Search NASA⌕ Search

SEARCH · Search NASA

Results for “Distributed 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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Stellar Mass Calibrations for Local Low-mass Galaxies

The stellar masses of galaxies are measured from integrated light via several methods—however, few of these methods were designed for low-mass (M ⋆ ≲ 10 8 M ⊙ ) “dwarf” galaxies, whose properties (e.g., stochastic star formation, low metallicity) pose unique challenges for estimating stellar masses. In this work, we quantify the precision and accuracy at which stellar masses of low-mass galaxies can be recovered using UV/optical/IR photometry. We use mock observations of 469 low-mass galaxies from a variety of models, including both semi-empirical models (GRUMPY and UniverseMachine-SAGA) and cosmological baryonic zoom-in simulations (MARVELous Dwarfs and FIRE-2), to test literature color–M ⋆ /L relations and multiwavelength spectral energy distribution (SED) mass estimators. We identify a list of “best practices” for measuring stellar masses of low-mass galaxies from integrated photometry. We find that literature color–M ⋆ /L relations are often unable to capture the bursty star formation histories (SFHs) of low-mass galaxies, and we develop an updated prescription for stellar mass based on g − r color that is better able to recover stellar masses for the bursty low-mass galaxies in our sample (with ∼0.1 dex precision). SED fitting can also precisely recover stellar masses of low-mass galaxies, but this requires thoughtful choices about the form of the assumed SFH: Parametric SFHs can underestimate stellar mass by as much as ∼0.4 dex, while nonparametric SFHs recover true stellar masses with insignificant offset (−0.03 ± 0.11 dex). Finally, we also caution that noninformative (wide) dust attenuation priors may introduce M ⋆ uncertainties of up to ∼0.6 dex.

de los Reyes, Mithi A. C. [Amherst College, MA (Un↗
DOE OSTI2021

Estimation of soil classes and their relationship to grapevine vigor in a Bordeaux vineyard: advancing the practical joint use of electromagnetic induction (EMI) and NDVI datasets for precision viticulture

Working within a vineyard in the Pessac Léognan Appellation of Bordeaux, France, this study documents the potential of using simple statistical methods with spatially-resolved and increasingly available electromagnetic induction (EMI) geophysical and normalized difference vegetation index (NDVI) datasets to accurately estimate Bordeaux vineyard soil classes and to quantitatively explore the relationship between vineyard soil types and grapevine vigor. First, co-located electrical tomographic tomography (ERT) and EMI datasets were compared to gain confidence about how the EMI method averaged soil properties over the grapevine rooting depth. Then, EMI data were used with core soil texture and soil-pit based interpretations of Bordeaux soil types (Brunisol, Redoxisol, Colluviosol and Calcosol) to estimate the spatial distribution of geophysically-identified Bordeaux soil classes. A strong relationship (r = 0.75, p < 0.01) was revealed between the geophysically-identified Bordeaux soil classes and NDVI (both 2 m resolution), showing that the highest grapevine vigor was associated with the Bordeaux soil classes having the largest clay fraction. The results suggest that within-block variability of grapevine vigor was largely controlled by variability in soil classes, and that carefully collected EMI and NDVI datasets can be exceedingly helpful for providing quantitative estimates of vineyard soil and vigor variability, as well as their covariation. The method is expected to be transferable to other viticultural regions, providing an approach to use easy-to-acquire, high resolution datasets to guide viticultural practices, including routine management and replanting.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Investigation of the effect of noise parameters of a 3D lidar on the error in estimating relief signatures of distant objects from 2D field intensity distributions of reflected radiation

We consider the influence of noise parameters of a 3D lidar and external background radiation on the error in estimating the relief signatures of distant objects from 2D field intensity distributions of the reflected radiation. A physical and mathematical model is developed to analyse the effect of noise. The dependence of the error in estimating the relief signatures at various signal-to-noise ratios is investigated taking into account the parameters of real multi-element photodetectors (MEPDs). The obtained results indicate the possibility of evaluating relief signatures of distant objects with high accuracy based on standard MEPDs simultaneously for a large number of points on the object’s surface with a signal-to-noise ratio of more than 4. (paper)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2001

The Spatial Distribution of Forest Biomass in the Brazilian Amazon: A Comparison of Estimates

