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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
NASA NTRS1982

Control systems synthesis for a large flexible space antenna

The problem of control systems synthesis is considered for controlling the rigid-body attitude and elastic motion of a large deployable space-based antenna. Two methods for control systems synthesis are considered. The first method utilizes the stability and robustness properties of the controller consisting of torque actuators and collocated attitude and rate sensors. The second method is based on the linear-quadratic-Gaussian (LQG) control theory. A combination of the two methods, which results in a two-level hierarchical control system, is also briefly discussed. The performance of the controllers is analyzed by computing the variances of pointing errors, feed misalignment errors and surface contour errors in the presence of sensor and actuator noise.

Joshi, S. M.↗
NASA NTRS1982

Attitude and vibration control of a large flexible space-based antenna

Control systems synthesis is considered for controlling the rigid body attitude and elastic motion of a large deployable space-based antenna. Two methods for control systems synthesis are considered. The first method utilizes the stability and robustness properties of the controller consisting of torque actuators and collocated attitude and rate sensors. The second method is based on the linear-quadratic-Gaussian control theory. A combination of the two methods, which results in a two level hierarchical control system, is also briefly discussed. The performance of the controllers is analyzed by computing the variances of pointing errors, feed misalignment errors and surface contour errors in the presence of sensor and actuator noise.

Joshi, S. M.↗
NASA NTRS1986

Initializing a flicker-noise generator

The behavior of the stationary process, y(s), resulting from a particular Barnes-Jarvis (1971) flicker-noise generator filter initialization is analyzed along with that of the usual output, y(p), and the results are compared to those derived from the theory of true nonstationary flicker noise. A computer simulation and theoretical analysis indicates that though y(s) and y(p) two-sample variances are almost the same, they have significantly different time-interval errors (TIE). As time increases, up to the useful life of the generator output, more and more of the y(s) TIE is found to be due to the transient part.

Greenhall, C. A.↗
DOE OSTI2024

Neural network denoising of x-ray images from high-energy-density experiments

Noise is a consistent problem for x-ray transmission images of High-Energy-Density (HED) experiments because it can significantly affect the accuracy of inferring quantitative physical properties from these images. We consider experiments that use x-ray area backlighting to image a thin layer of opaque material within a physics package to observe its hydrodynamic evolution. The spatial variance of the x-ray transmission across the system due to changing opacity serves as an analog for measuring density in this evolving layer. The noise in these images adds nonphysical variations in measured intensity, which can significantly reduce the accuracy of our inferred densities, particularly at small spatial scales. Denoising these images is thus necessary to improve our quantitative analysis, but any denoising method also affects the underlying information in the image. In this paper, we present a method for denoising HED x-ray images via a deep convolutional neural network model with a modified DenseNet architecture. In our denoising framework, we estimate the noise present in the real (data) images of interest and apply the inferred noise distribution to a set of natural images. These synthetic noisy images are then used to train a neural network model to recognize and remove noise of that character. We show that our trained denoiser network significantly reduces the noise in our experimental images while retaining important physical features.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Weak-Form Latent Space Dynamics Identification

This software showcases the enhanced capabilities of the Latent Space Dynamics Identification (LaSDI) algorithm through the application of the weak form, resulting in WLaSDI. WLaSDI first compresses the data, then projects it onto test functions, and subsequently learns the local latent space models. Notably, WLaSDI demonstrates significantly improved robustness to noise. Using weak-form equation learning techniques, WLaSDI achieves local latent space modeling. Compared to the standard sparse identification of nonlinear dynamics (SINDy) used in LaSDI, the variance reduction of the weak form ensures robust and precise latent space recovery, enabling fast, robust, and accurate simulations. We demonstrate the efficacy of WLaSDI against LaSDI using several common benchmark examples, including viscid and inviscid Burgers', radial advection, and heat conduction. For instance, in 1D inviscid Burgers' simulations with up to 100% Gaussian white noise, WLaSDI maintains relative errors consistently below 6%, whereas LaSDI errors can exceed 10,000%. Similarly, in radial advection simulations, WLaSDI keeps relative errors below 16%, compared to potential errors of up to 10,000% with LaSDI. Additionally, WLaSDI achieves significant speedups, such as a 140X speedup in 1D Burgers' simulations compared to the corresponding full order model.

