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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1998

Parameter Estimation of Actuators for Benchmark Active Control Technology (BACT) Wind Tunnel Model with Analysis of Wear and Aerodynamic Loading Effects

This report describes the development of transfer function models for the trailing-edge and upper and lower spoiler actuators of the Benchmark Active Control Technology (BACT) wind tunnel model for application to control system analysis and design. A simple nonlinear least-squares parameter estimation approach is applied to determine transfer function parameters from frequency response data. Unconstrained quasi-Newton minimization of weighted frequency response error was employed to estimate the transfer function parameters. An analysis of the behavior of the actuators over time to assess the effects of wear and aerodynamic load by using the transfer function models is also presented. The frequency responses indicate consistent actuator behavior throughout the wind tunnel test and only slight degradation in effectiveness due to aerodynamic hinge loading. The resulting actuator models have been used in design, analysis, and simulation of controllers for the BACT to successfully suppress flutter over a wide range of conditions.

Waszak, Martin R.↗
NASA NTRS2007

A Start Toward Micronucleus-Based Decompression Models; Altitude Decompression

Do gaseous micronuclei trigger the formation of bubbles in decompression sickness (DCS)? Most previous instructions for DCS prevention have been oriented toward supersaturated gas in tissue. We are developing a mathematical model that is oriented toward the expected behavior of micronuclei. The issue is simplified in altitude decompressions because the aviator or astronaut is exposed only to decompression, whereas in diving there is a compression before the decompression. The model deals with four variables: duration of breathing of 100% oxygen before going to altitude (O2 prebreathing), altitude of the exposure, exposure duration, and rate of ascent. Assumptions: a) there is a population of micronuclei of various sizes having a range of characteristics, b) micronuclei are stable until they grow to a certain critical nucleation radius, c) it takes time for gas to diffuse in or out of micronuclei, and d) all other variables being equal, growth of micronuclei upon decompression is more rapid at high altitude because of the rarified gas in the micronuclei. To estimate parameters, we use a dataset of 4,756 men in altitude chambers exposed to various combinations of the model s variables. The model predicts occurrence of DCS symptoms quite well. It is notable that both the altitude chamber data and the model show little effect of O2 prebreathing until it lasts more than 60 minutes; this is in contrast to a conventional idea that the benefit of prebreathing is directly due to exponential washout of tissue nitrogen. The delay in response to O2 prebreathing can be interpreted as time required for outward diffusion of nitrogen; when the micronuclei become small enough, they are disabled, either by crushing or because they cannot expand to a critical nucleation size when the subject ascends to altitude.

Van Liew, H. D.↗
NASA NTRS2011

Improving Simulated Soil Moisture Fields Through Assimilation of AMSR-E Soil Moisture Retrievals with an Ensemble Kalman Filter and a Mass Conservation Constraint

Model simulated soil moisture fields are often biased due to errors in input parameters and deficiencies in model physics. Satellite derived soil moisture estimates, if retrieved appropriately, represent the spatial mean of soil moisture in a footprint area, and can be used to reduce model bias (at locations near the surface) through data assimilation techniques. While assimilating the retrievals can reduce model bias, it can also destroy the mass balance enforced by the model governing equation because water is removed from or added to the soil by the assimilation algorithm. In addition, studies have shown that assimilation of surface observations can adversely impact soil moisture estimates in the lower soil layers due to imperfect model physics, even though the bias near the surface is decreased. In this study, an ensemble Kalman filter (EnKF) with a mass conservation updating scheme was developed to assimilate the actual value of Advanced Microwave Scanning Radiometer (AMSR-E) soil moisture retrievals to improve the mean of simulated soil moisture fields by the Noah land surface model. Assimilation results using the conventional and the mass conservation updating scheme in the Little Washita watershed of Oklahoma showed that, while both updating schemes reduced the bias in the shallow root zone, the mass conservation scheme provided better estimates in the deeper profile. The mass conservation scheme also yielded physically consistent estimates of fluxes and maintained the water budget. Impacts of model physics on the assimilation results are discussed.

