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At least 343 records · Page 19

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NASA NTRS2005

Plasma sheet turbulence observed by Cluster II

Cluster fluxgate magnetometer (FGM) and ion spectrometer (CIS) data are employed to analyze magnetic field fluctuations within the plasma sheet during passages through the magnetotail region in the summers of 2001 and 2002 and, in particular, to look for characteristics of magnetohydrodynamic (MHD) turbulence. Power spectral indices determined from power spectral density functions are on average larger than Kolmogorov's theoretical value for fluid turbulence as well as Kraichnan's theoretical value for MHD plasma turbulence. Probability distribution functions of the magnetic fluctuations show a scaling law over a large range of temporal scales with non-Gaussian distributions at small dissipative scales and inertial scales and more Gaussian distribution at large driving scales. Furthermore, a multifractal analysis of the magnetic field components shows scaling behavior in the inertial range of the fluctuations from about 20 s to 13 min for moments through the fifth order. Both the scaling behavior of the probability distribution functions and the multifractal structure function suggest that intermittent turbulence is present within the plasma sheet. The unique multispacecraft aspect and fortuitous spacecraft spacing allow us to examine the turbulent eddy scale sizes. Dynamic autocorrelation and cross correlation analysis of the magnetic field components allow us to determine that eddy scale sizes fit within the plasma sheet. These results suggest that magnetic field turbulence is occurring within the plasma sheet resulting in turbulent energy dissipation.

Weygand, James M.↗

NASA NTRS2014

Deducing Electronic Unit Internal Response During a Vibration Test Using a Lumped Parameter Modeling Approach

During random vibration testing of electronic boxes there is often a desire to know the dynamic response of certain internal printed wiring boards (PWBs) for the purpose of monitoring the response of sensitive hardware or for post-test forensic analysis in support of anomaly investigation. Due to restrictions on internally mounted accelerometers for most flight hardware there is usually no means to empirically observe the internal dynamics of the unit, so one must resort to crude and highly uncertain approximations. One common practice is to apply Miles Equation, which does not account for the coupled response of the board in the chassis, resulting in significant over- or under-prediction. This paper explores the application of simple multiple-degree-of-freedom lumped parameter modeling to predict the coupled random vibration response of the PWBs in their fundamental modes of vibration. A simple tool using this approach could be used during or following a random vibration test to interpret vibration test data from a single external chassis measurement to deduce internal board dynamics by means of a rapid correlation analysis. Such a tool might also be useful in early design stages as a supplemental analysis to a more detailed finite element analysis to quickly prototype and analyze the dynamics of various design iterations. After developing the theoretical basis, a lumped parameter modeling approach is applied to an electronic unit for which both external and internal test vibration response measurements are available for direct comparison. Reasonable correlation of the results demonstrates the potential viability of such an approach. Further development of the preliminary approach presented in this paper will involve correlation with detailed finite element models and additional relevant test data.

random vibration response↗

DOE OSTI2021

Bispectral analysis of broadband turbulence and geodesic acoustic modes in the T-10 tokamak

Local fluctuations of electrostatic potential, poloidal electric field, magnetic potential and electron density are simultaneously measured in the T-10 tokamak by a heavy ion beam probe (HIBP) having a five-slit energy analyser, which allows an estimate of the turbulent particle flux and $\boldsymbol {E}\times \boldsymbol {B}$ rotation velocity in the off-minor-axis gradient zone of the toroidal plasma column. The high spatial and temporal resolution of the modern multichannel HIBP makes it an effective tool to study plasma oscillations. Motivated by previous work that has documented time-resolved interactions between measured plasma parameters using correlation analysis (coherence of $E_{\textrm {pol}}$ and density $n_e$ , and cross-phase), a new result from bicorrelation analysis (bicoherence of magnetic potential $A_\zeta$ and density $n_e$ , and biphase) is reported for documenting the evidence of wave–wave coupling and energy transfer associated with the interaction between geodesic acoustic modes (GAM) and broadband, quasi-coherent modes.

