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NASA NTRS1988

Global distribution of seamounts from Seasat profiles

A new measurement techique based on a model of a Gaussian seamount loading a thin elastic lithosphere was developed to analyze seamounts that, until then, were not surveyed or seamounts with poor bathymetric coverage. The model predicts that the seamount diameter is equal to the peak-to-trough distance along the vertical deflection profile and that the flexural diameter of a seamount is related to the age of the lithosphere when the seamount formed. This model also suggests that these two measurements are relatively insensitive to the cross-track location from the seamount. These model predictions were confirmed using Seasat altimeter profiles crossing 14 surveyed seamounts in the Pacific. The analysis of the seamount distribution indicated considerable variations in population density and type across the oceans. Most notable among them are the absence of seamounts in the Atlantic, variations in population density across large fracture zones in the Pacific, and the prevalence of small signatures in the Indian Ocean.

Craig, Claire H.↗
DOE OSTI2022

Dark and luminous mass components of Omega Centauri from stellar kinematics

Here we combine proper motion data from Gaia EDR 3 and HST with line-of-sight velocity data to study the stellar kinematics of the ω Cen globular cluster. Using a steady-state, axisymmetric dynamical model, we measure the distribution of both the dark and luminous mass components. Assuming both Gaussian and Navarro–Frenk–White mass profiles, depending on the data set, we measure an integrated mass of ≲10 6 M ⊙ within the ω Cen half-light radius for a dark component that is distinct from the luminous stellar component. For the HST and radial velocity data, models with a non-luminous mass component are strongly statistically preferred relative to a stellar mass-only model with a constant mass-to-light ratio. While a compact core of stellar remnants may account for a dynamical mass up to ~5 x 10 5 M ⊙ , they likely cannot explain the higher end of the range. This leaves open the possibility that this non-luminous dynamical mass component comprises non-baryonic dark matter. In comparison to the dark matter distributions around dwarf spheroidal galaxies, the ω Cen dark mass component is much more centrally concentrated. Interpreting the non-luminous mass distribution as particle dark matter, we use these results to obtain the J-factor, which sets the sensitivity to the annihilation cross-section. For the data sets considered, the range of median J-factors is ~10 22 -10 24 GeV 2 cm -5 , which is larger than that obtained for any dwarf spheroidal galaxy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

G-Mapper: Learning a Cover in the Mapper Construction

The Mapper algorithm is a visualization technique in topological data analysis (TDA) that outputs a graph reflecting the structure of a given dataset. However, the Mapper algorithm requires tuning several parameters in order to generate a “nice” Mapper graph. This paper focuses on selecting the cover parameter. We present an algorithm that optimizes the cover of a Mapper graph by splitting a cover repeatedly according to a statistical test for normality. Our algorithm is based on G-means clustering, which searches for the optimal number of clusters in 𝑘-means by iteratively applying the Anderson–Darling test. Our splitting procedure employs a Gaussian mixture model to carefully choose the cover according to the distribution of the given data. In conclusion, experiments for synthetic and real-world datasets demonstrate that our algorithm generates covers so that the Mapper graphs retain the essence of the datasets, while also running significantly faster than a previous iterative method.

G-means clustering↗
DOE OSTI2021

Decentralized Voltage Control of Large-Scale Distribution System with PVs Based on MADRL

This paper proposes a model-free decentralized control framework for the voltage regulation of large-scale distribution systems through the coordinated control of PV inverters. This is achieved by developing a novel interaction mechanism between the surrogate model and the centralized training and decentralized execution multiagent deep reinforcement learning framework. Specifically, the sparse Gaussian processes regression method is first utilized to develop the surrogate model of the original distribution system for reward calculation during the training stage, where each agent represents a sub-region in the centralized fashion for coordination strategy learning. After that, the learned control rules are used to inform controllers within each sub-region for real-time decisions with only local measurements. Comparative tests among various methods on the EPRI Ckt5 test system demonstrate the effectiveness of the proposed method.

