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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Measurement of (anti)alpha production in central Pb–Pb collisions at $\sqrt{s_{NN}} = 5.02$ TeV

In this letter, measurements of (anti)alpha production in central (0–10%) Pb–Pb collisions at a center-of-mass energy per nucleon–nucleon pair of $\sqrt{s_{NN}} = 5.02$ TeV are presented, including the first measurement of an antialpha transverse-momentum spectrum. Owing to its large mass, the production of (anti)alpha is expected to be sensitive to different particle production models. The production yields and transverse-momentum spectra of nuclei are of particular interest because they provide a stringent test of these models. The averaged antialpha and alpha spectrum is compared to the spectra of lighter particles, by including it into a common blast-wave fit capturing the hydrodynamic-like flow of all particles. This fit is indicating that the (anti)alpha also participates in the collective expansion of the medium created in the collision. A blast-wave fit including only protons, (anti)alpha, and other light nuclei results in a similar flow velocity as the fit that includes all particles. A similar flow velocity, but a significantly larger kinetic freeze-out temperature is obtained when only protons and light nuclei are included in the fit. The coalescence parameter B 4 is well described by calculations from a statistical hadronization model but significantly underestimated by calculations assuming nucleus formation via coalescence of nucleons. Similarly, the (anti)alpha-to-proton ratio is well described by the statistical hadronization model. On the other hand, coalescence calculations including approaches with different implementations of the (anti)alpha substructure tend to underestimate the data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1981

Radiation budget measurement/model interface research

The NIMBUS 6 data were analyzed to form an up to date climatology of the Earth radiation budget as a basis for numerical model definition studies. Global maps depicting infrared emitted flux, net flux and albedo from processed NIMBUS 6 data for July, 1977, are presented. Zonal averages of net radiation flux for April, May, and June and zonal mean emitted flux and net flux for the December to January period are also presented. The development of two models is reported. The first is a statistical dynamical model with vertical and horizontal resolution. The second model is a two level global linear balance model. The results of time integration of the model up to 120 days, to simulate the January circulation, are discussed. Average zonal wind, meridonal wind component, vertical velocity, and moisture budget are among the parameters addressed.

Vonderhaar, T. H.↗
NASA NTRS2014

A Reliability Model for Ni-BaTiO3-Based (BME) Ceramic Capacitors

The evaluation of multilayer ceramic capacitors (MLCCs) with base-metal electrodes (BMEs) for potential NASA space project applications requires an in-depth understanding of their reliability. The reliability of an MLCC is defined as the ability of the dielectric material to retain its insulating properties under stated environmental and operational conditions for a specified period of time t. In this presentation, a general mathematic expression of a reliability model for a BME MLCC is developed and discussed. The reliability model consists of three parts: (1) a statistical distribution that describes the individual variation of properties in a test group of samples (Weibull, log normal, normal, etc.), (2) an acceleration function that describes how a capacitors reliability responds to external stresses such as applied voltage and temperature (All units in the test group should follow the same acceleration function if they share the same failure mode, independent of individual units), and (3) the effect and contribution of the structural and constructional characteristics of a multilayer capacitor device, such as the number of dielectric layers N, dielectric thickness d, average grain size r, and capacitor chip size S. In general, a two-parameter Weibull statistical distribution model is used in the description of a BME capacitors reliability as a function of time. The acceleration function that relates a capacitors reliability to external stresses is dependent on the failure mode. Two failure modes have been identified in BME MLCCs: catastrophic and slow degradation. A catastrophic failure is characterized by a time-accelerating increase in leakage current that is mainly due to existing processing defects (voids, cracks, delamination, etc.), or the extrinsic defects. A slow degradation failure is characterized by a near-linear increase in leakage current against the stress time; this is caused by the electromigration of oxygen vacancies (intrinsic defects). The two identified failure modes follow different acceleration functions. Catastrophic failures follow the traditional power-law relationship to the applied voltage. Slow degradation failures fit well to an exponential law relationship to the applied electrical field. Finally, the impact of capacitor structure on the reliability of BME capacitors is discussed with respect to the number of dielectric layers in an MLCC unit, the number of BaTiO3 grains per dielectric layer, and the chip size of the capacitor device.

