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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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175 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Filtering Methods for Error Reduction in Spacecraft Attitude Estimation Using Quaternion Star Trackers

Precision attitude determination for recent and planned space missions typically includes quaternion star trackers (ST) and a three-axis inertial reference unit (IRU). Sensor selection is based on estimates of knowledge accuracy attainable from a Kalman filter (KF), which provides the optimal solution for the case of linear dynamics with measurement and process errors characterized by random Gaussian noise with white spectrum. Non-Gaussian systematic errors in quaternion STs are often quite large and have an unpredictable time-varying nature, particularly when used in non-inertial pointing applications. Two filtering methods are proposed to reduce the attitude estimation error resulting from ST systematic errors, 1) extended Kalman filter (EKF) augmented with Markov states, 2) Unscented Kalman filter (UKF) with a periodic measurement model. Realistic assessments of the attitude estimation performance gains are demonstrated with both simulation and flight telemetry data from the Lunar Reconnaissance Orbiter.

Calhoun, Philip C.↗
NASA NTRS2019

Sensor Analysis, Modeling, and Test for Robust Propulsion System Autonomy

An approach is presented supporting analysis, modeling, and test validation of operational flight instrumentation (OFI) that facilitates critical functions for the Space Launch System (SLS) main propulsion system (MPS). Certain types of OFI sensors were shown to exhibit highly nonlinear and non-gaussian noise characteristics during acceptance testing, motivating the development of advanced modeling and simulation (M&S) capability to support algorithm verification and flight certification. Hardware model and algorithm simulation fidelity was informed by a risk scoring metric; redesign of high-risk algorithms using test-validated sensor models significantly improved their expected performance as evaluated using Monte Carlo acceptance sampling methods. Autonomous functions include closed-loop ullage pressure regulation, pressurant leak detection, and fault isolation for automated safing and crew caution and warning (C&W).

Orr, Jeb S.↗
NASA NTRS2019

Extended Line Spread Function of TES Microcalorimeters with Au/Bi Absorbers

Microcalorimeters have the potential to provide line shapes well-described by a single Gaussian broadening term of few-eV width. This attribute makes the detectors especially well suited for x-ray astrophysics observations; however, low-level non-Gaussian broadening terms are expected and must be characterized. These terms depend on the composition of the x-ray absorber, the detailed x-ray absorption physics, the device thermalization processes, and the incident x-ray energy. Here we present the first measurements targeted at understanding the extended line-spread function (LSF) of x-ray microcalorimeter pixels under development for the X-ray Integral Field Unit (XIFU) on the Athena X-ray Observatory. These pixels are comprised of Mo/Au transition-edge sensors (TESs) with overhanging electroplated Au/Bi absorbers. We measured the line shapes using monochromatic x-ray sources with < 1 eV width at several x-ray energies (0.85, 0.93, 1.25, 1.5, 5.4, and 8.0 keV) across the instrument bandpass (0.3-12 keV) and modeled the line profiles. These results are compared to the extended LSF of the Hitomi microcalorimeter pixels that used HgTe absorbers.

Superconducting devices↗
NASA NTRS2019

A Communication Channel Density Estimating Generative Adversarial Network

Autoencoder-based communication systems use neural network channel models to backwardly propagate message reconstruction error gradients across an approximation of the physical communication channel. In this work, we develop and test a new generative adversarial network (GAN) architecture for the purpose of training a stochastic channel approximating neural network. In previous research, investigators have focused on additive white Gaussian noise (AWGN) channels and/or simplified Rayleigh fading channels, both of which are linear and have well defined analytic solutions. Given that training a neural network is computationally expensive, channel approximation networks— and more generally the autoencoder systems—should be evaluated in communication environments that are traditionally difficult. To that end, our investigation focuses on channels that contain a combination of non-linear amplifier distortion, pulse shape filtering, intersymbol interference, frequency-dependent group delay, multipath, and non-Gaussian statistics. Each of our models are trained without any prior knowledge of the channel. We show that the trained models have learned to generalize over an arbitrary amplifier drive level and constellation alphabet. We demonstrate the versatility of our GAN architecture by comparing the marginal probability density function of several channel simulations with that of their corresponding neural network approximations

