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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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A Machine Learning-Based Method to Estimate Transformer Primary-Side Voltages with Limited Customer-Side AMI Measurements: Preprint

Distribution control applications such as volt/var optimization, network reconfiguration, and distribution automation require accurate knowledge of the distribution system state. The lack of sufficient sensors on the primary side of distribution networks often limits the accuracy of the control decisions by these applications. The deployment of advanced metering infrastructure (AMI) provides utilities an opportunity to translate the AMI data on the secondary onto the primary so that it can be used as pseudo-measurements to augment the limited existing measurements on the primary. This paper develops an approach for estimating service transformer primary-side voltages by using limited secondary-side AMI measurements. The estimated primary-side voltages can be used by utilities as pseudo-measurements for distribution control applications. The detailed secondary model topology, which is an essential input data for many existing algorithms, is not required for the proposed method. The performance of the proposed method is validated by using AMI measurements from the field and an actual distribution feeder model of San Diego Gas & Electric Company.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Assessing the nexus between groundwater and solar energy plants in a desert basin with a dual-model approach under uncertainty

Globally, many solar power plants and other types of renewable energy are being located in water-scarce regions. Many projects rely on groundwater resources whose sustainability is uncertain. In the Chuckwalla Basin in California, quantification of recharge and trans-valley underflow is needed to estimate the impacts of solar project withdrawals on the water table. However, such estimates are highly challenging due to data scarcity, heterogeneous soils and long residence times. Conventional assessment employs isolated groundwater models configured with crude and uniform estimates of recharge. Here, we employ a data-constrained surface subsurface processes model, PAWS+CLM, to provide an ensemble of recharges and underflows with perturbed parameters. Then, the Parameter Estimation (PEST) package is used to calibrate MODFLOW aquifer conductivity and filter out implausible recharges. The novel dual-model approach, potentially applicable in other arid regions, can effectively assimilate groundwater head observations, reject unrealistic parameters, and narrow the range of estimated drawdowns. Simulated recharge concentrates along alluvial fans at the mountain fronts and ephemeral washes where run-off water infiltrates. If an evenly distributed recharge was assumed, it resulted in under-estimated drawdown and larger uncertainty bounds. The withdrawals are approaching total inflow, suggesting the system will be nearing, if not exceeding, its sustainable groundwater production capacity, and a boom of such projects will not be sustainable. Especially, the cost/benefit of pumped-storage projects is called into question as the initial-fill phase depletes entire area’s recharge. Our study highlights the stress on groundwater resources of solar development, and that the speed of groundwater recovery does not indicate sustainability. Main point 1: A novel dual model approach, involving an integrated surface/subsurface model and a groundwater parameter-estimation model, was able to better constrain the model. Main point 2: The groundwater system may be nearing, if not exceeding, its sustainable groundwater production capacity and the speed of recovery is not indicative of sustainability. Main point 3: Results from using conventionally-assumed uniform recharge distort calibrated K fields and impacts assessment

14 SOLAR ENERGY↗
DOE OSTI2022

Daylight simulation workflows incorporating measured bidirectional scattering distribution functions

Daylight predictions of architectural spaces depend on good estimates of light transfer through skylights, windows and other fenestration systems. For clear glazing and painted surfaces, parametric transmission and reflection models have proven adequate, but there are many cases where light-scattering, semi-specular shading and daylighting materials defy simple characterization. Something as commonplace as fabric roller shades and venetian blinds may turn daylight prediction into guesswork, and numerous advanced systems on the market tuned specifically to enhance daylight are not sufficiently characterized to distinguish their performance. In this paper, we describe new tools available to handle novel and specialized fabrics, materials, and devices using data-driven modelling of bi-directional scattering distribution functions (BSDFs). These representations are usually tabulated at constant or adjustable angular resolution for efficient point-in-time and annual daylight simulations. We describe a variety of BSDF simulation workflows, including some of the tools and methods that make advanced analysis possible, and highlight some of the current challenges. We conclude with a discussion of future work and how such data might be created and shared worldwide.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Bayesian inference for the seismic moment tensor using regional waveforms and a data-derived distribution of velocity models

The largest source of uncertainty in any source inversion is the velocity model used to construct the transfer function employed in the forward model that relates observed ground motion to the seismic moment tensor. However, standard inverse procedures often does not quantify uncertainty in the seismic moment tensor due to error in the Green’s functions from uncertain event location and Earth structure. We attempt to incorporate this uncertainty into an estimation of the seismic moment tensor using a distribution of velocity models calculated in a prior effort based on different and complementary data sets. The posterior distribution of velocity models is then used to construct Green’s functions for use in Bayesian inference of an unknown seismic moment tensor using regional waveform data. The combined likelihood is estimated using data-specific error models and the posterior of the seismic moment tensor is estimated and can be interpreted in terms of most-probable source-type.