The amount of carbon released to the atmosphere as a result of deforestation is determined, in part, by the amount of carbon held in the biomass of the forests converted to other uses. Uncertainty in forest biomass is responsible for much of the uncertainty in current estimates of the flux of carbon from land-use change. We compared several estimates of forest biomass for the Brazilian Amazon, based on spatial interpolations of direct measurements, relationships to climatic variables, and remote sensing data. We asked three questions. First, do the methods yield similar estimates? Second, do they yield similar spatial patterns of distribution of biomass? And, third, what factors need most attention if we are to predict more accurately the distribution of forest biomass over large areas? Amazonian forests (including dead and below-ground biomass) vary by more than a factor of two, from a low of 39 PgC to a high of 93 PgC. Furthermore, the estimates disagree as to the regions of high and low biomass. The lack of agreement among estimates confirms the need for reliable determination of aboveground biomass over large areas. Potential methods include direct measurement of biomass through forest inventories with improved allometric regression equations, dynamic modeling of forest recovery following observed stand-replacing disturbances (the approach used in this research), and estimation of aboveground biomass from airborne or satellite-based instruments sensitive to the vertical structure plant canopies.

Houghton, R. A.↗
NASA NTRS2009

Exploring Alternate Parameterizations for Snowfall with Validation from Satellite and Terrestrial Radars

Increases in computational resources have allowed operational forecast centers to pursue experimental, high resolution simulations that resolve the microphysical characteristics of clouds and precipitation. These experiments are motivated by a desire to improve the representation of weather and climate, but will also benefit current and future satellite campaigns, which often use forecast model output to guide the retrieval process. Aircraft, surface and radar data from the Canadian CloudSat/CALIPSO Validation Project are used to check the validity of size distribution and density characteristics for snowfall simulated by the NASA Goddard six-class, single-moment bulk water microphysics scheme, currently available within the Weather Research and Forecast (WRF) Model. Widespread snowfall developed across the region on January 22, 2007, forced by the passing of a midlatitude cyclone, and was observed by the dual-polarimetric, C-band radar King City, Ontario, as well as the NASA 94 GHz CloudSat Cloud Profiling Radar. Combined, these data sets provide key metrics for validating model output: estimates of size distribution parameters fit to the inverse-exponential equations prescribed within the model, bulk density and crystal habit characteristics sampled by the aircraft, and representation of size characteristics as inferred by the radar reflectivity at C- and W-band. Specified constants for distribution intercept and density differ significantly from observations throughout much of the cloud depth. Alternate parameterizations are explored, using column-integrated values of vapor excess to avoid problems encountered with temperature-based parameterizations in an environment where inversions and isothermal layers are present. Simulation of CloudSat reflectivity is performed by adopting the discrete-dipole parameterizations and databases provided in literature, and demonstrate an improved capability in simulating radar reflectivity at W-band versus Mie scattering assumptions.

Molthan, Andrew L.↗
NASA NTRS2009

Exploring Alternative Parameterizations for Snowfall with Validation from Satellite and Terrestrial Radars

Increases in computational resources have allowed operational forecast centers to pursue experimental, high resolution simulations that resolve the microphysical characteristics of clouds and precipitation. These experiments are motivated by a desire to improve the representation of weather and climate, but will also benefit current and future satellite campaigns, which often use forecast model output to guide the retrieval process. The combination of reliable cloud microphysics and radar reflectivity may constrain radiative transfer models used in satellite simulators during future missions, including EarthCARE and the NASA Global Precipitation Measurement. Aircraft, surface and radar data from the Canadian CloudSat/CALIPSO Validation Project are used to check the validity of size distribution and density characteristics for snowfall simulated by the NASA Goddard six-class, single moment bulk water microphysics scheme, currently available within the Weather Research and Forecast (WRF) Model. Widespread snowfall developed across the region on January 22, 2007, forced by the passing of a mid latitude cyclone, and was observed by the dual-polarimetric, C-band radar King City, Ontario, as well as the NASA 94 GHz CloudSat Cloud Profiling Radar. Combined, these data sets provide key metrics for validating model output: estimates of size distribution parameters fit to the inverse-exponential equations prescribed within the model, bulk density and crystal habit characteristics sampled by the aircraft, and representation of size characteristics as inferred by the radar reflectivity at C- and W-band. Specified constants for distribution intercept and density differ significantly from observations throughout much of the cloud depth. Alternate parameterizations are explored, using column-integrated values of vapor excess to avoid problems encountered with temperature-based parameterizations in an environment where inversions and isothermal layers are present. Simulation of CloudSat reflectivity is performed by adopting the discrete-dipole parameterizations and databases provided in literature, and demonstrate an improved capability in simulating radar reflectivity at W-band versus Mie scattering assumptions.

