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DOE OSTI2026

Probing jet base emission of M87* with the 2021 Event Horizon Telescope observations

We investigate the presence and spatial characteristics of the jet base emission in M87* at 230 GHz, enabled by the significantly enhanced (u,v) coverage in the 2021 Event Horizon Telescope (EHT) observations. The integration of the 12−m Kitt Peak Telescope (USA) and NOEMA (France) stations into the array introduces two critical intermediate-length baselines to SMT (USA) and IRAM 30−m (Spain), providing sensitivity to emission structures at spatial scales of ∼250 μas and ∼2500 μas (∼ 0.02 pc and ∼ 0.02 pc). Without these new baselines, previous EHT observations of the source in 2017 and 2018 lacked the capability to constrain emission on large scales, where a “missing flux” of order ∼1 Jy is expected to reside. To probe these scales, we analyzed closure phases–robust against station-based gain calibration errors–and model the jet base emission using a simple Gaussian component offset from the compact ring emission at spatial separations > 100 μas. Our analysis revealed a Gaussian feature centered at (ΔRA ≈ 320 μ as, ΔDec. ≈ 60 μ as), projected separation of ≈ 5500 AU, with an estimated flux density of only ∼60 mJy, implying that most of the missing flux identified in previous EHT studies had to originate from different, larger scales. Brighter emission at the relevant spatial scales is firmly ruled out, and the data do not favor more complex models. This component aligns with the inferred position of the large-scale jet and is therefore physically consistent with the emission of the jet base. While our findings point to detectable jet base emission at 230 GHz, the limited coverage provided by only two intermediate baselines limits our ability to robustly reconstruct its morphology. Consequently, we treated the recovered Gaussian as an upper limit on the jet base flux density. Future EHT observations with expanded intermediate baseline coverage will be essential to constrain the structure and nature of this component with higher precision.

accretion, accretion disks↗

DOE OSTI2024

Cross Correlating Cosmological Probes for LSST & CMB-S4

The upcoming years will be populated with state-of-the-art Stage-IV cosmological surveys. This paper forecasts the cosmological information we expect to constrain using probes from the Rubin Observatory Legacy Survey of Space and Time (LSST) and CMB-S4. We explore the effectiveness of different large-scale-structure probes, including the power spectra of galaxy weak lensing, galaxy position, Cosmic Microwave Background (CMB) lensing, and their cross-correlations. We use correlated lognormal simulations with the expected redshift distributions, galaxy number densities, and noise levels of the LSST survey. For CMB weak lensing, we use CMB-S4 lensing simulations with anticipated noise levels to obtain the expected error bars of each probe. We investigate the constraining power of the cosmological parameters for the individual probes and their combinations in an idealized scenario. We do not take into account astrophysical and observational parameters such as galaxy bias variations and photometric redshift un- certainties. Overall, we find the auto-correlated probes hold stronger constraints on cosmological parameters than the cross-correlated probes due to kernel differences. However, the cross-correlation of galaxy clustering and CMB lensing is very comparable to the auto-correlation of galaxy clustering with only an 8% stronger constraint in S8. It will be important for future surveys to use these auto and cross probes in combination due to the different astrophysical and systematic effects involved in CMB and late-time galaxy data. When systematics are added to this analysis, the cross probes will serve as a check for systematic biases in individual probes. In addition, we find a significant increase in constraint using all four probes in combination. This can be illustrated by the increase in constraint of S8 by 93.2% comparing galaxy clustering auto-correlation to a combination of all probes. Our study is a first step toward forecasting the high-precision cosmological constraints we expect to obtain using the next generation of large-scale structure probes.

