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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Galactic Cosmic Ray Event-Based Risk Model (GERM) Code

This software describes the transport and energy deposition of the passage of galactic cosmic rays in astronaut tissues during space travel, or heavy ion beams in patients in cancer therapy. Space radiation risk is a probability distribution, and time-dependent biological events must be accounted for physical description of space radiation transport in tissues and cells. A stochastic model can calculate the probability density directly without unverified assumptions about shape of probability density function. The prior art of transport codes calculates the average flux and dose of particles behind spacecraft and tissue shielding. Because of the signaling times for activation and relaxation in the cell and tissue, transport code must describe temporal and microspatial density of functions to correlate DNA and oxidative damage with non-targeted effects of signals, bystander, etc. These are absolutely ignored or impossible in the prior art. The GERM code provides scientists data interpretation of experiments; modeling of beam line, shielding of target samples, and sample holders; and estimation of basic physical and biological outputs of their experiments. For mono-energetic ion beams, basic physical and biological properties are calculated for a selected ion type, such as kinetic energy, mass, charge number, absorbed dose, or fluence. Evaluated quantities are linear energy transfer (LET), range (R), absorption and fragmentation cross-sections, and the probability of nuclear interactions after 1 or 5 cm of water equivalent material. In addition, a set of biophysical properties is evaluated, such as the Poisson distribution for a specified cellular area, cell survival curves, and DNA damage yields per cell. Also, the GERM code calculates the radiation transport of the beam line for either a fixed number of user-specified depths or at multiple positions along the Bragg curve of the particle in a selected material. The GERM code makes the numerical estimates of basic physical and biophysical quantities of high-energy protons and heavy ions that have been studied at the NASA Space Radiation Laboratory (NSRL) for the purpose of simulating space radiation biological effects. In the first option, properties of monoenergetic beams are treated. In the second option, the transport of beams in different materials is treated. Similar biophysical properties as in the first option are evaluated for the primary ion and its secondary particles. Additional properties related to the nuclear fragmentation of the beam are evaluated. The GERM code is a computationally efficient Monte-Carlo heavy-ion-beam model. It includes accurate models of LET, range, residual energy, and straggling, and the quantum multiple scattering fragmentation (QMSGRG) nuclear database.

Cucinotta, Francis A.↗
NASA NTRS1991

The number counts and infrared backgrounds from infrared-bright galaxies

Extragalactic number counts and diffuse backgrounds at 25, 60, and 100 microns are predicted using new luminosity functions and improved spectral-energy distribution density functions derived from IRAS observations of nearby galaxies. Galaxies at redshifts z less than 3 that are like those in the local universe should produce a minimum diffuse background of 0.0085, 0.038, and 0.13 MJy/sr at 25, 60, and 100 microns, respectively. Models with significant luminosity evolution predict backgrounds about a factor of 4 greater than this minimum.

Hacking, P. B.↗
NASA NTRS1994

Search Space Characterization for a Telescope Scheduling Application

This paper presents a technique for statistically characterizing a search space and demonstrates the use of this technique within a practical telescope scheduling application. The characterization provides the following: (i) an estimate of the search space size, (ii) a scaling technique for multi-attribute objective functions and search heuristics, (iii) a "quality density function" for schedules in a search space, (iv) a measure of a scheduler's performance, and (v) support for constructing and tuning search heuristics. This paper describes the random sampling algorithm used to construct this characterization and explains how it can be used to produce this information. As an example, we include a comparative analysis of an heuristic dispatch scheduler and a look-ahead scheduler that performs greedy search.

