Search NASA⌕ Search

SEARCH · Search NASA

Results for “Function Estimation”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
NASA NTRS1988

Information content of radiometric and optical data during planetary encounter

An assessment is made of the feasibility of determining the flyby position and velocity parameters from optical data. Closed form solutions for the various parameter estimates are derived to show the functional dependence of the estimates on the nominal flyby parameters as well as the data arc characteristics. A strong correlation between the downtrack position and crosstrack velocity estimates is observed for the case of a short optical data arc, and a similarly strong correlation between the flyby planet mass and the flyby distance estimates is demonstrated for the case of radiometric data.

Kechichian, Jean A.↗
NASA NTRS2010

Preliminary Characterizations of Altair Lunar Lander Slosh Dynamics and Some Implications for TVC Design

During thrust maneuvers, the sloshing of fuel in partially filled tanks could interact with the controlled system in such a way as to cause the overall system to be unstable. This failure mode could be a result of poor coordination between systems engineers of Structures, Propulsion, GN&C, Mission Operations, and other subsystems. Attentions to the following is a minimum: Placements of fuel tanks, Tank shapes and baffle designs, Separation of TVC BW from slosh mode frequencies, Adequacy of excursion range of engine gimbal actuators in all engine burns, BW of sensors (e.g., gyroscopes) and actuators (e.g., engine gimbal), Structural frequencies of spacecraft and engine mounts, Prediction of S/C's c.m. location, canting of gimbal null axis, others. Check overall TVC performance via validated simulation test bed. Modeling fuel slosh during spacecraft thrusting maneuvers • Placement of fuel tanks to guarantee stable interactions between fuel slosh mode and the S/C rigid-body mode • Estimation of slosh frequencies (as a function of mission phases): – Estimation of slosh mode damping • Some implications for Thrust Vector Control design: – Selection of TVC controller bandwidth – Adequacy of the excursion range of engine gimbal actuator • TVC system design: Complex Interactions between GN&C, Propulsion, Structure, and Other Subsystems

Thrust vector control↗
DOE OSTI2021

An efficient method to estimate the probability density of seismic Green's functions

We present a computationally efficient method to approximate the probability distribution of seismic Green's functions given the uncertainty of an Earth model. The method is based on the Karhunen-Loève (KL) theorem and an approximation of the Green's function (or seismogram) covariance. Using Monte Carlo (MC) simulations as a control case, we demonstrate that our KL-based method can accurately reproduce a probability distribution of seismograms that results from an uncertain Earth model for a MC-derived seismogram covariance. We then describe a method to estimate the covariance of the seismograms resulting from those Earth models that is not based on MC simulations. We use the estimated Green's function covariance in conjunction with our KL-based method to produce a Green's function probability distribution, and compare that distribution to a Green's function probability distribution produced using a MC finite difference method. We find that the Green's function probability distribution approximated using our KL-based method generally mimics that produced using the MC simulations, especially for direct-arriving body waves. However the accuracy of the KL-based method generally decreases for later times in the simulated Green's function distribution.

58 GEOSCIENCES↗
DOE OSTI2019

Estimating covariance matrices for two- and three-point correlation function moments in Arbitrary Survey Geometries

ABSTRACT We present configuration-space estimators for the auto- and cross-covariance of two- and three-point correlation functions (2PCF and 3PCF) in general survey geometries. These are derived in the Gaussian limit (setting higher order correlation functions to zero), but for arbitrary non-linear 2PCFs (which may be estimated from the survey itself), with a shot-noise rescaling parameter included to capture non-Gaussianity. We generalize previous approaches to include Legendre moments via a geometry-correction function calibrated from measured pair and triple counts. Making use of importance sampling and random particle catalogues, we can estimate model covariances in fractions of the time required to do so with mocks, obtaining estimates with negligible sampling noise in ∼10 (∼100) CPU-hours for the 2PCF (3PCF) autocovariance. We compare results to sample covariances from a suite of BOSS DR12 mocks and find the matrices to be in good agreement, assuming a shot-noise rescaling parameter of 1.03 (1.20) for the 2PCF (3PCF). To obtain strongest constraints on cosmological parameters, we must use multiple statistics in concert; having robust methods to measure their covariances at low computational cost is thus of great relevance to upcoming surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Graphite Characterization: Baseline Variability Analysis Report