58 GEOSCIENCES↗
DOE OSTI2019

General-Surrogate Adaptive Sampling Using Interquartile Range for Design Space Exploration

Abstract A surrogate model is a common tool to approximate system response at untested points for design space exploration. Adaptive sampling has been studied for improving the accuracy of surrogates iteratively by introducing additional samples (simulations and experiments). New samples are often selected based on the estimated uncertainty in the design space. While some surrogates such as kriging have readily available uncertainty models for their predictions, other surrogates do not. Consequently, there have been studies of using the process of leaving-samples-out (LSO) used in cross-validation tools to estimate prediction uncertainty, such as the universal prediction distribution (UPD). In this paper, an adaptive sampling scheme for general surrogates is proposed based on LSO, similar to cross-validation and interquartile range (IQR). Multiple submodels are first developed from LSO. The uncertainty is then estimated from the IQR of these surrogates at a given point. New samples are added iteratively at the point with maximum IQR for design space exploration. The proposed scheme is illustrated using kriging, radial basis function, and neural network surrogates. The proposed scheme is evaluated using four algebraic test functions. Multiple sets of initial samples were produced to account for randomness. For these test functions, the proposed scheme was found to be more accurate and robust than kriging with its own uncertainty model. The proposed scheme was more accurate than the UPD for three out of the four test functions. For a fixed number of samples, the IQR-based adaptive sampling also proved to be more accurate than all-at-once sampling in most cases even when the estimated uncertainty was only mildly correlated with prediction errors.

Engineering↗
DOE OSTI2026

LandScan Mosaic Time Series Version 1.0: 1975-2024

The LandScan Mosaic Annual Global Ambient Population Time Series, Version 1.0, provides annual high-resolution global ambient population estimates from 1975 to 2024. The dataset is distributed as one Cloud-Optimized GeoTIFF per year on a globally aligned 3 arc-second WGS 84 grid. The 2024 layer is the LandScan Mosaic benchmark reference year, while the 1975–2023 layers are historical estimates generated using the LandCast backcasting framework.

Zimmer, Andrew [ORNL] (ORCID:0000000176838713)↗
DOE OSTI2022

Sensitivity of the U.S. economy to oil prices controlling for domestic production and imports

This paper investigates the sensitivity of U.S. economic performance to oil price changes, accounting for changes in the domestic petroleum supply-demand balance over the last decade. Herein, a non-linear (asymmetric) autoregressive distributed lag (NARDL) model is used to estimate the U.S. GDP elasticity with respect to the oil price, controlling for oil production, consumption and imports, and macroeconomic variables. The positive and negative components of the oil price both have statistically significant long-run impacts on the real U.S. GDP. The parameter estimates imply that a 1% positive and permanent oil price shock, all else the same, would have a long-run impact of –0.045% on the U.S. economy (an elasticity of –0.045), but short-run parameters on the positive oil price terms are not significant at the 10% level. The long-run parameter on the negative oil price component term implies an elasticity of –0.034. Thus, controlling for domestic U.S. oil production and oil trade, the long-run oil price elasticity of the U.S. GDP remains within the range of estimates from previous studies. The results also show that domestic oil production and consumption have short-run impacts on the U.S. GDP. The potential extent of interactions among these variables, and implications for the net economic impacts, under an oil price shock are subjects of future research.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Ionizing radiation from AGNs at z > 3.3 with the Subaru Hyper Suprime-Cam Survey and the CFHT Large Area U-band Deep Survey (CLAUDS)

