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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 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

Control of Thermo-Acoustics Instabilities: The Multi-Scale Extended Kalman Approach

"Multi-Scale Extended Kalman" (MSEK) is a novel model-based control approach recently found to be effective for suppressing combustion instabilities in gas turbines. A control law formulated in this approach for fuel modulation demonstrated steady suppression of a high-frequency combustion instability (less than 500Hz) in a liquid-fuel combustion test rig under engine-realistic conditions. To make-up for severe transport-delays on control effect, the MSEK controller combines a wavelet -like Multi-Scale analysis and an Extended Kalman Observer to predict the thermo-acoustic states of combustion pressure perturbations. The commanded fuel modulation is composed of a damper action based on the predicted states, and a tones suppression action based on the Multi-Scale estimation of thermal excitations and other transient disturbances. The controller performs automatic adjustments of the gain and phase of these actions to minimize the Time-Scale Averaged Variances of the pressures inside the combustion zone and upstream of the injector. The successful demonstration of Active Combustion Control with this MSEK controller completed an important NASA milestone for the current research in advanced combustion technologies.

Le, Dzu K.↗
DOE OSTI2016

Use of Criticality Eigenvalue Simulations for Subcritical Benchmark Evaluations

Subcritical multiplication experiments and simulations are important for a variety of applications including nonproliferation, safeguards, and criticality safety monitoring. Recently, subcritical experiments have been evaluated for inclusion in the International Criticality Safety Benchmark Evaluation Process (ICSBEP). In addition, new subcritical experiments have recently been designed. In these experiments, list-mode data (a list of each absorption in the neutron detector) is recorded and neutron noise analysis is used to determine the leakage multiplication (ML) of each configuration. One downside to the simulation of these types of experiments is that they are computationally expensive. This is because analog Monte Carlo with no variance reduction must be performed in order to maintain the time accuracy required in the measurement analysis. This work demonstrates that criticality eigenvalue simulations can be used to estimate sensitivities and uncertainties for three parameters: singles count rate (R{sub 1}), doubles count rate (R{sub 2}), and leakage multiplication (M{sub L}). This will provide a useful aid both in the design and evaluation of new subcritical measurements. (authors)

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS2002

Maximum Likelihood Estimation of the Broken Power Law Spectral Parameters with Detector Design Applications

The method of Maximum Likelihood (ML) is used to estimate the spectral parameters of an assumed broken power law energy spectrum from simulated detector responses. This methodology, which requires the complete specificity of all cosmic-ray detector design parameters, is shown to provide approximately unbiased, minimum variance, and normally distributed spectra information for events detected by an instrument having a wide range of commonly used detector response functions. The ML procedure, coupled with the simulated performance of a proposed space-based detector and its planned life cycle, has proved to be of significant value in the design phase of a new science instrument. The procedure helped make important trade studies in design parameters as a function of the science objectives, which is particularly important for space-based detectors where physical parameters, such as dimension and weight, impose rigorous practical limits to the design envelope. This ML methodology is then generalized to estimate broken power law spectral parameters from real cosmic-ray data sets.

Howell, Leonard W.↗
NASA NTRS1993

Remote sensing of submerged vegetation canopies for biomass estimation

The visible bands of the Landsat Thematic Mapper (TM) sensor were used in an empirical assessment of seagrass biomass on shallow banks near Lee Stocking Island in the Bahamas. The TM bands were transformed to minimize the depth-dependent variance in the bottom reflectance signal. Regression analyses were performed between the transformed bands and field measurements of seagrass standing crop (above-ground biomass). Regression equations using spectral data accounted for up to 80 per cent of the variability in seagrass biomass. The unexplained variance was ascribed to variations in bottom sediment color.

Armstrong, Roy A.↗
DOE OSTI2015

Integration of microseismic monitoring data into coupled flow and geomechanical models with ensemble Kalman filter

Abstract Hydraulic stimulation of low‐permeability rocks in enhanced geothermal systems, shale resources, and CO 2 storage aquifers can trigger microseismic events, also known as microearthquakes (MEQs). The distribution of microseismic source locations in the reservoir may reveal important information about the distribution of hydraulic and geomechanical rock properties. In this paper, we present a framework for conditioning heterogeneous rock permeability and geomechanical property distributions on microseismic data. To simulate the multiphysics processes in these systems, we combine a fully coupled flow and geomechanical model with the Mohr‐Coulomb type rock failure criterion. The resulting multiphysics simulation constitutes the forecast model that relates microseismic source locations to reservoir rock properties. We adopt this forward model in an ensemble Kalman filter (EnKF) data assimilation framework to jointly estimate reservoir permeability and geomechanical property distributions from injection‐induced microseismic response measurements. We show that integration of a large number of spatially correlated microseismic data with practical ensemble sizes can lead to severe underestimation of ensemble spread, and eventually ensemble collapse. To mitigate the variance underestimation issue, two low‐rank data representation schemes are presented and discussed. In the first approach, microseismic data are projected onto a low‐dimensional subspace defined by the left singular vectors of the perturbed observation matrix. The second method uses a coarser grid for representing the microseismic data. A series of numerical experiments is presented to evaluate the performance of the proposed methods and to illustrate their applicability for assimilating microseismic data into coupled flow and geomechanical forward models to estimate multiphysics rock properties.