reliability↗
NASA NTRS2014

Clustering Days with Similar Airport Weather Conditions

On any given day, traffic flow managers must often rely on past experience and intuition when developing traffic flow management initiatives that mitigate imbalances between the aircraft demand and the weather impacted airport capacity. The goal of this study was to build on recent efforts to apply data mining classification and clustering algorithms to vast archives of historical weather and air traffic data to identify patterns and past decisions that can ultimately inform day-of-operations decision-making. More specifically, this study identified similar weather impacted days at select U.S. airports, and analyzed the traffic management initiatives implemented on these representative days. The identification of the similar days was accomplished by applying a decision tree algorithm to the hourly Localized Aviation Model Output Statistics Program observations and the arrival delays for Newark Liberty International Airport. The branches from the trained decision tree were subsequently pruned to identify four weather conditions that resulted in medium to high delays for the arrivals scheduled to Newark in 2012. Using these weather conditions, four, daily airport-level Weather Impacted Traffic Index values were calculated using the Localized Aviation Model Output Statistics Program observations and the 2012 scheduled arrival counts from the FAAs Aviation System Performance Metric system. The four, daily Weather Impacted Traffic Index values for 2012 were subsequently clustered using an Expectation Maximization clustering algorithm, and nine unique types of weather days at Newark were identified. By far the most prominent type of day at Newark was a day associated with relatively good weather conditions, where there was little convective activity, winds were low, ceilings and visibility were high and there was little precipitation. Moderate levels of convective activity characterized the next most prominent type of day. Days with persistently high winds or low ceiling and visibility levels were relatively rare in 2012. Lastly, the frequency at which Ground Delay Programs, Ground Stops and Miles-in-Trail restrictions were implemented on each of the typical types of days at Newark were analyzed. Based on the results, it does appear as if the usage of Miles-in-Trail, Ground Delay Program and Ground Stop restrictions correlates well with the severity of the weather associated with each unique type of weather impacted day at Newark. Furthermore, the results demonstrate that it is feasible to use historical weather and air traffic archives to provide guidance on the types of traffic management restrictions to implement in response to the weather conditions impacting an airport.

traffic flow management↗
NASA NTRS2014

Clustering Days with Similar Airport Weather Conditions

On any given day, traffic flow managers must often rely on past experience and intuition when developing traffic flow management initiatives that mitigate imbalances between the aircraft demand and the weather impacted airport capacity. The goal of this study was to build on recent efforts to apply data mining classification and clustering algorithms to vast archives of historical weather and air traffic data to identify patterns and past decisions that can ultimately inform day-of-operations decision-making. More specifically, this study identified similar weather impacted days at select U.S. airports, and analyzed the traffic management initiatives implemented on these representative days. The identification of the similar days was accomplished by applying a decision tree algorithm to the hourly Localized Aviation Model Output Statistics Program observations and the arrival delays for Newark Liberty International Airport. The branches from the trained decision tree were subsequently pruned to identify four weather conditions that resulted in medium to high delays for the arrivals scheduled to Newark in 2012. Using these weather conditions, four, daily airport-level Weather Impacted Traffic Index values were calculated using the Localized Aviation Model Output Statistics Program observations and the 2012 scheduled arrival counts from the FAAs Aviation System Performance Metric system. The four, daily Weather Impacted Traffic Index values for 2012 were subsequently clustered using an Expectation Maximization clustering algorithm, and nine unique types of weather days at Newark were identified. By far the most prominent type of day at Newark was a day associated with relatively good weather conditions, where there was little convective activity, winds were low, ceilings and visibility were high and there was little precipitation. Moderate levels of convective activity characterized the next most prominent type of day. Days with persistently high winds or low ceiling and visibility levels were relatively rare in 2012. Lastly, the frequency at which Ground Delay Programs, Ground Stops and Miles-in-Trail restrictions were implemented on each of the typical types of days at Newark were analyzed. Based on the results, it does appear as if the usage of Miles-in-Trail, Ground Delay Program and Ground Stop restrictions correlates well with the severity of the weather associated with each unique type of weather impacted day at Newark. Furthermore, the results demonstrate that it is feasible to use historical weather and air traffic archives to provide guidance on the types of traffic management restrictions to implement in response to the weather conditions impacting an airport.

weather↗
NASA NTRS2011

Human Thermal Model Evaluation Using the JSC Human Thermal Database

The human thermal database developed at the Johnson Space Center (JSC) is used to evaluate a set of widely used human thermal models. This database will facilitate a more accurate evaluation of human thermoregulatory response using in a variety of situations, including those situations that might otherwise prove too dangerous for actual testing--such as extreme hot or cold splashdown conditions. This set includes the Wissler human thermal model, a model that has been widely used to predict the human thermoregulatory response to a variety of cold and hot environments. These models are statistically compared to the current database, which contains experiments of human subjects primarily in air from a literature survey ranging between 1953 and 2004 and from a suited experiment recently performed by the authors, for a quantitative study of relative strength and predictive quality of the models. Human thermal modeling has considerable long term utility to human space flight. Such models provide a tool to predict crew survivability in support of vehicle design and to evaluate crew response in untested environments. It is to the benefit of any such model not only to collect relevant experimental data to correlate it against, but also to maintain an experimental standard or benchmark for future development in a readily and rapidly searchable and software accessible format. The Human thermal database project is intended to do just so; to collect relevant data from literature and experimentation and to store the data in a database structure for immediate and future use as a benchmark to judge human thermal models against, in identifying model strengths and weakness, to support model development and improve correlation, and to statistically quantify a model s predictive quality.

