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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Multiscale analysis in solids with unseparated scales: fine-scale recovery, error estimation, and coarse-scale adaptivity

There are several engineering applications in which the assumptions of homogenization and scale separation may be violated, in particular, for metallic structures constructed through additive manufacturing. Instead of resorting to direct numerical simulation of the macroscale system with an embedded fine scale, an alternative approach is to use an approximate macroscale constitutive model, but then estimate the model-form error using a posteriori error estimation techniques and subsequently adapt the macroscale model to reduce the error for a given boundary value problem and quantity of interest. Here, we investigate this approach to multiscale analysis in solids with unseparated scales using the example of an additively manufactured metallic structure consisting of a polycrystalline microstructure that is neither periodic nor statistically homogeneous. As a first step to the general nonlinear case, we focus here on linear elasticity in which each grain within the polycrystal is linear elastic but anisotropic.

42 ENGINEERING↗
DOE OSTI2017

Aerosol and cloud microphysics covariability in the northeast Pacific boundary layer estimated with ship-based and satellite remote sensing observations: NE Pacific Aerosol-Cloud Interactions

Ship measurements collected over the northeast Pacific along transects between the port of Los Angeles (33.7°N, 118.2°W) and Honolulu (21.3°N, 157.8°W) during May to August 2013 were utilized to investigate the covariability between marine low cloud microphysical and aerosol properties. Ship-based retrievals of cloud optical depth (τ) from a Sun photometer and liquid water path (LWP) from a microwave radiometer were combined to derive cloud droplet number concentration Nd and compute a cloud-aerosol interaction (ACI) metric defined as ACICCN = ∂ ln(Nd)/∂ ln(CCN), with CCN denoting the cloud condensation nuclei concentration measured at 0.4% (CCN0.4) and 0.3% (CCN0.3) supersaturation. Analysis of CCN0.4, accumulation mode aerosol concentration (Na), and extinction coefficient (σext) indicates that Na and σext can be used as CCN0.4 proxies for estimating ACI. ACICCN derived from 10 min averaged Nd and CCN0.4 and CCN0.3, and CCN0.4 regressions using Na and σext, produce high ACICCN: near 1.0, that is, a fractional change in aerosols is associated with an equivalent fractional change in Nd. ACICCN computed in deep boundary layers was small (ACICCN = 0.60), indicating that surface aerosol measurements inadequately represent the aerosol variability below clouds. Satellite cloud retrievals from MODerate-resolution Imaging Spectroradiometer and GOES-15 data were compared against ship-based retrievals and further analyzed to compute a satellite-based ACICCN. Satellite data correlated well with their ship-based counterparts with linear correlation coefficients equal to or greater than 0.78. Combined satellite Nd and ship-based CCN0.4 and Na yielded a maximum ACICCN = 0.88–0.92, a value slightly less than the ship-based ACICCN, but still consistent with aircraft-based studies in the eastern Pacific.

Painemal, David↗
DOE OSTI2016

Estimating heterotrophic respiration at large scales: challenges, approaches, and next steps

Heterotrophic respiration (HR), the aerobic and anaerobic processes mineralizing organic matter, is a key carbon flux but one impossible to measure at scales significantly larger than small experimental plots. This impedes our ability to understand carbon and nutrient cycles, benchmark models, or reliably upscale point measurements. Given that a new generation of highly mechanistic, genomic-specific global models is not imminent, we suggest that a useful step to improve this situation would be the development of "Decomposition Functional Types" (DFTs). Analogous to plant functional types (PFTs), DFTs would abstract and capture important differences in HR metabolism and flux dynamics, allowing models to efficiently group and vary these characteristics across space and time. We argue that DFTs should be initially informed by top-down expert opinion, but ultimately developed using bottom-up, data-driven analyses, and provide specific examples of potential dependent and independent variables that could be used. We present and discuss an example clustering analysis to show how model-produced annual HR can be broken into distinct groups associated with global variability in biotic and abiotic factors, and demonstrate that these groups are distinct from already-existing PFTs. A similar analysis, incorporating observational data, could form a basis for future DFTs. Finally, we suggest next steps and critical priorities: collection and synthesis of existing data; more in-depth analyses combining open data with high-performance computing; rigorous testing of analytical results; and planning by the global modeling community for decoupling decomposition from fixed site data. These are all critical steps to build a foundation for DFTs in global models, thus providing the ecological and climate change communities with robust, scalable estimates of HR at large scales.

carbon cycle↗
DOE OSTI2018

Application of Unbiased Estimators to Group Classification Risk Estimation

We consider the problem of construction of point and interval estimators for the Bayesian risk of a group classification decision rule provided that the elements of training samples have distributions belonging to the same one-parameter exponential family. We propose a solution to this problem using the asymptotic normality and efficiency of an unbiased risk estimator. An example of application of the obtained theoretical results is given.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

astroABC : An Approximate Bayesian Computation Sequential Monte Carlo sampler for cosmological parameter estimation