Gibbins, Grace↗

DOE OSTI2018

A Complete and Accurate Description of Superconductivity of AlB{sub 2}-Type Structures from Phonon Dispersion Calculations

A predictive tool for the design of new, higher-temperature superconductors requires a simple, first-principles technique based on well-established axioms as embodied in density functional theory (DFT), without post-calculation corrections or increased levels of complexity. We show that anomalies in the calculated phonon dispersions of compounds with AlB{sub 2}-type structures are good descriptors of their superconducting transition temperatures. This ab initio methodology has proven robust for descriptions of the superconductivity of MgB{sub 2} and other related AlB{sub 2}-type structures, the different isotopic forms of MgB{sub 2}, a series of substituted MgB{sub 2} compositions with Al and with transition metals, the pressure dependence of the superconductivity of MgB{sub 2}, and various temperature effects. Calculated values are well correlated with experimentally determined values within experimental errors, when sufficiently fine k-grids are used to resolve Fermi surface details. This methodology provides invaluable insight on the mechanisms of superconductivity, can be extended to other crystal systems, and has been used to predict superconducting T{sub c} for new compounds.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2023

Operator inference with roll outs for learning reduced models from scarce and low-quality data

Data-driven modeling has become a key building block in computational science and engineering. However, data that are available in science and engineering are typically scarce, often polluted with noise and affected by measurement errors and other perturbations, which makes learning the dynamics of systems challenging. Here, in this work, we propose to combine data-driven modeling via operator inference with the dynamic training via roll outs of neural ordinary differential equations. Operator inference with roll outs inherits interpretability, scalability, and structure preservation of traditional operator inference while leveraging the dynamic training via roll outs over multiple time steps to increase stability and robustness for learning from low-quality and noisy data. Numerical experiments with data describing shallow water waves and surface quasi-geostrophic dynamics demonstrate that operator inference with roll outs provides predictive models from training trajectories even if data are sampled sparsely in time and polluted with noise of up to 10%.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Data-driven Whitney forms for structure-preserving control volume analysis

Control volume analysis models physics via the exchange of generalized fluxes between subdomains. Here, we introduce a scientific machine learning framework adopting a partition of unity architecture to identify physically-relevant control volumes, with generalized fluxes between subdomains encoded via Whitney forms. The approach provides a differentiable parameterization of geometry which may be trained in an end-to-end fashion to extract reduced models from full field data while exactly preserving physics. The architecture admits a data-driven finite element exterior calculus allowing discovery of mixed finite element spaces with closed form quadrature rules. An equivalence between Whitney forms and graph networks reveals that the geometric problem of control volume learning is equivalent to an unsupervised graph discovery problem. The framework is developed for manifolds in arbitrary dimension, with examples provided for H(div) problems in $\mathbb{R}$ establishing convergence and structure preservation properties. Finally, we consider a lithium-ion battery problem where we discover a reduced finite element space encoding transport pathways from high-fidelity microstructure resolved simulations. The approach reduces the 5.89M finite element simulation to 136 elements while reproducing pressure to under 0.1% error and preserving conservation.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

The Illumination of Thunderclouds by Lightning: 3. Retrieving Optical Source Altitude

Abstract Optical space‐based lightning sensors such as the Geostationary Lightning Mapper (GLM) detect and geolocate lightning by recording rapid changes in cloud top illumination. While lightning locations can be determined to within a pixel on the GLM imaging array, these instruments are not individually able to natively report lightning altitude. It has previously been shown that thunderclouds are illuminated differently based on the altitude of the optical source. In this study, we examine how altitude information can be extracted from the spatial distributions of GLM energy recorded from each optical pulse. We match GLM “groups” with Lightning Mapping Array (LMA) source data that accurately report the 3‐D positions of coincident Radio‐Frequency (RF) emitters. We then use machine learning methods to predict the mean LMA source altitudes matched to GLM groups using metrics from the optical data that describe the amplitude, breadth, and texture of the group spatial energy distribution. The resulting model can predict the LMA mean source altitude from GLM group data with a median absolute error of <1.5 km, which is sufficient to determine the location of the charge layer where the optical energy originated. This model is able to capture changes to the source altitude distribution in response to convective processes in the thunderstorm, and the GLM predictions can reveal the vertical structure of individual flashes ‐ enabling 3‐D flash geolocation with GLM for the first time. Future work will account for differences in thunderstorm charge/precipitation structures and viewing angle across the GLM Field of View.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

Accurate lattice geometrical parameters and bulk moduli from a semilocal density functional