Physics↗

NASA NTRS1980

A sunspot periodicity and the solar rotation

A least squares power spectrum analysis of daily sunspot numbers for the last 122 years yielded a statistically significant peak at 12.0715 plus or minus .002 days period. This feature at 11.685 days (sidereal) of the sunspot spectrum is discussed in relation to the peak at 12.22 days (sidereal) which Dicke found in his oblateness data. The data is attributed to the Sun's core if the core rotates at either 12.0715 days or 24.1430 days period (synodic). It is suggested that spacecraft observations combined with correlative analysis of solar surface features between eastern and western hemispheres could further reveal a basic core periodicity. A Dicke type space oblateness experiment is discussed for providing better photospheric observations than a ground instrument to determine the core periodicity.

Knight, J. W.↗

NASA NTRS1992

Variations in solar Lyman alpha irradiance on short time scales

Variations in solar UV irradiance at Lyman alpha are studied on short time scales (from days to months) after removing the long-term changes over the solar cycle. The SME/Lyman alpha irradiance is estimated from various solar indices using linear regression analysis. In order to study the nonlinear effects, Lyman alpha irradiance is modeled with a 5th-degree polynomial as well. It is shown that the full-disk equivalent width of the He line at 1083 nm, which is used as a proxy for the plages and active network, can best reproduce the changes observed in Lyman alpha. Approximately 72 percent of the solar-activity-related changes in Lyman alpha irradiance arise from plages and the network. The network contribution is estimated by the correlation analysis to be about 19 percent. It is shown that significant variability remains in Lyman alpha irradiance, with periods around 300, 27, and 13.5d, which is not explained by the solar activity indices. It is shown that the nonlinear effects cannot account for a significant part of the unexplained variation in Lyman alpha irradiance. Therefore, additional events (e.g., large-scale motions and/or a systematic difference in the area and intensity of the plages and network observed in the lines of Ca-K, He 1083, and Lyman alpha) may explain the discrepancies found between the observed and estimated irradiance values.

Pap, J. M.↗

NASA NTRS2018

Confirming Variability in the Secondary Eclipse Depth of the Super-Earth 55 Cancri e

We present a reanalysis of five transit and eight eclipse observations of the ultrashort-period super-Earth 55 Cancri e observed using the Spitzer Space Telescope during 2011-2013. We use pixel-level decorrelation to derive accurate transit and eclipse depths from the Spitzer data, and we perform an extensive error analysis. We focus on determining possible variability in the eclipse data, as was reported in Demory et al. From the transit data, we determine updated orbital parameters, yielding T (sub 0) = 2,455,733.0037 +/- 0.0002, P = 0.7365454 +/- 0.0000003 days, i = 83.5 +/- 1fdg3, and R p = 1.89 +/- 0.05 R ⊕. Our transit results are consistent with a constant depth, and we conclude that they are not variable. We find a significant amount of variability between the eight eclipse observations and confirm agreement with Demory et al. through a correlation analysis. We convert the eclipse measurements to brightness temperatures, and generate and discuss several heuristic models that explain the evolution of the planet's eclipse depth versus time. The eclipses are best modeled by a year-to-year variability model, but variability on shorter timescales cannot be ruled out. The derived range of brightness temperatures can be achieved by a dark planet with inefficient heat redistribution intermittently covered over a large fraction of the substellar hemisphere by reflective grains, possibly indicating volcanic activity or cloud variability. This time-variable system should be observable with future space missions, both planned (JWST) and proposed (i.e., ARIEL).

