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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Rapid analysis of composition and reactivity in cellulosic biomass feedstocks with near-infrared spectroscopy

Obtaining accurate chemical composition and reactivity (measures of carbohydrate release and yield) information for biomass feedstocks in a timely manner is necessary for the commercialization of biofuels. Our objective was to use near-infrared (NIR) spectroscopy and partial least squares (PLS) multivariate analysis to develop calibration models to predict the feedstock composition and the release and yield of soluble carbohydrates generated by a bench-scale dilute acid pretreatment and enzymatic hydrolysis assay. Major feedstocks included in the calibration models are corn stover, sorghum, switchgrass, perennial cool season grasses, rice straw, and miscanthus. Here are the results: We present individual model statistics to demonstrate model performance and validation samples to more accurately measure predictive quality of the models. The PLS-2 model for composition predicts glucan, xylan, lignin, and ash (wt%) with uncertainties similar to primary measurement methods. A PLS-2 model was developed to predict glucose and xylose release following pretreatment and enzymatic hydrolysis. An additional PLS-2 model was developed to predict glucan and xylan yield. PLS-1 models were developed to predict the sum of glucose/glucan and xylose/xylan for release and yield (grams per gram). The release and yield models have higher uncertainties than the primary methods used to develop the models. In conclusion, it is possible to build effective multispecies feedstock models for composition, as well as carbohydrate release and yield. The model for composition is useful for predicting glucan, xylan, lignin, and ash with good uncertainties. The release and yield models have higher uncertainties; however, these models are useful for rapidly screening sample populations to identify unusual samples.

09 BIOMASS FUELS↗
NASA NTRS1979

Future freeze forecasting

Real time GOES thermal data acquisition, an energy balance minimum temperature prediction model and a statistical model are incorporated into a minicomputer system. These components make up the operational "Satellite Freeze Forecast System" being used to aid NOAA, NWS forecasters in developing their freeze forecasts. The general concept of the system is presented in this paper. Specific detailed aspects of the system can be found in the reference cited.

Bartholic, J. F.↗
NASA NTRS1993

Segmentation Of Multifrequency, Multilook SAR Data

Segmentation of multifrequency, multilook synthetic-aperture radar (SAR) image intensity data into regions, within each of which backscattering characteristics of target scene considered homogeneous, enhanced by use of two statistical models. One represents statistics of multifrequency, multilook speckled intensities of SAR picture elements; other represents statistics of labels applied to regions into which picture elements grouped. Each region represents different type of terrain, terrain cover, or other surface; e.g., forest, agricultural land, sea ice, or water. Segmentation of image into regions of neighboring picture elements accomplished by method similar to that described in "Algorithms For Segmentation Of Complex-Amplitude SAR Data" (NPO-18524).

Rignot, Eric J.↗
DOE OSTI2018

Influence of chemical composition on biochemical methane potential of fruit and vegetable waste

Highlights: • Different mixtures of fruit and vegetable waste collected along one year were analysed. • Chemical composition showed great variation over time. • BMP ranged from 288 to 516 L{sub N} CH{sub 4} kg VS{sup −1} with 79% biodegradability average. • Lipid and high calorific value is well correlated to BMP. • Multiple regression models show good ability to predict methane potential. - Abstract: This study investigates the influence of chemical composition on the biochemical methane potential (BMP) of twelve different batches of fruit and vegetable waste (FVW) with different compositions collected over one year. BMP ranged from 288 to 516 L{sub N} CH{sub 4} kg VS{sup −1}, with significant statistical differences between means, which was explained by variations in the chemical composition over time. BMP was most strongly correlated to lipid content and high calorific values. Multiple linear regression was performed to develop statistical models to more rapidly predict methane potential. Models were analysed that considered chemical compounds and that considered only high calorific value as a single parameter. The best BMP prediction was obtained using the statistical model that included lipid, protein, cellulose, lignin, and high calorific value (HCV), with R{sup 2} of 92.5%; lignin was negatively correlated to methane production. Because HCV and lipids are strongly correlated, and because HCV can be determined more rapidly than overall chemical composition, HCV may be useful for predicting BMP.