Cognata, T.↗
NASA NTRS1990

Calculation of infrared limb emission by ozone in the terrestrial middle atmosphere. I - Source functions. II - Emission calculations

The departure from LTE in the vibration-rotation bands of ozone in the middle atmosphere is analyzed using two statistical equilibrium models: the energy gap model and the simplified single model. The diurnal variations in the fundamental band source functions nu(1) and nu(3) is determined by the diurnal change in the rate of ozone photolysis by the solar radiation. Source functions are presented for the vibration-rotation bands of ozone that emit in the 9-11-micron spectral interval, over an altitude range of 1-110 km. Results are also reported of the evaluation of the radiative transfer equation incorporating these source functions and energy level populations. Using the results of the two statistical equilibrium models, calculations are carried out of the spectrally integrated limb radiance for the limb viewing geometry for daytime conditions. Results indicate that the interpretation of the measurements of spectrally integrated limb radiance from ozone will be greatly complicated by the breakdown of LTE in the vibration-rotational bands of ozone.

Mlynczak, Martin G.↗
NASA NTRS2010

Verify MesoNAM Performance

The AMU conducted an objective analysis of the MesoNAM forecasts compared to observed values from sensors at specified KSC/CCAFS wind towers by calculating the following statistics to verify the performance of the model: 1) Bias (mean difference), 2) Standard deviation of Bias, 3) Root Mean Square Error (RMSE), and 4) Hypothesis test for Bias = O. The 45 WS LWOs use the MesoNAM to support launch weather operations. However, the actual performance of the model at KSC and CCAFS had not been measured objectively. The analysis compared the MesoNAM forecast winds, temperature and dew point to the observed values from the sensors on wind towers. The data were stratified by tower sensor, month and onshore/offshore wind direction based on the orientation of the coastline to each tower's location. The model's performance statistics were then calculated for each wind tower based on sensor height and model initialization time. The period of record for the data used in this task was based on the operational start of the current MesoNAM in mid-August 2006 and so the task began with the first full month of data, September 2006, through May 2010. The analysis of model performance indicated: a) The accuracy decreased as the forecast valid time from the model initialization increased, b) There was a diurnal signal in T with a cool bias during the late night and a warm bias during the afternoon, c) There was a diurnal signal in Td with a low bias during the afternoon and a high bias during the late night, and d) The model parameters at each vertical level most closely matched the observed parameters at heights closest to those vertical levels. The AMU developed a GUI that consists of a multi-level drop-down menu written in JavaScript embedded within the HTML code. This tool allows the LWO to easily and efficiently navigate among the charts and spreadsheet files containing the model performance statistics. The objective statistics give the LWOs knowledge of the model's strengths and weaknesses and the GUI allows quick access to the data which will result in improved forecasts for operations.

Bauman, William H., III↗
DOE OSTI2025

Implementation of disruptive designs for gas turbine components using direct energy deposition additive manufacturing

This research aims to develop a framework for establishing the correlation between in-situ monitoring data, process parameters, and microstructure evolution in blown-powder laser-directed energy deposition (DED) additive manufacturing (AM). To achieve this, a comprehensive manufacturing framework has been developed, spanning from in-situ data acquisition, melt-pool simulation, microstructure modeling, and statistical microstructure quantification. A machine learning-based surrogate model is constructed to predict melt pool geometry directly from in-situ coaxial camera data. The surrogate model is trained using outputs from a high-fidelity melt pool simulation, which provides accurate melt pool dimension data under varying process conditions. The predicted melt pool geometry is then used as input to a microstructure model to predict microstructural features. To rigorously compare and analyze microstructures, the project introduces statistical metrics that quantify differences based on key features such as morphology and texture. Microstructures are represented using advanced statistical descriptors including angular chord length distribution, two-point spatial statistics, orientation distribution function, and global spherical harmonic. These representations are used to compute four distinct “dissimilarity scores” that quantitatively capture differences in texture and morphology. This framework is demonstrated to enable automated calibration of simulation parameters by minimizing discrepancies between simulated and target microstructures. The technology developed in this project enables direct correlation between in-situ monitoring data and resulting microstructure, paving the way for adaptive microstructure control in metal AM. This capability strengthens the connection between process parameters and final material properties, facilitating more precise and reliable material design.

