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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 1,585 records · Page 88
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

An Investigation of the Kinematic and Microphysical Control of Lightning Rate, Extent and NOX Production using DC3 Observations and the NASA Lightning Nitrogen Oxides Model (LNOM)

The Deep Convective Clouds and Chemistry (DC3) experiment seeks to quantify the relationship between storm physics, lightning characteristics and the production of nitrogen oxides via lightning (LNOx). The focus of this study is to investigate the kinematic and microphysical control of lightning properties, particularly those that may govern LNOx production, such as flash rate, type and extent across Alabama during DC3. Prior studies have demonstrated that lightning flash rate and type is correlated to kinematic and microphysical properties in the mixed‐phase region of thunderstorms such as updraft volume and graupel mass. More study is required to generalize these relationships in a wide variety of storm modes and meteorological conditions. Less is known about the co‐evolving relationship between storm physics, morphology and three‐dimensional flash extent, despite its importance for LNOx production. To address this conceptual gap, the NASA Lightning Nitrogen Oxides Model (LNOM) is applied to North Alabama Lightning Mapping Array (NALMA) and Vaisala National Lightning Detection NetworkTM (NLDN) observations following ordinary convective cells through their lifecycle. LNOM provides estimates of flash rate, flash type, channel length distributions, lightning segment altitude distributions (SADs) and lightning NOx production profiles. For this study, LNOM is applied in a Lagrangian sense to multicell thunderstorms over Northern Alabama on two days during DC3 (21 May and 11 June 2012) in which aircraft observations of NOx are available for comparison. The LNOM lightning characteristics and LNOX production estimates are compared to the evolution of updraft and precipitation properties inferred from dual‐Doppler and polarimetric radar analyses applied to observations from a nearby radar network, including the UAH Advanced Radar for Meteorological and Operational Research (ARMOR). Given complex multicell evolution, particular attention is paid to storm morphology, cell mergers and possible dynamical, microphysical and electrical interaction of individual cells when testing various hypotheses.

Carey, Lawrence↗
NASA NTRS2014

The Kinematic and Microphysical Control of Lightning Rate, Extent and NOX Production

The Deep Convective Clouds and Chemistry (DC3) experiment seeks to quantify the relationship between storm physics, lightning characteristics and the production of nitrogen oxides via lightning (LNOx). The focus of this study is to investigate the kinematic and microphysical control of lightning properties, particularly those that may govern LNOx production, such as flash rate, type and extent across Alabama during DC3. Prior studies have demonstrated that lightning flash rate and type is correlated to kinematic and microphysical properties in the mixed-phase region of thunderstorms such as updraft volume and graupel mass. More study is required to generalize these relationships in a wide variety of storm modes and meteorological conditions. Less is known about the co-evolving relationship between storm physics, morphology and three-dimensional flash extent, despite its importance for LNOx production. To address this conceptual gap, the NASA Lightning Nitrogen Oxides Model (LNOM) is applied to North Alabama Lightning Mapping Array (NALMA) and Vaisala National Lightning Detection Network(TM) (NLDN) observations following ordinary convective cells through their lifecycle. LNOM provides estimates of flash rate, flash type, channel length distributions, lightning segment altitude distributions (SADs) and lightning NOx production profiles. For this study, LNOM is applied in a Lagrangian sense to multicell thunderstorms over Northern Alabama on two days during DC3 (21 May and 11 June 2012) in which aircraft observations of NOx are available for comparison. The LNOM lightning characteristics and LNOX production estimates are compared to the evolution of updraft and precipitation properties inferred from dual-Doppler and polarimetric radar analyses applied to observations from a nearby radar network, including the UAH Advanced Radar for Meteorological and Operational Research (ARMOR). Given complex multicell evolution, particular attention is paid to storm morphology, cell mergers and possible dynamical, microphysical and electrical interaction of individual cells when testing various hypotheses.