Li, Bailing↗
DOE OSTI2015

determining the mass of kepler-78b with nonparametric Gaussian process estimation

Kepler-78b is a transiting planet that is 1.2 times the radius of Earth and orbits a young, active K dwarf every 8 hr. The mass of Kepler-78b has been independently reported by two teams based on radial velocity (RV) measurements using the HIRES and HARPS-N spectrographs. Due to the active nature of the host star, a stellar activity model is required to distinguish and isolate the planetary signal in RV data. Whereas previous studies tested parametric stellar activity models, we modeled this system using nonparametric Gaussian process (GP) regression. We produced a GP regression of relevant Kepler photometry. We then use the posterior parameter distribution for our photometric fit as a prior for our simultaneous GP + Keplerian orbit models of the RV data sets. We tested three simple kernel functions for our GP regressions. Based on a Bayesian likelihood analysis, we selected a quasi-periodic kernel model with GP hyperparameters coupled between the two RV data sets, giving a Doppler amplitude of 1.86 ± 0.25 m s{sup −1} and supporting our belief that the correlated noise we are modeling is astrophysical. The corresponding mass of 1.87{sub −0.26}{sup +0.27} M{sub ⊕} is consistent with that measured in previous studies, and more robust due to our nonparametric signal estimation. Based on our mass and the radius measurement from transit photometry, Kepler-78b has a bulk density of 6.0{sub −1.4}{sup +1.9} g cm{sup −3}. We estimate that Kepler-78b is 32% ± 26% iron using a two-component rock-iron model. This is consistent with an Earth-like composition, with uncertainty spanning Moon-like to Mercury-like compositions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

MACHINE LEARNING-ENABLED PREDICTION OF TRANSIENT INJECTION MAP IN AUTOMOTIVE INJECTORS WITH UNCERTAINTY QUANTIFICATION

Accurate prediction of injection profiles is a critical aspect of linking injector operation with engine performance and emissions. However, highly resolved injector simulations can take one to two weeks of wall-clock time, which is incompatible with engine design cycles with desired turnaround times of less than a day. Hence, it is important to reduce the time-to-solution of the internal flow simulations by several orders of magnitude to make it compatible with engine simulations. This work demonstrates a data-driven approach for tackling the computational overhead of injector simulations, whereby the transient injection profiles are emulated for a side-oriented, single-hole diesel injector using a Bayesian machine-learning framework. First, an interpretable Bayesian learning strategy was employed to understand the effect of design parameters on the total void fraction field. Then, autoencoders are utilized for efficient dimensionality reduction of the flowfields. Gaussian process models are finally used to predict the spatiotemporal void fraction field at the injector exit for unknown operating conditions. The Gaussian process models produce principled uncertainty estimates associated with the emulated flowfields, which provide the engine designer with valuable information of where the data-driven predictions can be trusted in the design space. The Bayesian flowfield predictions are compared with the corresponding predictions from a deep neural network, which has been transfer-learned from static needle simulations from a previous work by the authors. The emulation framework can predict the void fraction field at the exit of the orifice within a few seconds, thus achieving a speed-up factor of up to 38 x 10(6) over the traditional simulation-based approach of generating transient injection maps.

machine learning↗
DOE OSTI2018

Estimation of the HIV-1 infection rate and the basic reproductive ratio

This paper proposes and analyzes an alternative method to estimate important parameters of the HIV-1 infection: the infection rate and the basic reproductive ratio (in the presence and absence of the immune response). These parameters are of great relevance, impacting on the infection chronicity. The method consists in comparing clinical data in the chronic stage with predicted equilibrium points of a mathematical model which describes the dynamics of HIV-1 in the acute infection phase. The fact that the method uses only clinical data relating to the chronic stage is an important aspect since most patients are diagnosed at this stage, and thus only have data available for this stage. A comparison between the proposed method and other estimation methods, as well as between numerical simulations, is presented and the results are quite satisfactory. In addition, by applying the proposed method for data of 31 patients, we estimate the basic reproductive ratio in the presence of the immune response and the HIV-1 infection rate for which we obtained a median of 1.95 (IQR 0.55) and 1.96×10{sup −5} mm{sup 3} virions{sup −1} day{sup −1} (IQR 3.52×10{sup −5}), respectively.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Data-driven discovery of dynamics from time-resolved coherent scattering