distribution system↗
DOE OSTI2025

A novel approach for large-scale wind energy potential assessment

Increasing wind energy generation is central to grid decarbonization, yet methods to estimate wind energy potential are not standardized, leading to inconsistencies and even skewed results. This study aims to improve the fidelity of wind energy potential estimates through an approach that integrates geospatial analysis and machine learning (i.e., Gaussian process regression). We demonstrate this approach to assess the spatial distribution of wind energy capacity potential in the Contiguous United States (CONUS). We find that the capacity-based power density ranges from 1.70 MW/km2 (25th percentile) to 3.88 MW/km2 (75th percentile) for existing wind farms in the CONUS. The value is lower in agricultural areas (2.73 ± 0.02 MW/km2, mean ± 95 % confidence interval) and higher in other land cover types (3.30 ± 0.03 MW/km2). Notably, advancements in turbine manufacturing could reduce power density in areas with lower wind speeds by adopting low specific-power turbines, but improve power density in areas with higher wind speeds (>8.35 m/s at 120m above the ground), highlighting opportunities for repowering existing wind farms. Wind energy potential is shaped by wind resource quality and is regionally characterized by land cover and physical conditions, revealing significant capacity potential in the Great Plains and Upper Texas. The results indicate that areas previously identified as hot spots using existing approaches (e.g., the west of the Rocky Mountains) may have a limited capacity potential due to low wind resource quality. Improvements in methodology and capacity potential estimates in this study could serve as a new basis for future energy systems analysis and planning.

Dai, Tao↗
DOE OSTI2022

A mixed, unified forward/inverse framework for earthquake problems: fault implementation and coseismic slip estimate

SUMMARY We introduce a new finite-element (FE) based computational framework to solve forward and inverse elastic deformation problems for earthquake faulting via the adjoint method. Based on two advanced computational libraries, FEniCS and hIPPYlib for the forward and inverse problems, respectively, this framework is flexible, transparent and easily extensible. We represent a fault discontinuity through a mixed FE elasticity formulation, which approximates the stress with higher order accuracy and exposes the prescribed slip explicitly in the variational form without using conventional split node and decomposition discrete approaches. This also allows the first order optimality condition, that is the vanishing of the gradient, to be expressed in continuous form, which leads to consistent discretizations of all field variables, including the slip. We show comparisons with the standard, pure displacement formulation and a model containing an in-plane mode II crack, whose slip is prescribed via the split node technique. We demonstrate the potential of this new computational framework by performing a linear coseismic slip inversion through adjoint-based optimization methods, without requiring computation of elastic Green’s functions. Specifically, we consider a penalized least squares formulation, which in a Bayesian setting—under the assumption of Gaussian noise and prior—reflects the negative log of the posterior distribution. The comparison of the inversion results with a standard, linear inverse theory approach based on Okada’s solutions shows analogous results. Preliminary uncertainties are estimated via eigenvalue analysis of the Hessian of the penalized least squares objective function. Our implementation is fully open-source and Jupyter notebooks to reproduce our results are provided. The extension to a fully Bayesian framework for detailed uncertainty quantification and non-linear inversions, including for heterogeneous media earthquake problems, will be analysed in a forthcoming paper.

58 GEOSCIENCES↗
DOE OSTI2022

The miniJPAS survey quasar selection – I. Mock catalogues for classification

In this series of papers, we employ several machine learning (ML) methods to classify the point-like sources from the miniJPAS catalogue, and identify quasar candidates. Since no representative sample of spectroscopically confirmed sources exists at present to train these ML algorithms, we rely on mock catalogues. In this first paper, we develop a pipeline to compute synthetic photometry of quasars, galaxies, and stars using spectra of objects targeted as quasars in the Sloan Digital Sky Survey . To match the same depths and signal-to-noise ratio distributions in all bands expected for miniJPAS point sources in the range 17.5 ≤ r < 24, we augment our sample of available spectra by shifting the original r-band magnitude distributions towards the faint end, ensure that the relative incidence rates of the different objects are distributed according to their respective luminosity functions, and perform a thorough modelling of the noise distribution in each filter, by sampling the flux variance either from Gaussian realizations with given widths, or from combinations of Gaussian functions. Finally, we also add in the mocks the patterns of non-detections which are present in all real observations. Although the mock catalogues presented in this work are a first step towards simulated data sets that match the properties of the miniJPAS observations, these mocks can be adapted to serve the purposes of other photometric surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Impact of baryonic feedback on HSC-Y1 weak lensing non-Gaussian statistics