Given the complexity of modern cosmological parameter inference where we arefaced with non-Gaussian data and noise, correlated systematics and multi-probecorrelated data sets, the Approximate Bayesian Computation (ABC) method is apromising alternative to traditional Markov Chain Monte Carlo approaches in thecase where the Likelihood is intractable or unknown. The ABC method is called"Likelihood free" as it avoids explicit evaluation of the Likelihood by using aforward model simulation of the data which can include systematics. Weintroduce astroABC, an open source ABC Sequential Monte Carlo (SMC) sampler forparameter estimation. A key challenge in astrophysics is the efficient use oflarge multi-probe datasets to constrain high dimensional, possibly correlatedparameter spaces. With this in mind astroABC allows for massive parallelizationusing MPI, a framework that handles spawning of jobs across multiple nodes. Akey new feature of astroABC is the ability to create MPI groups with differentcommunicators, one for the sampler and several others for the forward modelsimulation, which speeds up sampling time considerably. For smaller jobs thePython multiprocessing option is also available. Other key features include: aSequential Monte Carlo sampler, a method for iteratively adapting tolerancelevels, local covariance estimate using scikit-learn's KDTree, modules forspecifying optimal covariance matrix for a component-wise or multivariatenormal perturbation kernel, output and restart files are backed up everyiteration, user defined metric and simulation methods, a module for specifyingheterogeneous parameter priors including non-standard prior PDFs, a module forspecifying a constant, linear, log or exponential tolerance level,well-documented examples and sample scripts. This code is hosted online athttps://github.com/EliseJ/astroABC

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

Electrochemical state and internal variables estimation using a reduced-order physics-based model of a lithium-ion cell and an extended Kalman filter

This paper addresses the problem of estimating the present value of electrochemical internal variables in a lithium-ion cell in real time, using readily available measurements of cell voltage, current, and temperature. The variables that can be estimated include any desired set of reaction flux and solid and electrolyte potentials and concentrations at any set of one-dimensional spatial locations, in addition to more standard quantities such as state of charge. The method uses an extended Kalman filter along with a one-dimensional physics-based reduced-order model of cell dynamics. Simulations show excellent and robust predictions having dependable error bounds for most internal variables. (C) 2014 Elsevier B.V. All rights reserved.

Stetzel, KD↗
DOE OSTI2015

Comparison of Integral Parameter Estimated Uncertainties in Fast Neutron Systems with COMMARA-2.1 and COMMARA-2.0 Covariance Data

This paper shows the main differences between the COMMARA-2.0 and COMMARA-2.1 evaluated covariance data in the uncertainty estimation of integral parameters of interest for a large number of typical innovative fast neutron systems. The study provides some guidelines to the nuclear data evaluators on which materials and which reaction types need to be evaluated most accurately to achieve reliable results in computational analyses of similar systems. The assessment of the quality and reliability of the covariance data is outside the scope of the study.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

How Much Are We Saving after All? Characterizing the Effects of Commonly Varying Assumptions on Emissions and Damage Estimates in PJM

In recent years, several methods have emerged to estimate the emissions and health, environmental, and climate change damages avoided by interventions such as energy efficiency, demand response, and the integration of renewables. However, differing assumptions employed in these analyses could yield contradicting recommendations regarding intervention implementation. We test the magnitude of the effect of using different key assumptions—average vs marginal emissions, year of calculation, temporal and regional scope, and inclusion of nonemitting generation—to estimate Mid-Atlantic region power pool (PJM) emissions and damage factors. We further highlight the importance of factor selection by evaluating three illustrative 2017 power system examples in PJM. We find that for a simple building lighting intervention, using average emissions factors incorporating nonemitting generation underestimates avoided damages by 45% compared to marginal factors. For PJM demand response, outdated marginal emissions factors from 2016 overestimate avoided damages by 25% compared to 2017 factors. Our assessment of PJM summer load further suggests that fossil-only average emissions factors overestimate damages by 63% compared to average factors incorporating nonemitting generation. Here, we recommend that energy modelers carefully select appropriate emissions metrics when performing their analyses. Furthermore, since the U.S. electric grid is rapidly changing, we urge decision-makers to frequently update (and consider forecasting) grid emissions factors.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Collar Properties and Measurement Time Confer Minimal Bias Overall on Annual Soil Respiration Estimates in a Global Database