Smith, Aaron↗
NASA NTRS2020

Atacama Cosmology Telescope: Component-separated maps of CMB temperature and the thermal Sunyaev-Zel'dovich effect

Optimal analyses of many signals in the cosmic microwave background (CMB) require map-level extraction of individual components in the microwave sky, rather than measurements at the power spectrum level alone. To date, nearly all map-level component separation in CMB analyses has been performed exclusively using satellite data. In this paper, we implement a component separation method based on the internal linear combination (ILC) approach which we have designed to optimally account for the anisotropic noise (in the 2D Fourier domain) often found in ground-based CMB experiments. Using this method, we combine multi-frequency data from the Planck satellite and the Atacama Cosmology Telescope Polarimeter (ACTPol) to construct the first wide area (≈2100 sq. deg.), arcminute-resolution component-separated maps of the CMB temperature anisotropy and the thermal Sunyaev-Zel'dovich (tSZ) effect sourced by the inverse-Compton scattering of CMB photons off hot, ionized gas. Our ILC pipeline allows for explicit deprojection of various contaminating signals, including a modified blackbody approximation of the cosmic infrared background (CIB) spectral energy distribution. The cleaned CMB maps will be a useful resource for CMB lensing reconstruction, kinematic SZ cross-correlations, and primordial non-Gaussianity studies. The tSZ maps will be used to study the pressure pro les of galaxies, groups, and clusters through cross-correlations with halo catalogs, with dust contamination controlled via CIB deprojection. The data products described in this paper are available on LAMBDA.

Mathew S. Madhavacheril↗
NASA NTRS2021

Radar-Based Bayesian Estimation of Ice Crystal Growth Parameters within a Microphysical Model

The potential for polarimetric Doppler radar measurements to improve predictions of ice microphysical processes within an idealized model–observational framework is examined. In an effort to more rigorously constrain ice growth processes (e.g., vapor deposition) with observations of natural clouds, a novel framework is developed to compare simulated and observed radar measurements, coupling a bulk adaptive-habit model of vapor growth to a polarimetric radar forward model. Bayesian inference on key microphysical model parameters is then used, via a Markov chain Monte Carlo sampler, to estimate the probability distribution of the model parameters. The statistical formalism of this method allows for robust estimates of the optimal parameter values, along with (non-Gaussian) estimates of their uncertainty. To demonstrate this framework, observations from Department of Energy radars in the Arctic during a case of pristine ice precipitation are used to constrain vapor deposition parameters in the adaptive habit model. The resulting parameter probability distributions provide physically plausible changes in ice particle density and aspect ratio during growth. A lack of direct constraint on the number concentration produces a range of possible mean particle sizes, with the mean size inversely correlated to number concentration. Consistency is found between the estimated inherent growth ratio and independent laboratory measurements, increasing confidence in the parameter PDFs and demonstrating the effectiveness of the radar measurements in constraining the parameters. The combined Doppler and polarimetric observations produce the highest-confidence estimates of the parameter PDFs, with the Doppler measurements providing a stronger constraint for this case.