Carey, Lawrence↗
NASA NTRS1991

Surface and aerosol models for use in radiative transfer codes

Absolute reflectance-based radiometric calibrations of Landsat-5 Thematic Mapper (TM) are improved with the inclusion of a method to invert optical-depth measurements to obtain aerosol-particle size distributions, and a non-Lambertian surface reflectance model. The inverted size distributions can predict radiances varying from the previously assumed jungian distributions by as much as 5 percent, though the reduction in the estimated error is less than one percent. Comparison with measured diffuse-to-global ratios show that neither distribution consistently predicts the ratio accurately, and this is shown to be a large contributor to calibration uncertainties. An empirical model for the surface reflectance of White Sands, using a two-degree polynomial fit as a function of scattering angle, was employed. The model reduced estimated errors in radiance predictions by up to one percent. Satellite calibrations dating from October, 1984 were reprocessed using the improved methods and linear estimations of satellite counts per unit radiance versus time since launch were determined which showed a decrease over time for the first four bands.

Hart, Quinn J.↗
NASA NTRS2002

A Method to Retrieve Rainfall Rate Over Land from TRMM Microwave Imager Observations

Over tropical land regions, rain rate maxima in mesoscale convective systems revealed by the Precipitation Radar (PR) flown on the Tropical Rainfall Measuring Mission (TRMM) satellite are found to correspond to thunderstorms, i.e., Cbs. These Cbs are reflected as minima in the 85 GHz brightness temperature, T85, observed by the TRMM Microwave Imager (TMI) radiometer. Because the magnitude of TMI observations do not discriminate satisfactorily convective and stratiform rain, we developed here a different TMI discrimination method. In this method, two types of Cbs, strong and weak, are inferred from the Laplacian of T85 at minima. Then, to retrieve rain rate, where T85 is less than 270 K, a weak (background) rain rate is deduced using T85 observations. Furthermore, over a circular area of 10 km radius centered at the location of each T85 minimum, an additional Cb component of rain rate is added to the background rain rate. This Cb component of rain rate is estimated with the help of (T19-T37) and T85 observations. Initially, our algorithm is calibrated with the PR rain rate measurements from 20 MCS rain events. After calibration, this method is applied to TMI data taken from several tropical land regions. With the help of the PR observations, we show that the spatial distribution and intensity of rain rate over land estimated from our algorithm are better than those given by the current TMI-Version-5 Algorithm. For this reason, our algorithm may be used to improve the current state of rain retrievals on land.

Prabhakara, C.↗
DOE OSTI2020

Stochastic Fault Detection

This entry describes the state-of-the-art and future perspectives on stochastic fault detection, namely, stochastic fault detection and diagnosis (FDD). Both model-based and data-driven FDD methods for stochastic signals and systems have been included, where the use of hypothesis testing, Kalman filtering, system estimation, principal component analysis (PCA), and stochastic distribution control has been discussed for the construction of effective FDD algorithms. Indeed, stochastic FDD constitute an important and integrated part in developing fault-tolerant controls (FTC) for guaranteed safe operation of control systems, of which increased penetration of random factors is inevitable nowadays.

Wang, Aiping↗
DOE OSTI2025

Active and passive cooling approaches for a Southern California residential community

This study assesses cooling strategies in a low-income community in Southern California that lacks air conditioning and struggles with heat and air pollution. We used an urban building energy model and an electric distribution system model to evaluate active and passive cooling measures. The most effective space cooling measures were high-performance air-source heat pumps, cool coatings, window films, and harnessing the space cooling effect from heat pump water heaters. The results show that combining heat pump water heaters with window films and cool coatings reduces heat index hazard hours within buildings by 95 % to 99 % but increases total energy costs (equipment costs plus changes in utility bills) by 20 % to 60 % where the higher end includes building electrical upgrades. These measures also led to increased space heater use during colder months to avoid overcooling. Replacing conventional heaters with air source heat pumps eliminated unsafe indoor temperatures and reduced total energy use, but increased cost by 125 % to 150 %. In total, using heat pumps for space and water heating could reduce primary energy use by up to 57 %. The higher cost of active and passive cooling measures can be mitigated by existing and emerging incentive programs, especially those that support heat pumps. Electric distribution upgrades to support community electrification are estimated to increase utility costs by $\$$25 to $\$$40 per ratepayer per year. The results underscore the potential and challenges of adapting building infrastructure in communities at risk from climate and environmental stressors.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Modelling the ejection of primary aerosols during the fast pyrolysis of biomass anisotropic particles