Tarrahi, Mohammadali↗
DOE OSTI2025

Bayesian Entropy Neural Networks for physics-aware prediction

This article addresses the need for deep learning models to integrate well-defined constraints into their outputs, driven by their application in surrogate models, learning with limited data and partial information, and scenarios requiring flexible model behavior to incorporate non-data sample information. We introduce Bayesian Entropy Neural Networks (BENN), a framework grounded in Maximum Entropy (MaxEnt) principles, designed to impose constraints on Bayesian Neural Network (BNN) predictions. BENN is capable of constraining not only the predicted values but also their derivatives and variances, ensuring a more robust and reliable model output. To achieve simultaneous uncertainty quantification and constraint satisfaction, we employ the method of multipliers approach. This allows for the concurrent estimation of neural network parameters and the Lagrangian multipliers associated with the constraints. Our experiments, spanning diverse applications such as beam deflection modeling and microstructure generation, demonstrate the effectiveness of BENN. The results highlight significant improvements over traditional BNNs and showcase competitive performance relative to contemporary constrained deep learning methods.

14 SOLAR ENERGY↗
DOE OSTI2015

The γ-ray detected narrow-line Seyfert 1 Galaxy 1H 0323+342: SWIFT monitoring and Suzaku spectroscopy

As a radio-loud narrow-line Seyfert 1 galaxy (NLS1) detected by Fermi/LAT in GeV γ-rays, 1H 0323+342 is a remarkable active galactic nucleus showing properties characteristic of both NLS1s and blazars. Here we present results of simultaneous X-ray and UV/optical monitoring observations on 1H 0323+342 taken with the UV/Optical Telescope and X-ray Telescope onboard the Swift satellite over six years from 2006. Overall, the object showed statistically correlated variations in both the UV and X-ray bands on timescales of years as well as on timescales of days. A deep Suzaku observation reveals X-ray variability on timescales as short as a few tens of thousand seconds, and an X-ray spectrum typical of Seyfert galaxies. The broadband spectral energy distribution, for which the data of UV and X-ray observations taken on 2009 July 26–27 were used, can be well modeled with a simple one-zone leptonic jet model plus accretion disk/corona emission. The latter is predominantly responsible for the UV/optical and X-ray (0.3–10 keV) emission and their observed variations. The correlated UV–X-ray variability on the timescale of days is consistent with reprocessing of the X-ray radiation by the accretion disk. The shortest timescale and large normalized excess variance of the X-ray variability detected with Suzaku suggest a relatively small black hole mass of the order of 10{sup 7} M{sub ⊙}, consistent with the estimation based on the broad Hβ line in the optical.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

On the fate of coupled flat directions during inflation

We investigate the stochastic dynamics of the long wavelength modes of a generic light scalar field that during inflation is coupled to another scalar field. The coupling plays an important role for the fluctuation of the field amplitude and may block its initial growth. We find that such a blocking is avoided, albeit only temporarily, if the light scalar has an initial non-vanishing expectation value (φ) larger than a certain critical value, for which we provide an estimate. We also show that the field fluctuations will eventually reach an equilibrium amplitude provided inflation is sufficiently long-lasting. We present a novel, general expression for the variance (φ{sup 2}) that takes into account the coupling of the massless field and describes the growth during the epoch of quasi-free fluctuations as well as the late-time approach to the equilibrium.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Noise from undetected sources in Dark Energy Survey images

ABSTRACT For ground-based optical imaging with current CCD technology, the Poisson fluctuations in source and sky background photon arrivals dominate the noise budget and are readily estimated. Another component of noise, however, is the signal from the undetected population of stars and galaxies. Using injection of artifical galaxies into images, we demonstrate that the measured variance of galaxy moments (used for weak gravitational lensing measurements) in Dark Energy Survey (DES) images is significantly in excess of the Poisson predictions, by up to 30 per cent, and that the background sky levels are overestimated by current software. By cross-correlating distinct images of ‘empty’ sky regions, we establish that there is a significant image noise contribution from undetected static sources (US), which, on average, are mildly resolved at DES resolution. Treating these US as a stationary noise source, we compute a correction to the moment covariance matrix expected from Poisson noise. The corrected covariance matrix matches the moment variances measured on the injected DES images to within 5 per cent. Thus, we have an empirical method to statistically account for US in weak lensing measurements, rather than requiring extremely deep sky simulations. We also find that local sky determinations can remove most of the bias in flux measurements, at a small penalty in additional, but quantifiable, noise.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Quantifying Airport Employee Commuting and Related Energy Use: A Comparison of Six US Airports