Robert S. Schrom↗
NASA NTRSDate not supplied

Estimation of Timing Performance with Pulsed Laser Links for Small Satellite PNT

Satellite constellations are growing in size and in utility for applications as diverse as providing reliable, low-latency internet service to rural areas orEarth science missions.This expansion of satellite constellations brings attention to the need for responsive and reliable satellite communication.Current research in the PrecisionSpace Systems Laboratory at the University of Florida focuses on using pulsed laser communications as the method of relaying time signals instead of the radio frequency (RF)signals.The upcoming CLICK (CubeSat Laser Infrared CrosslinK) missions (Massachusetts Institute of Technology, University of Florida, NASA Ames Research Center) will demonstrate a spacecraft-to-spacecraft laser communication link and time-transfer.For pre-flight technology assessment of missions such as CLICK, a simulation of a spacecraft constellation in Earth orbit has been created to estimate clock synchronization and precision orbit determination based on measured instrumentation performance.We have developed a novel MATLAB-based numerical simulator to model spacecraft-to-spacecraft laser time-transfer and estimate the offset between the spacecraft clocks over time.This simulation includes timing errors associated with laser pulse generation and detection, as well as non-Gaussian clock drift models.The two on-board clocks modeled are a cesium-based Chip-Scale Atomic Clock (CSAC) and a rubidium-based Miniature Atomic Clock (MAC), both produced by Microchip.An example case of two spacecraft in a circular, low-Earth orbit receiving GPS position, GPS timing, and laser pulse time-of-flight measurements is simulated.The positions and velocities of the two spacecraft at a reference epoch and the constant clock model coefficients are estimated.Polynomial models of different orders were used as clock models. The effect of clock model order on the root-mean-square (RMS) of the clock error is apparent in the case using GPS and lasing measurements, showing that the clock model improves with increasing clock model order. Results compare the estimated clock model of a mission operation that only uses GPS measurements and a mission operation that uses both GPS and laser pulse time-of-flight measurements between spacecraft referenced to their on-board CSACs or on-board MACs. Including lasing measurements reduces the RMS clock model error by approximately 80% of the RMS of the cases with only GPS measurements.This simulation tool can be used to optimize the lasing operations schedule based on mission timing performance objectives. During this presentation, I will discuss the details of the simulation and clock model estimation, as well as discuss the results.I will compare the results of incorporating lasing timing measurements to using only GPS measurements and the outcomes of using different orders of polynomials.

Timing Performance↗
NASA NTRS2018

Performance Metrics for the Assessment of Satellite Data Products: An Ocean Color Case Study

Performance assessment of ocean color satellite data has generally relied on statistical metrics chosen for their common usage and the rationale for selecting certain metrics is infrequently explained. Commonly reported statistics based on mean squared errors, such as the coefficient of determination (r2), root mean square error, and regression slopes, are most appropriate for Gaussian distributions without outliers and, therefore, are often not ideal for ocean color algorithm performance assessment, which is often limited by sample availability. In contrast, metrics based on simple deviations, such as bias and mean absolute error, as well as pair-wise comparisons, often provide more robust and straightforward quantities for evaluating ocean color algorithms with non-Gaussian distributions and outliers. This study uses a SeaWiFS chlorophyll-a validation data set to demonstrate a framework for satellite data product assessment and recommends a multimetric and user-dependent approach that can be applied within science, modeling, and resource management communities.

remote sensing↗
NASA NTRS2022

Superclustering with the Atacama Cosmology Telescope and Dark Energy Survey. I. Evidence for Thermal Energy Anisotropy Using Oriented Stacking

The cosmic web contains filamentary structure on a wide range of scales. On the largest scales, superclustering aligns multiple galaxy clusters along intercluster bridges, visible through their thermal Sunyaev–Zel'dovich signal in the cosmic microwave background. We demonstrate a new, flexible method to analyze the hot gas signal from multiscale extended structures. We use a Compton y-map from the Atacama Cosmology Telescope (ACT) stacked on redMaPPer cluster positions from the optical Dark Energy Survey (DES). Cutout images from the y-map are oriented with large-scale structure information from DES galaxy data such that the superclustering signal is aligned before being overlaid. We find evidence of an extended quadrupole moment of the stacked y signal at the 3.5σ level, demonstrating that the large-scale thermal energy surrounding galaxy clusters is anisotropically distributed. We compare our ACT × DES results with the Buzzard simulations, finding broad agreement. Using simulations, we highlight the promise of this novel technique for constraining the evolution of anisotropic, non-Gaussian structure using future combinations of microwave and optical surveys.