Accurate prediction of lattice constants is very important in applications of density functional theory. In this work, we assess the efficacy of a non-empirical meta-generalized gradient approximation proposed by Tao and Mo (TM) by calculating the lattice constants as well as bulk moduli of 33 crystalline semiconductors within the TM scheme. We find that the TM functional is able to produce very accurate lattice constants, with a mean absolute error of 0.038 Å, and bulk moduli with a mean absolute error of 3.2 GPa, improving upon commonly-used semilocal density functionals, such as the LSDA, PBE, SOGGA, PBEsol, TPSS, M06L, and SCAN. The high computational efficiency and remarkable agreements with the corresponding experimental values suggest that the TM functional can be a very competitive candidate in electronic structure theory. We attribute the accuracy of the TM functional to be the result of its satisfaction of many exact or nearly-exact conditions related to the exchange-correlation energy and the associated hole, leading to an improved description of the short- as well as intermediate-range van der Waals interactions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

The impact of hurricane disturbances on a tropical forest: implementing a palm plant functional type and hurricane disturbance module in ED2-HuDi V1.0

Hurricanes commonly disturb and damage tropical forests. Hurricane frequency and intensity are predicted to change under the changing climate. The short-term impacts of hurricane disturbances to tropical forests have been widely studied, but the long-term impacts are rarely investigated. Modeling is critical to investigate the potential response of forests to future disturbances, particularly if the nature of the disturbances is changing with climate. Unfortunately, existing models of forest dynamics are not presently able to account for hurricane disturbances. Therefore, we implement the Hurricane Disturbance in the Ecosystem Demography model (ED2) (ED2-HuDi). The hurricane disturbance includes hurricane-induced immediate mortality and subsequent recovery modules. The parameterizations are based on observations at the Bisley Experimental Watersheds (BEW) in the Luquillo Experimental Forest in Puerto Rico. We add one new plant functional type (PFT) to the model – Palm, as palms cannot be categorized into one of the current existing PFTs and are known to be an abundant component of tropical forests worldwide. The model is calibrated with observations at BEW using the generalized likelihood uncertainty estimation (GLUE) approach. The optimal simulation obtained from GLUE has a mean relative error of –21 %, –12 %, and –15 % for stem density, basal area, and aboveground biomass, respectively. The optimal simulation also agrees well with the observation in terms of PFT composition (+1 %, –8 %, –2 %, and +9 % differences in the percentages of “Early”, “Mid”, “Late”, and “Palm” PFTs, respectively) and size structure of the forest (+0.8 % differences in the percentage of large stems). Lastly, using the optimal parameter set, we study the impact of forest initial condition on the recovery of the forest from a single hurricane disturbance. The results indicate that, compared to a no-hurricane scenario, a single hurricane disturbance has little impact on forest structure (+1 % change in the percentage of large stems) and composition (<1 % change in the percentage of each of the four PFTs) but leads to 5 % higher aboveground biomass after 80 years of succession. The assumption of a less severe hurricane disturbance leads to a 4 % increase in aboveground biomass.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Novel, Simplified Air Delivery System Design Gets Commercialized

Typical air delivery systems require contractors to design systems that are complex, difficult to integrate into the framing and structural components of a home, and often do not meet the optimization demands of newer energy-efficient homes. This complex process can be a burden to already strained construction labor costs. However, a new technology simplifies duct system design to improve comfort and eliminate sources of installation error for residential air delivery systems.

Building America↗

DOE OSTI2020

Novel, Simplified Air Delivery System Design Gets Commercialized

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2018

Conservative model reduction for finite-volume models

Here, this work proposes a method for model reduction of finite-volume models that guarantees the resulting reduced-order model is conservative, thereby preserving the structure intrinsic to finite-volume discretizations. The proposed reduced-order models associate with optimization problems characterized by a minimum-residual objective function and nonlinear equality constraints that explicitly enforce conservation over subdomains. Conservative Galerkin projection arises from formulating this optimization problem at the time-continuous level, while conservative least-squares Petrov–Galerkin (LSPG) projection associates with a time-discrete formulation. We equip these approaches with hyper-reduction techniques in the case of nonlinear flux and source terms, and also provide approaches for handling infeasibility. In addition, we perform analyses that include deriving conditions under which conservative Galerkin and conservative LSPG are equivalent, as well as deriving a posteriori error bounds. Numerical experiments performed on a parameterized quasi-1D Euler equation demonstrate the ability of the proposed method to ensure not only global conservation, but also significantly lower state-space errors than nonconservative reduced-order models such as standard Galerkin and LSPG projection.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