Tamburo, P.↗

NASA NTRS1995

The use of LinkWinds for the validation and analysis of 14 years of Microwave Sounder Unit daily global temperature anomaly data

Temperature data derived from the Microwave Sounder Unit (MSU) provides an opportunity for investigating atmospheric temperatures on a global scale since 1979. Fourteen years of global data sets of daily temperature anomalies within the lower stratosphere and lower troposphere are being generated at NASA Marshall Space Flight Center. LinkWinds, a visualization/analysis package under development at NASA Jet Propulsion Laboratory, has been extremely useful for validating and analyzing these data sets. LinkWinds provides the ability to interactively scroll and animate through the 10,220 images of temporal data, to selectively slice and view the data along latitude, longitude, or temporal axes, to interactively analyze spatial and temporal variability within the data, and to perform correlative analysis between various elements of the data. These capabilities have been invaluable in allowing the recognition of processing artifacts, as well as the effects that physical phenomena, such as the El Ninos effects and the Mt. Pinatubo eruption, have had on atmospheric temperatures.

Botts, Michael E.↗

NASA NTRS1995

A serach for moderate- and high-energy neturino emission correlated with gamma-ray bursts

A temporal correlation analysis between moderate- (60 Mev less than or equal to E(sub nu)greater than or equal to 2500 MeV) and high-energy (E(sub nu) greater than or equal to 2000 MeV) neutrino interactions consist of two types: the moderate-energy interactions that are contained within the volume of IMB-3 and the upward-going muons produced by high-energy nu(sub mu) interactions in the rock around the detector. No evidence is found for moderate- or high-energy neutrino emission from GRBs nor for any neutrino/neutrino correlation. The nonobservation of nu/GRB correlations allows upper limits to be placed on the neutrino flux associated with GRBs.

Becker-Szendy, R.↗

NASA NTRS1981

Space Shuttle main engine nozzle-steerhorn dynamics

On two occasions during the Space Shuttle main engine development, the LH2 feedline (called the steerhorn, because of its shape) failed during the cutoff transient. A dynamic test was undertaken, and an analytical model was developed and correlated to the dynamic test. Detailed models of the tube bundle were required to obtain the equivalent shell coefficients. All-shell models of the nozzle wall were found better than beam-shell models. The most difficult part of the structure to simulate was the felt-metal pad between the feedline and its mount, which introduced nonlinear stiffness and damping and led to the use of separate low amplitude and high amplitude models. The total structure was found to have 400 modes in the frequency range of interest, 0 to 500 Hz. Good test analysis correlation was obtained and a modified feedline configuration was found to demonstrate a 40% reduction of response stress from the original configuration.

Kiefling, L.↗

NASA NTRSDate not supplied

Statistical Process Control and Analysis on the Water Content Measurements in NASA Glenn’s Icing Research Tunnel

The Icing Research Tunnel at NASA Glenn follows the recommended practice for calibration outlined in SAE’s ARP5905. The calibration team has followed the schedule of a full calibration every five years with a check calibration done every six months following. The liquid water content of the IRT has maintained stability within the stated specifications of variation within +/- 10% of the curve fit equation generated from calibration data. Using past measurements and data trends, IRT characterization engineers wanted to develop methods for the ability to know when data were not within variation. Trends can be observed in the liquid water content measurement process by constructing statistical process control charts. This paper describes data processing procedures for the Multi-Element Sensor in the IRT, including collision efficiency corrections, canonical correlation analysis, process for rejection of data, and construction of control charts. Data are presented to display the control capability to meet defined liquid water content specifications of the IRT with the Multi-Element Sensor mounted in the center of the test section.

Statistical Process Control↗

NASA NTRSDate not supplied

Statistical Process Control and Analysis on the Water Content Measurements in NASA Glenn’s Icing Research Tunnel

Statistical Process Control↗

NASA NTRS2006

Exploration of OMI Products for Air Quality Applications Through Comparisons with Models and Observations