M. Lokken↗
NASA NTRS2021

A Cosmic Microscope for the Preheating Era

Light fields with spatially varying backgrounds can modulate cosmic preheating, and imprint the nonlinear effects of preheating dynamics at tiny scales on large scale fluctuations. This provides us a unique probe into the preheating era which we dub the “cosmic microscope”. We identify a distinctive effect of preheating on scalar perturbations that turns the Gaussian primordial fluctuations of a light scalar field into square waves, like a diode. The effect manifests itself as local non-Gaussianity. We present a model, “modulated partial preheating”, where this nonlinear effect is consistent with current observations and can be reached by near future cosmic probes.

Cosmology of Theories beyond the SM↗
NASA NTRS2022

Robust Vision-based Multi-spacecraft Guidance Navigation and Control using CNN-based Pose Estimation

In this paper, we present an end-to-end simulation framework for tracking an uncooperative Target spacecraft in Low Earth Orbit using a CubeSat-class Ego spacecraft outfitted with a camera. Currently, capturing high-fidelity realistic images in space for this scenario is difficult and exorbitantly expensive. Therefore, we developed a framework to simulate the spacecraft orbits in Basilisk software and generate high-fidelity realistic images of spacecraft in Unreal Engine, including the effects from Sun, Earth, Moon and stars. The Ego spacecraft uses cameras to capture images of the uncooperative Target and estimates its position and attitude using a CNN based 6DOF pose estimation pipeline, eliminating need for large SWAP-C(Size, Weight, Power and Cost) sensors like LIDAR or reliance on inter-spacecraft communication, This CNN, which is motivated by ESA’s Pose Estimation challenge of 2019, is trained using simulated data from our end-to-end simulation framework. We compare the performance of two distinct CNNbased algorithms for pose estimation along a nominal trajectory. In presence of non-Gaussian modeling uncertainties, the statedependent estimation error is characterized with a quadratic upper-bound. The quadratically-bounded error can be used by a robust controller to maneuver

Rahmani, Amir↗
NASA NTRSDate not supplied

Ensemble Forecasting/Assimilation/Emissions Estimation with WRF-Chem/DART: Accomplishments, Lessons Learned, and Future Plans

Over the past ten years, we have been conducting research on regional ensemble atmospheric composition forecasting/data assimilation/emissions estimation with WRF-Chem/DART. WRF- Chem/DART integrates the Weather Research and Forecasting model (WRF) with online chemistry (WRF-Chem) into the Data Assimilation Research Testbed (DART). DART is an ensemble data assimilation system based on the ensemble adjustment Kalman filter (EAKF) with adaptive inflation, localization (physical and state space), and an optional non-Gaussian formulation of the EAKF. DART includes assimilation of meteorological and limited chemical observations. WRF-Chem/DART extends DART to include assimilation of: MOPITT CO; IASI CO and O3; MODIS AOD; OMI O3, NO2, and SO2; TROPOMI CO, O3, NO2, and SO2, TES CO, CO2 (research mode), O3, NH3, and CH4 (research mode); CrIS CO, O3, NH3, CH4 (research mode), and PAN; SCIAMACHY NO2; GOME2a NO2; MLS O3 and HNO3; and proxy TEMPO O3, and NO2 satellite retrievals as raw retrievals or as ‘compact phase space retrievals’ (CPSRs) for profile retrievals. WRF-Chem/DART also assimilates in situ atmospheric composition measurements and uses the ‘state augmentation method’ for emissions estimation. In our presentation, we will provide an overview of WRF-Chem/DART: • applications and results; • lessons learned from: (i) independent versus joint assimilation; (ii) total/partial column versus profile retrieval assimilation; (iii) joint in situ and retrieval assimilation; (iv) assimilation at grid resolutions ranging from 100 km to 4 km; (v) dynamic emissions estimation; and • future work related to wildfire emissions estimation and intercomparison of CMAQ (online)/JEDI and CMAQ (online and offline)/DART.

WRF-Chem/DART↗