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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 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
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 OSTI2019

Simulations of high-velocity impacts on metal in preparation for the Psyche mission

In 2026, the NASA Discovery mission Psyche will orbit the asteroid (16) Psyche, the largest known metal-rich asteroid in the main belt. To estimate relative ages of the surface, identify re-surfacing events and better constrain Psyche's history, impact craters will be counted and characterized. No spacecraft has ever visited a metal-rich small body; therefore, laboratory-scale impact experiments and numerical simulations will play an important role in the interpretation of the mission's data. However, the planetary applications of high-velocity impacts have so far mostly been studied for silicate targets. Limited attention has been drawn to planetary objects predominantly made of metal, and more laboratory experiments and numerical calibrations are needed. As part of this effort, we present a suite of numerical simulations using an adaptative smoothed particles hydrodynamics numerical code (ASPH) reproducing a high-velocity impact experiment conducted on a steel target. This work primarily focuses on the influence of the chosen equation of state and initial distribution of flaws in the material on the estimated crater dimensions, damage and temperature. Additionally, we find that changing the EOS and initial flaw distribution affects the crater dimensions, though for a vast majority of cases the dimensions remain within 20% of the experimental values. The target is in most cases only locally weakened but not fully damaged, independently from the EOS chosen. Finally, temperatures at the impact point and around the forming crater can reach values above the melting point of iron at <100 GPa, which is in agreement with experimental observations. These results allow us to speculate on the differences expected between the surfaces of visited silicate-rich asteroids and that of the metal-rich target of the Psyche mission.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Sensor enabled data-driven predictive analytics for modeling and control with high penetration of DERs in distribution systems

The electric power grid is undergoing a tremendous transformation due to the increasing penetration of renewable energy resources beginning with wind and more recently with the distributed energy resources (DERs) such as solar and battery storage. DERs have dramatically changed the role of the distribution systems in the overall power grid, and they are expected to contribute a significant portion of power generation in the future. If current trends for DERs continue, system operation and control will need to change dramatically for improved grid reliability and resiliency. As renewable resources increase in penetration, new and challenging operational, planning, and design problems are expected to emerge. Some of the key challenges that arise in the planning and operation of the future grid are: 1) Quantifying the impact of high DER penetration in distribution systems on bulk grid behavior over multiple time scales. 2) Identifying whether a particular DER configuration/settings have a large impact on the overall grid behavior. These challenges can be addressed in an offline manner using detailed T&D grid models and they can also be addressed in an online manner using sensor measurements. In particular, the advancement and planned growth in sensor technology in power grid over various voltage levels provide us with a unique opportunity to tackle these challenges from a data analytic perspective without needing detailed T&D grid models. A few questions that naturally arise when addressing the challenges from DERs using sensor data are: 1) How can we use limited sensor measurements to monitor & control voltage stability and small signal stability of the bulk system? 2) How can we ensure that the developed data analytic methods are robust to data availability and quality issues? 3) How can we compute the developed analytics in a scalable manner using streaming measurements? In this project, we addressed the aforementioned challenges arising from DERs and answered the questions raised above on how to effectively use the sensor measurements to enhance the reliability and performance of the electric grid. Thus, the overarching goal of this project is to develop effective reduced/representative system models from data that make the computational complexity sufficiently manageable so as to be useful to simulate, analyze, and even control complex non-linear power systems dynamics with large penetrations of DERs. In order to achieve the objective, the project team established a four-fold technical approach 1) Formulated a combined transmission-distribution co-simulation framework for data generation and validation, 2) Derived reduced/representative models of power systems based on data-driven methods for efficient computation and appropriate representation of system behavior, 3) Developed data driven characterization of power system behavior based on transfer operator theory, machine learning and optimization for model estimation, 4) Incorporated a scalable data management and processing architecture using distributed Kafka streaming applications that coordinate input data streams to the developed data analytics. The key accomplishments of the project are: 1) Development of a scalable multi-timescale T&D co-simulation framework (both for steady state and for dynamic co-simulation) using commercial solvers (PSSE and GridLAB-D). The steady-state T&D co-simulation interface is shared with our industry partner (PJM). 2) A structured reduced order dynamic model of distribution systems that can represent partial motor stalling along with a systematic procedure to derive the model parameters. 3) A PMU based online method to monitor, localize and mitigate fault-induced delayed voltage recovery using DER reactive support and load control in distribution systems. 4) Development of linear operator based robust methodologies for dynamic state estimation, uncertainty quantification, system identification and trajectory prediction for power system dynamics. 5) An adaptive damping control for utilizing wind energy resources to provide oscillation damping and system stability. 6) Implementation of Kafka-based framework for efficient processing of streaming data using Linux-based local virtual environment.

DER integration↗
DOE OSTI2021

Experimental 3-dimensional tracking of the dynamics of a single electron in the Fermilab Integrable Optics Test Accelerator (IOTA)

We present the results of experimental studies on the transverse and longitudinal dynamics of a single electron in the IOTA storage ring. IOTA is a flexible machine dedicated to beam physics experiments with electrons and protons. A method was developed to reliably inject and circulate a controlled number of electrons in the ring. A key beam diagnostic system is the set of sensitive high-resolution digital cameras for the detection of synchrotron light emitted by the electrons. With 60–130 electrons in the machine, we measured beam lifetime and derived an absolute calibration of the optical system. At exposure times of 0.5 s, the cameras were sensitive to individual electrons. Camera images were used to reconstruct the time evolution of oscillation amplitudes of a single electron in all 3 degrees of freedom. The evolution of amplitudes directly showed the interplay between synchrotron-radiation damping, quantum excitations, and scattering with the residual gas. From the distribution of measured single-electron oscillation amplitudes, we deduced transverse emittances, momentum spread, damping times, and beam energy. Estimates of residual-gas density and composition were calculated from the measured distributions of vertical scattering angles. Combining scattering and lifetime data, we also provide an estimate of the aperture of the ring. To our knowledge, this is the first time that the dynamics of a single electron are tracked in all three dimensions with digital cameras in a storage ring.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Local Power-Voltage Sensitivity and Thevenin Impedance Estimation from Phasor Measurements