The Ozone Monitoring Instrument (OMI) on board NASA s Aura satellite was launched in July 2004, and is now providing daily global observations of total column ozone, NO2, and SO2, as well as aerosol information. Algorithms have also been developed to produce daily tropospheric ozone and NO2 products. The tropospheric ozone product reported here is a tropospheric residual computed through use of Aura Microwave Limb Sounder (MLS) ozone profile data to quantify stratospheric ozone. We are investigating the applicability of OMI products for use in air quality modeling, forecasting, and analysis. These investigations include comparison of the OMI tropospheric O3 and NO2 products with global and regional models and with lower tropospheric aircraft observations. Large-scale transport of pollution seen in the OM1 tropospheric O3 data is compared with output from NASA's Global Modeling Initiative global chemistry and transport model. On the regional scale we compare the OMI tropospheric O3 and NO2 with fields from the National Oceanic and Atmospheric Administration and Environmental Protection Agency (NOAA/EPA) operational Eta/CMAQ air quality forecasting model over the eastern United States. This 12-km horizontal resolution model output is roughly of equivalent resolution to the OMI pixel data. Correlation analysis between lower tropospheric aircraft O3 profile data taken by the University of Maryland over the Mid-Atlantic States and OMI tropospheric column mean volume mixing ratio for O3 will be presented. These aircraft data are representative of the lowest 3 kilometers of the atmosphere, the region in which much of the locally-generated and regionally-transported ozone exists.

Pickering, K. E.↗

NASA NTRS1985

Evidence for collisionless conduction fronts in impulsive solar flares

The present paper is concerned with an analysis of new observations of impulsive hard X-ray and microwave bursts. The results are compared with predictions of a thermal flare model. A description is presented of the thermal model with conduction-front confinement, taking into account the development and previous applications of the model, the high-energy limit of confinement and its implications for microwave emission, the formation of a test based on the revised predictions of the model, and derived parameters. The observations and the test of the model are discussed, giving attention to instrumentation, event selection, selection of a homogeneous sample of impulsive rises,. observed and derived parameters for each rise, a correlation analysis of observed and predicted rise times, uncertainties in the measurements, consistency checks, and a comparison with results of Crannell et al. (1978).

Batchelor, D. A.↗

DOE OSTI2020

Large-eddy simulation study on cycle-to-cycle variation of knocking combustion in a spark-ignition engine

The cycle-to-cycle variation in the knock intensity is commonly encountered under abnormal combustion conditions. The severity of these abnormal combustion events can vary significantly, and the efficiency of engines at high loads is limited in practice by heavy knocking phenomena. Since, a thorough analysis of such recurrent but non-cyclic phenomena via experiments alone becomes highly cumbersome, in the present work, a multi-cycle large-eddy simulation study was performed to quantitatively predict cyclic variability in the combustion process and cyclic knock intensity variability in a direct injection spark-ignition engine. To account for the turbulence-chemistry interaction effects on flame propagation, the G-equation combustion model was used. Detailed chemistry was solved outside the flame front with a toluene primary reference fuel skeletal kinetic mechanism. For both the mild knock and heavy knock conditions, the numerical results were validated against experimental measurements. Based on the simulation results, a correlation analysis was performed considering combustion phasing, peak cylinder pressure and maximum amplitude of pressure oscillation. Furthermore, a detailed three-dimensional spatial analysis illustrated the evolution of auto-ignition kernel development and propagation of pressure waves during knocking combustion for three typical cycles with different knock intensities. In this process, it was found that an early occurrence of auto-ignition in the end gas was prone to high knock intensity. Although multiple auto-ignition kernels were observed in different cycles, the degree of coupling between chemical heat release and pressure waves varied, thereby leading to different maximum amplitude of pressure oscillation values.

