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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Solar wind charge exchange contribution to the ROSAT all sky survey maps

DXL (Diffuse X-ray emission from the Local Galaxy) is a sounding rocket mission designed to estimate the contribution of solar wind charge eXchange (SWCX) to the diffuse X-ray background and to help determine the properties of the Local Hot Bubble. The detectors are large area thin-window proportional counters with a spectral response that is similar to that of the PSPC used in the ROSAT All Sky Survey (RASS). A direct comparison of DXL and RASS data for the same part of the sky viewed from quite different vantage points in the solar system, and the assumption of approximate isotropy for the solar wind, allowed us to quantify the SWCX contribution to all six RASS bands (R1–R7, excluding R3). We find that the SWCX contribution at l=140{sup ∘},b=0{sup ∘}, where the DXL path crosses the Galactic plane, is 33%±6%(statistical)±12%(systematic) for R1, 44%±6%±5% for R2, 18%±12%±11% for R4, 14%±11%±9% for R5, and negligible for the R6 and R7 bands. Reliable models for the distribution of neutral H and He in the solar system permit estimation of the contribution of interplanetary SWCX emission over the the whole sky and correction of the RASS maps. We find that the average SWCX contribution in the whole sky is 26%±6%±13% for R1, 30%±4%±4% for R2, 8%±5%±5% for R4, 6%±4%±4% for R5, and negligible for R6 and R7.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

VST ATLAS galaxy cluster catalogue I: cluster detection and mass calibration

Taking advantage of ∼4700 deg 2 optical coverage of the Southern sky offered by the VST ATLAS survey, we construct a new catalogue of photometrically selected galaxy groups and clusters using the orca cluster detection algorithm. The catalogue contains ∼22 000 detections with N 200 > 10 and ∼9000 with N 200 > 20. We estimate the photometric redshifts of the clusters using machine learning and find the redshift distribution of the sample to extend to z ∼ 0.7, peaking at z ∼ 0.25. We calibrate the ATLAS cluster mass-richness scaling relation using masses from the MCXC, Planck, ACT DR5, and SDSS redMaPPer cluster samples. We estimate the ATLAS sample to be > 95 per cent complete and > 85 per cent pure at z < 0.35 and in the M 200m >1 x 10 14 h -1 M ⊙ mass range. At z < 0.35, we also find the ATLAS sample to be more complete than redMaPPer, recovering a ~ 40 per cent higher fraction of Abell clusters. This higher sample completeness places the amplitude of the z < 0.35 ATLAS cluster mass function closer to the predictions of a ΛCDM model with parameters based on the Planck CMB analyses, compared to the mass functions of the other cluster samples. However, strong tensions between the observed ATLAS mass functions and models remain. We shall present a detailed cosmological analysis of the ATLAS cluster mass functions in paper II. In the future, optical counterparts to X-ray-detected eROSITA clusters can be identified using the ATLAS sample. The catalogue is also well suited for auxiliary spectroscopic target selection in 4MOST. The ATLAS cluster catalogue is publicly available at http://astro.dur.ac.uk/cosmology/vstatlas/cluster_catalogue/.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Development of Waste Inventory for Probabilistic Performance Assessment Modeling - 19361

Estimation of radioactive waste inventory is a key initial step in the development of an environmental fate and transport model for performance assessment (PA) of radioactive waste sites. PA model output is often sensitive to parameters that represent inventory as a source term, yet estimation is a difficult problem. Inventory estimation for a probabilistic performance assessment (PPA) is related to, but distinct from, that performed for waste excavation, characterization, and offsite disposal. The latter appropriately focuses on obtaining the most detailed information possible on specific hazards and locations; however, these questions cannot be fully answered without intrusive site characterization efforts. This type of characterization is often not feasible when assembling information regarding a source term in a PPA model. Development of waste inventory for a PPA is one piece of a larger hierarchical model. The physical process model represented by Goldsim computer code represents one level of the hierarchy, and the second level of the hierarchy involves estimation of inputs such as inventory, transport, and risk parameters. The estimation of inputs is not a straightforward problem. The parameters of a computer model often do not have an equivalence to the physical quantities they describe, and estimation of the physical quantities themselves can be difficult due to limitations of available information. Inventory information for the West Valley Site in upstate New York is based on data from historical shipping and disposal forms. In the 1990's, the West Valley Demonstration Project (WVDP) compiled information from existing databases and historical waste records into one complete modern database. A conceptual model was then applied and used with recorded information to characterize the waste mass and radionuclide composition for each waste burial. Information was ultimately aggregated over all waste records to estimate the mass of each radionuclide within each trench, hole, or caisson. Three decades later, we revisit the master database, make updates with newly-identified information, and incorporate the conceptual model previously used for inventory estimation in the hierarchical modeling framework. We characterize uncertainty using probability distributions for waste mass and radionuclide composition at the individual shipment or individual burial level. Uncertainty incorporated at the lowest level of the hierarchy is ultimately carried through to uncertainty in PPA model endpoints. Sensitivity analysis of early model results is then applied to identify influential inputs and guide subsequent efforts towards parameters that drive risk. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Quantum-Inspired Power System Reliability Assessment