This paper describes how to use voltage phasor measurements to produce a sensitivity matrix that describes how real and reactive power injections at a node on a distribution network affect the local voltage magnitude and angle. Rather than estimating the sensitivity directly, the voltage phasor measurements and power commands/measurements are used to estimate the unbalanced, three-phase Thevenin impedance. The Thevenin impedance estimation is conducted using recursive least squares on temporal difference measurements. The Thevenin impedance and voltage phasor measurement are then used to build the local power-voltage sensitivity matrix with the closed form expression for the Jacobian of the power flow manifold. Hardware-in-the-loop simulations with phasor measurement units providing real phasor measurements are used to evaluate the recursive temporal difference Thevenin impedance estimation and Thevenin-based power-voltage sensitivity methods.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

A novel closed-form inversion of the convection–diffusion equation for rapid convection, diffusion, and source profile estimation

To simplify and routinize particle transport analysis in fusion devices, a novel closed form linear inversion of the 1-D convection diffusion equation to estimate diffusion and convection profiles D(r ⃗ ), v(r ⃗ ) and source distribution s(r ⃗ ), of a single species from measured data is derived and demonstrated on synthetic data. Profile estimates of D(r ⃗ ), v(r ⃗ ), s(r ⃗ ) and their uncertainties are given as a matrix expression constructed directly from the incoming density data of the transported species in space and time, as well as physics assumptions such as particle conservation and experimental geometry. The derived matrix expression can be applied to a pumped or non-pumped recycling species, or a non-recycling species that is effectively “pumped” by plasma-facing surfaces.

Hinson, Edward [ORNL] (ORCID:000000019713140X)↗
DOE OSTI2020

Machine Learning Methods for Connection RTT and Loss Rate Estimation Using MPI Measurements Under Random Losses

Scientific computations are expected to be increasingly distributed across wide-area networks, and Message Passing Interface (MPI) has been shown to scale to support their communications over long distances. Application-level measurements of MPI operations reflect the connection Round-Trip Time (RTT) and loss rate, and machine learningmethods have been previously developed to estimate them under deterministic periodic losses. In this paper, we consider more complex, random losses with uniform, Poisson and Gaussian distributions. We study five disparate machine leaning methods, with linear and non-linear, and smooth and non-smooth properties, to estimate RTT and loss rate over 10 Gbps connections with 0–366 ms RTT. The diversity and complexity of these estimators combined with the randomness of losses and TCP’s non-linear response together rule out the selection of a single best among them; instead, we fuse them to retain their design diversity. Overall, the results show that accurate estimates can be generated at low loss ratesbut become inaccurate at loss rates 10% and higher, thereby illustrating both their strengths and limitations.

Rao, Nageswara S.↗
DOE OSTI2025

Hourly Electricity Demand Profiles for Each County in the Contiguous United States

This dataset provides estimated hourly electricity demand for each county in the contiguous United States from 2016-2023. The demand profiles represent the sum of two components: (1) Weighted averages of reported hourly demand profiles for North American Electric Reliability Corporation balancing authority (BA) regions and subregions, scaled to match annual estimates of county-level retail sales and direct use of electricity and weighted by the estimated percentage of county load served by each BA region or subregion. (2) Weighted averages of modeled hourly, county- and sector-level distributed photovoltaic (DPV) capacity factor profiles, scaled to match annual estimates of on-site consumption of DPV-generated electricity for each county and weighted by the percentage of consumption attributable to each sector Annual county-level retail sales are estimated by aggregating utility-reported sales to the state level and allocating the results to counties according to each county's share of state population. Annual county-level direct use is calculated by aggregating power plant-reported direct use values. Annual county-level on-site consumption of DPV-generated electricity is estimated by aggregating utility-reported net metering data to determine the amount of DPV-generated electricity sold back to the grid for each state, subtracting those values from modeled state-level DPV generation estimates, and allocating the results to counties according to each county's share of statewide modeled DPV generation. The open-source Python code used to develop this dataset is available at "Historical Load Data Repository" link below.

14 SOLAR ENERGY↗
DOE OSTI2023

Ensemble Estimation of Historical Evapotranspiration for the Conterminous U.S.

Abstract Evapotranspiration (ET) is the largest component of the water budget, accounting for the majority of the water available from precipitation. ET is challenging to quantify because of the uncertainties associated with the many ET equations currently in use, and because observations of ET are uncertain and sparse. In this study, we combine information provided by available ET data and equations to produce a new monthly data set for ET for the conterminous U.S. (CONUS). These maps are produced from 1895 to 2018 at an 800 m spatial scale, marking a finer resolution than currently available products over this time period. In our approach, the relative performance of a suite of ET equations is assessed using water balance, flux tower, and remotely sensed ET estimates. At the observation locations, we use error distributions to quantify relative weights for the equations and use these in a modified Bayesian model averaging weighted ensemble approach. The relative weights are spatially generalized using a random forest regression, which is applied to wall‐to‐wall explanatory variable maps to generate CONUS‐wide relative weight maps and ensemble estimates. We assess the performance of the ensemble using a reserved subset of the observations and compare this performance against other national‐scale map products for historical to modern ET. The ensemble ET maps are shown to provide an improved accuracy over the alternative comparison products. These ET maps could be useful for a variety of hydrologic modeling and assessment applications that benefit from a long record, such as the study of periods of water scarcity through time.

Environmental Sciences & Ecology↗