Coherent X-ray scattering (CXS) techniques are capable of interrogating dynamics of nano- to mesoscale materials systems at time scales spanning several orders of magnitude. However, obtaining accurate theoretical descriptions of complex dynamics is often limited by one or more factors—the ability to visualize dynamics in real space, computational cost of high-fidelity simulations, and effectiveness of approximate or phenomenological models. In this work, we develop a data-driven framework to uncover mechanistic models of dynamics directly from time-resolved CXS measurements without solving the phase reconstruction problem for the entire time series of diffraction patterns. Our approach uses neural differential equations to parameterize unknown real-space dynamics and implements a computational scattering forward model to relate real-space predictions to reciprocal-space observations. This method is shown to recover the dynamics of several computational model systems under various simulated conditions of measurement resolution and noise. Moreover, the trained model enables estimation of long-term dynamics well beyond the maximum observation time, which can be used to inform and refine experimental parameters in practice. Finally, we demonstrate an experimental proof-of-concept by applying our framework to recover the probe trajectory from a ptychographic scan. Our proposed framework bridges the wide existing gap between approximate models and complex data.

36 MATERIALS SCIENCE↗
NASA NTRS1990

Attitude determination and parameter estimation using vector observations - Application

This paper presents tests of a new method for the simultaneous estimation of spacecraft attitude and sensor biases, based on a quaternion estimation algorithm minimizing Wahba's loss function. The new method is compared with a conventional batch least-squares differential correction algorithm. The estimates are based on data from strapdown gyros and star trackers, simulated with varying levels of Gaussian noise for both inertially-fixed and earth-pointing attitudes. Both algorithms solve for the spacecraft attitude and the gyro drift rate biases. In the majority of tests performed, the two methods converge to the same estimates in the same number of iterations, but the new algorithm requires about 60 percent more computational effort. Some cases were found in which the new method converges in fewer iterations than the differential correction, and some for which the differential correction requires fewer iterations.

Markley, F. Landis↗
NASA NTRS2012

The Size Evolution of Passive Galaxies: Observations From the Wide-Field Camera 3 Early Release Science Program

We present the size evolution of passively evolving galaxies at z approximately 2 identified in Wide-Field Camera 3 imaging from the Early Release Science program. Our sample was constructed using an analog to the passive BzK galaxy selection criterion, which isolates galaxies with little or no ongoing star formation at z greater than approximately 1.5. We identify 30 galaxies in approximately 40 arcmin(sup 2) to H less than 25 mag. By fitting the 10-band Hubble Space Telescope photometry from 0.22 micrometers less than approximately lambda (sub obs) 1.6 micrometers with stellar population synthesis models, we simultaneously determine photometric redshift, stellar mass, and a bevy of other population parameters. Based on the six galaxies with published spectroscopic redshifts, we estimate a typical redshift uncertainty of approximately 0.033(1+z).We determine effective radii from Sersic profile fits to the H-band image using an empirical point-spread function. By supplementing our data with published samples, we propose a mass-dependent size evolution model for passively evolving galaxies, where the most massive galaxies (M(sub ∗) approximately 10(sup 11) solar mass) undergo the strongest evolution from z approximately 2 to the present. Parameterizing the size evolution as (1 + z)(sup - alpha), we find a tentative scaling of alpha approximately equals (−0.6 plus or minus 0.7) + (0.9 plus or minus 0.4) log(M(sub ∗)/10(sup 9 solar mass), where the relatively large uncertainties reflect the poor sampling in stellar mass due to the low numbers of highredshift systems. We discuss the implications of this result for the redshift evolution of the M(sub ∗)-R(sub e) relation for red galaxies.

fundamental parameters↗
DOE OSTI2025

Solving high-dimensional inverse problems using amortized likelihood-free inference with noisy and incomplete data

Here, we present a likelihood-free probabilistic inversion method based on normalizing flows for high-dimensional inverse problems. The proposed method is composed of two complementary networks: a summary network for data compression and an inference network for parameter estimation. The summary network encodes raw observations into a fixed-size vector of summary features, while the inference network generates samples of the approximate posterior distribution of the model parameters based on these summary features. The posterior samples are produced in a deep generative fashion by sampling from a latent Gaussian distribution and passing these samples through an invertible transformation. We construct this invertible transformation by sequentially alternating conditional invertible neural network and conditional neural spline flow layers. The summary and inference networks are trained simultaneously. We apply the proposed method to an inversion problem in groundwater hydrology to estimate the posterior distribution of the log-conductivity field conditioned on spatially sparse time-series observations of the system’s hydraulic head responses. The conductivity field is represented with 706 degrees of freedom in the considered problem. Comparison with the likelihood-based iterative ensemble smoother PEST-IES method demonstrates that the proposed method accurately estimates the parameter posterior distribution and the observations’ predictive posterior distribution at a fraction of the inference time of PEST-IES.