To enable an in-depth study of power system operation and planning, the assessment of standard reliability indices is inevitable. The Monte Carlo Simulation (MCS) approach is a broadly used method in replacing the analytical methods in reliability indices assessment. The accuracy of MCS, however, highly depends on the sampling size, and hence, a complicated system with large number of components requires a large sampling size and daunting computational effort. To address this shortcoming, we, in this paper attempt to take advantage of potentials of the quantum computing (QC) for power system reliability assessment by realizing the following contributions: 1) an innovative quantum model designed for reliability assessment; 2) a quantum circuit that achieves the quadratic speed up compared to the classical MCS method; 3) an efficient quantum amplitude estimation (QAE) algorithm to accurately evaluate the reliability indices. The accuracy and efficacy of the quantum reliability method are extensively verified and demonstrated on both radial and mesh distribution systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Information content of spatially distributed ground-based measurements for hydrologic-parameter calibration in mixed rain-snow mountain headwaters

Parameters in hydrologic models used in mixed rain-snow regions are often uncertain to calibrate and overfitted on streamflow. To contribute addressing these challenges, we used an algorithm that assesses modeling performances through time (Dynamic Identifiability Analysis) to quantify the information content of spatially distributed ground-based measurements for identifying optimal parameter values in the Precipitation Runoff Modeling System (PRMS) model. Including spatially distributed ground-based measurements in Identifiability Analysis allowed us to unambiguously estimate more parameter values than only using streamflow (seven parameters instead of two out of a pool of thirty-three). Peaks in information gain were obtained when using dew-point temperature to identify precipitation phase-partitioning parameters. Multi-attribute identifiability analysis also yielded optimal parameter values that were temporally less variable than those estimated using streamflow alone. Overall, identifying parameter values using ground-based measurements improved the simulation of key drivers of the surface-water budget, such as air temperature and precipitation-phase partitioning. However, parameters simulating surface-to-subsurface mass fluxes like snow accumulation and melt or evapotranspiration were poorly identified by any attribute and so emerged as key sources of predictive uncertainty for this distributed-parameter hydrologic model. In conclusion, this work demonstrates the value of expanded ground-based measurements for identifying parameters in distributed-parameter hydrologic models and so diagnosing their conceptual uncertainty across the water budget.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Distributed data management on Belle II

The Belle II experiment started taking physics data in April 2018 with an estimated total volume of all files including raw events, Monte-Carlo and skim statistics of 340 petabytes expected by the end of operations in the late-2020s. Originally designed as a fully integrated component of the BelleDIRAC production system, the Belle II distributed data management (DDM) software needs to manage data across about 29 storage elements worldwide for a collaboration of nearly 1000 physicists. By late 2018, this software required significant performance improvements to meet the requirements of physics data taking and was seriously lacking in automation. Rucio, the DDM solution created by ATLAS, was an obvious alternative but required tight integration with BelleDIRAC and a seamless yet non-trivial migration. This contribution describes the work done on both DDM options, the current status of the software running successfully in production and the problems associated with trying to balance long-term operations cost against short term risk. .