Bresina, John↗
NASA NTRS2021

The GF Convection Parameterization: Recent Developments, Extensions, and Applications

Recent developments and options in the GF (Grell and Freitas, 2014, Freitas et al.,2018) convection parameterization are presented. The parameterization has been expanded to atrimodal spectral size to simulate three convection modes: shallow, congestus and deep. Incontrast to usual entrainment/detrainment assumptions, we assume that Beta Functions (BFs),commonly applied to represent Probability Density Functions (PDF’s), can be used tocharacterize the vertical mass flux profiles for the three modes, and use the BFs to deriveentrainment and detrainment rates. We also added a new closure for non-equilibrium convectionthat improved the simulation of the diurnal cycle of convection, with a better representation ofthe transition from shallow to deep convection regimes over land. The transport of chemicalconstituents (including wet deposition) can be treated inside the GF scheme. The tracer transportis handled in flux form and is mass conserving. Finally, the cloud microphysics has beenextended to include the ice phase to simulate the conversion from liquid water to ice in updraftswith resulting additional heat release and the melting from snow to rain.

Saulo R De Freitas↗
NASA NTRS2023

Influence of Cation Species on Thermal Expansion of Y2Si2O7–Gd2Si2O7 Solid Solutions

Mixtures of Y 2 Si 2 O 7 and Gd 2 Si 2 O 7 were synthesized by solid-state reaction at 1600°C and characterized via in situ x-ray diffraction (XRD) to determine their coefficients of thermal expansion (CTE). All solid solutions within the system exhibited the orthorhombic δ-RE 2 Si 2 O 7 (Pna2 1 ) structure. Thermal expansion measurements of Y 2 Si 2 O 7 and Gd 2 Si 2 O 7 correlated well with reported values in literature, and all synthesized solid solutions exhibited CTEs between Y 2 Si 2 O 7 and Gd 2 Si 2 O 7 . Generally, there was a slight decrease in CTE exhibited by the materials with increasing Gd 2 Si 2 O 7 content, with Gd 2 Si 2 O 7 having the lowest CTEs and Y 2 Si 2 O 7 the highest CTEs. The decrease in CTE was attributed to stronger bonds of Gd-O over Y-O, as determined by calculated crystal orbital Hamilton populations using density functional theory. However, such differences were very small and crystal structure was the dominating factor in CTE trends.

rare earth silicates↗
NASA NTRS2022

Ab initio Screening of Refractory Nitrides and Carbides for High Temperature Hydrogen Permeation Barriers

Density functional theory was used to screen eleven refractory materials – two pure metals, six nitrides, and three carbides–as high-temperature hydrogen permeation barriers to prevent hydrogen embrittlement. Activation energies were calculated for atomic hydrogen (H) diffusion into the first subsurface layer from the lowest energy surface of the high-temperature phase of each candidate material. The candidate barrier materials with the highest activation energies are h-BN, c-BN, HfN, and ZrN with predicted barriers of 3.25 eV, 3.23 eV, 3.14 eV, and 2.76 eV, respectively. Strain energies, Bader charges, and density of states were calculated for the diffusing H at the relaxed initial state and the transition state to provide insight into contributing factors to high energy barriers. The diffusing H atom in materials with the highest predicted barriers are protic. In addition, interstitial H atoms induce mid-gap states in the density of states of both BN polymorphs. The nitrogen retention of each nitride material at high temperatures was predicted using nitrogen vacancy formation energies with respect to gaseous nitrogen. Experimental evaluation of nitrogen retention in h-BN, ZrN, and TiN confirmed their resistance to nitrogen loss at 1773 K. However, of these nitrides, TiN is predicted to be the least stable. This work identifies multiple promising materials that are predicted to be effective hydrogen barriers at high temperatures and that are stable at temperatures above 2700 K, with BN predicted to perform best.

Density Functional Theory (DFT)↗
NASA NTRS1993

Geometric model from microscopic theory for nuclear absorption

A parameter-free geometric model for nuclear absorption is derived herein from microscopic theory. The expression for the absorption cross section in the eikonal approximation, taken in integral form, is separated into a geometric contribution that is described by an energy-dependent effective radius and two surface terms that cancel in an asymptotic series expansion. For collisions of light nuclei, an expression for the effective radius is derived from harmonic oscillator nuclear density functions. A direct extension to heavy nuclei with Woods-Saxon densities is made by identifying the equivalent half-density radius for the harmonic oscillator functions. Coulomb corrections are incorporated, and a simplified geometric form of the Bradt-Peters type is obtained. Results spanning the energy range from 1 MeV/nucleon to 1 GeV/nucleon are presented. Good agreement with experimental results is obtained.

