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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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DOE OSTI2017

Orbits for 18 Visual Binaries and Two Double-line Spectroscopic Binaries Observed with HRCAM on the CTIO SOAR 4 m Telescope, Using a New Bayesian Orbit Code Based on Markov Chain Monte Carlo

We present orbital elements and mass sums for 18 visual binary stars of spectral types B to K (five of which are new orbits) with periods ranging from 20 to more than 500 yr. For two double-line spectroscopic binaries with no previous orbits, the individual component masses, using combined astrometric and radial velocity data, have a formal uncertainty of ∼0.1 M{sub ⊙}. Adopting published photometry and trigonometric parallaxes, plus our own measurements, we place these objects on an H-R diagram and discuss their evolutionary status. These objects are part of a survey to characterize the binary population of stars in the Southern Hemisphere using the SOAR 4 m telescope+HRCAM at CTIO. Orbital elements are computed using a newly developed Markov chain Monte Carlo (MCMC) algorithm that delivers maximum-likelihood estimates of the parameters, as well as posterior probability density functions that allow us to evaluate the uncertainty of our derived parameters in a robust way. For spectroscopic binaries, using our approach, it is possible to derive a self-consistent parallax for the system from the combined astrometric and radial velocity data (“orbital parallax”), which compares well with the trigonometric parallaxes. We also present a mathematical formalism that allows a dimensionality reduction of the feature space from seven to three search parameters (or from 10 to seven dimensions—including parallax—in the case of spectroscopic binaries with astrometric data), which makes it possible to explore a smaller number of parameters in each case, improving the computational efficiency of our MCMC code.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Initial study of Schroedinger eigenmaps for spectral target detection

Spectral target detection refers to the process of searching for a specific material with a known spectrum over a large area containing materials with different spectral signatures. Traditional target detection methods in hyperspectral imagery (HSI) require assuming the data fit some statistical or geometric models and based on the model, to estimate parameters for defining a hypothesis test, where one class (i.e., target class) is chosen over the other classes (i.e., background class). Nonlinear manifold learning methods such as Laplacian eigenmaps (LE) have extensively shown their potential use in HSI processing, specifically in classification or segmentation. Recently, Schroedinger eigenmaps (SE), which is built upon LE, has been introduced as a semisupervised classification method. In SE, the former Laplacian operator is replaced by the Schroedinger operator. The Schroedinger operator includes by definition, a potential term V that steers the transformation in certain directions improving the separability between classes. In this regard, we propose a methodology for target detection that is not based on the traditional schemes and that does not need the estimation of statistical or geometric parameters. This method is based on SE, where the potential term V is taken into consideration to include the prior knowledge about the target class and use it to steer the transformation in directions where the target location in the new space is known and the separability between target and background is augmented. An initial study of how SE can be used in a target detection scheme for HSI is shown here. Inscene pixel and spectral signature detection approaches are presented. Finally, the HSI data used comprise various target panels for testing simultaneous detection of multiple objects with different complexities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Extensive analysis of reconstruction algorithms for DESI 2024 baryon acoustic oscillations

Reconstruction of the baryon acoustic oscillation (BAO) signal has been a standard procedure in BAO analyses over the past decade and has helped to improve the BAO parameter precision by a factor of ∼2 on average. The Dark Energy Spectroscopic Instrument (DESI) BAO analysis for the first year (DR1) data uses the “standard” reconstruction framework, in which the displacement field is estimated from the observed density field by solving the linearized continuity equation in redshift space, and galaxy and random positions are shifted in order to partially remove non-linearities. There are several approaches to solving for the displacement field in real survey data, including the multigrid (MG), iterative Fast Fourier Transform (iFFT), and iterative Fast Fourier Transform particle (iFFTP) algorithms. In this work, we analyze these algorithms and compare them with various metrics including two-point statistics and the displacement itself using realistic DESI mocks. We focus on three representative DESI samples, the emission line galaxies (ELG), quasars (QSO), and the bright galaxy sample (BGS), which cover the extreme redshifts and number densities, and potential wide-angle effects. We conclude that the MG and iFFT algorithms agree within 0.4% in post-reconstruction power spectrum on BAO scales with the RecSym convention, which does not remove large-scale redshift space distortions (RSDs), in all three tracers. The RecSym convention appears to be less sensitive to displacement errors than the RecIso convention, which attempts to remove large-scale RSDs. However, iFFTP deviates from the first two; thus, we recommend against using iFFTP without further development. In addition, we provide the optimal settings for reconstruction for five years of DESI observation. The analyses presented in this work pave the way for DESI DR1 analysis as well as future BAO analyses.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Dwarf spheroidal J-factor likelihoods for generalized NFW profiles