A model for the fast pyrolysis of anisotropic biomass particles is presented which considers bubbling dynamics within the liquid intermediate phase (metaplast) and aerosol ejection from this phase. The model employs the population balance equation and the method of moments to estimate the production rate and resultant size distribution of aerosol ejections, incorporating a detailed CRECK reaction mechanism, and considers the effect of anisotropic biomass microstructure on the intraparticle transport of mass and energy. Here, this study investigates the impact of particle size, heating rate (heat transfer coefficient), and lignocellulosic composition on aerosol ejection. The model predicts that, at high heating rates (convective heat transfer coefficient of 359 W/m 2 .K), aerosols can contribute over 20% to the heavy fraction yield in bio-oil for small particles (1 mm diameter, 4 mm length). The model can predict aerosol size distribution and surface area, indicating an average size of 20 μm for bubbles and 5 μm for aerosols during increased bubble production and aerosol ejection rates. These findings are consistent with prior experimental results and provide essential information for future modeling of extra-particle reactions of the aerosols as they progress through the reactor.

09 BIOMASS FUELS↗
DOE OSTI2020

Predicting the proximity to macroscopic failure using local strain populations from dynamic in situ X-ray tomography triaxial compression experiments on rocks

Predicting the proximity of large-scale dynamic failure is a critical concern in the engineering and geophysical sciences. Here we use evolving contractive, dilatational, and shear strain deformation preceding failure in dynamic X-ray tomography experiments to examine which strain components best predict the proximity to failure. We develop machine learning models to predict the proximity to failure using time series of three-dimensional local incremental strain tensor fields acquired in rock deformation experiments under stress conditions of the upper crust. Three-dimensional scans acquired in situ throughout triaxial compression experiments provide a distribution of density contrasts from which we estimate the three-dimensional incremental strain that accumulates between each scan acquisition. Training machine learning models on multiple experiments of six rock types provides suites of feature importance that indicate the predictive power of each feature. Comparing the average importance of groups of features that include information about each strain component quantifies the ability of the contractive, dilatational and shear strain to predict the proximity of macroscopic failure. A total of 24 models of four machine learning algorithms with six rock types indicate that 1) the dilatational strain provides the best predictive power of the strain components, and 2) the intermediate values (25th-75th percentile) of the strain population provide the best predictive power of the statistics of the strain populations. In addition, the success of the predictions of models trained on one rock type and tested on other rock types quantifies the similarities and differences of the precursory strain accumulation process in the six rock types. These similarities suggest the potential existence of a unified theory of brittle rock deformation for a range of rock types.

58 GEOSCIENCES↗
DOE OSTI2021

Analysis of incentive policies for phosphorus recovery at livestock facilities in the Great Lakes area