John, Sarah↗
NASA NTRS2018

The Kepler Light Curves of AGN: A Detailed Analysis

We present a comprehensive analysis of 21 light curves of Type 1 active galactic nuclei (AGN) from the Kepler spacecraft. First, we describe the necessity and development of a customized pipeline for treating Kepler data of stochastically variable sources like AGN. We then present the light curves, power spectral density functions (PSDs), and flux histograms. The light curves display an astonishing variety of behaviors, many of which would not be detected in ground-based studies, including switching between distinct flux levels. Six objects exhibit PSD flattening at characteristic timescales that roughly correlate with black hole mass. These timescales are consistent with orbital timescales or free-fall accretion timescales. We check for correlations of variability and high-frequency PSD slope with accretion rate, black hole mass, redshift, and luminosity. We find that bolometric luminosity is anticorrelated with both variability and steepness of the PSD slope. We do not find evidence of the linear rms-flux relationships or lognormal flux distributions found in X-ray AGN light curves, indicating that reprocessing is not a significant contributor to optical variability at the 0.1%-10% level.

power spectral density functions(PSDs)↗
NASA NTRS2022

High-precision predictions of properties of chemically disordered crystals

Multiple scattering theory (MST) combined with density functional theory (DFT) allows to predict properties of chemically disordered materials from the first principles. However, such predictions often suffer from the systematic errors, which depend on crystal geometry. Each computed property of a particular crystal structure typically has a relatively small random error and a larger systematic error. Cancellation of systematic errors allows more accurate predictions. We propose a computational methodology based on the subtraction of the systematic errors in MST. To exemplify it, we apply it to the precipitated alloys. Considering precipitation strengthening in Ni superalloys, we compute the relative enthalpies of the competing Ni_3(Al_{1-x}Ti_x)_1 crystal structures with a chemical disorder on the Al+Ti sublattice. Such predicted composition-structure-property dependencies are useful for the guided design of the next-generation alloys with improved strength. Our predictions are validated by comparison with the results of other DFT methods (having a higher computational cost) and with experiment.

density functional theory↗
NASA NTRS2024

Theoretical Prediction of Thermal Expansion Anisotropy for Y 2 Si 2 O 7 Environmental Barrier Coatings Using a Deep Neural Network Potential and Comparison to Experiment

Environmental barrier coatings (EBCs) are an enabling technology for silicon carbide (SiC)-based ceramic matrix composites (CMCs) in extreme environments such as gas turbine engines. However, development of new coating systems is hindered by the large design space and difficulty in predicting properties for these materials. Density Functional Theory (DFT) has successfully been used to model and predict some thermodynamic and thermo-mechanical properties of high-temperature ceramics for EBCs, although these calculations are challenging due to their high computational costs. In this work, we use machine learning to train a deep neural network potential (DNP) for Y 2 Si 2 O 7 , which is then applied to calculate thermodynamic and thermo-mechanical properties at near-DFT accuracy much faster and using less computational resources than DFT. We use this DNP to predict phonon-based thermodynamic properties of Y 2 Si 2 O 7 with good agreement to DFT and experiments. We also utilize the DNP to calculate the anisotropic, lattice direction-dependent coefficients of thermal expansion (CTEs) for Y 2 Si 2 O 7 . Molecular dynamics trajectories using the DNP correctly demonstrate accurate prediction of the anisotropy of the CTE in good agreement with diffraction experiments. In the future, this DNP could be applied to accelerate additional property calculations for Y 2 Si 2 O 7 compared to DFT or experiments.

rare earth silicates↗
NASA NTRSDate not supplied

Machine Learning Approaches for Rare-Earth Silicate Environmental Barrier Coating Thermochemical and Thermomechanical Property Predictions