09 BIOMASS FUELS↗
DOE OSTI2021

Determination of the 60 Zn level density from neutron evaporation spectra

Nuclear reactions of interest for astrophysics and applications often rely on statistical model calculations for nuclear reaction rates, particularly for nuclei far from β stability. However, statistical model parameters are often poorly constrained, where experimental constraints are particularly sparse for exotic nuclides. For example, our understanding of the breakout from the NiCu cycle in the astrophysical rp-process is currently limited by uncertainties in the statistical properties of the proton-rich nucleus 60 Zn. We have determined the nuclear level density of 60 Zn using neutron evaporation spectra from 58 Ni( 3 He,n) measured at the Edwards Accelerator Laboratory. We compare our results to a number of theoretical predictions, including phenomenological, microscopic, and shell-model-based approaches. Notably, we find the 60 Zn level density is somewhat lower than expected for excitation energies populated in the 59 Cu(p,γ) 60 Zn reaction under rp-process conditions. This includes a level density plateau from roughly 5 to 6 MeV excitation energy, which is counter to the usual expectation of exponential growth and all theoretical predictions that we explore. Here, a determination of the spin distribution at the relevant excitation energies in 60 Zn is needed to confirm that the Hauser-Feshbach formalism is appropriate for the 59 Cu(p,γ) 60 Zn reaction rate at x-ray burst temperatures

59 ≤ A ≤ 89↗
DOE OSTI2019

Hierarchical Modeling and Statistical Calibration for Photometric Redshifts

The cosmological exploitation of modern photometric galaxy surveys requires both accurate (unbiased) and precise (narrow) redshift probability distributions derived from broadband photometry. Existing methodologies do not meet these requirements. Standard template fitting delivers interpretable models and errors, but lacks the flexibility to learn inaccuracies in the observed photometry or spectral templates. Machine learning addresses those issues, but requires representative training data, and the resulting models and uncertainties cannot be interpreted in the context of a physical model or outside of the training data. We present a hierarchical modeling approach simultaneously addressing the issues of flexibility, interpretability, and generalization. It combines template fitting with flexible (machine-learning-like) models to correct the spectral templates, model their redshift distributions, and recalibrate the photometric observations. By optimizing the full posterior distribution of the model and solving for its (thousands of) parameters, one can perform a global statistical calibration of the data and the spectral energy distribution (SED) model. We apply this approach to the public Dark Energy Survey Science Verification data and show that it provides more accurate and compact redshift posterior distributions than existing methods, as well as insights into residual photometric and SED systematics. Here, the model is causal and makes predictions for future data (e.g., additional photometric bandpasses), and its internal parameters and components are interpretable. This approach does not formally require the training data to be complete or representative; in principle, it can even work in regimes in which few or no spectroscopic redshifts are available.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1986

Statistics of the geomagnetic secular variation for the past 5Ma

A new statistical model is proposed for the geomagnetic secular variation over the past 5Ma. Unlike previous models, the model makes use of statistical characteristics of the present day geomagnetic field. The spatial power spectrum of the non-dipole field is consistent with a white source near the core-mantle boundary with Gaussian distribution. After a suitable scaling, the spherical harmonic coefficients may be regarded as statistical samples from a single giant Gaussian process; this is the model of the non-dipole field. The model can be combined with an arbitrary statistical description of the dipole and probability density functions and cumulative distribution functions can be computed for declination and inclination that would be observed at any site on Earth's surface. Global paleomagnetic data spanning the past 5Ma are used to constrain the statistics of the dipole part of the field. A simple model is found to be consistent with the available data. An advantage of specifying the model in terms of the spherical harmonic coefficients is that it is a complete statistical description of the geomagnetic field, enabling us to test specific properties for a general description. Both intensity and directional data distributions may be tested to see if they satisfy the expected model distributions.