ABSTRACT Indirect detection strategies of particle dark matter (DM) in Dwarf spheroidal satellite galaxies (dSphs) typically entail searching for annihilation signals above the astrophysical background. To robustly compare model predictions with the observed fluxes of product particles, most analyses of astrophysical data – which are generally frequentist – rely on estimating the abundance of DM by calculating the so-called J factor. This quantity is usually inferred from the kinematic properties of the stellar population of a dSph using the Jeans equation, commonly by means of Bayesian techniques that entail the presence (and additional systematic uncertainty) of prior choice. Here, extending earlier work, we develop a scheme to derive the profile likelihood for J factors of dwarf spheroidals for models with five or more free parameters. We validate our method on a publicly available simulation suite, released by the Gaia Challenge, finding satisfactory statistical properties for bias and probability coverage. We present the profile likelihood function and maximum likelihood estimates for the J-factor of 10 dSphs. As an illustration, we apply these profile likelihoods to recently published analyses of γ-ray data with the Fermi Large Area Telescope to derive new, consistent upper limits on the DM annihilation cross-section. We do this for a subset of systems, generally referred to as classical dwarfs. The implications of these findings for DM searches are discussed, together with future improvements and extensions of this technique.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Smart thermostat data-driven U.S. residential occupancy schedules and development of a U.S. residential occupancy schedule simulator

Occupancy schedule is one of the key inputs in Building Energy Modeling (BEM) to reflect the interaction between buildings and occupants. Over the past decades, standardized occupancy schedules, developed mainly by engineering rule-of-thumb, have been widely used in BEM due to its simplicity and lack of real measured occupancy data. However, the BEM community has recognized their association with uncertainty and reliability in simulation results from BEM. This study introduces representative occupancy schedules in the U.S. residential buildings, derived from a large smart thermostat dataset and time-series K-means clustering, and an open-source tool to generate a stochastic residential occupancy schedule. Over 90,000 residential occupancy schedules were estimated from the ecobee Donate Your Data dataset. Then, the representative occupancy schedules were identified through clustering. This study further investigated the impacts of three parameters (day, house type, and state) on residential occupancy schedules. Then, a tool, the Residential Occupancy Schedule Simulator (ROSS), is developed using the representative occupancy schedules derived in this study. Details of this tool are presented in this paper. In conclusion, the derived representative occupancy schedules and the ROSS tool can help improve the energy modeling of residential buildings.

42 ENGINEERING↗
DOE OSTI2017

Magnetized Reverse Shock: Density-fluctuation-induced Field Distortion, Polarization Degree Reduction, and Application to GRBs

The early optical afterglow emission of several gamma-ray bursts (GRBs) shows a high linear polarization degree (PD) of tens of percent, suggesting an ordered magnetic field in the emission region. The light curves are consistent with being of a reverse shock (RS) origin. However, the magnetization parameter, σ , of the outflow is unknown. If σ is too small, an ordered field in the RS may be quickly randomized due to turbulence driven by various perturbations so that the PD may not be as high as observed. Here we use the “Athena++” relativistic MHD code to simulate a relativistic jet with an ordered magnetic field propagating into a clumpy ambient medium, with a focus on how density fluctuations may distort the ordered magnetic field and reduce PD in the RS emission for different σ values. For a given density fluctuation, we discover a clear power-law relationship between the relative PD reduction and the σ value of the outflow. Such a relation may be applied to estimate σ of the GRB outflows using the polarization data of early afterglows.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Affinity propagation clustering of full-field, high-spatial-dimensional measurements for robust output-only modal identification: A proof-of-concept study