Environmental barrier coatings (EBCs) are a necessary enabling technology for the transition from superalloys to silicon carbide (SiC) ceramic matrix composites (CMCs) in gas turbine engines for increased efficiency and decreased fuel costs. SiC-based CMCs are prone to oxidation-based degradation in the engine hot section, and rare-earth (RE) silicates are promising candidates for EBCs due to their close thermal expansion match to the composite substrate and oxidation resistance. However, the design of EBCs is hindered by the large chemical space of candidate materials and the difficulty in obtaining material properties for engineering optimization. This is especially difficult as research continues into mixed-cation or “high-entropy” RE silicates. First-principles computational methods such as density functional theory (DFT) are highly effective at calculating material properties to guide coating design but are limited by their computational cost. Atomistic simulations have the potential to both accelerate property calculations and expand the properties able to be calculated due to their lower computational compared to DFT. However, they require interatomic potentials (IAPs) specific to the material system of interest, and, to our knowledge, there are no suitable IAPs for RE silicates. Machine learning (ML) is a promising technique to accelerate material property predictions indirectly by generating IAPs for atomistic simulations or via direct prediction. In this work, we present two ML approaches to accelerate the calculation of RE silicate properties relevant to EBC design: 1) a ML-derived interatomic potential (IAP) for atomistic simulations of yttrium disilicate (Y2Si2O7) from DFT training data, and 2) a neural network (NN) model to directly predict thermochemical properties of RE silicates and oxides directly from easily obtainable unit cell parameters. Classical MD simulations using the IAP yield lattice properties and bond lengths in good agreement with both DFT and experimental results from x-ray diffraction. Thermodynamic properties calculated using the finite-displacement phonon method and quasi-harmonic approximation were orders of magnitude faster than DFT with good agreement to the DFT results. The IAP was also used to calculate properties such as coefficient of thermal expansion (CTE) that require large simulation supercells and are therefore difficult with DFT. The IAP correctly predicted the anisotropic nature of the CTE in three different phases of Y2Si2O7. The NN model predicts constant pressure heat capacity, Cp, orders of magnitude faster than DFT calculations, which can enable its use as a surrogate model for multiscale simulations. The two methods presented in this work demonstrate the utility of ML for accelerating the prediction of RE silicate properties, which can in turn accelerate EBC design and optimization.

machine learning↗
NASA NTRS1993

Geometric model for nuclear absorption from microscopic theory

A parameter-free geometric model for nuclear absorption is derived from microscopic theory. The expression for the absorption cross section in the eikonal approximation taken in integral form is separated into a geometric contribution, described by an energy-dependent effective radius, and two surface terms which are shown to cancel in an asymptotic series expansion. For collisions of light nuclei, an expression for the effective radius is derived using harmonic-oscillator nuclear density functions. A direct extension to heavy nuclei with Woods-Saxon densities is made by identifying the equivalent half density radius for the harmonic-oscillator functions. Coulomb corrections are incorporated and a simplified geometric form of the Bradt-Peters type obtained. Results spanning the energy range of 1 MeV/nucleon to 1 GeV/nucleon are presented. Good agreement with experimental results are obtained.

John, S.↗
NASA NTRS1981

Characterization, parameter estimation, and aircraft response statistics of atmospheric turbulence

A nonGaussian three component model of atmospheric turbulence is postulated that accounts for readily observable features of turbulence velocity records, their autocorrelation functions, and their spectra. Methods for computing probability density functions and mean exceedance rates of a generic aircraft response variable are developed using nonGaussian turbulence characterizations readily extracted from velocity recordings. A maximum likelihood method is developed for optimal estimation of the integral scale and intensity of records possessing von Karman transverse of longitudinal spectra. Formulas for the variances of such parameter estimates are developed. The maximum likelihood and least-square approaches are combined to yield a method for estimating the autocorrelation function parameters of a two component model for turbulence.