Livestock operations have been highly intensified over the last decades, resulting in the advent of large concentrated animal feeding operations (CAFOs). Intensification decreases production costs but also leads to substantial environmental impacts. Specifically, nutrient runoff from livestock waste results in eutrophication, harmful algal blooms, and hypoxia. The implementation of nutrient recovery systems in CAFOs can abate nutrient releases and negative ecosystem responses, although they might negatively affect the economic performance of CAFOs. We design and analyze potential incentive policies for the deployment of phosphorus recovery technologies at CAFOs considering the geospatial vulnerability to nutrient pollution. The case study demonstration consists of 2217 CAFOs in the U.S. Great Lakes area. The results reveal that phosphorus recovery is more economically viable in the largest CAFOs due to economies of scale, although they also represent the largest eutrophication threats. For small and medium-scale CAFOs, phosphorus credits progressively improve the profitability of nutrient management systems. The integration of biogas production does not improve the economic performance of phosphorus recovery systems at most of CAFOs, as they lack enough size to be cost-effective. Furthermore, phosphorus recovery proves to be economically beneficial by comparing the net costs of nutrient management systems with the negative economic impact derived from phosphorus releases. The incentives necessary for avoiding up to 20.7.10 3 ton/year phosphorus releases and achieve economic neutrality in the Great Lakes area are estimated at $223 million/year. Additionally, the fair distribution of limited incentives is studied using a Nash allocation scheme, determining the break-even point for allocating monetary resources.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Quantification of Shallow Defect Suppression in Aminosilane-Passivated Lead Halide Perovskites via Optical and Electrochemical Methods

We combine fluence-dependent photoluminescence (PL) and electrochemical characterization to estimate defect-dependent trapping rates, energetic distributions, and densities of defects, in lead halide perovskite films Cs 0.1 FA 0.9 PbI 3 and Cs 0.17 FA 0.83 Pb­(I 0.75 Br 0.25 ) 3 , before and after trap passivation with vapor-deposited (3-aminopropyl)­trimethoxysilane (APTMS). For both compositions, the PL studies show that energetically shallow subgap near-valence defect densities lead to bimolecular nonradiative recombination which is reduced by 92% after APTMS treatment. Electrochemical characterization of these same active layers shows for the first time that APTMS passivation impacts mainly on near-valence (mobile anionic) defects that decrease in density by 4 orders of magnitude, from 10 18 cm –3 to 10 14 cm –3 , as a result of silane modification. Here, the combination of PL and electrochemical characterization promises a unique approach to speciation and quantification of a broader distribution of trap states in perovskites and provides straightforward assessments of the efficacy of defect mitigation strategies.

Defects↗
DOE OSTI2021

Computational Framework for Machine-Learning-Enabled 13 C Fluxomics

13 C metabolic flux analysis (MFA) has emerged as a powerful tool for synthetic biology. This optimization-based approach suffers long computation time and unstable solutions depending on the initial guess. Here, we develop a machine-learning-based framework for 13 C fluxomics. Specifically, training and test data sets are generated by metabolic network decomposition and flux sampling, in which flux ratios at metabolic nodes and simulated labeling patterns of metabolites are used as training targets and features, respectively. To improve prediction accuracy and simplify the model, automated processes are developed for flux ratio selection based on solvability and feature screening based on importance. We found that predictive performance can be significantly improved using both amino acids and central carbon metabolites in comparison with amino acids alone. Together with measured external fluxes, the predicted flux ratios determine the mass balance system, yielding global flux distributions. This approach is validated by flux estimation using both simulated and experimental data in comparison with canonical 13 C MFA. The approach represents a reliable fluxomics method readily applicable to high-throughput metabolic phenotyping, which highlights the advances of intelligent learning algorithms in synthetic biology, specifically in the Test and Learn stage of the Design-Build-Test-Learn cycle.

13C metabolic flux analysis↗
DOE OSTI2023

A Decision-Relevant Factor-Fixing Framework: Application to Uncertainty Analysis of a High-Dimensional Water Quality Model