Determination of the model order is a challenging problem in system identification, especially in output-only or operational modal identification where some modes are weakly excited. While existing methods such as the stabilization diagram method (spectral information) are effective, they do not scale to high-dimensional data, which is usually needed for high-fidelity characterization of structural dynamics and has been made available in the emerging full-field measurement techniques using optical methods such as photogrammetry and laser vibrometers. In this proof-of-concept study we present a new non-parametric, data-driven approach for robust output-only identification of high-spatial-dimensional modal parameters of basic structures by efficiently processing and interactively exploiting the full-field measurement (i.e., very dense spatial measurement points). Specifically, we first over-estimate the system model once, producing a pool of candidate modes associated with their modal frequencies and full-field, high-spatial-dimensional mode shapes. This is accomplished by a data-driven method termed affinity propagation clustering (APC), where the active clusters, which are the active modes in our formulations, emerge from the "message-passing" procedure and does not require a pre-determination of the cluster number (mode or model order). Next, rather than using the spectral information to distinguish the physical and spurious modes in the stabilization diagram method, we exploit and visualize the spatial, full-field mode shape associated with each candidate mode to do so. We conduct extensive experiments on basic structural models with comparisons to a few existing methods. The results indicate that the new method is computationally efficient for identifying high-spatial-dimensional modal parameters, and robust to identify weak modes by exploiting the full-field measurement. We also discuss its applicability and limitations for structures with complex geometry (shapes).

42 ENGINEERING↗
DOE OSTI2024

Data-Driven Digital Twin for Reliability Assessment of DC/DC Buck Converter

In commercial applications, the operation of DC/DC converters significantly impacts overall system performance and long-term reliability. This study introduces a data-driven digital twin (DT) approach for estimating critical degradation parameters of DC/DC BUCK converter under steady-state condition. Initially, a circuit-level MATLAB/Simulink digital model (DM C ) is refined against a hardware prototype’s switching model dataset using offline particle swarm optimization. The optimized digital model’s steady-state response is then verified with its average model response while varying the duty and load. Subsequently, degradation profiles are imposed on the inductor, capacitor, MOSFET in the DMC. A large dataset is generated from this model, allowing training, validation, and testing of machine learning (ML) models for component health regression tasks. The proposed method employs random forest ML models, achieving impressive regression results with a squared R value as high as 0.99978 and a root mean square error of 4.2× 10 –6 . The method is further validated on a medium power level DC/DC BUCK prototype with varying load conditions, and includes the analysis of MOSFET’s on-resistance under degradation conditions. This data-driven DT method shows promise for identifying parasitic degradation and ohmic loss parameters, enhancing converter reliability assessments in a non-invasive, generalized, and computationally efficient manner.

14 SOLAR ENERGY↗
DOE OSTI2018

Updated Kd Estimates for Modeling Sorption of Cs, Sr, and Pu Isotopes in Hydrostratigraphic Units of Rainier Mesa and Shoshone Mountain