Choi, Youngsoo↗
NASA NTRS1995

The Third-Difference Approach to Modified Allan Variance

This study gives strategies for estimating the modified Allan variance (mvar) and formulas for computing the equivalent degrees of freedom (edf) of the estimators. A third-difference formulation of mvar leads to a tractable formula for edf in the presence of power-law phase noise. The effect of estimation stride on edf is tabulated. First-degree rational-function approximations for edf are derived.

Allan variance frequency instability statistical m↗
NASA NTRS1972

Digital simulation of hybrid loop operation in RFI backgrounds.

A digital computer model for Monte-Carlo simulation of an imperfect second-order hybrid phase-locked loop (PLL) operating in radio-frequency interference (RFI) and Gaussian noise backgrounds has been developed. Characterization of hybrid loop performance in terms of cycle slipping statistics and phase error variance, through computer simulation, indicates that the hybrid loop has desirable performance characteristics in RFI backgrounds over the conventional PLL or the costas loop.

Ziemer, R. E.↗
NASA NTRS1973

Short-term frequency stability of the Rb-87 maser.

Measurements of the short-term stability of the Rb-87 maser as a function of the maser power output and the receiver cutoff frequency are reported. The experimental data are compared to theoretical results obtained from an approximate theory. In this theory the transfer function of the maser for thermal noise is derived, and the spectral density of the phase fluctuations is calculated. An analytical expression for the 'Allan variance' is also given. A comparison of the stability of the Rb-87 maser with existing frequency standards shows its superiority for averaging times less than 1 sec.

Tetu, M.↗
NASA NTRS1991

Carrier-to-noise power estimation for the Block 5 Receiver

Two possible algorithms for the carrier to noise power (P sub c/N sub 0) estimation in the Block V Receiver are analyzed and their performances compared. The expected value and the variance of each estimator algorithm are derived. The two algorithms examined are known as the I arm estimator, which relies on samples from only the in-phase arm of the digital phase lock loop, and the IQ arm estimator, which uses both in-phase and quadrature-phase arm signals. The IQ arm algorithm is currently implemented in the Advanced Receiver II (ARX II). Both estimators are biased. The performance degradation due to phase jitter in the carrier tracking loop is taken into account. Curves of the expected value and the signal to noise ratio of the P sub c/N sub 0 estimators vs. actual P sub c/N sub 0 are shown. From this, it is clear that the I arm estimator performs better than the IQ arm estimator when the data to noise power ratio (P sub d/N sub 0) is high, i.e., at high P sub c/N sub 0 values and a significant modulation index. When P sub d/N sub 0 is low, the two estimators have essentially the same performance.

Monk, A. M.↗
DOE OSTI2023

Quantifying Uncertainty of Deep Reinforcement Learning Based Decision Making for Operations and Maintenance of Nuclear Power Plant

This paper summarizes research that integrates condition monitoring and prognostics with decision making for nuclear power plant operations and maintenance. As part of this research, we have developed an online asset management tool to help reduce life-cycle maintenance and repair costs. Using the latest advancements in condition monitoring, supply chain analytics, and deep reinforcement learning, we have created a predictive maintenance tool that can optimize the maintenance and spare-part management of a repairable nuclear system. To demonstrate these methods, preliminary studies were conducted on a simple, representative maintenance system undergoing a stochastic degradation process that requires repairs or replacement to continue operation. Through Monte Carlo simulations, we were able to reduce maintenance spending by approximately 50% compared to optimized, time-based maintenance strategies. Not only does the decision maker reduce the average life-cycle costs, it also minimizes the chance of high cost scenarios, lowering the variance of the expected cost distributions, and reducing overall financial risk. Furthermore, this work also studies the ability of the decision maker to handle various levels of noise from observation uncertainty. By introducing uncertainty into the decision-making process, we have quantified the robustness and resiliency of the decision maker, as well as identified necessary levels of observability to demonstrate cost effectiveness.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Analysis of the MUSIC 3 He Multiplicity Data