Molthan, Andrew L.↗
DOE OSTI2021

A Scalable Gradient Free Method for Bayesian Experimental Design with Implicit Models

Bayesian experimental design (BED) is to answer the question that how to choose designs that maximize the information gathering. For implicit models, where the likelihood is intractable but sampling is possible, conventional BED methods have difficulties in efficiently estimating the posterior distribution and maximizing the mutual information (MI) between data and parameters. Recent work proposed the use of gradient ascent to maximize a lower bound on MI to deal with these issues. However, the approach requires a sampling path to compute the pathwise gradient of the MI lower bound with respect to the design variables, and such a pathwise gradient is usually inaccessible for implicit models. In this paper, we propose a novel approach that leverages recent advances in stochastic approximate gradient ascent incorporated with a smoothed variational MI estimator for efficient and robust BED. Without the necessity of pathwise gradients, our approach allows the design process to be achieved through a unified procedure with an approximate gradient for implicit models. Several experiments show that our approach outperforms baseline methods, and significantly improves the scalability of BED in high-dimensional problems.

Zhang, Jiaxin↗
DOE OSTI2022

Spatial and temporal prediction of radiation dose rates near Fukushima Daiichi Nuclear Power Plant

In this paper, we have developed a methodology to estimate the spatiotemporal distribution of radiation air dose rates around the Fukushima Daiichi Nuclear Power Plant (FDNPP). In our exploratory data analysis, we found that (1) the temporal evolution of dose rates is composed of a log-linear decay trend and fluctuations of air dose rates that are spatially correlated among adjacent monitoring posts; and (2) the slope of the log-linear environmental decay trend can be represented as a function of the apparent initial dose rates, coordinate position, land-use type, and soil type. From these observations, we first estimated the log-linear decay trend at each location based on these predictors, using the random forest method. We then developed a modified Kalman filter coupled with a Gaussian process model to estimate the dose-rate time series at a given location and time. We applied this method to the Fukushima evacuation zone (as of March 2017), which included 17 monitoring post locations (with monitoring datasets collected between 2014 and 2018) and generated a time series of dose-rate maps. Our results show that this approach allows us to produce accurate spatial and temporal predictions of radiation dose-rate maps using limited spatiotemporal measurements.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2023

Recovering non-Maxwellian particle velocity distribution functions from collective Thomson-scattered spectra

Collective optical Thomson scattering (TS) is a diagnostic commonly used to characterize plasma parameters. These parameters are typically extracted by a fitting algorithm that minimizes the difference between a measured scattered spectrum and an analytic spectrum calculated from the velocity distribution function (VDF) of the plasma. However, most existing TS analysis algorithms assume that the VDFs are Maxwellian, and applying an algorithm that makes this assumption does not accurately extract the plasma parameters of a non-Maxwellian plasma due to the effect of non-Maxwellian deviations on the TS spectra. We present new open-source numerical tools for forward modeling analytic spectra from arbitrary VDFs and show that these tools are able to more accurately extract plasma parameters from synthetic TS spectra generated by non-Maxwellian VDFs compared to standard TS algorithms. Estimated posterior probability distributions of fits to synthetic spectra for a variety of example non-Maxwellian VDFs are used to determine uncertainties in the extracted plasma parameters and show that correlations between parameters can significantly affect the accuracy of fits in plasmas with non-Maxwellian VDFs.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Detailed Characterization of Vitrinite-Rich Subbituminous and Bituminous Coals for Utilization in Carbon Fiber Precursor Production