There has been growing research interest in understanding the energy signatures of airport access and egress as air travel continues to grow. However, these findings generally focus on airport access from the perspective of air travelers rather than ground access modes by airport employees. Using a data set of employment records, this paper uses residential location data of airport employees to estimate the energy implications of employee commuting at six major US airports based on commuting mode choice information from the US Census. This analysis reveals a high degree of variance in commuting energy usage by employees between different airports.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

ON MORE SENSITIVE PERIODOGRAM STATISTICS

Period searches in event data have traditionally used the Rayleigh statistic, R {sup 2}. For X-ray pulsars, the standard has been the Z {sup 2} statistic, which sums over more than one harmonic. For γ-rays, the H-test, which optimizes the number of harmonics to sum, is often used. These periodograms all suffer from the same problem, namely artifacts caused by correlations in the Fourier components that arise from testing frequencies with a non-integer number of cycles. This article addresses this problem. The modified Rayleigh statistic is discussed, its generalization to any harmonic, R{sub k}{sup 2}, is formulated, and from the latter, the modified Z {sup 2} statistic, Z{sup 2}, is constructed. Versions of these statistics for binned data and point measurements are derived, and it is shown that the variance in the uncertainties can have an important influence on the periodogram. It is shown how to combine the information about the signal frequency from the different harmonics to estimate its value with maximum accuracy. The methods are applied to an XMM-Newton observation of the Crab pulsar for which a decomposition of the pulse profile is presented, and shows that most of the power is in the second, third, and fifth harmonics. Statistical detection power of the R{sub k}{sup 2} statistic is superior to the FFT and equivalent to the Lomb--Scargle (LS). Response to gaps in the data is assessed, and it is shown that the LS does not protect against the distortions they cause. The main conclusion of this work is that the classical R {sup 2} and Z {sup 2} should be replaced by R{sub k}{sup 2} and Z{sup 2} in all applications with event data, and the LS should be replaced by the R{sub k}{sup 2} when the uncertainty varies from one point measurement to another.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1987

A photometric search for solar giant convection cells

The photometric contrast of solar giant convection cells is limited using 526.6 nm continuum images obtained on 15 days in May 1985. The rms of the giant cell intensity pattern must be less than or equal to the observed rms on spatial scales of 80 to 240 Mm, which is 0.023 percent or, equivalently, 0.33 K. However, the spatial scale and time-scale dependence of the variance demonstrate that giant cells are not the source of the observed variance. Consequently, a tighter constraint on the rms of the giant cell pattern may be placed, namely 0.016 percent or 0.23 K. This limit is consistent with temperature perturbations estimated from recent nonlinear simulations of global-scale solar convection. This limit on the rms of the giant cell pattern is used to estimate that the contribution of giant cells to the fluctuation of the solar irradiance on a one-month time-scale is less than 0.00003 S.

Chiang, W.-H.↗
NASA NTRS2005

Global Precipitation Variations and Long-term Changes Derived from the GPCP Monthly Product

Global and large regional rainfall variations and possible long-term changes are examined using the 25-year (1979-2004) monthly dataset from the Global Precipitation Climatology Project (GPCP). The emphasis is to discriminate among the variations due to ENSO, volcanic events and possible long-term changes. Although the global change of precipitation in the data set is near zero, the data set does indicate an upward trend (0.13 mm/day/25yr) and a downward trend (-0.06 mm/day/25yr) over tropical oceans and lands (25S-25N), respectively. This corresponds to a 4% increase (ocean) and 2% decrease (land) during this time period. Techniques are applied to attempt to eliminate variations due to ENSO and major volcanic eruptions. The impact of the two major volcanic eruptions over the past 25 years is estimated to be about a 5% reduction in tropical rainfall. The modified data set (with ENSO and volcano effect removed) retains the same approximate change slopes, but with reduced variance leading to significance tests with results in the 90-95% range. Inter-comparisons between the GPCP, SSWI (1988-2004), and TRMM (1998-2004) rainfall products are made to increase or decrease confidence in the changes seen in the GPCP analysis.

Adler, Robert F.↗
NASA NTRS2011

Updating the Standard Spatial Observer for Contrast Detection

Watson and Ahmuada (2005) constructed a Standard Spatial Observer (SSO) model for foveal luminance contrast signal detection based on the Medelfest data (Watson, 1999). Here we propose two changes to the model, dropping the oblique effect from the CSF and using the cone density data of Curcio et al. (1990) to estimate the variation of sensitivity with eccentricity. Dropping the complex images, and using medians to exclude outlier data points, the SSO model now accounts for essentially all the predictable variance in the data, with an RMS prediction error of only 0.67 dB.