Constable, C. G.↗
NASA NTRS1988

Statistics of the geomagnetic secular variation for the past 5 m.y

A new statistical model is proposed for the geomagnetic secular variation over the past 5Ma. Unlike previous models, the model makes use of statistical characteristics of the present day geomagnetic field. The spatial power spectrum of the non-dipole field is consistent with a white source near the core-mantle boundary with Gaussian distribution. After a suitable scaling, the spherical harmonic coefficients may be regarded as statistical samples from a single giant Gaussian process; this is the model of the non-dipole field. The model can be combined with an arbitrary statistical description of the dipole and probability density functions and cumulative distribution functions can be computed for declination and inclination that would be observed at any site on Earth's surface. Global paleomagnetic data spanning the past 5Ma are used to constrain the statistics of the dipole part of the field. A simple model is found to be consistent with the available data. An advantage of specifying the model in terms of the spherical harmonic coefficients is that it is a complete statistical description of the geomagnetic field, enabling us to test specific properties for a general description. Both intensity and directional data distributions may be tested to see if they satisfy the expected model distributions.

Constable, C. G.↗
DOE OSTI2014

Decoherence-induced conductivity in the one-dimensional Anderson model

We study the effect of decoherence on the electron transport in the one-dimensional Anderson model by means of a statistical model [1, 2, 3, 4, 5]. In this model decoherence bonds are randomly distributed within the system, at which the electron phase is randomized completely. Afterwards, the transport quantity of interest (e.g. resistance or conductance) is ensemble averaged over the decoherence configurations. Averaging the resistance of the sample, the calculation can be performed analytically. In the thermodynamic limit, we find a decoherence-driven transition from the quantum-coherent localized regime to the Ohmic regime at a critical decoherence density, which is determined by the second-order generalized Lyapunov exponent (GLE) [4].

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Misclassification in Workers’ Telecommuting Frequency Choices Using a Generalized Extreme Value Model

Telecommuting frequency is a response variable collected in travel surveys and is, therefore, prone to errors leading to mismeasurements or misclassification. Misclassification of explanatory variables is a common risk when using statistical modeling techniques. We define “misclassification” as a response reported or recorded in the wrong category; for example, a variable is recorded as a 1 when it should be 0. Here, in this context, this study aims to develop a statistical model to analyze telecommuting data which accounts for potential misclassification errors by building on existing literature in econometrics. The empirical analysis was undertaken using the 2017 National Household Travel Survey (NHTS) and the general extreme value (GEV) models available in the literature. Specifically, the frequency of telecommuting days was analyzed using the negative binomial (NB) model recast as the multinomial logit (MNL) model. By nature—and consistent with other studies—NHTS data are prone to errors that can be classified as intentional or unintentional misinformation provided by the person being interviewed. Ignoring these errors while modeling telecommuting frequencies using standard discrete count models can result in biased parameter estimates. The misclassification parameter was calculated for both over-reporting and under-reporting scenarios. The misclassification errors can be as high as 14% over-reported and 10% under-reported, particularly for the neighboring values. Statistical fit comparison between the models shows that models that ignore misclassification have worse data fit and biased parameter estimates with significant policy implications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Photo response of 164 Dy

Background: Little data is available for the pygmy dipole resonance (PDR) in axially deformed nuclei. Photon-scattering experiments are complicated by high level densities in the PDR region and the small energy difference of transitions to the ground state and to excited states. Purpose: In this work, we report on an experimental study of the low-energy dipole strength distribution of the well-deformed nucleus 164 Dy between 4.0–7.7 MeV. Methods: The low-lying photoresponse of 164 Dy has been investigated using the method of nuclear resonance fluorescence using a quasimonochromatic linearly polarized $\textit{γ}$-ray beam in the energy range of 4.0–7.7 MeV in steps of 0.2 MeV. Results: For excitation energies between 4 MeV and 5 MeV, sufficiently low level densities allow for the identification of individual states, including level energies, reduced transition widths and branching ratios. Energy-averaged mean decay branching ratios, mean population ratios and partial absorption cross sections were determined above 5 MeV up to the neutron-separation threshold at 7.7 MeV. A Lorentzian-shaped enhancement of the partial photo absorption cross section followed by decays back to the ground-state band is found at 6.10(5) MeV with a width of 0.77(23) MeV. A comparison with results from complementary measurements is performed using the framework of the statistical model. Conclusions: The experimental results for the mean population ratios deviate systematically from the statistical model simulation by 30(6)%. However, they are in agreement within one standard deviation of the simulation.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