A measurement campaign called the Measurement of Uranium Subcritical and Critical (MUSiC) was performed on a range of configurations of highly-enriched uranium (HEU) from December 2020 through April of 2021. While part of the focus was to measure reactor kinetics parameters on delayed supercritical systems, an additional focus was performing neutron noise measurements on subcritical configurations from deeply subcritical to nearly delayed critical. Multiple detector systems were used to perform these measurements, such as a 3 He multiplicity detector called the Neutron Multiplicity Array Detector (NoMAD) and a liquid scintillator system called the Rossi-α Measurement Rapid Organic Discriminating Detector (RAM-RODD). Also included were a scintillator system from the University of Michigan and a set of small 3He tubes that have previously been used to measure Rossi-α values on near-critical systems. The focus of this paper will be a comparison of prospective analysis methods for the NoMAD measurements. Previous subcritical measurements at the National Criticality Experiments Research Center (NCERC) submitted to the International Criticality Safety Benchmark Evaluation Project (ICSBEP) used the Hage-Cifarelli formalism of the Feynman Variance-to-Mean method. This relies on the time correlations of neutron detections to infer the spontaneous fission rate and neutron multiplication of a system through binning the time tagged detections and analyzing resulting histograms of the numbers of counts. However, there are other neutron noise methods that rely on similar processes, such as the Hansen-Dowdy formalism which uses a slightly different methodology to extract multiplication from the neutron multiplicity counting moments. Comparisons can be made between these experimental results and those obtained through simulations to validate or identify deficiencies in analysis, detection methods, or the underlying nuclear data. Different time gating strategies and their effects on count rate uncertainties are also investigated.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1982

Application of a recursive distortion estimator to the geodetic correction of thematic mapper imagery

It is pointed out that the higher resolution provided by the Thematic Mapper increases the demands on the accuracy needed by the ground processing in correcting for geodetic errors deriving from internal misalignments and uncertainties in the knowledge of spacecraft ephemeris and attitude. In addition, the Thematic Mapper will also process longer imagery intervals than previous missions. The recursive distortion estimator to be used is a Kalman filter. Here, a minimum variance spacecraft state error vector is estimated for known initial covariance of the elements of that vector and known image noise. Tests of the recursive distortion estimator with various spacecraft models carried out using a simulation of real world state vector dynamics are described. A determination is made of the density of control points needed to meet specified geometric correction requirements; it is expressed as a function of imagery interval length and control point measurement error.

Arnold, P.↗
NASA NTRS1984

Relationship between Allan variances and Kalman Filter parameters

A relationship was constructed between the Allan variance parameters (H sub z, H sub 1, H sub 0, H sub -1 and H sub -2) and a Kalman Filter model that would be used to estimate and predict clock phase, frequency and frequency drift. To start with the meaning of those Allan Variance parameters and how they are arrived at for a given frequency source is reviewed. Although a subset of these parameters is arrived at by measuring phase as a function of time rather than as a spectral density, they all represent phase noise spectral density coefficients, though not necessarily that of a rational spectral density. The phase noise spectral density is then transformed into a time domain covariance model which can then be used to derive the Kalman Filter model parameters. Simulation results of that covariance model are presented and compared to clock uncertainties predicted by Allan variance parameters. A two state Kalman Filter model is then derived and the significance of each state is explained.

Vandierendonck, A. J.↗
DOE OSTI2024

Weak-form latent space dynamics identification

Recent work in data-driven modeling has demonstrated that a weak formulation of model equations enhances the noise robustness of a wide range of computational methods. In this paper, we demonstrate the power of the weak form to enhance the LaSDI (Latent Space Dynamics Identification) algorithm, a recently developed data-driven reduced order modeling technique. We introduce a weak form-based version WLaSDI (Weak-form Latent Space Dynamics Identification). WLaSDI first compresses data, then projects onto the test functions and learns the local latent space models. Notably, WLaSDI demonstrates significantly enhanced robustness to noise. With WLaSDI, the local latent space is obtained using weak-form equation learning techniques. Compared to the standard sparse identification of nonlinear dynamics (SINDy) used in LaSDI, the variance reduction of the weak form guarantees a robust and precise latent space recovery, hence allowing for a fast, robust, and accurate simulation. We demonstrate the efficacy of WLaSDI vs. LaSDI on several common benchmark examples including viscid and inviscid Burgers', radial advection, and heat conduction. For instance, in the case of 1D inviscid Burgers' simulations with the addition of up to 100% Gaussian white noise, the relative error remains consistently below 6% for WLaSDI, while it can exceed 10,000% for LaSDI. Similarly, for radial advection simulations, the relative errors stay below 15% for WLaSDI, in stark contrast to the potential errors of up to 10,000% with LaSDI. Moreover, speedups of several orders of magnitude can be obtained with WLaSDI. For example applying WLaSDI to 1D Burgers' yields a 140X speedup compared to the corresponding full order model.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Automated scanning probe microscopy of combinatorial ferroelectric libraries: Gaussian-process-guided exploration and noise-aware experiment planning