Abstract Measuring the soil‐to‐atmosphere carbon dioxide (CO 2 ) flux (soil respiration, R S ) is important to understanding terrestrial carbon balance and to forecasting climate change. Such measurements are frequently made using measurement collars permanently inserted into the soil surface. However, differences in measurement duration and frequency, as well as collar properties, may lead to biases in the estimation of annual R S . Using a newly updated global R S database (SRDB‐V5), we investigated the annual R S bias associated with five methodological factors: collar height, collar coverage area, collar insertion depth, measurement duration, and measurement frequency. We found that annual R S was negatively correlated with collar insertion depth, consistent with the idea that collar insertion cuts roots and thus reduces R S . Annual R S was also negatively related with collar height and collar coverage area, perhaps because uniform head‐space mixing is difficult to achieve in larger volume chambers; however, these effects were quantitatively small (bias of ~2% to 10% of mean R S ). We found no correlation of measurement duration or measurement frequency with annual R S . These findings suggest that variation in R S methodology generally introduces minimal bias overall. Therefore, compilations of minimally adjusted annual R S measurements provide a reliable resource for synthesis studies, global annual R S modeling, and investigation of how soil carbon responds to climate change.

Jian, Jinshi↗
DOE OSTI2021

A New Approach for Simultaneous Estimation of Entrainment and Detrainment Rates in Non- Precipitating Shallow Cumulus

A new approach is developed for estimating entrainment and detrainment rates in cumulus clouds based on aircraft observations. Equations relating entrainment and detrainment rates to gross entrainment and detrainment are derived. This approach is applied to the Holistic Interactions of Shallow Clouds, Aerosols, and Land-Ecosystems field campaign, supported by the U.S. Department of Energy's Atmospheric Radiation Measurement program. The results show that both entrainment and detrainment rates decrease with increasing height. Sensitivity tests with different detrained air assumptions yield similar results. The entrainment and detrainment rates can reproduce the cloud thermodynamic variables. Partial correlation analysis indicates that entrainment rate is positively correlated with environmental relative humidity (RH), and detrainment rate is negatively correlated with environmental RH and positively correlated with entrainment rate. This new approach can be applied to other cloud observations to obtain a data set of entrainment and detrainment rates in cumulus clouds.

Zhu, Lei↗
DOE OSTI2015

Influence of Network Model Detail on Estimated Health Effects of Drinking Water Contamination Events

Network model detail can influence the accuracy of results from analyses of water distribution systems. Previous work has shown the limitations of skeletonized network models when considering water quality and hydraulic effects. Loss of model detail is potentially less important for aggregated effects such as the systemwide health effects associated with a contamination event, but has received limited attention. The influence of model detail on such effects is examined here by comparing results obtained for contamination events using three large network models and several skeletonized versions of the models. Loss of model detail decreases the accuracy of estimated aggregated adverse effects related to contamination events. It has the potential to have a large negative influence on the results of consequence assessments and the design of contamination warning systems. However, the adverse influence on analysis results can be minimized by restricting attention to high percentile effects (i.e., 95th percentile or higher).

Davis, Michael J.↗
DOE OSTI2014

Parameter estimation for chaotic systems using a hybrid adaptive cuckoo search with simulated annealing algorithm

This paper introduces a novel hybrid optimization algorithm to establish the parameters of chaotic systems. In order to deal with the weaknesses of the traditional cuckoo search algorithm, the proposed adaptive cuckoo search with simulated annealing algorithm is presented, which incorporates the adaptive parameters adjusting operation and the simulated annealing operation in the cuckoo search algorithm. Normally, the parameters of the cuckoo search algorithm are kept constant that may result in decreasing the efficiency of the algorithm. For the purpose of balancing and enhancing the accuracy and convergence rate of the cuckoo search algorithm, the adaptive operation is presented to tune the parameters properly. Besides, the local search capability of cuckoo search algorithm is relatively weak that may decrease the quality of optimization. So the simulated annealing operation is merged into the cuckoo search algorithm to enhance the local search ability and improve the accuracy and reliability of the results. The functionality of the proposed hybrid algorithm is investigated through the Lorenz chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the method can estimate parameters efficiently and accurately in the noiseless and noise condition. Finally, the results are compared with the traditional cuckoo search algorithm, genetic algorithm, and particle swarm optimization algorithm. Simulation results demonstrate the effectiveness and superior performance of the proposed algorithm.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2013