Linear sorption (Kd) coefficients in bulk porous (matrix) rock were estimated for modeling of radionuclide transport in groundwater at Rainier Mesa (RM) and Shoshone Mountain (SM). These Kd estimates were derived from a “component additivity approach” applied in other areas of the Nevada National Security Site. This approach utilizes two types of variables: 1. average mass percentages of sorbing minerals: smectite, mica/illite, hematite, calcite, and zeolite determined from X-ray diffraction (XRD); and 2. linear coefficients derived from reactive chemistry models of surface complexation/ion exchange processes in representative groundwater. This study estimates Kd for Cs, Pu, and Sr isotopes using the same coefficients for component additivity approach applied to RM and SM in the earlier report, but instead uses an updated XRD mineralogy database that expands on the original XRD database. As transport modeling scope for RM/SM evolved since 2011, new considerations have emerged: Transport modeling or sorbing radionuclides has expanded from RM proper to adjacent areas; Probabilistic modeling approaches require examination of transport parameter uncertainty; and, More XRD data will improve estimates of Kd with consideration of uncertainty in mineralogy. This report addresses the above considerations by expanding the analysis of XRD data from two boreholes on RM to over twenty boreholes on RM, SM, and adjacent areas. This analysis focuses on XRD data relevant to potential radionuclide transport pathways sourced from RM and SM.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2017

Drifting oscillations in axion monodromy

In this paper, we study the pattern of oscillations in the primordial power spectrum in axion monodromy inflation, accounting for drifts in the oscillation period that can be important for comparing to cosmological data. In these models the potential energy has a monomial form over a super-Planckian field range, with superimposed modulations whose size is model-dependent. The amplitude and frequency of the modulations are set by the expectation values of moduli fields. We show that during the course of inflation, the diminishing energy density can induce slow adjustments of the moduli, changing the modulations. We provide templates capturing the effects of drifting moduli, as well as drifts arising in effective field theory models based on softly broken discrete shift symmetries, and we estimate the precision required to detect a drifting period. A non-drifting template suffices over a wide range of parameters, but for the highest frequencies of interest, or for sufficiently strong drift, it is necessary to include parameters characterizing the change in frequency over the e-folds visible in the CMB. Finally, we use these templates to perform a preliminary search for drifting oscillations in a part of the parameter space in the Planck nominal mission data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Role of the likelihood for elastic scattering uncertainty quantification

In the last decade, uncertainty quantification (UQ) for optical model potentials (OMPs) has become a focal point for nuclear reaction theory, and several competing approaches for OMP UQ have recently been developed. Here, we clarify recent efforts to compare frequentist and Bayesian approaches in the context of OMP UQ [G. B. King et al., Phys. Rev. Lett. 122, 232502 (2019)]. We replicate a portion of that OMP UQ study but use independent statistical tools. Specifically, we compare two methods for OMP parameter inference from elastic scattering data: the Levenberg-Marquardt algorithm for χ 2 minimization on one hand and Markov chain Monte Carlo (MCMC) sampling on the other. Separately, we assess the common practice of using a renormalized likelihood (χ 2 /N), N being the number of data points, instead of the canonical weighted-least-squares likelihood (χ 2 ), as a way of accounting for unknown data correlations. Here, we show that for a generic linear model and for a five-parameter OMP analysis, frequentist and uniform-prior Bayesian approaches recover the same optimum and uncertainty estimates—not systematically larger uncertainties for the Bayesian approach, as was concluded in G. B. King et al., Phys. Rev. Lett. 122, 232502 (2019). Further, we show that if an additional, near-degenerate parameter is introduced into the same OMP analysis such that the parameter posterior becomes non-Gaussian, then covariance-based estimates of uncertainty become unreliable. Finally, we show that regardless of optimization approach, if χ 2 /N is used for the likelihood, the resulting parametric uncertainties increase by $\sqrt{N}$, and that this is responsible for the conclusions drawn in the revisited study. Based on our replication results, we find that a fortuitous cancellation of unreported errors and the renormalization factor can lead to improvement in empirical coverages, as was the case in the original comparative study. We emphasize that developing and applying a realistic likelihood function is an essential task in a UQ analysis, and that several recent UQ studies that employed a renormalized likelihood (i.e., including a 1/N factor) may have yielded unrealistically large uncertainties for elastic-scattering observables. If the parameter posterior deviates from multivariate-normal, a sampling-based approach like MCMC has a clear advantage over methods that assume the Laplace approximation holds. We note that empirical coverage can serve as an important internal check for the analyst whose model or data may have additional, unaccounted-for uncertainties.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Decentralized Data-Driven Estimation of Generator Rotor Speed and Inertia Constant Based on Adaptive Unscented Kalman Filter