The Idaho National INL’s Baseline Material Property program provides many measurements of different material properties on a variety of nuclear graphite grades being investigated for irradiation-induced changes in material properties. The baseline material properties provide an unirradiated reference for comparison with measurements of those properties after irradiation. This study describes how uncertainty, or variance, in estimates of important material properties, like compressive strength, may depend on sample size, sample distribution and parameter estimation methods. Compressive strength data from two large collections of specimens from a billet of PCEA graphite (sample size = 230) and a billet of IG-110 graphite (sample size = 48) are used to illustrate these dependencies. The grades of graphite used represent approximate end members in terms of relative variability in strength measurements. Monte Carlo simulation and bootstrapping methods were used to examine variance in Weibull-parameter estimates as a function of sample size. Results were consistent with what previous statistical analyses have shown about the relationship between sample size and width of the confidence interval for maximum likelihood (ML) estimated parameters when samples are repeatedly drawn from a Weibull distribution. As a function of sample size, the precision of the modulus estimate increases faster than that of the characteristic strength. The modulus maximum likelihood estimates (MLEs) are biased for low sample sizes. The American Society for Testing and Materials document ASTM D7846-16 provides equations that describe the dependence of the variance of parameter MLEs on sample size. Using the same methodology, we show that the polynomials provided for variance dependence on sample size are inaccurate for sample sizes greater than ~120. We provide approximations to that relationship that illustrate relative constancy for sample size > ~10, in the logspace relationship of the modulus variance and the log transformed characteristic strength variance. Bootstrapping provided a means of comparing Weibull parameters obtained using ASME’s guidance for specimen collection to those obtained using different weighting in a case where strength is related to position in a billet and to grain orientation and a single distribution is used to characterize the billet. ASME specifies that samples be collected with equal representation in all locations and grain orientations. Parameter values obtained using ASME’s method were different from those obtained using random sampling of the complete data sets, and this was particularly true for the PCEA data. In addition, while the ASME approach yielded more conservative parameters for the PCEA data, the opposite was true for the IG-110 data. The difference observed for the PCEA data set likely reflects the larger number of against-grain specimens collected, which had generally higher strength than the with-grain specimens. This emphasizes the importance of the dependence of strength on location or grain orientation and suggests that it may be prudent to weight specimen collection toward the weaker subgroups if a conservative estimate as desired, as the equal weighting method recommended by ASME would will not always provide the most conservative Weibull parameters. Because spatial correlations commonly exist in certain grades of graphite, future work may involve developing a methodology for parameter inference that relaxes the assumption of independent specimens. ASTM and ASME codes recommend different parameter estimation methods and models of the Weibull distribution in different situations. Descriptions of the effects on parameter estimates for the example billets used in this study provide an example of how the method may affect the parameter estimate, and how—in this case—that compares to differences due to other factors, such as sample size and dependence on grain orientation or sample location.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1982

Quantiles, parametric-select density estimation, and bi-information parameter estimators

A quantile-based approach to statistical analysis and probability modeling of data is presented which formulates statistical inference problems as functional inference problems in which the parameters to be estimated are density functions. Density estimators can be non-parametric (computed independently of model identified) or parametric-select (approximated by finite parametric models that can provide standard models whose fit can be tested). Exponential models and autoregressive models are approximating densities which can be justified as maximum entropy for respectively the entropy of a probability density and the entropy of a quantile density. Applications of these ideas are outlined to the problems of modeling: (1) univariate data; (2) bivariate data and tests for independence; and (3) two samples and likelihood ratios. It is proposed that bi-information estimation of a density function can be developed by analogy to the problem of identification of regression models.

Parzen, E.↗
DOE OSTI2021

Approximating and incorporating model uncertainty in an inversion for seismic source functions: Preliminary results

We present preliminary work on propagating model uncertainty into the estimation of the time domain source time functions of the seismic source. Our method is based on an estimated model covariance function, which we estimate from the data. The model covariance function is then used to construct a suite of surrogate Greens functions which we use in a Monte Carlo type inversion scheme. The result is a probability density function of the six independent source time functions, each of which corresponds to an individual component of the seismic moment tensor. We compare the results of our method with those obtained using a computationally expensive finite difference Monte Carlo method and find that our new method produces results that are deficient in low frequencies. The advantage of our new method, which we term the Karhunen-Loeve Monte Carlo (KLMC) method, is that is several orders of magnitude faster than our current method, which uses a finite difference scheme to produce the suite of forward models.