ABSTRACT We use deep and wide imaging data from the CFHT Large Area U-band Deep Survey (CLAUDS) and the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) to constrain the ionizing radiation (Lyman Continuum, LyC) escape fraction from active galactic nuclei (AGNs) at z ∼ 3–4. For 94 AGNs with spectroscopic redshifts at 3.3 < z < 4.0, we use their U/i-band flux ratios to estimate LyC transmission of individual AGNs. The distribution of their LyC transmission shows values lower than the range of LyC transmission values for IGM of the same redshift range, which suggests that LyC escape fraction of AGNs at z > 3.3 is considerably lower than unity in most cases. We do not find any trend in LyC transmission values depending on their UV luminosities. Based on the photometry of stacked images we find the average flux ratio of LyC and non-ionizing UV photons escaping from the objects (fLyC/fUV)out = 0.182 ± 0.043 for AGNs at 3.3 < z < 3.6, which corresponds to LyC escape fraction fesc = 0.303 ± 0.072 if we assume a fiducial intrinsic SED of AGN. Based on the estimated LyC escape fraction and the UV luminosity function (UVLF) of AGNs, we argue that UV-selected AGNs’ contribution to the LyC emissivity at the epoch is minor, although the size of their contribution largely depends on the shape of the UVLF.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Correlation function distributions for O ( N ) lattice field theories in the disordered phase

Numerical computations in strongly interacting quantum field theories are often performed using Monte Carlo sampling methods. A key task in these calculations is to estimate the value of a given physical quantity from the distribution of stochastic samples that are generated using the Monte Carlo method. Typically, the sample mean and sample variance are used to define the expectation values and uncertainties of computed quantities. However, the Monte Carlo sample distribution contains more information than these basic properties, and it is useful to investigate it more generally. In this work, the exact form of the probability distributions of two-point correlation functions at zero momentum in O ( N ) lattice field theories in the disordered phase and in infinite volume are determined. These distributions allow for a robust investigation of the efficacy of the Monte Carlo sampling procedure and are shown also to allow for improved estimators of the target physical quantity to be constructed. The theoretical expectations are shown to agree with numerical calculations in the O ( 2 ) model. Published by the American Physical Society 2024

Astronomy & Astrophysics↗
DOE OSTI2018

WIPP PA Sensitivity Studies in Support of CRA-2014 Completeness Determination and Compliance Decision - 18209

The Waste Isolation Pilot Plant (WIPP) has been developed by the U.S. Department of Energy (DOE) for the geologic (deep underground) disposal of transuranic (TRU) waste. Containment of TRU waste at the WIPP is regulated by the U.S. Environmental Protection Agency (EPA). The DOE demonstrates compliance with the containment requirements by means of performance assessment (PA) calculations. WIPP PA calculations estimate the probability and consequence of potential radionuclide releases from the repository to the accessible environment for a regulatory period of 10,000 years after facility closure. Since TRU waste was first received at the WIPP in 1999, compliance recertification applications (CRAs) have been required by the EPA on five-year cycle. The latest CRA (CRA-2014) was submitted by the DOE to the EPA in 2014. As part of the application, a formal set of PA calculations (CRA-2014 PA) was performed and results were documented in detail. The CRA-2014 PA incorporated several parameter updates based on new data, as well as the implementation of a different model for panel closures, barriers planned for the opening of each waste panel. The results presented as part of the CRA-2014 showed via the required complementary cumulative distribution function (CCDF) release curves that the estimated releases were within compliance limits. As part of their process for reviewing the completeness of the CRA-2014 [1], the EPA formally requested that the DOE perform sensitivity studies based on EPA-requested changes to model parameters. The DOE performed and documented the results of three sensitivity studies described below: CRA14-SEN2 [2], CRA14-SEN3 [3], and CRA14-SEN4 [4]. In each case, the new analysis results were compared to CRA- 2014 PA (CRA14) results to determine the impact of the EPA-requested changes. CRA14-SEN2: the application of EPA-requested modified parameters in the operations and experimental (non-waste) areas of the repository to simulate an accelerated (instantaneous) creep closure, the inclusion of capillary pressure effects on relative permeability, and an increase in initial/residual brine saturation and residual gas saturation. The modifications to the repository model resulted in increased pressures and decreased brine saturations in waste areas and increased pressures and brine saturations in the operations and experimental areas. However, the overall effects on total high-probability mean releases from the repository were entirely insignificant. It was concluded that the modeling assumptions associated with the operations and experimental areas of the repository have an insignificant effect on the prediction of total releases from the repository and/or adequacy of the current (CRA14) model to demonstrate compliance with the regulatory limits. CRA14-SEN3: the application of EPA-requested modified parameters in the panel closure areas and operations and experimental (non-waste) areas of the repository to simulate an accelerated (instantaneous) creep closure, the inclusion of capillary pressure effects on relative permeability, and an increase in initial/residual brine saturation and residual gas saturation. The modifications to the repository model predominantly result in increased pressures and decreased brine saturations in waste areas, increased pressures and brine saturations in the operations and experimental areas, and decreased brine and gas flows across all panel closures. The pressure increases in repository waste regions yielded slightly decreased brine saturations (on average) in those areas. However, the overall effects on total high-probability mean releases from the repository are entirely insignificant. It was concluded that the modeling assumptions made for this sensitivity study which were associated with the panel closures and north end areas of the repository have only a small effect on the prediction of total releases from the repository and/or adequacy of the current (CRA14) model to demonstrate compliance with the regulatory limits. CRA14-SEN4: the application of several changes to the current WIPP PA model (CRA14), including: parameter changes, use of an updated code, and a revised computational grid for the BRAGFLO code. The modifications to the repository model resulted in increased releases in all primary release mechanisms. The impacts of each EPA-requested change to CRA14 were analyzed with respect to each release mechanism in the CRA14-SEN4 study. Overall, total high-probability predicted mean releases from the repository were increased by about 15%. Total low-probability predicted mean releases were increased by about 107%. It was concluded that the EPA-requested changes to the CRA14 result in increases to the predicted total releases from the repository. However, releases calculated in the CRA14-SEN4 analysis remained below regulatory limits, demonstrating continued compliance of the WIPP. In January 2017, the EPA determined that CRA-2014 was complete and then began a technical evaluation of CRA-2014 to determine whether the WIPP continued to comply with the radiation protection standards for disposal. During their evaluation, the EPA utilized the sensitivity study results to inform their ultimate determination in July 2017 that the WIPP continues to comply with regulatory standards. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Experimental investigation of the effect of pore size distribution on nano-particle capture efficiency within ceramic particulate filters