This paper proposes an online decentralized and data-driven method for synchronous generator rotor speed and inertia estimation. First, an analytical relationship between the generator terminal voltage/current phasors and its internal rotor speed is derived using the Thevenin equivalent. The parameters of the latter are obtained via a recursive estimator, which allows us to estimate the generator's internal rotor speed from voltage/current phasors without the need for any generator information. Second, we reformulate the equivalent swing equation into the Kalman filter form, which enables us to use a priori-estimated internal rotor speed along with measured real and reactive power injections to obtain an estimate of the inertia for each online synchronous generator. This is achieved through an adaptive unscented Kalman filter. We demonstrate that the use of terminal bus frequency to approximate the rotor speed for inertia estimation by existing methods leads to large estimation biases/errors. Numerical simulations carried out on the IEEE 39-bus and Texas 2000-bus systems reveal that our proposed method is consistently more accurate than existing methods. Moreover, our proposed method is insensitive to both the type and location of system disturbances, and it is robust to different levels of measurement noise.

bus frequency↗
DOE OSTI2016

Nitrate vulnerability projections from Bayesian inference of multiple groundwater age tracers

Nitrate is a major source of contamination of groundwater in the United States and around the world. We tested the applicability of multiple groundwater age tracers ( 3 H, 3 He, 4 He, 14 C, 13 C, and 85 Kr) in projecting future trends of nitrate concentration in 9 long-screened, public drinking water wells in Turlock, California, where nitrate concentrations are increasing toward the regulatory limit. Very low 85 Kr concentrations and apparent 3 H/ 3 He ages point to a relatively old modern fraction (40–50 years), diluted with pre-modern groundwater, corroborated by the onset and slope of increasing nitrate concentrations. An inverse Gaussian–Dirac model was chosen to represent the age distribution of the sampled groundwater at each well. Model parameters were estimated using a Bayesian inference, resulting in the posterior probability distribution – including the associated uncertainty – of the parameters and projected nitrate concentrations. Three scenarios were considered, including combined historic nitrate and age tracer data, the sole use of nitrate and the sole use of age tracer data. Each scenario was evaluated based on the ability of the model to reproduce the data and the level of reliability of the nitrate projections. The tracer-only scenario closely reproduced tracer concentrations, but not observed trends in the nitrate concentration. Both cases that included nitrate data resulted in good agreement with historical nitrate trends. Use of combined tracers and nitrate data resulted in a narrower range of projections of future nitrate levels. However, use of combined tracer and nitrate resulted in a larger discrepancy between modeled and measured tracers for some of the tracers. In conclusion, despite nitrate trend slopes between 0.56 and 1.73 mg/L/year in 7 of the 9 wells, the probability that concentrations will increase to levels above the MCL by 2040 are over 95% for only two of the wells, and below 15% in the other wells, due to a leveling off of reconstructed historical nitrate loadings to groundwater since about 1990.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

An atomic cluster expansion potential for twisted multilayer graphene

Twisted multilayer graphene, characterized by its moiré patterns arising from inter-layer rotational misalignment, serves as a rich platform for exploring quantum phenomena. Machine learning interatomic potentials (MLIPs) are a promising approach to model such systems. Our work develops a method to generate training and test datasets for fitting MLIPs that capture all possible misalignments but remain small-scale to facilitate efficient data generation and parameter estimation. To achieve this, we generate configurations with periodic boundary conditions suitable for density functional theory calculations, and then introduce an internal twist and shift within those supercell structures. Using this technique, supplemented with an active learning workflow, we fit an Atomic Cluster Expansion potential for simulating twisted multilayer graphene and test it for accuracy and robustness on a range of simulation tasks.