Factor Fixing (FF) is a common method for reducing the number of model parameters to lower computational cost. FF typically starts with distinguishing the insensitive parameters from the sensitive and pursues uncertainty quantification (UQ) on the resulting reduced-order model, fixing each insensitive parameter at a fixed value. There is a need, however, to expand such a common approach to consider the effects of decision choices in the FF-UQ procedure on metrics of interest. Therefore, to guide the use of FF and increase confidence in the resulting dimension-reduced model, we propose a new adaptive framework consisting of four principles: (a) re-parameterize the model first to reduce obvious non-identifiable parameter combinations, (b) focus on decision relevance especially with respect to errors in quantities of interest (QoI), (c) conduct adaptive evaluation and robustness assessment of errors in the QoI across FF choices as sample size increases, and (d) reconsider whether fixing is warranted. The framework is demonstrated on a spatially-distributed water quality model. The error in estimates of QoI caused by FF can be estimated using a Polynomial Chaos Expansion (PCE) surrogate model. Built with 70 model runs, the surrogate is computationally inexpensive to evaluate and can provide global sensitivity indices for free. For the selected catchment, just two factors may provide an acceptably accurate estimate of model uncertainty in the average annual load of Total Suspended Solids (TSS), suggesting that reducing the uncertainty in these two parameters is a priority for future work before undertaking further formal uncertainty quantification.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Compositional heterogeneity in Cs y FA 1- y Pb(Br x I 1- x ) 3 perovskite films and its impact on phase behavior

Hybrid organic inorganic lead halide perovskite semiconductors of the form Cs y FA 1-y Pb(Br x I 1-x ) 3 are promising candidate materials for high-efficiency photovoltaics. Notably, cation and anion substitution can be used to tune the band gaps to optimize performance and improve stability. However, multi-component materials can be prone to compositional and structural inhomogeneity and the extent, length scale and impact of this heterogeneity on lead halide perovskite properties are not well understood. Here we use synchrotron X-ray diffraction to probe the evolution of crystal structure across the tetragonal-to-cubic phase transition for a series of Cs y FA 1-y Pb(Br x I 1-x ) 3 thin films with x = 0.05 to 0.3 and y = 0.17 to 0.40. We find that the transition occurs across a broad temperature range of approximately 40 °C, much larger than for pure compounds such as MAPbI 3 and MAPbBr 3 . Finally, we hypothesize that this smearing of the phase transition is due to compositional inhomogeneities that give rise to a distribution of local transition temperatures and we estimate the composition varies by about 10% to 15% with likely greater heterogeneity for the halide anion than the cation. This approach of mapping the transition is a simple and effective method of assessing heterogeneity, enabling evaluation of its impact.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Emergency department visits in California associated with wildfire PM 2.5 : differing risk across individuals and communities

The threats to human health from wildfires and wildfire smoke (WFS) in the United States (US) are increasing due to continued climate change. A growing body of literature has documented important adverse health effects of WFS exposure, but there is insufficient evidence regarding how risk related to WFS exposure varies across individual or community level characteristics. To address this evidence gap, we utilized a large nationwide database of healthcare utilization claims for emergency department (ED) visits in California across multiple wildfire seasons (May through November, 2012–2019) and quantified the health impacts of fine particulate matter <2.5 μm (PM 2.5 ) air pollution attributable to WFS, overall and among subgroups of the population. We aggregated daily counts of ED visits to the level of the Zip Code Tabulation Area (ZCTA) and used a time-stratified case-crossover design and distributed lag non-linear models to estimate the association between WFS and relative risk of ED visits. We further assessed how the association with WFS varied across subgroups defined by age, race, social vulnerability, and residential air conditioning (AC) prevalence. Over a 7 day period, PM 2.5 from WFS was associated with elevated risk of ED visits for all causes (1.04% (0.32%, 1.71%)), non-accidental causes (2.93% (2.16%, 3.70%)), and respiratory disease (15.17% (12.86%, 17.52%)), but not with ED visits for cardiovascular diseases (1.06% (–1.88%, 4.08%)). Analysis across subgroups revealed potential differences in susceptibility by age, race, and AC prevalence, but not across subgroups defined by ZCTA-level Social Vulnerability Index scores. These results suggest that PM 2.5 from WFS is associated with higher rates of all cause, non-accidental, and respiratory ED visits with important heterogeneity across certain subgroups. Notably, lower availability of residential AC was associated with higher health risks related to wildfire activity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

The Completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: Large-scale structure catalogues for cosmological analysis