Baryonic feedback is a major systematic in weak lensing cosmology. Its most studied effect is the suppression of the lensing power spectrum, a second-order statistic, on small scales. Motivated by the growing interest in statistics beyond the second order, we investigate the effect of baryons on lensing non-Gaussian statistics and the resulting biases in the matter clustering amplitude S 8 = σ 8 Ω m / 0.3 . We focus on the Subaru Hyper Suprime-Cam Year 1 (HSC-Y1) data which, with its high source number density, closely resembles those expected from the upcoming Euclid and Rubin Legacy Survey of Space and Time. We study four non-Gaussian statistics of convergence maps—peak counts, minimum counts, the probability distribution function, and the scattering transform coefficients—in addition to the usual power spectrum. We first estimate the biases in S 8 using mock observations built from the IllustrisTNG and BAHAMAS hydrodynamical simulations and theoretical models built from dark-matter-only simulations. We find up to 1 σ bias in S 8 when the smallest scales (2 arcmin) and the highest feedback level are considered. We then analyze the HSC-Y1 data and compare the S 8 obtained for each statistic with different smoothing scales or scale cuts. As we expect that baryons mostly affect the small scales, comparing the results obtained from including and excluding small scales can indicate the level of impact from baryons. With HSC data, we find only minor ( ≤ 0.5 σ ) differences in S 8 for all statistics, even when considering very small scales (2 arcmin). Our results suggest that the effect of baryons is insignificant at the level of HSC-Y1 down to 2 arcmin for all statistics examined here, or it is canceled by other scale-dependent systematics.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Real-time deep-learning inversion of seismic full waveform data for CO 2 saturation and uncertainty in geological carbon storage monitoring

Deep-learning inversion has recently drawn attention in geological carbon storage research due to its potential of imaging and monitoring carbon storage in real time, significantly improving efficiency and safety of carbon storage operations. We present a deep-learning full waveform inversion method that after the neural network has been trained can image CO 2 saturation and its uncertainty in real time. Our deep-learning inversion method is based on the U-Net architecture with the neural network trained on pairs of synthetic seismic data and CO 2 saturation models. Accordingly, our training establishes a mapping relationship between seismic data and CO 2 saturation models and once fully trained directly estimates CO 2 saturation as a function of subsurface location. We further quantify uncertainties of CO 2 saturation estimates using the Monte Carlo dropout method and a bootstrap aggregating method. For this proof-of-concept study, the CO 2 training models and data are derived from the Kimberlina 1.2 model, a hypothetical 3D geological carbon storage model that is constructed based on various geological and hydrological data from the Southern San Joaquin Basin, California. We perform deep-learning inversion experiments using noise-free and noisy training and test data sets and compare the results. Our modelling experiments show that (1) the deep-learning inversion can estimate 2D distributions of CO 2 fairly well even in the presence of Gaussian random noise and (2) both CO 2 saturation imaging and uncertainty quantification can be done in real time. Our results suggest that the deep-learning inversion method can serve as a robust real-time monitoring tool for geological carbon storage and/or other time-varying reservoir/aquifer properties that result from injection, extraction, and/or other subsurface transport phenomena.

