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Results for “empirical analysis”

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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

An Attempt to Measure the Traffic Impact of Airline Alliances

This paper analyzes the effects of airline alliances on the allied partners output by comparing the traffic change observed between the pre- and the post-alliance period. First, a simple methodology based on traffic passenger modelling is developed, and then an empirical analysis is conducted using time series from four global strategic alliances (Wings, Star Alliance, oneworld and SkyTeam) and 124 alliance routes. The analysis concludes that, all other things being equal, strategic alliances do lead to a 9.4%, on average, improvement in passenger volume.

Iatrou, Kostas↗
NASA NTRS2013

A Supplementary Clear-Sky Snow and Ice Recognition Technique for CERES Level 2 Products

Identification of clear-sky snow and ice is an important step in the production of cryosphere radiation budget products, which are used in the derivation of long-term data series for climate research. In this paper, a new method of clear-sky snow/ice identification for Moderate Resolution Imaging Spectroradiometer (MODIS) is presented. The algorithm's goal is to enhance the identification of snow and ice within the Clouds and the Earth's Radiant Energy System (CERES) data after application of the standard CERES scene identification scheme. The input of the algorithm uses spectral radiances from five MODIS bands and surface skin temperature available in the CERES Single Scanner Footprint (SSF) product. The algorithm produces a cryosphere rating from an aggregated test: a higher rating corresponds to a more certain identification of the clear-sky snow/ice-covered scene. Empirical analysis of regions of interest representing distinctive targets such as snow, ice, ice and water clouds, open waters, and snow-free land selected from a number of MODIS images shows that the cryosphere rating of snow/ice targets falls into 95% confidence intervals lying above the same confidence intervals of all other targets. This enables recognition of clear-sky cryosphere by using a single threshold applied to the rating, which makes this technique different from traditional branching techniques based on multiple thresholds. Limited tests show that the established threshold clearly separates the cryosphere rating values computed for the cryosphere from those computed for noncryosphere scenes, whereas individual tests applied consequently cannot reliably identify the cryosphere for complex scenes.

Radkevich, Alexander↗
NASA NTRS2012

View Angle Effects on MODIS Snow Mapping in Forests

Binary snow maps and fractional snow cover data are provided routinely from MODIS (Moderate Resolution Imaging Spectroradiometer). This paper investigates how the wide observation angles of MODIS influence the current snow mapping algorithm in forested areas. Theoretical modeling results indicate that large view zenith angles (VZA) can lead to underestimation of fractional snow cover (FSC) by reducing the amount of the ground surface that is viewable through forest canopies, and by increasing uncertainties during the gridding of MODIS data. At the end of the MODIS scan line, the total modeled error can be as much as 50% for FSC. Empirical analysis of MODIS/Terra snow products in four forest sites shows high fluctuation in FSC estimates on consecutive days. In addition, the normalized difference snow index (NDSI) values, which are the primary input to the MODIS snow mapping algorithms, decrease as VZA increases at the site level. At the pixel level, NDSI values have higher variances, and are correlated with the normalized difference vegetation index (NDVI) in snow covered forests. These findings are consistent with our modeled results, and imply that consideration of view angle effects could improve MODIS snow monitoring in forested areas.

MODIS↗
NASA NTRS2013

Causes and Predictability of the 2012 Great Plains Drought

Central Great Plains precipitation deficits during May-August 2012 were the most severe since at least 1895, eclipsing the Dust Bowl summers of 1934 and 1936. Drought developed suddenly in May, following near-normal precipitation during winter and early spring. Its proximate causes were a reduction in atmospheric moisture transport into the Great Plains from the Gulf of Mexico. Processes that generally provide air mass lift and condensation were mostly absent, including a lack of frontal cyclones in late spring followed by suppressed deep convection in summer owing to large-scale subsidence and atmospheric stabilization. Seasonal forecasts did not predict the summer 2012 central Great Plains drought development, which therefore arrived without early warning. Climate simulations and empirical analysis suggest that ocean surface temperatures together with changes in greenhouse gases did not induce a substantial reduction in summertime precipitation over the central Great Plains during 2012. Yet, diagnosis of the retrospective climate simulations also reveals a regime shift toward warmer and drier summertime Great Plains conditions during the recent decade, most probably due to natural decadal variability. As a consequence, the probability for severe summer Great Plains drought may have increased in the last decade compared to the 1980s and 1990s, and the so-called tail-risk for severe drought may have been heightened in summer 2012. Such an extreme drought event was nonetheless still found to be a rare occurrence within the spread of 2012 climate model simulations. Implications of this study's findings for U.S. seasonal drought forecasting are discussed.