42 ENGINEERING↗

DOE OSTI2025

Pavement condition and climatic data in southeast Texas: A dataset for evaluating flood impacts on pavement performance

Effective pavement maintenance is essential for economic stability, optimal network performance, and roadway safety. Achieving this requires thorough evaluation of pavement conditions, including structural integrity, surface roughness, and distress characteristics. Pavement performance indicators play a critical role in influencing vehicle safety and ride quality. Recent advances have emphasized the use of data-driven modeling to anticipate pavement behavior, with the goal of optimizing resource allocation and refining Maintenance and Rehabilitation (M&R) strategies through accurate condition assessment. A foundational requirement for these modeling efforts is the availability of standardized, high-quality datasets that can support robust and reproducible infrastructure analysis. This data article presents a comprehensive dataset assembled to facilitate pavement performance prediction, with a geographic focus on Southeast Texas, particularly the flood-vulnerable area of Beaumont. The dataset encompasses pavement and traffic attributes, meteorological records, flood simulation outputs, ground deformation measurements, and topographic indices, enabling detailed examination of both load-associated and non-load-associated degradation mechanisms. Data preprocessing was performed using ArcGIS Pro, Microsoft Excel, and Python to ensure consistency and usability in data-driven modeling applications, including machine learning workflows. Key contributions of this dataset include its utility in analyzing the climatic and environmental factors affecting pavement conditions, identifying critical predictive features, and enabling in-depth correlation analysis across diverse variables. By filling existing gaps in input variable selection resources, this dataset supports the development of predictive tools for estimating future maintenance demand and enhancing the resilience of pavement networks in flood-impacted areas. The resource highlights the importance of standardized datasets for advancing pavement management practices and provides a robust foundation for ongoing infrastructure performance modeling.

42 ENGINEERING↗

DOE OSTI2023

Community Resilience Indicator Analysis: Commonly Used Indicators from Peer-Reviewed Research (Updated for Research Published 2003-2021)

In 2017, FEMA’s National Integration Center (NIC) Technical Assistance (TA) Branch identified a need to establish a data-driven basis for prioritizing locations for TA investment and guiding local emergency management planning. To achieve this goal, FEMA tasked Argonne National Laboratory (Argonne) with identifying commonly used indicators of community resilience across the landscape of published peer-reviewed research. FEMA and Argonne completed the first Community Resilience Indicator Analysis (CRIA) in 2018 and repeated the process in 2022. The CRIA process begins with a literature review and cataloguing of published peer-reviewed assessment methodologies on social vulnerability and community resilience. The literature review findings are then filtered by inclusion criteria established by the CRIA research team to ensure the methodologies are: (1) Quantitative, (2) Data and methodology are publicly available, (3) Calculated at the county level or lower, (4) Examine generalized hazard risk (rather than a singular hazard), and (5) Focused on pre-disaster community conditions. After this, the research team identifies the commonly used indicators across these methodologies and selects the best data source for each indicator. Finally, the research team bins the data for visual display, conducts a correlation analysis and creates a composite index, the FEMA Community Resilience Index (FEMA CRI). In 2018, the CRIA identified eight resilience and vulnerability assessment methodologies and 20 commonly used indicators (indicators used in three or more of the eight methodologies). The FEMA CRI in 2018 was created from these 20 indicators and was produced for at the county level. The 2022 CRIA updated the literature review to expand the list of methodologies examined and followed the same process, resulting in an analysis of 14 methodologies published between 2003 and 2021 and 22 indicators identified as commonly used (indicators used in five or more of the 14 methodologies). In 2022, the research team produced the FEMA CRI at the county and the census tract levels. To make the CRIA data more accessible and more actionable, each individual indicator and the FEMA CRI is binned and included in FEMA’s Resilience Analysis and Planning Tool (RAPT). RAPT enables emergency managers and community partners to quickly visualize relative differences in potential resilience by county, tribe and census tract. By reviewing the data for each of these 22 indicators individually, emergency managers can gain insights for targeted outreach strategies, planning, mitigation investments and response and recovery operations. Communities, regional governments and others can use this data to better understand potential challenges to resilience. As the social science field of examining and validating indicators of resilience evolves, FEMA will update RAPT to provide emergency managers and community partners with additional data and tools to inform planning, mitigation, response and recovery. It is important to understand that the role of the emergency manager is not to change or to “improve” the data, but to plan appropriately for the community characteristics reflected in the data. These datasets are community characteristics that researchers have identified as important considerations for resilience. For example, people with disabilities may have greater challenges to be resilient to disasters. If a community has a high population of people with disabilities, the emergency manager(s) may need to create tailored preparedness outreach programs and strategies to ensure those residents have support if evacuation is necessary. Rather than label these indicators as an absolute measure of resilience, FEMA considers “potential challenges to resilience” a better frame to understand these indicators. Everyone is vulnerable to disasters. While scholars theorize that certain characteristics may make an individual or a household more socially vulnerable, the data does not reflect measures that individuals and/or communities have taken to address potential challenges, such as emergency management planning and outreach or household preparedness measures. To aid emergency managers in understanding how to use these indicators, calling them potential challenges to resilience supports a more positive and strategic application of the data in all phases of emergency management.