42 ENGINEERING↗
NASA NTRS1975

Development of a method for optimal maneuver analysis of complex space missions

A system that allows mission planners to find optimal multiple-burn space trajectories easily is described. Previously developed methods with different gravity assumptions perform the optimization function. The power of these programs is extended by a method of costate estimation. A penalty function method of constraining coast arc times to be positive is included. The capability of the method is demonstrated by finding the optimal control for three different space missions. These include a shuttle abort-once-around mission and two- and three-burn geosynchronous satellite-placement missions.

Mcadoo, S. F., Jr.↗
NASA NTRS1987

Observations of the Jovian UV aurora by Voyager

The observations of the Jovian aurora made by the Voyager UV spectrometer (UVS) during 1979 are analyzed, with special consideration given to the model fitting process. Several different estimates of the function specifying auroral arc geometry were tried, with the Broadfoot et al. (1981) estimate, found by probing with the tip of the UVS slit, giving the best agreement; this agreement is slightly better than the agreement with the Io torus footprint computed by Roederer et al. (1977). The results suggest that the UVS observations are more sensitive to the surface field geometry than are the Voyager flyby in situ observations. The results of a study of intensity maximum positions indicate that the particles exciting the spatially variable portion of the aurora are drifting west, implying either that these particles are electrons or that they are positive ions drifting east more slowly than the corotation lag of the Io torus region carries them to the west. The latter case is most consistent with the high-energy charged particle measurements.

Herbert, Floyd↗
NASA NTRS1989

Numerical optimization in Hilbert space using inexact function and gradient evaluations

Trust region algorithms provide a robust iterative technique for solving non-convex unstrained optimization problems, but in many instances it is prohibitively expensive to compute high accuracy function and gradient values for the method. Of particular interest are inverse and parameter estimation problems, since function and gradient evaluations involve numerically solving large systems of differential equations. A global convergence theory is presented for trust region algorithms in which neither function nor gradient values are known exactly. The theory is formulated in a Hilbert space setting so that it can be applied to variational problems as well as the finite dimensional problems normally seen in trust region literature. The conditions concerning allowable error are remarkably relaxed: relative errors in the gradient error condition is automatically satisfied if the error is orthogonal to the gradient approximation. A technique for estimating gradient error and improving the approximation is also presented.

Carter, Richard G.↗
NASA NTRS2013

Comparative Analysis of Alternative Spectral Bands of CO2 and O2 for the Sensing of CO2 Mixing Ratios

We performed comparative studies to establish favorable spectral regions and measurement wavelength combinations in alternative bands of CO2 and O2, for the sensing of CO2 mixing ratios (XCO2) in missions such as ASCENDS. The analysis employed several simulation approaches including separate layers calculations based on pre-analyzed atmospheric data from the modern-era retrospective analysis for research and applications (MERRA), and the line-byline radiative transfer model (LBLRTM) to obtain achievable accuracy estimates as a function of altitude and for the total path over an annual span of variations in atmospheric parameters. Separate layer error estimates also allowed investigation of the uncertainties in the weighting functions at varying altitudes and atmospheric conditions. The parameters influencing the measurement accuracy were analyzed independently and included temperature sensitivity, water vapor interferences, selection of favorable weighting functions, excitations wavelength stabilities and other factors. The results were used to identify favorable spectral regions and combinations of on / off line wavelengths leading to reductions in interferences and the improved total accuracy.

Pliutau, Denis↗
NASA NTRS2012

Global Air Quality and Health Co-benefits of Mitigating Near-term Climate Change Through Methane and Black Carbon Emission Controls

