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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Toward Improved Regional Hydrological Model Performance Using State-Of-The-Science Data-Informed Soil Parameters

Accurate soil moisture and streamflow data are an aspirational need of many hydrologically relevant fields. Model simulated soil moisture and streamflow hold promise but models require validation prior to application. Calibration methods are commonly used to improve model fidelity but misrepresentation of the true dynamics remains a challenge. In this study, we leverage soil parameter estimates from the Soil Survey Geographic (SSURGO) database and the probability mapping of SSURGO (POLARIS) to improve the representation of hydrologic processes in the Weather Research and Forecasting Hydrological modeling system (WRF-Hydro) over a central California domain. Our results show WRF-Hydro soil moisture exhibits increased correlation coefficients ( r ), reduced biases, and increased Kling-Gupta Efficiencies (KGEs) across seven in situ soil moisture observing stations after updating the model's soil parameters according to POLARIS. Compared to four well-established soil moisture data sets including Soil Moisture Active Passive data and three Phase 2 North American Land Data Assimilation System land surface models, our POLARIS-adjusted WRF-Hydro simulations produce the highest mean KGE (0.69) across the seven stations. More importantly, WRF-Hydro streamflow fidelity also increases, especially in the case where the model domain is set up with SSURGO-informed total soil thickness. The magnitude and timing of peak flow events are better captured, r increases across nine United States Geological Survey stream gages, and the mean KGE across seven of the nine gages increases from 0.12 to 0.66. Our pre-calibration parameter estimate approach, which is transferable to other spatially distributed hydrological models, can substantially improve a model's performance, helping reduce calibration efforts and computational costs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

PyCBC Inference: A Python-based Parameter Estimation Toolkit for Compact Binary Coalescence Signals

We introduce new modules in the open-source PyCBC gravitational-wave astronomy toolkit that implement Bayesian inference for compact-object binary mergers. We review the Bayesian inference methods implemented and describe the structure of the modules. Here, we demonstrate that the PyCBC Inference modules produce unbiased estimates of the parameters of a simulated population of binary black hole mergers. We show that the parameters' posterior distributions obtained using our new code agree well with the published estimates for binary black holes in the first Advanced LIGO–Virgo observing run.

79 ASTRONOMY AND ASTROPHYSICS↗
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 OSTI2019

A biokinetic model for trivalent or hexavalent chromium in adult humans

Chromium exists in several oxidation states, with the trivalent state (Cr(III)) being the dominant naturally occurring form. Chromium in other oxidation states tends to be converted to the trivalent oxide in the natural environment and in biological systems. Chromium(III) has been shown to be an essential nutrient for humans and several non-human species. Chromium(VI), the second most stable form of chromium, is an important environmental contaminant that is mostly of industrial origin and is associated with lung cancer and nose tumours in chromium workers. This paper proposes a biokinetic model for chromium that addresses the distinctive behaviours of Cr(III) and Cr(VI) following uptake to blood of an adult human. Additionally, the model is based on biokinetic data derived from relatively short-term studies involving administration of chromium tracers to adult human subjects or laboratory animals, supplemented with data on the long-term distribution of chromium in adult humans as estimated from autopsy measurements. The model is part of a comprehensive update of biokinetic models of the International Commission on Radiological Protection, used to project or evaluate radiation doses from occupational intake of radionuclides.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
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 OSTI2019

Reconstructing small-scale lenses from the cosmic microwave background temperature fluctuations

Cosmic microwave background (CMB) lensing is a powerful probe of the matter distribution in the Universe. The standard quadratic estimator, which is typically used to measure the lensing signal, is known to be suboptimal for low-noise polarization data from next-generation experiments. In this paper, we explain why the quadratic estimator will also be suboptimal for measuring lensing on very small scales, even for measurements in temperature where this estimator typically performs well. Though maximum likelihood methods could be implemented to improve performance, we explore a much simpler solution, revisiting a previously proposed method to measure lensing that involves a direct inversion of the background gradient. An important application of this simple formalism is the measurement of cluster masses with CMB lensing. We find that directly applying a gradient inversion matched filter to simulated lensed images of the CMB can tighten constraints on cluster masses compared to the quadratic estimator. While the difference is not relevant for existing surveys, for future surveys it can translate to significant improvements in mass calibration for distant clusters, where galaxy lensing calibration is ineffective due to the lack of enough resolved background galaxies. Improvements can be as large as ~50 per cent for a cluster at z = 2 and a next-generation CMB experiment with 1 μK arcmin noise, and over an order of magnitude for lower noise levels. For future surveys, this simple matched filter or gradient inversion method approaches the performance of maximum likelihood methods, at a fraction of the computational cost.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Constraints for stellar electron-capture rates on 86 Kr via the 86 Kr(t, 3 He +γ) 86 Br reaction and the implications for core-collapse supernovae

Background: In the late stages of stellar core collapse just prior to core bounce, electron captures on medium-heavy nuclei drive deleptonization. Therefore, simulations require the use of accurate reaction rates. Nuclei with neutron number near N=50 above atomic number Z=28 play an important role. Rates presently used in astrophysical simulations rely primarily on a relatively simple single-state approximation. In order to improve the accuracy of the astrophysical simulations, experimental data are needed to test the electron-capture rates and to guide the development of better theoretical models and astrophysical simulations. Purpose: The purpose of the present work was to measure the Gamow-Teller transition strength from 86 Kr to 86 Br, to derive the stellar electron-capture rates based on the extracted strengths, and to compare the derived rates with rates based on shell-model and quasiparticle random-phase approximation (QRPA) Gamow-Teller strengths calculations, as well as the single-state approximation. An additional purpose was to test the impact of using improved electron-capture rates on the late evolution of core-collapse supernovae. Method: The Gamow-Teller strengths from 86 Kr were extracted from the 86 Kr(t, 3 He +γ) charge-exchange reaction at 115MeV/u. The electron-capture rates were calculated as a function of stellar density and temperature. Besides the case of 86 Kr, the electron-capture rates based on the QRPA calculations were calculated for 78 additional isotopes near N=50 above Z=28. The impact of using these rates instead of those based on the single-state approximation is studied in a spherically symmetrical simulation of core collapse just prior to bounce. Results: The derived electron-capture rates on 86 Kr from the experimental Gamow-Teller strength distribution are much smaller than the rates estimated based on the single-state approximation. Rates based on Gamow-Teller strengths estimated in shell-model and QRPA calculations are more accurate. The core-collapse supernova simulation with electron-capture rates based on the QRPA calculations indicate a significant reduction in the deleptonization during the collapse phase. Conclusions: It is important to utilize microscopic theoretical models that are tested by experimental data to constrain and estimate Gamow-Teller strengths and derived electron-capture rates for nuclei near N=50 that are inputs for astrophysical simulations of core-collapse supernovae and their multimessenger signals, such as the emission of neutrinos and gravitational waves.

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 OSTI2019

Chemical Composition Analysis for X-Ray Transport Container Scans

It is important for national security to control the movement of dangerous or strategically cargo such as explosives, radioactive materials, rare and precious metals. This control can be provided by scanning transport containers by gamma rays. In this report the existing technique for scanning (dual energy method) is considered and the alternative method based on measuring the energy distribution of gamma rays is proposed. For estimation perspectives of the proposing method, the corresponding simulation was conducted by using the GEANT4 toolkit. The example of the algorithm of reconstruction the chemical composition of the scanned object is also considered. In addition the experiment for estimation energy resolution of the detector based on a scintillation crystal BGO and SiPM was carried out.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