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Determination of nuclear PDFs using Markov chain Monte Carlo methods

Global QCD analyses of nuclear parton distribution functions (nPDFs) have traditionally relied on the Hessian method for uncertainty estimation. However, the inherent Gaussian approximation and reliance on local curvature often prove insufficient for nPDF fits, which are frequently characterized by limited data constraints and non-Gaussian likelihoods. In this paper, we present the first nPDF determination based on Markov Chain Monte Carlo (MCMC) techniques, implemented within the nCTEQ framework using an adaptive Metropolis-Hastings algorithm. The MCMC approach enables a direct mapping of the posterior distribution and reveals a highly nontrivial parameter-space structure, including multiple modes and pronounced non-Gaussian behavior, particularly for the valence PDFs. We perform the first single-nucleus global analysis of lead PDFs using exclusively lead data and compare it to a multi-nuclei fit employing a standard analytic A dependence. The inclusion of lighter nuclei reduces quark uncertainties and modifies the shape of the lead PDFs, while leaving the gluon distribution largely unaffected. A complementary Hessian analysis exposes systematic limitations of the Gaussian approximation. Our results demonstrate that MCMC methods provide a more reliable framework for uncertainty quantification in nPDF determinations.

Derakhshanian, N. [Institute of Nuclear Physics Po↗
DOE OSTI2024

Using automated machine learning for the upscaling of gross primary productivity

Estimating gross primary productivity (GPP) over space and time is fundamental for understanding the response of the terrestrial biosphere to climate change. Eddy covariance flux towers provide in situ estimates of GPP at the ecosystem scale, but their sparse geographical distribution limits larger-scale inference. Machine learning (ML) techniques have been used to address this problem by extrapolating local GPP measurements over space using satellite remote sensing data. However, the accuracy of the regression model can be affected by uncertainties introduced by model selection, parameterization, and choice of explanatory features, among others. Recent advances in automated ML (AutoML) provide a novel automated way to select and synthesize different ML models. In this work, we explore the potential of AutoML by training three major AutoML frameworks on eddy covariance measurements of GPP at 243 globally distributed sites. We compared their ability to predict GPP and its spatial and temporal variability based on different sets of remote sensing explanatory variables. Explanatory variables from only Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data and photosynthetically active radiation explained over 70 % of the monthly variability in GPP, while satellite-derived proxies for canopy structure, photosynthetic activity, environmental stressors, and meteorological variables from reanalysis (ERA5-Land) further improved the frameworks' predictive ability. We found that the AutoML framework Auto-sklearn consistently outperformed other AutoML frameworks as well as a classical random forest regressor in predicting GPP but with small performance differences, reaching an r 2 of up to 0.75. We deployed the best-performing framework to generate global wall-to-wall maps highlighting GPP patterns in good agreement with satellite-derived reference data. This research benchmarks the application of AutoML in GPP estimation and assesses its potential and limitations in quantifying global photosynthetic activity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Distributed Wind Resource Assessment: State of the Industry

In support of the U.S. Department of Energy (DOE) Wind and Water Power Technologies Office (WWPTO) goals, researchers from DOE's National Renewable Energy Laboratory (NREL), National Wind Technology Center (NWTC) are investigating the Distributed Wind Resource Assessment (DWRA) process, which includes pre-construction energy estimation as well as turbine site suitability assessment. DWRA can have a direct impact on the Wind Program goals of maximizing stakeholder confidence in turbine performance and safety as well as reducing the levelized cost of energy (LCOE). One of the major components of the LCOE equation is annual energy production. DWRA improvements can maximize the annual energy production, thereby lowering the overall LCOE and improving stakeholder confidence in the distributed wind technology sector by providing more accurate predictions of power production. Over the long term, one of the most significant benefits of a more defined DWRA process could be new turbine designs, tuned to site-specific characteristics that will help the distributed wind industry follow a similar trajectory to the low-wind-speed designs in the utility-scale industry sector. By understanding the wind resource better, the industry could install larger rotors, capture more energy, and as a result, increase deployment while lowering the LCOE. a direct impact on the Wind Program goals of maximizing stakeholder confidence in turbine performance and safety as well as reducing the levelized cost of energy (LCOE). One of the major components of the LCOE equation is annual energy production. DWRA improvements can maximize the annual energy production, thereby lowering the overall LCOE and improving stakeholder confidence in the distributed wind technology sector by providing more accurate predictions of power production. Over the long term, one of the most significant benefits of a more defined DWRA process could be new turbine designs, tuned to site-specific characteristics that will help the distributed wind industry follow a similar trajectory to the low-wind-speed designs in the utility-scale industry sector. By understanding the wind resource better, the industry could install larger rotors, capture more energy, and as a result, increase deployment while lowering the LCOE.

17 WIND ENERGY↗
DOE OSTI2023

Viability of Reclaiming Municipal Wastewater for Potential Microalgae-Based Biofuel Production in the U.S.