ABSTRACT We present large-scale structure catalogues from the completed extended Baryon Oscillation Spectroscopic Survey (eBOSS). Derived from Sloan Digital Sky Survey (SDSS) IV Data Release 16 (DR16), these catalogues provide the data samples, corrected for observational systematics, and random positions sampling the survey selection function. Combined, they allow large-scale clustering measurements suitable for testing cosmological models. We describe the methods used to create these catalogues for the eBOSS DR16 Luminous Red Galaxy (LRG) and Quasar samples. The quasar catalogue contains 343 708 redshifts with 0.8 < z < 2.2 over 4808 deg2. We combine 174 816 eBOSS LRG redshifts over 4242 deg2 in the redshift interval 0.6 < z < 1.0 with SDSS-III BOSS LRGs in the same redshift range to produce a combined sample of 377 458 galaxy redshifts distributed over 9493 deg2. Improved algorithms for estimating redshifts allow that 98 per cent of LRG observations result in a successful redshift, with less than one per cent catastrophic failures (Δz > 1000 km s−1). For quasars, these rates are 95 and 2 per cent (with Δz > 3000 km s−1). We apply corrections for trends between the number densities of our samples and the properties of the imaging and spectroscopic data. For example, the quasar catalogue obtains a χ2/DoF = 776/10 for a null test against imaging depth before corrections and a χ2/DoF= 6/8 after. The catalogues, combined with careful consideration of the details of their construction found here-in, allow companion papers to present cosmological results with negligible impact from observational systematic uncertainties.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Comparison of Load Models for Estimating Electrical Efficiency in DC Microgrids: Preprint

This paper compares several electrical load models for estimating the efficiency of DC vs. AC distribution in microgrids. Candidate models include energy balance, harmonic power flow, and time-domain modeling. Model results are compared with numerical studies and validated with experimental measurements. Based on quantitative and qualitative considerations, the most appropriate load modeling approach for larger-scale DC distribution efficiency studies is proposed.

DC distribution↗
DOE OSTI2022

Comparative analysis of energy deposition modes available in Serpent 2 within the framework of the supercritical water reactor - Fuel qualification test reactor physics benchmark

A joint European Canadian Chinese development of a supercritical water-cooled small modular reactor (SCW-SMR) technology is in progress since September 2020 in the framework of a Horizon 2020 project called ECC-SMART. As a main purpose of the project, proper estimates of energy deposition and its spatial distribution are prerequisites for the accurate analysis of safety related parameters of the SCW-SMR concept under development. A supercritical water reactor fuel computational benchmark model, provided by Canadian Nuclear Laboratories, was applied for detailed comparison of different energy deposition calculation options available in the Serpent 2 Monte Carlo code. The effect of energy deposition options on the normalization of the results as well as on the spatial distribution of the energy deposition are discussed. Consistent energy deposition calculation methods are presented between three Monte Carlo codes, viz., Serpent 2, MCNP6 and OpenMC. Although resource-intensive, the use of the coupled neutron-photon transport mode of Serpent 2 is recommended for accurate spatial and quantitative characterization of energy deposition in the SCW-SMR fuel assemblies, accounting for both neutron and photon heating of all the materials. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Generating Uncertainty Distributions for Seismic Signal Onset Times

Signal arrival-time estimation plays a critical role in a variety of downstream seismic analyses, including location estimation and source characterization. Any arrival-time errors propagate through subsequent data-processing results. In this article, we detail a general framework for refining estimated seismic signal arrival times along with full estimation of their associated uncertainty. Using the standard short-term average/long-term average threshold algorithm to identify a search window, we demonstrate how to refine the pick estimate through two different approaches. In both cases, new waveform realizations are generated through bootstrap algorithms to produce full a posteriori estimates of uncertainty of onset arrival time of the seismic signal. The onset arrival uncertainty estimates provide additional data-derived information from the signal and have the potential to influence seismic analysis along several fronts.

58 GEOSCIENCES↗