58 GEOSCIENCES↗
DOE OSTI2025

The Dark Matter Content of Milky Way Dwarf Spheroidal Galaxies: Draco, Sextans, and Ursa Minor

The Milky Way Survey of the Dark Energy Spectroscopic Instrument (DESI) has so far observed three classical dwarf spheroidal galaxies (dSphs): Draco, Sextans, and Ursa Minor. Based on the observed line-of-sight velocities and metallicities of their member stars, we apply the axisymmetric Jeans Anisotropic Multi-Gaussian Expansion modeling (JAM) approach to recover their inner dark matter distributions. In particular, both the traditional single-population Jeans model and the multiple population chemodynamical model are adopted. With the chemodynamical model, we divide member stars of each dSph into metal-rich and metal-poor populations. The metal-rich populations are more centrally concentrated and dynamically colder, featuring lower velocity dispersion profiles than the metal-poor populations. We find a diversity of the inner density slopes γ of dark matter halos, with the best constraints by the single-population or chemodynamical models consistent with each other. The inner density slopes are $0.7{1}_{-0.35}^{+0.34}$, $0.2{6}_{-0.12}^{+0.22}$, and $0.3{3}_{-0.16}^{+0.20}$ for Draco, Sextans, and Ursa Minor, respectively. We also present the measured astrophysical J and D factors of the three dSphs. Our results indicate that the study of the dark matter content of dSphs through stellar kinematics is still subject to uncertainties behind both the methodology and the observed data, through comparisons with previous measurements and datasets.

Dark matter↗
NASA NTRS1978

Digital image centering. II

Digital image centering algorithms were compared in a test involving microdensitometer raster scans of a refractor parallax series consisting of 22 stars on 26 plates. The highest accuracy in determining stellar image positions was provided by an algorithm which involved fitting of a symmetric Gaussian curve and a flat background to the image marginal density distributions. Algorithms involving transmission marginals instead of density marginals were found to be less accurate. The repeatability and computational efficiency of the digital image centering technique were also studied.

Auer, L. H.↗
NASA NTRS1982

The rotation of comet nuclei

Spin-vector research on cometary nuclei is reviewed with emphasis on the actual determination of rotation period and spin-axis orientation. The rotation periods of 47 comets are compared with those of 41 asteroids with diameters of not more than 40 km. It is shown that the median periods for the comets is 15.0 hr as compared with 6.8 hr for the asteroids and that the preliminary distribution curve for the logarithms of the comet periods is not Gaussian and is flatter than the corresponding curve for the asteroids. Slow accumulation at low relative velocities is suggested as the cause of the longer comet rotation periods.

Whipple, F. L.↗
NASA NTRS1993

Finite element large deflection random response of thermally buckled plates

The governing nonlinear equations of motion are derived for a rectangular isotropic plate subjected to a combined thermal-acoustic loading. The thermal loading is taken to be a steady state temperature distribution, and the acoustic loading is considered to be a stationary Gaussian pressure with zero mean and uniform magnitude and phase over the plate surface. With these types of loads, the thermal-acoustic analysis can be separated into two problems: (1) thermal post-buckling; and (2) random vibration of the thermally buckled plate. Since both analyses are nonlinear, iterative techniques are used to solve each. For the random vibration analysis, the method of equivalent linearization is used; both auto- and cross-correlation terms are included. Results obtained using the present formulation are compared with previous classical equivalent linearization results.

Locke, J. E.↗
NASA NTRS1993

On the Effects of a Spacecraft Subcarrier Unbalanced Modulator

This paper presents mathematical models with associated analysis of the deleterious effects which a spacecraft's subcarrier unbalanced modulator has on the performance of a phase-modulated residual carrier communications link. The undesired spectral components produced by the phase and amplitude imbalances in the subcarrier modulator can cause (1) potential interference to the carrier tracking and (2) degradation in the telemetry bit signal-to-noise ratio (SNR). A suitable model for the unbalanced modulator is developed and the threshold levels of undesired components that fall into the carrier tracking loop are determined. The distribution of the carrier phase error caused by the additive White Gaussian noise (AWGN) and undesired component at the residual RF carrier is derived for the limiting cases. Further, this paper analyses the telemetry bit signal-to-noise ratio degradations due to undesirable spectral components as well as the carrier tracking phase error induced by phase and amplitude imbalances. Numerical results which indicate the sensitivity of the carrier tracking loop and the telemetry symbol-error rate (SER) to various parameters of the models are also provided as a tool in the design of the subcarrier balanced modulator.