The effect of the pore size distribution on size-resolved filtration efficiency was investigated for two ceramic particulate filters using particulate matter (PM) generated by a spark-ignition direct-injection engine fueled with gasoline. The cordierite filter tested had a porosity of 43%, a median pore diameter (d_50) of 12 µm, and a wide pore size distribution with a lognormal standard deviation (s’) of 0.3. The aluminum titanate filter had very similar porosity, d_50, and thickness, but significantly narrower pore size distribution (s’ = 0.1). The testing of two filters under identical experimental conditions enabled the impact of the pore size distribution on filtration performance to be evaluated. Filtration experiments were performed focusing on just the filter wall, starting from a clean filter until the transition to cake filtration (filtration efficiency > 99%). Time-resolved particle size distribution measurements were used to evaluate the progression of filtration performance and estimate trapped mass within the filter. The aluminum titanate filter, with a narrow pore size distribution, exhibited significantly better diffusion capture efficiency. The negative impact of higher flow velocity on diffusion capture efficiency was more pronounced for a narrower pore size distribution. Flow distributions measured using capillary-flow porometry were used to develop a cylindrical pore flow model to understand the impact of the differences in pore size distribution on observed trends in diffusion capture efficiency within a clean filter. The model helps demonstrate how large pores on the high end of a wider pore size distribution can carry a large fraction of the flow, with less efficient capture of small particles by diffusion. The experimental results and data demonstrate that the bubble point diameter and width of the pore size distribution significantly influence diffusion capture efficiencies for filters with very similar median pore diameter, porosity, and thickness.

aerosol filtration, Aftertreatment, ceramic exhaus↗
DOE OSTI2024

The impact of sampling scale: A comparison of methods for estimating external contaminant exposure in free-ranging wildlife