Chemical structures of candidate coals (one subbituminous coal from the Powder River Basin and three bituminous Eastern US coals) were examined for potential low-cost carbon fiber precursor production. Here, the structural evaluation examined the carbon skeleton ( 13 C nuclear magnetic resonance (NMR) and high-resolution transmission electron microscopy (HRTEM)), the heteroatom functionality (X-ray photoelectron spectroscopy (XPS) Fourier transform infrared spectroscopy (FTIR) and NMR), the structural ordering and distribution of PAH sizes (HRTEM), and an estimation of the molecular weight distribution (laser desorption ionization mass spectrometry, LDIMS). The molecular compositional distributions were also evaluated for a mixed solvent extract (atmospheric pressure photoionization, 21 T Fourier transform ion cyclotron resonance mass spectrometry, FT-ICR MS). Significant structural differences existed between the subbituminous and bituminous coals, as expected with coalification (with the bituminous coals having a higher carbon content, lower oxygen content, higher aromaticity values, larger cluster sizes, and so forth). While the bituminous coals were similar in structure (close in rank with Blue Gem being hvAb and the rest being hvBb), structural differences were still evident. Specifically, structural similarities were evident for the average properties of Herrin and Springfield coals: same rank, similar moisture, and volatile matter yields, along with similar aromaticity and carbon and hydrogen content. However, significant structural differences were observed at the molecular level by 21 T FT-ICR MS, which showed that the Springfield coal was structurally more complex. Specifically, 21740, 16931, 30190, and 12982 unique elemental compositions were identified for the Monarch, Herrin, Springfield, and Blue Gem coals, further illustrating the complexity of the coal.

01 COAL, LIGNITE, AND PEAT↗
NASA NTRS1973

Electron emission produced by photointeractions in a slab target

The current density and energy spectrum of escaping electrons generated in a uniform plane slab target which is being irradiated by the gamma flux field of a nuclear reactor are calculated by using experimental gamma energy transfer coefficients, electron range and energy relations, and escape probability computations. The probability of escape and the average path length of escaping electrons are derived for an isotropic distribution of monoenergetic photons. The method of estimating the flux and energy distribution of electrons emerging from the surface is outlined, and a sample calculation is made for a 0.33-cm-thick tungsten target located next to the core of a nuclear reactor. The results are to be used as a guide in electron beam synthesis of reactor experiments.

Thinger, B. E.↗
DOE OSTI2022

Characterization of Temperature Heterogeneity in Utility-Scale Power Plant Boilers by Spatially Distributed Ultrasonic Measurements

In extreme environments, even hardened insertion sensors fail quickly. For such environments, we have developed an ultrasonic (US) method for measuring the spatial distribution of temperatures in solid materials and, specifically, across containments of extreme processes. By deploying US sensors in multiple locations, spatial heterogeneity in temperatures inside harsh environments may be characterized. In this note, we update our progress on testing the developed approach in an industrial setting and its performance in describing the temperature distribution inside a 500 MW (electrical) coalfired utility boiler of an electrical power generation plant. We use waveguides (WG) structured to contain echogenic features that produce a train of echoes in response to an excitation pulse. The time of flight between echoes encodes the information on the temperature distribution in the corresponding segment of the WG, which we reconstruct using its parametrization. Five waveguides were welded to the boiler’s heat exchange surface (waterwall) and different locations at the same boiler elevation and produced reliable US waveforms 18 months after the installation. For several weeks during the latest trial, we performed US measurements in five locations spanning half of the boiler’s 14-meter width. The measurements were acquired during regular operation, including load cycling to adjust for demand and intermittent contributions of renewable power sources, and during boiler shutdown for emergency maintenance and the subsequent restart. The interpretation of US waveforms by signal processing resulted in an accurate estimation of temperature distribution along the waveguides. We captured daily cyclical variations in demand-following load. Measurements performed in multiple locations revealed unexpected temperature variations across the boiler. We conclude that an array of developed sensors can provide responsive and spatial temperature measurements while maintaining functionality despite prolonged exposure to an extreme environment.

Walton, Kenneth↗
DOE OSTI2019

Exploring the Use of FHWA Truck Traffic Volume and Weight Data to Support National Truck Freight Mobility Study

The goal of this exploratory study was to investigate alternative data sources and develop methodologies to estimate trends and adjustment factors that could be applied to calibrate statistics generated from the outdated 2002 VIUS. The main focus of this study was on utilizing the FHWA’s traffic data (i.e., VTRIS and TMAS), along with other supplemental freight data (i.e., FAF and CFS), to examine changes in truck volumes and truck weight distributions over time. Ultimately, this effort seeks to develop certain adjustments/factors that could be used to realign perspective VIUS data. Unfortunately, as pointed out previously in this technical memorandum, there are no readily available data sources that could be used to explicitly validate the estimated results from this study. This study compared the estimated distributions from the 2002 VTRIS to the 2002 VIUS data to establish linkages between the two datasets. With distributions and patterns generated based on VTRIS and TMAS data series for additional years, trend factors were then estimated and applied to “update” VIUS-based information. This study resulted in three key highlights: (1) Updated estimates on truck volume and truck VMT can be utilized to better estimate on energy uses by truck. (2) Vehicle class distribution, percent/share of empty trucks, and average truck payload could be updated by utilizing more current annual data series such as VTRIS and TMAS. (3) Additional information could be estimated (e.g., distance distribution by operational type) by integrating other data sources, such as CFS and FAF, instead of relying on the 2002 VIUS data.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS1976