New compositional data on ancient mortars and plasters from Pompeii (Campania – Southern Italy): Archaeometric results and considerations about their time evolution

Highlights: • Mortar samples from Pompeii, with different constructive functions, were studied. • Characterization of the materials can be used for preparing compatible mortars. • Analogies and differences were highlighted to identify the constructive phases. • Preliminary statistic models were proposed to discriminate materials and constructive phases. Twenty-six samples of mortar used for various construction applications (joint mortars, floor mortars, filling mortars and plasters) were collected from different areas of the archaeological site of Pompeii (Campania - Sothern Italy). The mortar samples belong to various historical periods covering approximately 2000 years, from the 2nd century BC to the post excavation period (18–19th century). The samples were characterized employing Optical Microscopy (OM), X-ray Powder Diffraction (XRPD), X-ray Fluorescence (XRF), Electron Probe Micro Analysis (EPMA) and Raman Spectroscopy. Moreover, image analysis was carried out using JMicroVision software in order to determine the percentages of binder, macropores and aggregate, in terms of crushed ceramic fragments (cocciopesto), rock fragments and monocrystals. The application of these techniques allowed obtaining a complete chemical and minero-petrographic characterization of the samples, which may prove useful for preparing compatible repair mortars for future restoration works. The archaeometric study provided new data on the production technology of the mortars and the analogies and differences observed among the samples, enabled us to distinguish different constructive phases, thus confirming or rebutting the archaeological hypothesis on the dating of some samples. By combining compositional variables through the discriminant analysis, it was also possible to analyse the time evolution of the materials and to construct preliminary multivariate statistical models, helpful to identify the various typologies of mortars used in the different historical periods.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Risk-Adaptive Experimental Design for High-Consequence Systems: LDRD Final Report

Constructing accurate statistical models of critical system responses typically requires an enormous amount of data from physical experiments or numerical simulations. Unfortunately, data generation is often expensive and time consuming. To streamline the data generation process, optimal experimental design determines the 'best' allocation of experiments with respect to a criterion that measures the ability to estimate some important aspect of an assumed statistical model. While optimal design has a vast literature, few researchers have developed design paradigms targeting tail statistics, such as quantiles. In this project, we tailored and extended traditional design paradigms to target distribution tails. Our approach included (i) the development of new optimality criteria to shape the distribution of prediction variances, (ii) the development of novel risk-adapted surrogate models that provably overestimate certain statistics including the probability of exceeding a threshold, and (iii) the asymptotic analysis of regression approaches that target tail statistics such as superquantile regression. To accompany our theoretical contributions, we released implementations of our methods for surrogate modeling and design of experiments in two complementary open source software packages, the ROL/OED Toolkit and PyApprox.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1990

The beam filling error in the Nimbus 5 electronically scanning microwave radiometer observations of Global Atlantic Tropical Experiment rainfall

A comparison of rain rates retrieved from the Nimbus 5 electronically scanning microwave radiometer brightness temperatures and observed from shipboard radars during the Global Atlantic Tropical Experiment (GATE) phase I shows that the beam filling error is the major source of discrepancy between the two. When averaged over a large scene (the GATE radar array, 400 km in diameter), the beam filling error is quite stable, being 50 percent of the observed rain rate. This suggests the simple procedure of multiplying retrieved rain rates by 2 (correction factor). A statistical model of the beam filling error is developed by envisioning an idealized instrument field-of-view that encompasses an entire gamma distribution of rain rates. A modeled correction factor near 2 is found for rain rate and temperature characteristics consistent with GATE conditions. The statistical model also suggests that the correction factor varies from 1.5 to 2.5 for suppressed to enhanced tropical convective regimes, and decreases to 1.5 as the freezing level and average depth of the rain column decreases to 2.5 km.