Combinatorial materials libraries provide an efficient route for mapping composition–property relationships, but their broader impact depends on rapid, quantitative, and functionally relevant characterization. Scanning Probe Microscopy (SPM), including piezoresponse force microscopy (PFM), offers significant potential for quantitative, functionally relevant combi-library readouts. Here, we implement a fully automated SPM workflow for ferroelectric combinatorial libraries and benchmark Gaussian-process-based Bayesian optimization strategies for autonomous experiment planning. The workflow integrates automated probe motion, contact optimization, imaging, and dual amplitude resonance tracking-PFM spectroscopy, and uses scalarized spectroscopic observables to guide subsequent measurements. Stage motion, probe engagement, in-contact tuning, imaging, spectroscopy, and the choice of the next measurement location all proceed without human input. We demonstrate the approach on Sm-doped BiFeO 3 and Zn x Mg 1−x O libraries. By comparing vanilla Bayesian optimization with a measured-noise variant, we show that explicit treatment of local reproducibility can improve modeling of composition-dependent response when the measured variance is physically meaningful, but can also reduce robustness when variability is dominated by outliers or topographic artifacts. Furthermore, these results establish automated SPM as a bridge between combinatorial synthesis and quantitative functional characterization.

Liu, Yu [University of Tennessee, Knoxville, TN (U↗
NASA NTRS1999

Clustering of the Diffuse Infrared Light from the COBE DIRBE Maps: Power Spectrum Analysis and Excess Isotropic Component of Fluctuations - 3

The cosmic infrared background (CIB) radiation is the cosmic repository for energy release throughout the history of the universe. The spatial fluctuations of the CIB resulting from galaxy clustering are expected to be at least a few percent on scales of a degree, depending on the luminosity and clustering history of the early universe. Using the all-sky data from the COBE DIRBE instrument at wavelengths 1.25 - 100 microns we attempt to measure the CIB fluctuations. In the near-IR, foreground emission is dominated by small scale structure due to stars in the Galaxy. There we find a strong correlation between the amplitude of the fluctuations and Galactic latitude after removing bright foreground stars. Using data outside the Galactic plane (absolute value of b > 20 deg) and away from the center (90 deg < l < 270 deg) we extrapolate the amplitude of the fluctuations to cosec absolute value of b = 0. We find a positive intercept of delta.F(sub rms) = 15.5(sup +3.7, sub -7.0), 5.9(sup +1.6, sub -3.7), 2.4(sup +0.5, sub -0.9), 2.0(sup +0.25, sub -0.5) nW/sq m.sr at 1.25, 2.2, 3.5 and 4.9 microns respectively, where the errors are the range of 92% confidence limits. For color subtracted maps between band 1 and 2 we find the isotropic part of the fluctuations at 7.6(sup +1.2, sub -2.4) nW/sq m.sr. Based on detailed numerical and analytic models, this residual is not likely to originate from the Galaxy, our clipping algorithm, or instrumental noise. We demonstrate that the residuals from the fit used in the extrapolation are distributed isotropically and suggest that this extra variance may result from structure in the CIB. We also obtain a positive intercept from a linear combination of maps at 1.25 and 2.2 microns. For 2 deg < theta < 15 deg, a power-spectrum analysis yields limits of (theta/5 deg) x delta.F(sub rms)(theta) < 6, 2.5, 0.8, 0.5 nW/sq m.sr at 1.25, 2.2, 3.5 and 4.9 microns respectively. From 10 - 100 microns, the dominant foregrounds are emission by dust in the Solar system and the Galaxy. There the upper limits on the CIB fluctuations are below 1 nW/sq m.sr and are lowest (< equal 0.5 nW/sq m.sr) at 25 microns.

Kashlinsky, A.↗
NASA NTRS2016

Femtosecond Photon-Counting Receiver

An optical correlation receiver is described that provides ultra-precise distance and/or time/pulse-width measurements even for weak (single photons) and short (femtosecond) optical signals. A new type of optical correlation receiver uses a fourth-order (intensity) interferometer to provide micron distance measurements even for weak (single photons) and short (femtosecond) optical signals. The optical correlator uses a low-noise-integrating detector that can resolve photon number. The correlation (range as a function of path delay) is calculated from the variance of the photon number of the difference of the optical signals on the two detectors. Our preliminary proof-of principle data (using a short-pulse diode laser transmitter) demonstrates tens of microns precision.

Photons↗