Forecasts↗
NASA NTRS2015

A Framework of Working Across Disciplines in Early Design and R&D of Large Complex Engineered Systems

This paper examines four primary methods of working across disciplines during R&D and early design of large-scale complex engineered systems such as aerospace systems. A conceptualized framework, called the Combining System Elements framework, is presented to delineate several aspects of cross-discipline and system integration practice. The framework is derived from a theoretical and empirical analysis of current work practices in actual operational settings and is informed by theories from organization science and engineering. The explanatory framework may be used by teams to clarify assumptions and associated work practices, which may reduce ambiguity in understanding diverse approaches to early systems research, development and design. The framework also highlights that very different engineering results may be obtained depending on work practices, even when the goals for the engineered system are the same.

McGowan, Anna-Maria Rivas↗
NASA NTRS2018

Surprising Results from Large Crowds Using Micro-Purchase Challenges - Using Contests on Freelancing Communities to Source Innovative, Impactful and Cost-Effective Solutions

Our world is more connected than ever before. The new digital economy is empowering platforms and crowds to become a progressively strategic way for organizations to innovate ahead of their competition. Existing research shows the effectiveness and quality of solutions crowdsourcing yields, yet few organizations genuinely understand it nor are leveraging those solutions to unlock the full range of benefits. Moreover, early adopters often face structural and financial barriers towards evangelizing digital platforms at scale within their organizations. NASA is an exception - being an advocate of the field since 2010, it has paved the path for large organizations to follow. An empirical analysis is conducted on NASA's Center of Excellence for Collaborative Innovation (CoECI) micro-purchase challenges on a crowd-based platform to assess the cost-savings, quality of work, time for work turnaround and brand effects of using this problem-solving mechanism. The results proved to provide a tangible impact on all four parameters. As such, micro-purchases could become a compelling entry-point for organizations who are willing to experiment and subsequently build a convincing business case to present to stakeholders. The paper concludes with NASA's learnings, supplemented by literature, on how to redesign business processes, change conventional thinking and create an organization that will transform its future with crowds.

Tang, Sarah↗
NASA NTRS2001

Laboratory characterization of silicon avalanche photodiodes (APD) for pulse position modulation (PPM) detection

Two commercially available large area silicon avalanche photodiodes (APD) were characterized in the laboratory. Specifically, the response of the APD’s to a sequence of 8-bit pulse position modulated (256-PPM) laser pulses, with and without background noise, was recorded and stored for post analysis. Empirical probability distribution functions (pdf) were constructed from the signal and noise slot data and compared to pdf’s predicted by an analytical model based on Webb/Gaussian statistics. The limited pulse sequence was also used to generate bit-error rate (BER) versus signal photons per pulse values, albeit with large error bars. These BER measurements were also compared to results predicted using the Gaussian and Webb/Gaussian models for APD channel statistics. While the measurements qualitatively reflect features predicted by theory, significant quantitative deviations are displayed between the measurements and theory. The source of these discrepancies is not currently well understood, but it is surmised that inaccurate knowledge of detector parameters such as gain and noise equivalent temperature models may explain the discrepancies.