The impacts of contaminants on wildlife are dose dependent, and thus being able to track or predict exposure following contamination events is important for monitoring ecosystem health. However, the ability to track exposure in free-ranging wildlife is often severely limited. Consequently, researchers have predominantly relied on simple methods for estimating contaminant exposures in wildlife with little regard for spatial contaminant heterogeneity or an animal’s use of diverse habitats. We evaluated the influence sampling scale (i.e., how finely contaminant distribution and organism’s spatial use of the landscape is mapped) has on (1) realism and (2) conservativeness of exposure estimates. To do this, we monitored the actual exposure of wild boar (Sus scrofa) in Fukushima, Japan to radioactive contamination using GPS27 coupled contaminant monitors placed on individual animals. We compared empirical exposures to estimates generated by combining varying amounts of information about an individual boar’s location and/or movement, with the distribution of contamination on the landscape. Here we found that the most realistic exposure estimates were produced when finer-scale contaminant distribution surveys (e.g., airborne surveys) were combined with more accurate estimates of an individual’s space use (e.g., home ranges or core areas). Importantly, estimates of exposure based on single point surveys at a trap site (a simple method commonly used in the literature), did not correlate with actual exposure rates, suggesting dose-effects studies using this method may result in spurious conclusions. These results suggest that researchers seeking realistic estimates of exposure, such as in dose-effect studies, should ensure they have adequately accounted for fine-scale contaminant distribution patterns and areas of higher use by study organisms. However, conservative estimates of exposure i.e., intentionally over-predicting exposure as is done in initial tiers of ecological risk analyses) were not as scale sensitive and could be achieved with a single known location and coarse contaminant distribution maps.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2021

Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of methane fluxes measured by eddy-covariance require gap-filling to estimate annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for methane, with regards both to the best model algorithms and predictors. In this study, we address the need for standardization by synthesizing results of gap-filling methods applied at 17 wetland sites spanning boreal to tropical regions including all major wetlands classes and two rice paddies. We introduce new procedures for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with robust uncertainty estimates. We tested a conventional method (marginal distribution sampling) and four machine learning algorithms - penalized linear regression, artificial neural networks, random forests, and boosted decision trees - and four predictor sets, including temporal, meteorological, ecosystem carbon and energy flux, and soil predictors. We find that the conventional method can achieve similar median performance to the machine learning models but is worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. Finally, we gap-fill and provide summary evaluation metrics for all 81 sites in the FLUXNET-CH4 community dataset and publicly release the python code for model development, evaluation, and uncertainty estimation.

42 ENGINEERING↗
DOE OSTI2020

Effects of social structure and management on risk of disease establishment in wild pigs

Contact heterogeneity among hosts determines invasion and spreading dynamics of infectious disease, thus its characterization is essential for identifying effective disease control strategies. Yet, little is known about the factors shaping contact networks in many 28 wildlife species and how wildlife management actions might affect contact networks. Wild pigs in North America are an invasive, socially-structured species that pose a health concern for domestic swine given their ability to transmit numerous devastating diseases 2 such as African swine fever (ASF). Using proximity loggers and GPS data from 48 wild pigs in Florida and South Carolina, USA, we employed a probabilistic framework to estimate weighted contact networks. We determined the effects of sex, social group, and spatial distribution (monthly home range overlap and distance) on wild pig contact. We also estimated the impacts of management-induced perturbations on contact and inferred their effects on ASF establishment in wild pigs with simulation. Social group membership was the primary factor influencing contacts. Between-group contacts depended primarily on space use characteristics, with fewer contacts among groups separated by >2 km and no contacts among groups >4 km apart within a month. Modeling ASF dynamics on the contact network demonstrated that indirect contacts resulting from baiting (a typical method of attracting wild pigs or game species to a site to enhance recreational hunting) increased the risk of disease establishment by ~33% relative to direct contact. Low-intensity population reduction (<5.9% of the population) had no detectable impact on contact structure but reduced predicted ASF establishment risk relative to no population reduction. Here, we demonstrate an approach for understanding the relative role of spatial, social, and individual-level characteristics in shaping contact networks and predicting their effects on disease establishment risk, thus providing insight for optimizing disease control in spatially- and socially-structured wildlife species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

On the estimation of the local dark matter density using the rotation curve of the Milky Way