2D materials↗
DOE OSTI2022

The impact of tomographic redshift bin width errors on cosmological probes

ABSTRACT Systematic errors in the galaxy redshift distribution n(z) can propagate to systematic errors in the derived cosmology. We characterize how the degenerate effects in tomographic bin widths and galaxy bias impart systematic errors on cosmology inference using observational data from the Deep Lens Survey. For this we use a combination of galaxy clustering and galaxy–galaxy lensing. We present two end-to-end analyses from the catalogue level to parameter estimation. We produce an initial cosmological inference using fiducial tomographic redshift bins derived from photometric redshifts, then compare this with a result where the redshift bins are empirically corrected using a set of spectroscopic redshifts. We find that the derived parameter S8 ≡ σ8(Ωm/.3)1/2 decreases from $0.841^{+0.062}_{-0.061}$ to $0.781^{0.061}_{0.054}$ upon correcting the n(z) errors in the second method.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Vibrational response and temperature of shock-compressed Pt: In situ extended x-ray absorption fine structure measurements to 325 GPa

Single pulse (~100 ps duration) extended x-ray absorption fine structure (EXAFS) measurements were obtained in laser shocked Pt to examine atomic vibrational properties and to determine temperatures for shock pressures from 72-325 GPa. Pt x-ray absorption spectra were recorded while a planar shock wave was propagating through the Pt sample. The absorption spectrum corresponding to the shocked Pt was obtained by subtracting the ambient Pt contribution from the measured absorption spectrum. Fits to the shocked state EXAFS data provided Pt lattice parameters consistent with the known Pt Hugoniot and the mean squared relative displacements (MSRDs) for nearest neighbor Pt atoms. Pt temperatures in the shocked state, estimated from the MSRDs using the correlated Debye model and correlated Einstein models, were consistent with both the Hugoniot temperatures calculated by integration along the Hugoniot and with the Hugoniot temperatures from published first-principles calculations. However, the agreement between the Hugoniot temperatures from EXAFS measurements and the calculated Hugoniot temperatures is not as good above 200 GPa, likely due to anharmonic effects. Furthermore, the present results demonstrate that single-pulse synchrotron EXAFS measurements in laser-shocked solids are useful for quantitative temperature determination and for examination of vibrational properties, including anharmonicity, at extreme pressure-temperature conditions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

A Bayesian nonparametric analysis for zero-inflated multivariate count data with application to microbiome study

High-throughput sequencing technology has enabled researchers to profile microbial communities from a variety of environments, but analysis of multivariate taxon count data remains challenging. Here, we develop a Bayesian nonparametric (BNP) regression model with zero inflation to analyse multivariate count data from microbiome studies. A BNP approach flexibly models microbial associations with covariates, such as environmental factors and clinical characteristics. The model produces estimates for probability distributions which relate microbial diversity and differential abundance to covariates, and facilitates community comparisons beyond those provided by simple statistical tests. We compare the model to simpler models and popular alternatives in simulation studies, showing, in addition to these additional community-level insights, it yields superior parameter estimates and model fit in various settings. The model's utility is demonstrated by applying it to a chronic wound microbiome data set and a Human Microbiome Project data set, where it is used to compare microbial communities present in different environments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Yttrium solid phase equation of state with uncertainty quantification

We discuss the development of an equation of state (EOS) for solid yttrium up to roughly 80 GPa. The EOS makes use of different experimental datasets, including measurements of the isobaric density, isobaric specific heat, room temperature isotherm, and principal shock Hugoniot. The fitting procedure is carried out using Markov Chain Monte Carlo (MCMC), where we fit model parameters for both the cold curve and ion thermal models simultaneously. The results show close agreement with experimental data and provide meaningful uncertainty estimates on the model parameters. This work serves as a first step towards a multiphase EOS for yttrium, which will include higher pressure solid phases (> 80 GPa), as well as modeling of the liquid phase.

36 MATERIALS SCIENCE↗