Tropospheric ozone and black carbon (BC), a component of fine particulate matter (PM < or = 2.5 microns in aerodynamic diameter; PM2.5), are associated with premature mortality and they disrupt global and regional climate. Objectives: We examined the air quality and health benefits of 14 specific emission control measures targeting BC and methane, an ozone precursor, that were selected because of their potential to reduce the rate of climate change over the next 20-40 years. Methods: We simulated the impacts of mitigation measures on outdoor concentrations of PM2.5 and ozone using two composition-climate models, and calculated associated changes in premature PM2.5‑ and ozone-related deaths using epidemiologically derived concentration-response functions. Results: We estimated that, for PM2.5 and ozone, respectively, fully implementing these measures could reduce global population-weighted average surface concentrations by 23-34% and 7-17% and avoid 0.6-4.4 and 0.04-0.52 million annual premature deaths globally in 2030. More than 80% of the health benefits are estimated to occur in Asia. We estimated that BC mitigation measures would achieve approximately 98% of the deaths that would be avoided if all BC and methane mitigation measures were implemented, due to reduced BC and associated reductions of nonmethane ozone precursor and organic carbon emissions as well as stronger mortality relationships for PM2.5 relative to ozone. Although subject to large uncertainty, these estimates and conclusions are not strongly dependent on assumptions for the concentration-response function. Conclusions: In addition to climate benefits, our findings indicate that the methane and BC emission control measures would have substantial co-benefits for air quality and public health worldwide, potentially reversing trends of increasing air pollution concentrations and mortality in Africa and South, West, and Central Asia. These projected benefits are independent of carbon dioxide mitigation measures. Benefits of BC measures are underestimated because we did not account for benefits from reduced indoor exposures and because outdoor exposure estimates were limited by model spatial resolution.

air quality↗
DOE OSTI2022

Towards mapping biodiversity from above: Can fusing lidar and hyperspectral remote sensing predict taxonomic, functional, and phylogenetic tree diversity in temperate forests?

Abstract Aim Rapid global change is impacting the diversity of tree species and essential ecosystem functions and services of forests. It is therefore critical to understand and predict how the diversity of tree species is spatially distributed within and among forest biomes. Satellite remote sensing platforms have been used for decades to map forest structure and function but are limited in their capacity to monitor change by their relatively coarse spatial resolution and the complexity of scales at which different dimensions of biodiversity are observed in the field. Recently, airborne remote sensing platforms making use of passive high spectral resolution (i.e., hyperspectral) and active lidar data have been operationalized, providing an opportunity to disentangle how biodiversity patterns vary across space and time from field observations to larger scales. Most studies to date have focused on single sites and/or one sensor type; here we ask how multiple sensor types from the National Ecological Observatory Network’s Airborne Observation Platform (NEON AOP) perform across multiple sites in a single biome at the NEON field plot scale (i.e., 40 m × 40 m). Location Eastern USA. Time period 2017–2018. Taxa studied Trees. Methods With a fusion of hyperspectral and lidar data from the NEON AOP, we assess the ability of high resolution remotely sensed metrics to measure biodiversity variation across eastern US temperate forests. We examine how taxonomic, functional, and phylogenetic measures of alpha diversity vary spatially and assess to what degree remotely sensed metrics correlate with in situ biodiversity metrics. Results Models using estimates of forest function, canopy structure, and topographic diversity performed better than models containing each category alone. Our results show that canopy structural diversity, and not just spectral reflectance, is critical to predicting biodiversity. Main conclusions We found that an approach that jointly leverages spectral properties related to leaf and canopy functional traits and forest health, lidar derived estimates of forest structure, fine‐resolution topographic diversity, and careful consideration of biogeographical differences within and among biomes is needed to accurately map biodiversity variation from above.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The effect of quasar redshift errors on Lyman-α forest correlation functions

ABSTRACT Using synthetic Lyman-α forests from the Dark Energy Spectroscopic Instrument (DESI) survey, we present a study of the impact of errors in the estimation of quasar redshift on the Lyman-α correlation functions. Estimates of quasar redshift have large uncertainties of a few hundred km s−1 due to the broadness of the emission lines and the intrinsic shifts from other emission lines. We inject Gaussian random redshift errors into the mock quasar catalogues, and measure the auto-correlation and the Lyman-α-quasar cross-correlation functions. We find a smearing of the BAO feature in the radial direction, but changes in the peak position are negligible. However, we see a significant unphysical correlation for small separations transverse to the line of sight which increases with the amplitude of the redshift errors. We interpret this contamination as a result of the broadening of emission lines in the measured mean continuum, caused by quasar redshift errors, combined with the unrealistically strong clustering of the simulated quasars on small scales.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Hydrological niche segregation defines forest structure and drought tolerance strategies in a seasonal Amazon forest

