/model under construction.

42 ENGINEERING↗
DOE OSTI2016

Sequential experimental design based generalised ANOVA

Over the last decade, surrogate modelling technique has gained wide popularity in the field of uncertainty quantification, optimization, model exploration and sensitivity analysis. This approach relies on experimental design to generate training points and regression/interpolation for generating the surrogate. In this work, it is argued that conventional experimental design may render a surrogate model inefficient. In order to address this issue, this paper presents a novel distribution adaptive sequential experimental design (DA-SED). The proposed DA-SED has been coupled with a variant of generalised analysis of variance (G-ANOVA), developed by representing the component function using the generalised polynomial chaos expansion. Moreover, generalised analytical expressions for calculating the first two statistical moments of the response, which are utilized in predicting the probability of failure, have also been developed. The proposed approach has been utilized in predicting probability of failure of three structural mechanics problems. It is observed that the proposed approach yields accurate and computationally efficient estimate of the failure probability.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Can Angiotensin-Converting Enzyme Inhibitors Reduce the Incidence, Severity, and Duration of Radiation Proctitis?

Purpose: To determine whether participants taking angiotensin-converting enzyme inhibitors (ACEIs) and treated with radical radiation therapy with neoadjuvant/adjuvant hormone therapy have less incidence, severity, and duration of radiation proctitis. Methods and Materials: A propensity score analysis of 817 patients who underwent radical radiation therapy with neoadjuvant or adjuvant hormone therapy as primary line management in a cohort study during 2009 to 2013 was conducted. Patients were stratified as follows: group 1, hypertensive patients taking ACEIs (as a study group); group 2, nonhypertensive patients not taking ACEIs; and group 3, hypertensive patients not taking ACEIs (both as control groups). The incidence, severity, and duration of proctitis were the main outcome. χ{sup 2} tests, Mann-Whitney U tests, analysis of variance, risk ratio (RR), confidence interval (CI), Kaplan-Meier plots, and log-rank tests were used. Results: The mean age of the participants was 68.91 years, with a follow-up time of 3.38 years. Based on disease and age-matched comparison, there was a statistically significant difference of proctitis grading between the 3 groups: χ{sup 2} (8, n=308) = 72.52, P<.001. The Mann-Whitney U test indicated that grades of proctitis were significantly lower in hypertensive patients taking ACEIs than in nonhypertensive patients not taking ACEIs and hypertensive patients not taking ACEIs (P<.001). The risk ratio (RR) of proctitis in hypertensive patients taking ACEIs was significantly lower than in hypertensive patients not taking ACEIs (RR 0.40, 95% CI 0.30-0.53, P<.001) and in nonhypertensive patients not taking ACEIs (RR 0.58, 95% CI 0.44-0.77, P<.001). Time to event analysis revealed that hypertensive patients taking ACEIs were significantly different from the control groups (P<.0001). Furthermore, hypertensive patients taking ACEIs had significantly faster resolution of proctitis (P<.0001). Conclusion: Patients who were taking ACEIs were significantly less likely to have high-grade proctitis after radical radiation therapy with neoadjuvant or adjuvant hormone therapy (P<.001). The intake of ACEIs was significantly associated with a reduced risk of radiation-induced proctitis and also with acceleration of its resolution.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2026

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↗
DOE OSTI2025

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↗
NASA NTRS2006

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↗
DOE OSTI2016

The Influence of PV Module Materials and Design on Solder Joint Thermal Fatigue Durability

Finite element model (FEM) simulations have been performed to elucidate the effect of flat plate photovoltaic (PV) module materials and design on PbSn eutectic solder joint thermal fatigue durability. The statistical method of Latin Hypercube sampling was employed to investigate the sensitivity of simulated damage to each input variable. Variables of laminate material properties and their thicknesses were investigated. Using analysis of variance, we determined that the rate of solder fatigue was most sensitive to solder layer thickness, with copper ribbon and silicon thickness being the next two most sensitive variables. By simulating both accelerated thermal cycles (ATCs) and PV cell temperature histories through two characteristic days of service, we determined that the acceleration factor between the ATC and outdoor service was independent of the variables sampled in this study. This result implies that an ATC test will represent a similar time of outdoor exposure for a wide range of module designs. This is an encouraging result for the standard ATC that must be universally applied across all modules.

14 SOLAR ENERGY↗
NASA NTRS2006

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.↗