Short, David A.↗
DOE OSTI2019

Parallel Hybrid Metaheuristics with Distributed Intensification and Diversification for Large-scale Optimization in Big Data Statistical Analysis

Important insights into many problems that are traditionally analyzed via statistical models can be obtained by re-formulating and evaluating within a large-scale optimization framework. The theoretical underpinnings of the statistical model often shift the goal of the solution space traversal from a traditional search for a single optimal solution to a traversal with the purpose of yielding a set of high quality, independent solutions. We examine statistical frameworks with astronomical solution spaces where the independence requirement constitutes a significant additional challenge for standard optimization methodologies. We design a hybrid metaheuristic with specialized intensification and diversification protocols in the base search algorithm. We extend our algorithm to the high-performance-computing realm using the Stampede2 supercomputer. We experimentally demonstrate the effectiveness of our algorithm to utilize multiple processors to collaboratively hill climb, broadcast messages to one another regarding landscape characteristics, diversify across the solution landscape, and request aid in climbing particularly difficult peaks.

Cho, Wendy K. Tam↗
DOE OSTI2021

Sustainability Trait Modeling of Field-Grown Switchgrass ( Panicum virgatum ) Using UAV-Based Imagery

Unmanned aerial vehicles (UAVs) provide an intermediate scale of spatial and spectral data collection that yields increased accuracy and consistency in data collection for morphological and physiological traits than satellites and expanded flexibility and high-throughput compared to ground-based data collection. In this study, we used UAV-based remote sensing for automated phenotyping of field-grown switchgrass (Panicum virgatum), a leading bioenergy feedstock. Using vegetation indices calculated from a UAV-based multispectral camera, statistical models were developed for rust disease caused by Puccinia novopanici, leaf chlorophyll, nitrogen, and lignin contents. For the first time, UAV remote sensing technology was used to explore the potentials for multiple traits associated with sustainable production of switchgrass, and one statistical model was developed for each individual trait based on the statistical correlation between vegetation indices and the corresponding trait. Also, for the first time, lignin content was estimated in switchgrass shoots via UAV-based multispectral image analysis and statistical analysis. The UAV-based models were verified by ground-truthing via correlation analysis between the traits measured manually on the ground-based with UAV-based data. The normalized difference red edge (NDRE) vegetation index outperformed the normalized difference vegetation index (NDVI) for rust disease and nitrogen content, while NDVI performed better than NDRE for chlorophyll and lignin content. Overall, linear models were sufficient for rust disease and chlorophyll analysis, but for nitrogen and lignin contents, nonlinear models achieved better results. As the first comprehensive study to model switchgrass sustainability traits from UAV-based remote sensing, these results suggest that this methodology can be utilized for switchgrass high-throughput phenotyping in the field.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Experiments, Statistical Analysis, and Modeling to Evaluate the Porosity Influence in SPS Coatings

Suspension plasma spray (SPS) is far more complicated than conventional plasma spray and requires a deep knowledge about the influence of process parameters and their correlations. In this study, YSZ coatings were manufactured by SPS with six different process parameters such as plasma power, suspension mass load, original powder size, substrate surface topology, spray distance, and spray step. Then, the porosity of as-prepared coatings was investigated by image method and X-ray transmission technique. A multivariate analysis on the collected experimental data was carried out by employing mathematical statistics methods. The results showed that: 1) Coating porosity has a negative correlation with plasma power and suspension mass load and a positive correlation with the original powder size, spray distance, spray step, and substrate roughness; 2) Spraying distance is the main factor affecting to coating porosity, followed by suspension mass load and substrate surface roughness, respectively. A linear model for porosity prediction was developed and was verified by experiments. Furthermore, the mechanism by which process parameters influence coating porosity is additionally discussed.

36 MATERIALS SCIENCE↗