Nguyen, Tien Manh↗
NASA NTRS2004

Determining Sizes of Particles in a Flow from DPIV Data

A proposed method of measuring the size of particles entrained in a flow of a liquid or gas would involve utilization of data from digital particle-image velocimetry (DPIV) of the flow. That is to say, with proper design and operation of a DPIV system, the DPIV data could be processed according to the proposed method to obtain particle sizes in addition to particle velocities. As an additional benefit, one could then compute the mass flux of the entrained particles from the particle sizes and velocities. As in DPIV as practiced heretofore, a pulsed laser beam would be formed into a thin sheet to illuminate a plane of interest in a flow field and the illuminated plane would be observed by means of a charge-coupled device (CCD) camera aimed along a line perpendicular to the illuminated plane. Unlike in DPIV as practiced heretofore, care would be taken to polarize the laser beam so that its electric field would lie in the illuminated plane, for the reason explained in the next paragraph. The proposed method applies, more specifically, to transparent or semitransparent spherical particles that have an index of refraction different from that of the fluid in which they are entrained. The method is based on the established Mie theory, which describes the scattering of light by diffraction, refraction, and specular reflection of light by such particles. In the case of a particle illuminated by polarized light and observed in the arrangement described in the preceding paragraph, the Mie theory shows that the image of the particle on the focal plane of the CCD camera includes two glare spots: one attributable to light reflected toward the camera and one attributable to light refracted toward the camera. The distance between the glare spots is a known function of the size of the particle, the indices of refraction of the particle material, and design parameters of the camera optics. Hence, the size of a particle can be determined from the distance between the glare spots. The proposed method would be implemented in an algorithm that would automatically identify, and measure the distance between, the glare spots for each particle for which a suitable image has been captured in a DPIV image frame. The algorithm (see figure) would begin with thresholding of data from the entire image frame to reduce noise, thereby facilitating discrimination of particle images from the background and aiding in the separation of overlapping particles. It is important not to pick a threshold level so high that the light intensity between a given pair of glare spots does not fall below the threshold value, leaving the glare spots disconnected. The image would then be scanned in a sequence of rows and columns of pixels to identify groups of adjacent pixels that contain nonzero brightnesses and that are surrounded by pixels of zero brightness. Each such group would be assumed to constitute the image of one particle. Each such group would be further analyzed to determine whether the image was saturated; saturated particle images must be rejected because the locations of glare spots in saturated images cannot accurately be determined. Within each unsaturated particle image, the centroids (deemed to be the locations) of the glare spots would be determined by means of gradients of brightness distributions and three-point horizontal and three-point vertical Gaussian estimates based on the brightness values of the brightest pixels and the pixels adjacent to them. If the brightness of a given particle image contained only one peak, then it would be assumed that a second glare spot did not exist and that image would be rejected.

Wernet, M. P.↗
NASA NTRS2018

Examination of Annular-Electrode Spark Discharges in Flowing Oxygen - An Overview

A parametric study of annular spark gaps, pressures, and spark discharges in flowing oxygen gas was performed with a Champion spark exciter. The range of the pressure-distance product for the experiment is from approximately 50 torr-cm to 2500 torr-cm. Measurements of breakdown voltage qualitatively trend with Paschen's curve. Spark duration remained constant until the pressure-distance product exceeded 200 torr-cm, and then steadily increased. The mean spark energy increases linearly with the pressure-distance on a log-log plot indicating that a definite power relationship exists. The distribution of sparks at low energies and low pressures is not Gaussian and has no dominant peaks. Moderate and high spark energies are bimodal, with the dominant mode near 80 mJ. As pressure increases, dominant and secondary modes approach the same probability.

Darren C. Tinker↗