conditional invertible neural network↗
DOE OSTI2016

Applying Bayesian parameter estimation to relativistic heavy-ion collisions: Simultaneous characterization of the initial state and quark-gluon plasma medium

We quantitatively estimate properties of the quark-gluon plasma created in ultrarelativistic heavy-ion collisions utilizing Bayesian statistics and a multiparameter model-to-data comparison. The study is performed using a recently developed parametric initial condition model, T R ENTo, which interpolates among a general class of particle production schemes, and a modern hybrid model which couples viscous hydrodynamics to a hadronic cascade.We calibrate the model to multiplicity, transverse momentum, and flow data and report constraints on the parametrized initial conditions and the temperature-dependent transport coefficients of the quark-gluon plasma. We show that initial entropy deposition is consistent with a saturation-based picture, extract a relation between the minimum value and slope of the temperature-dependent specific shear viscosity, and find a clear signal for a nonzero bulk viscosity.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1984

Flight investigation of various control inputs intended for parameter estimation

An experiment assessing the stability and control derivatives resulting from various control inputs was undertaken using the F-8 digital fly by wire aircraft. Improved control inputs have been proposed as a means of making stability and contol derivative estimation more efficient, thus reducing the cost of flight testing and data analysis. The subject inputs were either generated by the pilot or preprogrammed in a remote ground computer and telemetered to the aircraft. Nine preprogrammed inputs and three pilot generated inputs were assessed at subsonic and supersonic flight conditions, and both unaugmented and highly augmented flight control systems were used. Effects of input amplitude were also assessed. The inputs were divided into two general types - sinusoidal or with corners (a rapid and distinct change in slope). The inputs with corners, performed in the unaugmented mode, produced the best sets of stability and control derivatives. The simplest of these inputs, the pilot generated doublet, produced sets of derivatives as good as those produced by the more complex inputs. Small inputs produced worse derivatives than the unaugmented mode, and sinusoidal inputs produced worse derivatives than corner containing inputs.

Shafer, M. F.↗
NASA NTRS1997

Analysis of Particle Image Velocimetry (PIV) Data for Acoustic Velocity Measurements

Acoustic velocity measurements were taken using Particle Image Velocimetry (PIV) in a Normal Incidence Tube configuration at various frequency, phase, and amplitude levels. This report presents the results of the PIV analysis and data reduction portions of the test and details the processing that was done. Estimates of lower measurement sensitivity levels were determined based on PIV image quality, correlation, and noise level parameters used in the test. Comparison of measurements with linear acoustic theory are presented. The onset of nonlinear, harmonic frequency acoustic levels were also studied for various decibel and frequency levels ranging from 90 to 132 dB and 500 to 3000 Hz, respectively.

Blackshire, James L.↗
NASA NTRS2012

Method and apparatus for autonomous, in-receiver prediction of GNSS ephemerides

Methods and apparatus for autonomous in-receiver prediction of orbit and clock states of Global Navigation Satellite Systems (GNSS) are described. Only the GNSS broadcast message is used, without need for periodic externally-communicated information. Earth orientation information is extracted from the GNSS broadcast ephemeris. With the accurate estimation of the Earth orientation parameters it is possible to propagate the best-fit GNSS orbits forward in time in an inertial reference frame. Using the estimated Earth orientation parameters, the predicted orbits are then transformed into Earth-Centered-Earth-Fixed (ECEF) coordinates to be used to assist the GNSS receiver in the acquisition of the signals. GNSS satellite clock states are also extracted from the broadcast ephemeris and a parameterized model of clock behavior is fit to that data. The estimated modeled clocks are then propagated forward in time to enable, together with the predicted orbits, quicker GNSS signal acquisition.