Biswas, A.↗
NASA NTRSDate not supplied

Hierarchical screening for Li-based solid electrolytes using fast, interpretable machine-learned potentials

Li-based solid-state electrolyte materials enable safer, all-solid-state batteries but the computational search for candidates with favorable stability and Li-ion conductivity is challenging due to the size of the search space and the cost of evaluating transport properties with ab initio methods. The prohibitive cost of high-throughput screening with DFT has lead to the development of surrogate models using geometric analysis, empirical potentials, and descriptors for ionic transport. Here, I will discuss a hierarchical screening approach for identifying promising materials using a combination of density functional theory, bond-valence methods, and machine learning potentials generated with the Ultra-Fast Force Fields (UF3) framework. We show how the inexpensive bond-valence method can be used to guide the generation of training samples for machine learning, in addition to filtering candidates. Finally, we apply the hierarchical workflow to screen for ionic conductivity across a database of Li-containing compounds.

Materials discovery↗
NASA NTRSDate not supplied

Hierarchical Screening for Li-Based Solid Electrolytes Using Fast, Interpretable Machine-Learned Potentials

Li-based solid-state electrolyte materials enable safer, all-solid-state batteries but the computational search for candidates with favorable stability and Li-ion conductivity is challenging due to the size of the search space and the cost of evaluating transport properties with ab initio methods. The prohibitive cost of high-throughput screening with DFT has lead to the development of surrogate models using geometric analysis, empirical potentials, and descriptors for ionic transport. Here, I will discuss a hierarchical screening approach for identifying promising materials using a combination of density functional theory, bond-valence methods, and machine learning potentials generated with the Ultra-Fast Force Fields (UF3) framework. We show how the inexpensive bond-valence method can be used to guide the generation of training samples for machine learning, in addition to filtering candidates.

Materials discovery↗
NASA NTRS1986

Investigation of seasonal variability of the wind stress curl over the North Atlantic Ocean by means of empirical orthogonal function analysis

The seasonal variability of the wind stress curl over the North Atlantic is investigated by means of empirical orthogonal function (EOF) analysis. The curl field is calculated from 1 year of First Global GARP Experiment wind data. It was found that 44 percent of the variability is contained in four significant eigenvectors. Their spatial patterns are characterized by basin-sized oscillations with larger amplitude to the north of 40 deg N. Their associated time series coefficients have the highest amplitude during the winter and show a tendency toward a white frequency spectrum which nevertheless exhibits noticeable peaks or gaps at certain frequencies. Physically, the first EOF is seen as the seasonal fluctuations of the mean wind stress curl pattern. Five other eigenvectors are also found to be above the noise level, but they account for only a smaller percentage of variability (19 percent). They are characterized by smaller spatial scales than the basin size. Their time series coefficients show a whiter frequency spectrum.

Barnier, B.↗
NASA NTRS1989

Empirical orthogonal function analysis of cloud-containing coastal zone color scanner images of northeastern North American coastal waters

Empirical-orthogonal-function (EOF) analyses were carried out on 36 images of the Mid-Atlantic Bight and the Gulf of Maine, obtained by the CZCS aboard Nimbus 7 for the time period from February 28 through July 9, 1979, with the purpose of determining pigment concentrations in coastal waters. The EOF procedure was modified so as to include images with significant portions of data missing due to cloud obstruction, making it possible to estimate pigment values in areas beneath clouds. The results of image analyses explained observed variances in pigment concentrations and showed a south-to-north pattern corresponding to an April Mid-Atlantic Bight bloom and a June bloom over Nantucket Shoals and Platts Bank.

Eslinger, David L.↗
NASA NTRS2012

A High-resolution Model of Field-aligned Currents Through Empirical Orthogonal Functions Analysis (MFACE)

Ten years of CHAMP magnetic field measurements are integrated into MFACE, a model of field-aligned currents (FACs) using empirical orthogonal functions (EOFs). EOF1 gives the basic Region-1/Region-2 pattern varying mainly with the interplanetary magnetic field Bz component. EOF2 captures separately the cusp current signature and By-related variability. Compared to existing models, MFACE yields significantly better spatial resolution, reproduces typically observed FAC thickness and intensity, improves on the magnetic local time (MLT) distribution, and gives the seasonal dependence of FAC latitudes and the NBZ current signature. MFACE further reveals systematic dependences on By, including 1) Region-1/Region-2 topology modifications around noon; 2) imbalance between upward and downward maximum current density; 3) MLT location of the Harang discontinuity. Furthermore, our procedure allows quantifying response times of FACs to solar wind driving at the bow shock nose: we obtain 20 minutes and 35-40 minutes lags for the FAC density and latitude, respectively.