Reclaimed municipal wastewater is a crucial component in biofuel production, especially in regions experiencing increasing freshwater scarcity. However, accurately estimating the potential for fuel production is challenging because of the uneven distribution of biofuel feedstock regions and wastewater treatment plants (WWTPs). This study assesses the viability of using reclaimed municipal water for algal biomass production in pond systems co-located with WWTPs under scenarios driven by biomass production and based on water transport logistics. We performed state- and county-level analysis of reclaimed water resources throughout the United States based on WWTP facility data. We overlaid these data onto estimated algae facility sites and examined the temporal resource availability to address seasonal variations in cultivation demand. Our findings reveal that 2694 billion liters per year of reclaimed water could potentially be used to produce 42.2 million metric tons (ash-free dry weight) of algal biomass, equivalent to 29.2 billion liters of renewable diesel equivalent (RDe). The use of reclaimed water would double current national water reuse and expand such reuse significantly in 455 counties across the United States. However, when we limit the construction of algae facilities to counties that can fully meet their water demand in order to minimize water transport burdens, the available supply decreases by 80%, to 512 billion liters, resulting in annual production of 12.2 billion liters of RDe, which still doubles current biodiesel production. Our analysis highlights the degree to which the location and flow of WWTPs and water transport affect the deployment of algae biofuel facilities and tradeoffs. These findings underscore the importance of improving the current WWTP infrastructure for reclaimed water reuse, especially in southern states.

09 BIOMASS FUELS↗
DOE OSTI2025

Transverse momentum distributions at large- x

We investigate the collinear matching of transverse momentum dependent (TMD) distributions at large values of x, computing and resumming the leading large-x asymptotics for matching coefficients. The large-x resummation is done directly within TMD distributions, ensuring the process-independence of the result. The derived resummation formulas are valid for all TMD distributions (except the pretzelosity). Their application improves perturbative convergence, provides practical estimation for unknown higher-order contributions, and sets restrictions for the nonperturbative part of models. Using the known anomalous dimensions, resummation can reach N 3 LL, often exceeding the accuracy of known coefficient functions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

A Two-Step Time-Series Data Clustering Method for Building-Level Load Profile: Preprint

Residential and commercial buildings have huge potential to contribute value to improve grid resilience by participating grid services. To reveal the significant value, it is critical to estimate the grid service capability from these buildings. Unlike the large-scale distributed energy resources such as wind and solar farms, those buildings need to participate grid services in aggregation, not by individual. Therefore, it is important to appropriately group buildings for aggregation. In this paper, we develop a load profile clustering method to classify the building-level load profiles for grid service capability estimation. In our two-step clustering approach, we first calculate the total load consumption for each building, clustering the load profiles based on energy consumption level. Then, we further cluster the load profiles in each energy cluster based on the load shape. The parameter selection for each clustering step is discussed. The proposed method is applied on actual building-level load profiles, and the results have proved the effectiveness of this method.

advanced metering infrastructure (AMI)↗
DOE OSTI2021

Clutter Distributions for Tomographic Image Standardization in Ground-Penetrating Radar

Multistatic ground-penetrating radar (GPR) signals can be imaged tomographically to produce 3-D distributions of image intensities. In the absence of objects of interest, these intensities can be considered to be estimates of clutter. These clutter intensities spatially vary over several orders of magnitude and vary across different arrays, which makes a direct comparison of these raw intensities difficult. However, by gathering statistics on these intensities and their spatial variation, a variety of metrics can be determined. In this study, the clutter distribution is found to fit better to a two-parameter Weibull distribution than Gaussian or log-normal distributions. Based on the spatial variation of the two Weibull parameters, scale and shape, more information may be gleaned from these data. How well the GPR array is illuminating various parts of the ground, in depth and cross track, may be determined from the spatial variation of the Weibull scale parameter, which may in turn be used to estimate an effective attenuation coefficient in the soil. The transition in depth from clutter- to noise-limited conditions (which is one possible definition of GPR penetration depth) can be estimated from the spatial variation of the Weibull shape parameter. Lastly, the underlying clutter distributions also provide an opportunity to standardize image intensities to determine when a statistically significant deviation from background (clutter) has occurred, which is convenient for buried threat detection algorithm development that needs to be robust across multiple different arrays.