The numerical evaluation of maximum-likelihood estimates of the parameters for a mixture of normal distributions from partially identified samples

Likelihood equations determined by the two types of samples which are necessary conditions for a maximum-likelihood estimate are considered. These equations, suggest certain successive-approximations iterative procedures for obtaining maximum-likelihood estimates. These are generalized steepest ascent (deflected gradient) procedures. It is shown that, with probability 1 as N sub 0 approaches infinity (regardless of the relative sizes of N sub 0 and N sub 1, i=1,...,m), these procedures converge locally to the strongly consistent maximum-likelihood estimates whenever the step size is between 0 and 2. Furthermore, the value of the step size which yields optimal local convergence rates is bounded from below by a number which always lies between 1 and 2.

Walker, H. F.↗
NASA NTRS1976

The numerical evaluation of maximum-likelihood estimates of the parameters for a mixture of normal distributions from partially identified samples

Likelihood equations determined by the two types of samples which are necessary conditions for a maximum-likelihood estimate were considered. These equations suggest certain successive approximations iterative procedures for obtaining maximum likelihood estimates. The procedures, which are generalized steepest ascent (deflected gradient) procedures, contain those of Hosmer as a special case.

Walker, H. F.↗
NASA NTRS1995

The frequency and distribution of high-velocity gas in the Galaxy

The purpose of this study was to estimate the frequency and distribution of high-velocity gas in the Galaxy using UV absorption line measurements from archival high-dispersion IUE spectra and to identify particularly interesting regions for future study. Approximately 500 spectra have been examined. The study began with the creation of a database of all 0 and B stars with b less than or = to 30 deg observed with IUE at high dispersion over its 18-year lifetime. The original database of 2500 unique objects was reduced to 1200 objects which had optimal exposures available. The next task was to determine the distances of these stars so the high-velocity structures could be mapped in the Galaxy. Spectroscopic distances were calculated for each star for which photometry was available. The photometry was acquired for each star using the SIMBAD database. Preference was given to the ubvy system where available; otherwise the UBV system was used.

Nichols, Joy S.↗
DOE OSTI2023

A Graph Convolutional Network for Active Distribution System Anomaly Detection Considering Measurement Spatial-Temporal Correlations

The accuracy of distribution system state estimation may be significantly impacted by the existence of bad measure-ments and unexpected topology errors. This paper proposes a data-driven Graph Convolutional Network (GCN) for anomaly detection, including bad measurements and topology change events. Compared to many existing machine learning approaches, the proposed approach embeds both spatial-temporal measure-ment correlations, which allows us to detect and distinguish different anomalies. Numerical results carried out on the IEEE 37-node system demonstrate that the proposed-based method can obtain high accuracy in detecting bad data and topology changes as compared to other approaches, even in the presence of high PV penetrations.

active distribution system↗
DOE OSTI2024

Decentralized Microgrid Protection Through Relative Fault Direction Classification: Preprint

Protection in inverter-based resources (IBRs) dominated microgrids generally face significant challenges due to the low fault current and inconsistent fault behaviors from IBRs. Recently, machine learning-based approaches have attracted considerable attention to address these challenges. This paper introduces a novel decentralized protection strategy for microgrids. The proposed method decomposes the protection challenge into several distributed learning tasks, enabling individual relays to autonomously determine the direction of faults using a binary classification framework based on support vector machine (SVM) algorithms. Following the distributed fault direction estimation, classifier outcomes are shared among neighboring relays, facilitating a local decision-making process to ascertain the presence of faults within the neighborhood. Finally, a tripping signal is generated based on the classifier results of each relay to operate the circuit breaker. To test and validate this approach, a 100% renewable microgrid model is simulated in MATLAB/Simulink. In the numerical analysis, the application of SVM classifiers in our approach yields impressive results: an average relay classification accuracy of 98%, and a 96% accuracy in circuit breaker control. These findings highlight the potential of machine-learning-based approaches in enhancing the efficiency and reliability of microgrid protection systems.

decentralized algorithm↗