CHAMP↗
DOE OSTI2021

A study of DC electrical breakdown in liquid helium through analysis of the empirical breakdown field distributions

Here we report results from a study on electrical breakdown in liquid helium using near-uniform-field stainless steel electrodes with a stressed area of ~0.7cm 2 . The distribution of the breakdown field is obtained for temperatures between 1.7 K and 4.0 K, pressures between the saturated vapor pressure and 626 Torr, and with electrodes of different surface polishes. A data-based approach for determining the electrode-surface-area scaling of the breakdown field is presented. The dependence of the breakdown probability on the field strength as extracted from the breakdown field distribution data is used to show that breakdown is a surface phenomenon closely correlated with Fowler–Nordheim field emission from asperities on the cathode. We show that the results from this analysis provide an explanation for the supposed electrode gap-size effect and also allow for a determination of the breakdown-field distribution for arbitrary shaped electrodes. Most importantly, the analysis method presented in this work can be extended to other noble liquids to explore the dependencies for electrical breakdown in those media.

74 ATOMIC AND MOLECULAR PHYSICS↗
NASA NTRS2004

Three dimensional empirical mode decomposition analysis apparatus, method and article manufacture

An apparatus and method of analysis for three-dimensional (3D) physical phenomena. The physical phenomena may include any varying 3D phenomena such as time varying polar ice flows. A repesentation of the 3D phenomena is passed through a Hilbert transform to convert the data into complex form. A spatial variable is separated from the complex representation by producing a time based covariance matrix. The temporal parts of the principal components are produced by applying Singular Value Decomposition (SVD). Based on the rapidity with which the eigenvalues decay, the first 3-10 complex principal components (CPC) are selected for Empirical Mode Decomposition into intrinsic modes. The intrinsic modes produced are filtered in order to reconstruct the spatial part of the CPC. Finally, a filtered time series may be reconstructed from the first 3-10 filtered complex principal components.

Gloersen, Per↗
NASA NTRS1988

Semi-empirical crack tip analysis

Experimentally observed crack opening displacements are employed as the solution of the multiple crack interaction problem. Then the near and far fields are reconstructed analytically by means of the double layer potential technqiue. Evaluation of the effective stress intensity factor resulting from the interaction of the main crack and its surrounding crazes in addition to the remotely applied load is presented as an illustrative example. It is shown that crazing (as well as microcracking) may constitute an alternative mechanism to Dugdale-Berenblatt models responsible for the cancellation of the singularity at the crack tip.

Chudnovsky, A.↗
NASA NTRSDate not supplied

Combining Data with Physical Knowledge for Uncertainty Quantification in Certification and Reliability Analysis

Unifying empirical data with predictive models can enable engineering cost-savings through certification by analysis and reliability-based design. Both concepts require rigorous uncertainty quantification (UQ) and robust understanding and treatment of relevant physics. Combining sampling-based UQ algorithms with high-fidelity simulations creates a computational bottleneck that is often alleviated through the use of machine learning (ML). ML can be used to create computationally efficient surrogates for simulations of complex or high-dimensional physical interactions (e.g., multi-phase interactions associated with melt pools in laser powder bed fusion or spatially-dependent material properties in functionally graded materials). However, negative side effects of ML may include a lack of interpretability and negative correlation between event rarity and simulation accuracy due to a lack of training data. As such, it is important to infuse ML algorithms with physics-based guardrails to provide confidence in their predictions. This talk will provide a brief review of recent NASA research at this intersection of physics-based simulation, ML, and UQ with a focus on certification and reliability analysis.

uncertainty quantification↗