Critical Assessment of Electronic Structure Descriptors for Predicting Perovskite Catalytic Properties

The discovery and design of materials which can efficiently catalyze the oxygen reduction and evolution reactions at reduced temperatures is important for facilitating the widespread adoption of fuel cell and electrolyzer technologies. Numerous studies have produced correlations between catalytic properties, such as oxygen surface exchange or electrode area specific resistance (ASR), and properties of the catalyst material. However, correlations have historically been limited in scope (e.g., using only a few materials or at a single temperature) and it has been difficult to provide detailed assessments of their robustness. Here, in this study, we assess the ability of the O p-band center electronic structure descriptor, obtained from density functional theory (DFT) calculations, to correlate with oxygen surface exchange rates, diffusivities, and area specific resistances for a large database of perovskite oxide catalytic properties. By data mining the literature, we obtain 747 catalytic property value data points spanning 299 unique perovskite compositions from 313 studies. We assess linear correlations of each property with the O p-band center and find generally modest correlations that are qualitatively useful (prediction mean absolute errors of about 0.5 log units are typical), where the correlations are improved at higher temperatures (e.g., 800 °C vs. 500 °C) and significantly improve when considering fits to the subset of materials which have multiple independent measurements. These findings suggest that the spread of property data is significantly influenced by experimental uncertainty, and subsequent measurements of additional materials will likely improve the O p-band center correlations.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2019

Performance of Laser-Based Electronic Devices for Structural Analysis of Amazonian Terra-Firme Forests

Tropical vegetation biomass represents a key component of the carbon stored in global forest ecosystems. Estimates of aboveground biomass commonly rely on measurements of tree size (diameter and height) and then indirectly relate, via allometric relationships and wood density, to biomass sampled from a relatively small number of harvested and weighed trees. Recently, however, novel in situ remote sensing techniques have been proposed, which may provide nondestructive alternative approaches to derive biomass estimates. Nonetheless, we still lack knowledge of the measurement uncertainties, as both the calibration and validation of estimates using different techniques and instruments requires consistent assessment of the underlying errors. To that end, we investigate different approaches estimating the tropical aboveground biomass in situ. We quantify the total and systematic errors among measurements obtained from terrestrial light detection and ranging (LiDAR), hypsometer-based trigonometry, and traditional forest inventory. We show that laser-based estimates of aboveground biomass are in good agreement (<10% measurement uncertainty) with traditional measurements. However, relative uncertainties vary among the allometric equations based on the vegetation parameters used for parameterization. We report the error metrics for measurements of tree diameter and tree height and discuss the consequences for estimated biomass. Despite methodological differences detected in this study, we conclude that laser-based electronic devices could complement conventional measurement techniques, thereby potentially improving estimates of tropical vegetation biomass.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Comparison of Conventional and Constrained Variational Methods for Computing Large-Scale Budgets and Forcing Fields

Analyses of atmospheric heat and moisture budgets serve as an effective tool to study convective characteristics over a region and to provide large-scale forcing fields for various modeling applications. This paper examines two popular methods for computing large-scale atmospheric budgets: the conventional budget method (CBM) using objectively gridded analyses based primarily on radiosonde data and the constrained variational analysis (CVA) approach which supplements vertical profiles of atmospheric fields with measurements at the top of the atmosphere and at the surface to conserve mass, water, energy, and momentum. Successful budget computations are dependent on accurate sampling and analyses of the thermodynamic state of the atmosphere and the divergence field associated with convection and the large-scale circulation that influences it. Utilizing analyses generated from data taken during Dynamics of the Madden-Julian Oscillation (DYNAMO) field campaign conducted over the central Indian Ocean from October to December 2011, we evaluate the merits of these budget approaches and examine their limitations. While many of the shortcomings of the CBM, in particular effects of sampling errors in sounding data, are effectively minimized with CVA, accurate large-scale diagnostics in CVA are dependent on reliable background fields and rainfall constraints. Finally, for the DYNAMO analyses examined, the operational model fields used as the CVA background state provided wind fields that accurately resolved the vertical structure of convection in the vicinity of Gan Island. However, biases in the model thermodynamic fields were somewhat amplified in CVA resulting in a convective environment much weaker than observed.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Model-observation discrepancies in Arctic moisture intrusions: causes and pathways for improved simulation