The rotation curve of the Milky Way is commonly used to estimate the local dark matter density ρDM,&odot;. However, the estimates are subject to the choice of the distribution of baryons needed in this type of studies. In this work we explore several Galactic mass models that differ in the distribution of baryons and dark matter, in order to determine ρDM,&odot;. For this purpose we analyze the precise circular velocity curve measurement of the Milky Way up to ~ 25 kpc from the Galactic centre obtained from Gaia DR2 [1]. We find that the estimated value of ρDM,&odot; stays robust to reasonable changes in the spherical dark matter halo. However, we show that ρDM,&odot; is affected by the choice of the model for the underlying baryonic components. In particular, we find that ρDM,&odot; is mostly sensitive to uncertainties in the disk components of the Galaxy. We also show that, when choosing one particular baryonic model, the estimate of ρDM,&odot; has an uncertainty of only about 10% of its best-fit value, but this uncertainty gets much bigger when we also consider the variation of the baryonic model. In particular, the rotation curve method does not allow to exclude the presence of an additional very thin component, that can increase ρDM,&odot; by more than a factor of 8 (the thin disk could even be made of dark matter). Therefore, we conclude that exclusively using the rotation curve of the Galaxy is not enough to provide a robust estimate of ρDM,&odot;. For all the models that we study without the presence of an additional thin component, our resulting estimates of the local dark matter density take values in the range ρDM,&odot; ≃ 0.3–0.4 GeV/cm 3 , consistent with many of the estimates in the literature.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Increased signal-to-noise ratios within experimental field trials by regressing spatially distributed soil properties as principal components

Environmental variability poses a major challenge to any field study. Researchers attempt to mitigate this challenge through replication. Thus, the ability to detect experimental signals is determined by the degree of replication and the amount of environmental variation, noise, within the experimental system. A major source of noise in field studies comes from the natural heterogeneity of soil properties which create microtreatments throughout the field. In addition, the variation within different soil properties is often nonrandomly distributed across a field. We explore this challenge through a sorghum field trial dataset with accompanying plant, microbiome, and soil property data. Diverse sorghum genotypes and two watering regimes were applied in a split-plot design. We describe a process of identifying, estimating, and controlling for the effects of spatially distributed soil properties on plant traits and microbial communities using minimal degrees of freedom. Importantly, this process provides a method with which sources of environmental variation in field data can be identified and adjusted, improving our ability to resolve effects of interest and to quantify subtle phenotypes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

CoSHA: Code for Stellar Properties Heuristic Assignment—for the MaStar Stellar Library

We introduce CoSHA: a Code for Stellar properties Heuristic Assignment. In order to estimate the stellar properties, CoSHA implements a Gradient Tree Boosting algorithm to label each star across the parameter space (T eff , $\mathrm{log}g$, [Fe/H], and [α/Fe]). We use CoSHA to estimate the stellar atmospheric parameters of 22,000 unique stars in the MaNGA Stellar Library (MaStar). To quantify the reliability of our approach, we run internal tests, using both the Göttingen Stellar Library (a theoretical library) and the first data release of MaStar, and external tests, by comparing the resulting distributions in the parameter space with the APOGEE estimates of the same properties. In summary, our parameter estimates span the ranges T eff = [2900, 12,000] K, $\mathrm{log}g=[-0.5,5.6]$, [Fe/H] = [-3.74, 0.81], and αM = [-0.22, 1.17]. We report internal (external) uncertainties of the properties of ${\sigma }_{{T}_{\mathrm{eff}}}\sim 43(240)$ K, ${\sigma }_{\mathrm{log}g}\sim 0.2(0.4)$, σ [Fe/H] ~ 0.16(0.24), and σ [α/Fe] ~ 0.09(0.08). These uncertainties are comparable to those of other methods with similar objectives. Despite the fact that CoSHA is not aware of the spatial distributions of these physical properties in the Milky Way, we are able to recover the main trends known in the literature. The catalog of physical properties for MaStar can be accessed online.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Influence of Common Assumptions Regarding Aerosol Composition and Mixing State on Predicted CCN Concentration

A 4-year record of aerosol size and hygroscopic growth factor distributions measured at the Department of Energy’s Southern Great Plains (SGP) site in Oklahoma, U.S. were used to estimate supersaturation (S)-dependent cloud condensation nuclei concentrations (N CCN ). Baseline or reference N CCN (S) spectra were estimated using κ-Köhler Theory without any averaging of the measured distributions by creating matrices of size- and hygroscopicity-dependent number concentration (N) and then integrating for S > critical supersaturation (S c ) calculated for the same size and hygroscopicity pairs. Moreover, those estimates were first compared with directly measured N CCN at the same site. Subsequently, N CCN was calculated using the same dataset but with an array of simplified treatments in which the aerosol was assumed to be either an internal or an external mixture and the hygroscopicity either assumed or based on averages derived from the growth factor distributions. The CCN spectra calculated using the simplified treatments were compared with those calculated using the baseline approach to evaluate the error introduced with commonly used approximations.

54 ENVIRONMENTAL SCIENCES↗