42 ENGINEERING↗
DOE OSTI2018

Two-Step Estimation in a Heteroscedastic Linear Regression Model

We study the problem of estimating a parameter in some heteroscedastic linear regression model in the case where the regressors consist of all order statistics based on the sample of identically distributed not necessarily independent observations with finite second moment. It is assumed that the random errors depend on the parameter and distributions of the corresponding regressors. We propose a two-step procedure for finding explicit asymptotically normal estimators.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Numerical characterization of support recovery in sparse regression with correlated design

Sparse regression is employed in diverse scientific settings as a feature selection method. A pervasive aspect of scientific data is the presence of correlations between predictive features. These correlations hamper both feature selection and estimation and jeopardize conclusions drawn from estimated models. On the other hand, theoretical results on sparsity-inducing regularized regression have largely addressed conditions for selection consistency via asymptotics, and disregard the problem of model selection, whereby regularization parameters are chosen. In this numerical study, we address these issues through exhaustive characterization of the performance of several regression estimators, coupled with a range of model selection strategies. These estimators and selection criteria were examined across correlated regression problems with varying degrees of signal to noise, distributions of non-zero model coefficients, and model sparsity. Our results reveal a fundamental tradeoff between false positive and false negative control in all regression estimators and model selection criteria examined. Additionally, we numerically explore a transition point modulated by the signal-to-noise ratio and spectral properties of the design covariance matrix at which the selection accuracy of all considered algorithms degrades. Overall, we find that SCAD coupled with BIC or empirical Bayes model selection performs the best feature selection across the regression problems considered.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Flexible classification with spatial quantile comparison and novel statistical features applied to spent nuclear fuel analysis

Multivariate classification algorithms are a common tool in contemporary data science that have begun to be applied to nuclear forensic analyses. Using a multivariate generalization of quantile–quantile plots for comparing unknown statistical distributions, a new model-free multivariate classifier called the Quantile–Quantile Comparator has been developed and tested on the analysis of simulated irradiated nuclear fuel. A method to estimate the sample-specific probability of having made an incorrect classification decision by analyzing the distribution of quantile comparisons has been developed, and methods have been developed to analyze the partition of the known data library used to analyze unknown data. By analyzing this partition, the analyst may identify classes that are likely to be confused, identify test data that are inconsistent with the data library and return a “None-of-the-Above” classification decision, and optimize the class boundaries. In this paper, the development of these capabilities is described, and a case study using real experimental assay data collected from nuclear fuel extracted from the newly-released In conclusion, Spent Fuel Isotopic Composition Database to demonstrate the efficacy of these capabilities is presented.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Estimating the color lifetime of energetic quarks

Using a simple geometric framework with a realistic nuclear density distribution, we fit published HERMES data to determine fundamental properties of hadronization using the nuclear medium as a spatial analyzer. Our approach uses a fit to the transverse momentum broadening observable and the hadronic multiplicity ratio; the simultaneous fit to two different observables strongly constrains the outcome. Using the known sizes of the target nuclei, we extract the color lifetime, finding a z h -dependent range of values from 2 to 8 fm/c for these data. We also extract estimates for the qˆ transport coefficient characterizing the strength of the interaction between the quark and the cold nuclear medium, finding an average value of 0.035±0.011 GeV 2 /fm. With a three-parameter model we obtain satisfactory fits to the data with a goodness-of-fit parameter χ 2 /dof of 1.1 or less. In a secondary fit of the results from that model we independently find a value for the Lund String Model string tension of 1.00±0.05 GeV/fm. We evaluated the sensitivity for extracting quark energy loss and effective in-medium hadronic cross sections using four-parameter variants of the model, finding large uncertainties in both cases. Our results suggest that hadronic interaction of forming hadrons in the nuclear medium is the primary dynamical cause of meson attenuation in the HERMES data, with quark energy loss playing a more minor role.

Electron-ion collider↗
DOE OSTI2020

A practical and efficient approach for Bayesian quantum state estimation

Bayesian inference is a powerful paradigm for quantum state tomography, treating uncertainty in meaningful and informative ways. Yet the numerical challenges associated with sampling from complex probability distributions hampers Bayesian tomography in practical settings. In this article, we introduce an improved, self-contained approach for Bayesian quantum state estimation. Leveraging advances in machine learning and statistics, our formulation relies on highly efficient preconditioned Crank–Nicolson sampling and a pseudo-likelihood. We theoretically analyze the computational cost, and provide explicit examples of inference for both actual and simulated datasets, illustrating improved performance with respect to existing approaches.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