Mark, W. D.↗
NASA NTRS1991

Computational modelling of dump combustors flowfield

A computational model aimed at predicting the flowfield of dump combustors is presented. The turbulent combustion model is based on the conserved scalar approach and on a convenient specification of its probability density function, which reduces the computation of the mean density to a closed form. Turbulence is modeled by means of the k-epsilon model. The averaged conservation equations are solved by a technique based on a staggered grid and on the SIMPLE solver. The computational model is applied to a simple dump combustor to assess the computer time requirements and accuracy. The turbulent combustion model is shown to reduce the computer time by an order of magnitude when compared to evaluating the mean density by numerical quadrature.

Lentini, D.↗
NASA NTRS1992

Reactant conversion in homogeneous turbulence: Mathematical modeling, computational validations and practical applications

Closed form analytical expressions are obtained for predicting the limited rate of reactant conversion in a binary reaction of the type F + rO yields (1 + r) Product in unpremixed homogeneous turbulence. These relations are obtained by means of a single point Probability Density Function (PDF) method based on the Amplitude Mapping Closure. It is demonstrated that with this model, the maximum rate of the reactants' decay can be conveniently expressed in terms of definite integrals of the Parabolic Cylinder Functions. For the cases with complete initial segregation, it is shown that the results agree very closely with those predicted by employing a Beta density of the first kind for an appropriately defined Shvab-Zeldovich scalar variable. With this assumption, the final results can also be expressed in terms of closed form analytical expressions which are based on the Incomplete Beta Functions. With both models, the dependence of the results on the stoichiometric coefficient and the equivalence ratio can be expressed in an explicit manner. For a stoichiometric mixture, the analytical results simplify significantly. In the mapping closure, these results are expressed in terms of simple trigonometric functions. For the Beta density model, they are in the form of Gamma Functions. In all the cases considered, the results are shown to agree well with data generated by Direct Numerical Simulations (DNS). Due to the simplicity of these expressions and because of nice mathematical features of the Parabolic Cylinder and the Incomplete Beta Functions, these models are recommended for estimating the limiting rate of reactant conversion in homogeneous reacting flows. These results also provide useful insights in assessing the extent of validity of turbulence closures in the modeling of unpremixed reacting flows. Some discussions are provided on the extension of the model for treating more complicated reacting systems including realistic kinetics schemes and multi-scalar mixing with finite rate chemical reactions in more complex configurations.

Madnia, C. K.↗
NASA NTRSDate not supplied

Uncertainty Estimates for Sonic-Boom Pressure Signatures and Loudness Carpets

A non-intrusive uncertainty quantification method is applied to computational analysis of supersonic, low-boom aircraft. The mean and standard deviation statistics of the pressure waveforms and loudness metrics are evaluated through use of numerical quadrature. The probability density function (p.d.f.) of these outputs is evaluated via kernel density estimation. The simulations use an inviscid, embedded-boundary Cartesian-mesh flow solver in the nearfield combined with an augmented Burgers’ equation solver for propagation in the farfield. The results show that the p.d.f. of the waveform is bimodal at shocks, which makes the mean and standard deviation statistics inappropriate. Despite this limitation, we show that the moment statistics can provide effective assessment of discrepancies when comparing with experimental data. This is demonstrated by presenting uncertainty analysis of a wind-tunnel test and showing that we significantly improve the predictions when we include the test uncertainties in the simulation. Normal distributions are obtained for the ground signature and loudness metrics, which is primarily due to the careful shaping of the low-boom waveform. Separation of variables and error control are used to reduce computational cost. We demonstrate that this is an efficient approach in the sense of balancing numerical errors in the statistics quadrature with discretization errors in the solvers.

ARMD↗
NASA NTRS1982

A bootstrap approach to bump hunting

An important question in cluster analysis and pattern recognition is the determination of the number of clusters into which a given population should be divided. Frequently, particularly when certain specific clustering methods are being used, the number of clusters is taken to be equal to the number of modes, or local maxima, in the probability density function underlying the given data set. The use of kernal density estimates in mode estimation is discussed. The test statistic to be used is defined and a bootstrap technique for assessing significance is given. An illustrative application is followed by an examination of the asymptotic behavior of the test statistic.

Silverman, B. W.↗