CARBON-ENHANCED METAL-POOR STARS IN SDSS/SEGUE. I. CARBON ABUNDANCE ESTIMATION AND FREQUENCY OF CEMP STARS

We describe a method for the determination of stellar [C/Fe] abundance ratios using low-resolution (R = 2000) stellar spectra from the Sloan Digital Sky Survey (SDSS) and its Galactic sub-survey, the Sloan Extension for Galactic Understanding and Exploration (SEGUE). By means of a star-by-star comparison with a set of SDSS/SEGUE spectra with available estimates of [C/Fe] based on published high-resolution analyses, we demonstrate that we can measure [C/Fe] from SDSS/SEGUE spectra with S/N ≥ 15 Å{sup –1} to a precision better than 0.35 dex for stars with atmospheric parameters in the range T {sub eff} = [4400, 6700] K, log g = [1.0, 5.0], [Fe/H] = [–4.0, +0.5], and [C/Fe] = [–0.25, +3.5]. Using the measured carbon-to-iron abundance ratios obtained by this technique, we derive the frequency of carbon-enhanced stars ([C/Fe] ≥ +0.7) as a function of [Fe/H], for both the SDSS/SEGUE stars and other samples from the literature. We find that the differential frequency slowly rises from almost zero to about 14% at [Fe/H] ∼ –2.4, followed by a sudden increase, by about a factor of three, to 39% from [Fe/H] ∼ –2.4 to [Fe/H] ∼ –3.7. Although the number of stars known with [Fe/H] < –4.0 remains small, the frequency of carbon-enhanced metal-poor (CEMP) stars below this value is around 75%. We also examine how the cumulative frequency of CEMP stars varies across different luminosity classes. The giant sample exhibits a cumulative CEMP frequency of 32% for [Fe/H] ≤ –2.5, 31% for [Fe/H] ≤ –3.0, and 33% for [Fe/H] ≤ –3.5; a roughly constant value. For the main-sequence turnoff stars, we obtain a lower cumulative CEMP frequency, around 10% for [Fe/H] ≤ –2.5, presumably due to the difficulty of identifying CEMP stars among warmer turnoff stars with weak CH G-bands. The dwarf population displays a large change in the cumulative frequency for CEMP stars below [Fe/H] = –2.5, jumping from 15% for [Fe/H] ≤ –2.5 to about 75% for [Fe/H] ≤ –3.0. When we impose a restriction with respect to distance from the Galactic mid-plane (|Z| < 5 kpc), the frequency of the CEMP giants does not increase at low metallicity ([Fe/H] < –2.5), but rather decreases due to the dilution of C-rich material in stars that have undergone mixing with CNO-processed material from their interiors. The frequency of CEMP stars near the main-sequence turnoff, which are not expected to have experienced mixing, increases for [Fe/H] ≤ –3.0. The general rise in the global CEMP frequency at low metallicity is likely due to the transition from the inner-halo to the outer-halo stellar populations with declining metallicity and increasing distance from the plane.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

The effect of weak lensing on distance estimates from supernovae

Using a sample of 608 Type Ia supernovae from the SDSS-II and BOSS surveys, combined with a sample of foreground galaxies from SDSS-II, we estimate the weak lensing convergence for each supernova line of sight. We find that the correlation between this measurement and the Hubble residuals is consistent with the prediction from lensing (at a significance of 1.7σ). Strong correlations are also found between the residuals and supernova nuisance parameters after a linear correction is applied. When these other correlations are taken into account, the lensing signal is detected at 1.4σ. We show, for the first time, that distance estimates from supernovae can be improved when lensing is incorporated, by including a new parameter in the SALT2 methodology for determining distance moduli. The recovered value of the new parameter is consistent with the lensing prediction. Using cosmic microwave background data from WMAP7, H {sub 0} data from Hubble Space Telescope and Sloan Digital Sky Survey (SDSS) Baryon acoustic oscillations measurements, we find the best-fit value of the new lensing parameter and show that the central values and uncertainties on Ω {sub m} and w are unaffected. The lensing of supernovae, while only seen at marginal significance in this low-redshift sample, will be of vital importance for the next generation of surveys, such as DES and LSST, which will be systematics-dominated.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Maximum a posteriori Ly α estimator (MAPLE): band power and covariance estimation of the 3D Ly α forest power spectrum

We present a novel maximum a posteriori estimator to jointly estimate band powers and the covariance of the three-dimensional power spectrum (P3D) of Ly $\alpha$ forest flux fluctuations, called MAPLE. Our Wiener-filter based algorithm reconstructs a window-deconvolved P3D in the presence of complex survey geometries typical for Ly $\alpha$ surveys that are sparsely sampled transverse to and densely sampled along the line of sight. We demonstrate our method on idealized Gaussian random fields with two selection functions: (i) a sparse sampling of 30 background sources per square degree designed to emulate the current Dark Energy Spectroscopic Instrument; (ii) a dense sampling of 900 background sources per square degree emulating the upcoming Prime Focus Spectrograph Galaxy Evolution Survey. Our proof-of-principle shows promise, especially since the algorithm can be extended to marginalize jointly over nuisance parameters and contaminants, i.e. offsets introduced by continuum fitting. Our code is implemented in JAX and is publicly available on GitHub.

79 ASTRONOMY AND ASTROPHYSICS↗