The relationship between rooting depth and above-ground hydraulic traits can potentially define drought resistance strategies that are important in determining species distribution and coexistence in seasonal tropical forests, and understanding this is important for predicting the effects of future climate change in these ecosystems. We assessed the rooting depth of 12 dominant tree species (representing c. 42% of the forest basal area) in a seasonal Amazon forest using the stable isotope ratios (δ 18 O and δ 2 H) of water collected from tree xylem and soils from a range of depths. We took advantage of a major ENSO-related drought in 2015/2016 that caused substantial evaporative isotope enrichment in the soil and revealed water use strategies of each species under extreme conditions. We measured the minimum dry season leaf water potential both in a normal year (2014; Ψ non-ENSO ) and in an extreme drought year (2015; Ψ ENSO ). Furthermore, we measured xylem hydraulic traits that indicate water potential thresholds trees tolerate without risking hydraulic failure (P 50 and P 88 ). We demonstrate that coexisting trees are largely segregated along a single hydrological niche axis defined by root depth differences, access to light and tolerance of low water potential. These differences in rooting depth were strongly related to tree size; diameter at breast height (DBH) explained 72% of the variation in the δ 18 Oxylem. Additionally, δ 18 O xylem explained 49% of the variation in P50 and 70% of P88, with shallow-rooted species more tolerant of low water potentials, while δ 18 O of xylem water explained 47% and 77% of the variation of minimum Ψ non-ENSO and Ψ ENSO . We propose a new formulation to estimate an effective functional rooting depth, i.e. the likely soil depth from which roots can sustain water uptake for physiological functions, using DBH as predictor of root depth at this site. Based on these estimates, we conclude that rooting depth varies systematically across the most abundant families, genera and species at the Tapajós forest, and that understorey species in particular are limited to shallow rooting depths. Our results support the theory of hydrological niche segregation and its underlying trade-off related to drought resistance, which also affect the dominance structure of trees in this seasonal eastern Amazon forest. Synthesis. Our results support the theory of hydrological niche segregation and demonstrate its underlying trade-off related to drought resistance (access to deep water vs. tolerance of very low water potentials). We found that the single hydrological axis defining water use traits was strongly related to tree size, and infer that periodic extreme droughts influence community composition and the dominance structure of trees in this seasonal eastern Amazon forest.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1992

Tables Of Gaussian-Type Orbital Basis Functions

NASA technical memorandum contains tables of estimated Hartree-Fock wave functions for atoms lithium through neon and potassium through krypton. Sets contain optimized Gaussian-type orbital exponents and coefficients, and near Hartree-Fock quality. Orbital exponents optimized by minimizing restricted Hartree-Fock energy via scaled Newton-Raphson scheme in which Hessian evaluated numerically by use of analytically determined gradients.

Partridge, Harry↗
NASA NTRSDate not supplied

A Thermodynamic Paradigm for Studying Disease Vector’s Habitats & Life Cycles Using NASA’s NextGen Remote Sensing Instruments

Global public health is entering a new informational age through the use of spatial models of disease vector/host ecologies driven by the use of remotely sensed data to measure environmental and structural factors critical in determining disease vector habitats, distributions, life cycles, and host interactions. The vector habitat microclimates can be quantified in terms of the surface energy budget measured by satellites. The epidemiological equations (processes) can be adapted and modified to explicitly incorporate environmental factors and interfaces required by a specific disease and its vector/host cycle. Remote sensing can be used to measure or evaluate or estimate both environment (state functions) and interface (process functions). Remote sensing can be used to measure or evaluate or estimate both environment (state functions) and interface (process functions) defining vector habitats. The products of remote sensing can be integrated directly into the epidemiological equations to significantly enhance our understanding of disease vector’s life cycles and habitats. The next generation of NASA’s remote sensing instruments recently operational will provide a significant enhancement in our ability to study disease vector’s life cycles and habitats. Three new instruments have become operational on the International Space Station (ISS) In June 2018, ECOSTRESS (The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station), a five channel, thermal IR instrument with 70 m resolution and approximately 4-5 day repeat cycle of day/night pairs along with DESISa hyperspectral sensor system 235 channels (between 400 and 1000 nanometers), 30 m resolution, and data only being acquired on demand. In November 2018. GEDI (Global Ecosystem Dynamics Investigation). GEDI is a high-resolution laser ranging of Earth’s forests and topography. These instruments set the stage for NASA’s new global designated observables set of remote sensing measurements collecting hyperspectral and multispectral thermal data called SBG (Surface Biology Geology). The SBG is currently in the planning stage. The availability and use of level 1-4 data products generated from these ISS instruments will be discussed.

thermodynamics↗