Bar-Sever, Yoaz E.↗
NASA NTRS1991

Global ocean tide mapping using TOPEX/Poseidon altimetry

The investigation's main goals are to produce accurate tidal maps of the main diurnal, semidiurnal, and long-period tidal components in the world's deep oceans. This will be done by the application of statistical estimation techniques to long time series of altimeter data provided by the TOPEX/POSEIDON mission, with additional information provided by satellite tracking data. In the prelaunch phase, we will use in our simulations and preliminary work data supplied by previous oceanographic missions, such as Seasat and Geosat. These results will be of scientific interest in themselves. The investigation will also be concerned with the estimation of new values, and their uncertainties, for tidal currents and for the physical parameters appearing in the Laplace tidal equations, such as bottom friction coefficients and eddy viscosity coefficients. This will be done by incorporating the altimetry-derived charts of vertical tides as boundary conditions in the integration of those equations. The methodology of the tidal representation will include the use of appropriate series expansions such as ocean-basin normal modes and spherical harmonics. The results of the investigation will be space-determined tidal models of coverage and accuracy superior to that of the present numerical models of the ocean tides, with the concomitant benefits to oceanography and associated disciplinary fields.

Sanchez, Braulio V.↗
NASA NTRS2022

L-Band Microwave Satellite Data and Model Simulations Over the Dry Chaco to Estimate Soil Moisture, Soil Temperature, Vegetation and Soil Salinity

The Dry Chaco in South America is a semi-arid ecoregion prone to dryland salinization. In this region, we investigated coarse-scale surface soil moisture (SM), soil temperature, soil salinity and vegetation, using L-band microwave brightness temperature (TB) observations and retrievals from the Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellite missions, Catchment Land Surface Model (CLSM) simulations, and in situ measurements within 26 sampled satellite pixels. Across these 26 sampled pixels, the satellite-based SM outperformed CLSM SM compared to field data, and forward L-band TB simulations derived from in situ SM and temperature performed better than those derived from CLSM estimates relative to SMOS TB observations. The surface salinity for the sampled pixels was on average only 4 mg/g and only locally influenced the TB simulations, when including salinity in the dielectric mixing model of the forward radiative transfer model (RTM) simulations. To explore the potential of retrieving salinity together with other RTM parameters to optimize TB simulations over the entire Dry Chaco, the RTM was inverted using 10 years of multi-angular SMOS TB data and constraints of CLSM SM and temperature. However, the latter modeled SM was not sufficiently accurate and factors such as open surface water were missing in the background constraints, so that the salinity retrievals effectively represented a bulk correction of the dielectric constant, rather than salinity per se. However, the retrieval of vegetation, scattering albedo and surface roughness resulted in realistic values.

L-Band↗
DOE OSTI2022

Dark Energy Survey Year 3 results: calibration of lens sample redshift distributions using clustering redshifts with BOSS/eBOSS

ABSTRACT We present clustering redshift measurements for Dark Energy Survey (DES) lens sample galaxies used in weak gravitational lensing and galaxy clustering studies. To perform these measurements, we cross-correlate with spectroscopic galaxies from the Baryon Acoustic Oscillation Survey (BOSS) and its extension, eBOSS. We validate our methodology in simulations, including a new technique to calibrate systematic errors that result from the galaxy clustering bias, and we find that our method is generally unbiased in calibrating the mean redshift. We apply our method to the data, and estimate the redshift distribution for 11 different photometrically selected bins. We find general agreement between clustering redshift and photometric redshift estimates, with differences on the inferred mean redshift found to be below |Δz| = 0.01 in most of the bins. We also test a method to calibrate a width parameter for redshift distributions, which we found necessary to use for some of our samples. Our typical uncertainties on the mean redshift ranged from 0.003 to 0.008, while our uncertainties on the width ranged from 4 to 9 per cent. We discuss how these results calibrate the photometric redshift distributions used in companion papers for DES Year 3 results.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2016

Determination of Celestial Body Principal Axes via Gravity Field Estimation

The determination of physical parameters of celestial bodies provides critical navigational and scientific information. Determining the mass, rotation state, and density distribution is an important task of the navigation team on an operational mission. One representation of the internal density distribution, spherical harmonics, may be leveraged to compute the orientation of the principal axis frame of the celestial body, which in turn informs the dynamics of the body’s motion. Using data from NASA’s Dawn mission to the massive asteroids (1) Ceres and (4) Vesta, a method is proposed that refines the knowledge of the principal axis frame by utilizing estimated spherical harmonic coefficients. Applying an iterative rotation scheme to the solved-for body-fixed frame leads to convergence on a frame that is very close to the actual dynamical principal axis frame.

Takahashi, Yu↗

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