36 MATERIALS SCIENCE↗
NASA NTRS1974

Statistical time-dependent model for the interstellar gas

We present models for temperature and ionization structure of low, uniform-density (approximately 0.3 per cu cm) interstellar gas in a galactic disk which is exposed to soft X rays from supernova outbursts occurring randomly in space and time. The structure was calculated by computing the time record of temperature and ionization at a given point by Monte Carlo simulation. The calculation yields probability distribution functions for ionized fraction, temperature, and their various observable moments. These time-dependent models predict a bimodal temperature distribution of the gas that agrees with various observations. Cold regions in the low-density gas may have the appearance of clouds in 21-cm absorption. The time-dependent model, in contrast to the steady-state model, predicts large fluctuations in ionization rate and the existence of cold (approximately 30 K), ionized (ionized fraction equal to about 0.1) regions.

Gerola, H.↗
NASA NTRS1982

Curve fitting and modeling with splines using statistical variable selection techniques

The successful application of statistical variable selection techniques to fit splines is demonstrated. Major emphasis is given to knot selection, but order determination is also discussed. Two FORTRAN backward elimination programs, using the B-spline basis, were developed. The program for knot elimination is compared in detail with two other spline-fitting methods and several statistical software packages. An example is also given for the two-variable case using a tensor product basis, with a theoretical discussion of the difficulties of their use.

Smith, P. L.↗
NASA NTRS1989

A parameterization of eddy transfer coefficients for two-level seasonal statistical dynamical zonally averaged models

This paper examines a parameterization of a quasi-geostrophic eddy transport that takes into account the time variation of eddy transfer coefficients according to Green's (1970) theory. It was found that, in the original eddy transfer relationship of Green, connecting the integral of the northward eddy entropy flux through midlatitudes with the second power of the difference in 500-mb entropy across the region of baroclinic activity, a value of 4 for the exponent is obtained when the temperature gradients at 500 mb are used. When the gradients at 1000 mb are used, an exponent of 1.5 is obtained. The differences in the powers in the eddy transfer relation were explored in a two-level zonally averaged model. It was found that an appropriate choice of power may be of special importance if the model is devised to simulate the seasonal climate cycle or to test astronomical changes inducing different seasonalities.

Neeman, Binyamin U.↗
NASA NTRS1997

Stochastic-Dynamical Modeling of Space Time Rainfall

The focus of this research work is the elucidation of the physical origins of the observed extreme-rainfall variability over tropical oceans. The quantitative results of this work may be used to establish links between deterministic models of the mesoscale and synoptic scale with statistical descriptions of the temporal variability of local tropical oceanic rainfall. In addition, they may be used to quantify the influence of measurement error in large-scale forcing and cloud scale observations on the accuracy of local rainfall variability inferences, important for hydrologic studies. A simple statistical-dynamical model, suitable for use in repetitive Monte Carlo experiments, is formulated as a diagnostic tool for this purpose. Stochastic processes with temporal structure and parameters estimated from observed large-scale data represent large-scale forcing.

Georgankakos, Konstantine P.↗
NASA NTRS2001

Error Estimation of An Ensemble Statistical Seasonal Precipitation Prediction Model

This NASA Technical Memorandum describes an optimal ensemble canonical correlation forecasting model for seasonal precipitation. Each individual forecast is based on the canonical correlation analysis (CCA) in the spectral spaces whose bases are empirical orthogonal functions (EOF). The optimal weights in the ensemble forecasting crucially depend on the mean square error of each individual forecast. An estimate of the mean square error of a CCA prediction is made also using the spectral method. The error is decomposed onto EOFs of the predictand and decreases linearly according to the correlation between the predictor and predictand. Since new CCA scheme is derived for continuous fields of predictor and predictand, an area-factor is automatically included. Thus our model is an improvement of the spectral CCA scheme of Barnett and Preisendorfer. The improvements include (1) the use of area-factor, (2) the estimation of prediction error, and (3) the optimal ensemble of multiple forecasts. The new CCA model is applied to the seasonal forecasting of the United States (US) precipitation field. The predictor is the sea surface temperature (SST). The US Climate Prediction Center's reconstructed SST is used as the predictor's historical data. The US National Center for Environmental Prediction's optimally interpolated precipitation (1951-2000) is used as the predictand's historical data. Our forecast experiments show that the new ensemble canonical correlation scheme renders a reasonable forecasting skill. For example, when using September-October-November SST to predict the next season December-January-February precipitation, the spatial pattern correlation between the observed and predicted are positive in 46 years among the 50 years of experiments. The positive correlations are close to or greater than 0.4 in 29 years, which indicates excellent performance of the forecasting model. The forecasting skill can be further enhanced when several predictors are used.

Shen, Samuel S. P.↗
NASA NTRS2002

The spectral correlation of functional clouds: a statistical test for theoretical models

We compute the spectral correlation function (SCF) of CO J=1-0 maps of moleculary cloud complexes. The SCF is a power law over approximately an order of magnitude in spatial separation in every map. The power law slope of the SCF, its normalization, and the spectral line width averaged over the whole map, are computed for all the observational maps.

turbulence kinematics↗