Ahumada, Albert J.↗
DOE OSTI2018

Performance of Solar-Induced Chlorophyll Fluorescence in Estimating Water-Use Efficiency in a Temperate Forest

Water-use efficiency (WUE) is a critical variable describing the interrelationship between carbon uptake and water loss in land ecosystems. Different WUE formulations (WUEs) including intrinsic water use efficiency (WUEi), inherent water use efficiency (IWUE), and underlying water use efficiency (uWUE) have been proposed. Based on continuous measurements of carbon and water fluxes and solar-induced chlorophyll fluorescence (SIF) at a temperate forest, we analyze the correlations between SIF emission and the different WUEs at the canopy level by using linear regression (LR) and Gaussian processes regression (GPR) models. Overall, we find that SIF emission has a good potential to estimate IWUE and uWUE, especially when a combination of different SIF bands and a GPR model is used. At an hourly time step, canopy-level SIF emission can explain as high as 65% and 61% of the variances in IWUE and uWUE. Specifically, we find that (1) a daily time step by averaging hourly values during daytime can enhance the SIF-IWUE correlations, (2) the SIF-IWUE correlations decrease when photosynthetically active radiation and air temperature exceed their optimal biological thresholds, (3) a low Leaf Area Index (LAI) has a negative effect on the SIF-IWUE correlations due to large evaporation fluxes, (4) a high LAI in summer also reduces the SIF-IWUE correlations most likely due to increasing scattering and (re)absorption of the SIF signal, and (5) the observation time during the day has a strong impact on the SIF-IWUE correlations and SIF measurements in the early morning have the lowest power to estimate IWUE due to the large evaporation of dew. This study provides a new way to evaluate the stomatal regulation of plant-gas exchange without complex parameterizations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

IMPROVING COSMOLOGICAL DISTANCE MEASUREMENTS USING TWIN TYPE IA SUPERNOVAE

We introduce a method for identifying “twin” Type Ia supernovae (SNe Ia) and using them to improve distance measurements. This novel approach to SN Ia standardization is made possible by spectrophotometric time series observations from the Nearby Supernova Factory (SNfactory). We begin with a well-measured set of SNe, find pairs whose spectra match well across the entire optical window, and then test whether this leads to a smaller dispersion in their absolute brightnesses. This analysis is completed in a blinded fashion, ensuring that decisions made in implementing the method do not inadvertently bias the result. We find that pairs of SNe with more closely matched spectra indeed have reduced brightness dispersion. We are able to standardize this initial set of SNfactory SNe to 0.083 ± 0.012 mag, implying a dispersion of 0.072 ± 0.010 mag in the absence of peculiar velocities. We estimate that with larger numbers of comparison SNe, e.g., using the final SNfactory spectrophotometric data set as a reference, this method will be capable of standardizing high-redshift SNe to within 0.06–0.07 mag. These results imply that at least 3/4 of the variance in Hubble residuals in current SN cosmology analyses is due to previously unaccounted-for astrophysical differences among the SNe.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Conditional Karhunen-Loève expansion for uncertainty quantification and active learning in partial differential equation models

We use a conditional Karhunen-Lo` eve (KL) model to quantify and reduce uncertainty in a stochastic partial differential equation (SPDE) problem with partially-known space-dependent coefficient, Y (x). We assume that a small number of Y (x) measurements are available and model Y (x) with a KL expansion. We achieve reduction in uncertainty by conditioning the KL expansion coefficients on measurements. We consider two approaches for conditioning the KL expansion: In Approach 1, we condition the KL model first and then truncate it. In Approach 2, we first truncate the KL expansion and then condition it. We employ the conditional KL expansion together with Monte Carlo and sparse grid collocation methods to compute the moments of the solution of the SPDE problem. Uncertainty of the problem is further reduced by adaptively selecting additional observation locations using two active learning methods. Method 1 minimizes the variance of the PDE coefficient, while Method 2 minimizes the variance of the solution of the PDE. We demonstrate that conditioning leads to dimension reduction of the KL representation of Y (x). For a linear diffusion SPDE with uncertain log-normal coefficient, we show that Approach 1 provides a more accurate approximation of the conditional log-normal coefficient and solution of the SPDE than Approach 2 for the same number of random dimensions in a conditional KL expansion. Furthermore, Approach 2 provides a good estimate for the number of terms of the truncated KL expansion of the conditional field of Approach 1. Finally, we demonstrate that active learning based on Method 2 is more efficient for uncertainty reduction in the SPDE’s states (i.e., it leads to a larger reduction of the variance) than active learning using Method 2.

Conditioned Karhunen-Lo` eve expanion, machine lea↗