Arctic moisture intrusions (MIs), narrow filaments of strong moisture transport, are key drivers of poleward moisture flux and Arctic weather extremes, yet their representation in climate models is poorly understood. Using a new Arctic MI detection algorithm, we document persistent biases across three CMIP generations (CMIP3–CMIP6): models overestimate MI occurrence over the Pacific sector and underestimate it over the Atlantic sector. These errors stem from misrepresented midlatitude westerly jets, with an equatorward North Atlantic jet associated with too few Atlantic MIs, and a poleward, weakened North Pacific jet linked to too many Pacific MIs. Experiments that correct sea surface temperature and sea ice concentration biases and increase atmospheric resolution improve jet structure and MI statistics, while a cloud-locking simulation indicates that better high-frequency cloud–radiation–circulation interactions can yield further gains. Our results clarify pathways to reducing long-standing MI and jet biases, providing guidance for improving simulations of Arctic and midlatitude climate.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

A generalized Ewald decomposition for screened Coulomb interactions

Medium-range interactions occur in a wide range of systems, including charged-particle systems with varying screening lengths. We generalize the Ewald method to charged systems described by interactions involving an arbitrary dielectric response function ϵ(𝐤). We provide an error estimate and optimize the generalization to find the break-even parameters that separate a neighbor list-only algorithm from the particle-particle particle-mesh algorithm. We examine the implications of different choices of the screening length for the computational cost of computing the dynamic structure factor. We then use our new method in molecular dynamics simulations to compute the dynamic structure factor for a model plasma system and examine the wave-dispersion properties of this system.

Dharuman, Gautham↗

DOE OSTI2018

Atomic-position independent descriptor for machine learning of material properties

The high-throughput screening of periodic inorganic solids using machine learning methods requires atomic positions to encode structural and compositional details into appropriate material descriptors. These atomic positions are not available a priori for new materials, which severely limits exploration of novel materials. In this work, we overcome this limitation by using only crystallographic symmetry information in the structural description of materials. We show that for materials with identical structural symmetry, machine learning is trivial, and accuracies similar to that of density functional theory calculations can be achieved by using only atomic numbers in the material description. For machine learning of formation energies of bulk crystalline solids, this simple material descriptor is able to achieve prediction mean absolute errors of only 0.07 eV/at on a test dataset consisting of more than 85 000 diverse materials. Finally, this atomic-position independent material descriptor presents a new route of materials discovery wherein millions of materials can be screened by training a machine learning model over a drastically reduced subspace of materials.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Crystal growth, microstructure, and physical properties of SrMnSb 2

We report on the crystal and magnetic structures, magnetic, and transport properties of SrMnSb 2 single crystals grown by the self-flux method. Magnetic susceptibility measurements reveal an antiferromagnetic (AFM) transition at T N = 295(3) K. Above T N , the susceptibility slightly increases and forms a broad peak at T ~ 420 K, which is a typical feature of two-dimensional magnetic systems. Neutron diffraction measurements on single crystals confirm the previously reported C-type AFM structure below T N . Both de Haas-van Alphen (dHvA) and Shubnikov-de Haas (SdH) effects are observed in SrMnSb2 single crystals. Analysis of the oscillatory component by a Fourier transform shows that the prominent frequencies obtained by the two different techniques are practically the same within error regardless of sample size or saturated magnetic moment. Transmission electron microscopy (TEM) reveals the existence of stacking faults in the crystals, which result from a horizontal shift of Sb atomic layers suggesting possible ordering of Sb vacancies in the crystals. Increase of temperature in susceptibility measurements leads to the formation of a strong peak at T ~ 570 K that upon cooling under magnetic field the susceptibility shows a ferromagnetic transition at T C ~ 580 K. Neutron powder diffraction on crushed single-crystals does not support an FM phase above T N . Furthermore, X-ray magnetic circular dichroism (XMCD) measurements of a single crystal at the L 2,3 edge of Mn shows a signal due to induced canting of AFM moments by the applied magnetic field. All evidence strongly suggests that a chemical transformation at the surface of single crystals occurs above 500 K concurrently producing a minute amount of ferromagnetic impurity phase.

36 MATERIALS SCIENCE↗