99 GENERAL AND MISCELLANEOUS↗

NASA NTRS2016

Imperfection Insensitivity Analyses of Advanced Composite Tow-Steered Shells

Two advanced composite tow-steered shells, one with tow overlaps and another without overlaps, were previously designed, fabricated and tested in end compression, both without cutouts, and with small and large cutouts. In each case, good agreement was observed between experimental buckling loads and supporting linear bifurcation buckling analyses. However, previous buckling tests and analyses have shown historically poor correlation, perhaps due to the presence of geometric imperfections that serve as failure initiators. For the tow-steered shells, their circumferential variation in axial stiffness may have suppressed this sensitivity to imperfections, leading to the agreement noted between tests and analyses. To investigate this further, a numerical investigation was performed in this study using geometric imperfections measured from both shells. Finite element models of both shells were analyzed first without, and then, with measured imperfections that were then, superposed in different orientations around the shell longitudinal axis. Small variations in both the axial prebuckling stiffness and global buckling load were observed for the range of imperfections studied here, which suggests that the tow steering, and resulting circumferentially varying axial stiffness, may result in the test-analysis correlation observed for these shells.

Wu, K. Chauncey↗

DOE OSTI2020

Characterization and identification of HPC applications at leadership computing facility

High Performance Computing (HPC) is an important method for scientific discovery via large-scale simulation, data analysis, or artificial intelligence. Leadership-class supercomputers are expensive, but essential to run large HPC applications. The Petascale era of supercomputers began in 2008, with the first machines achieving performance in excess of one petaflops, and with the advent of new supercomputers in 2021 (e.g., Aurora, Frontier), the Exascale era will soon begin. However, the high theoretical computing capability (i.e., peak FLOPS) of a machine is not the only meaningful target when designing a supercomputer, as the resources demand of applications varies. A deep understanding of the characterization of applications that run on a leadership supercomputer is one of the most important ways for planning its design, development and operation. In order to improve our understanding of HPC applications, user demands and resource usage characteristics, we perform correlative analysis of various logs for different subsystems of a leadership supercomputer. This analysis reveals surprising, sometimes counter-intuitive patterns, which, in some cases, conflicts with existing assumptions, and have important implications for future system designs as well as supercomputer operations. For example, our analysis shows that while the applications spend significant time on MPI, most applications spend very little time on file I/O. Combined analysis of hardware event logs and task failure logs show that the probability of a hardware FATAL event causing task failure is low. Combined analysis of control system logs and file I/O logs reveals that pure POSIX I/O is used more widely than higher level parallel I/O. Based on holistic insights of the application gained through combined and co-analysis of multiple logs from different perspectives and general intuition, we engineer features to "fingerprint" HPC applications. We use t-SNE (a machine learning technique for dimensionality reduction) to validate the explainability of our features and finally train machine learning models to identify HPC applications or group those with similar characteristic. To the best of our knowledge, this is the first work that combines logs on file I/O, computing, and inter-node communication for insightful analysis of HPC applications in production.

Liu, Zhengchun↗