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DOE OSTI2021

Microtearing modes as the source of magnetic fluctuations in the JET pedestal

Abstract We report on a detailed study of magnetic fluctuations in the JET pedestal, employing basic theoretical considerations, gyrokinetic simulations, and experimental fluctuation data to establish the physical basis for their origin, role, and distinctive characteristics. We demonstrate quantitative agreement between gyrokinetic simulations of microtearing modes (MTMs) and two magnetic frequency bands with corresponding toroidal mode numbers n = 4 and 8. Such disparate fluctuation scales, with substantial gaps between toroidal mode numbers, are commonly observed in pedestal fluctuations. Here we provide a clear explanation, namely the alignment of the relevant rational surfaces (and not others) with the peak in the ω * profile, which is localized in the steep gradient region of the pedestal. We demonstrate that a global treatment is required to capture this effect. Nonlinear simulations suggest that the MTM fluctuations produce experimentally-relevant transport levels and saturate by relaxing the background electron temperature gradient, slightly downshifting the fluctuation frequencies from the linear predictions. Scans in collisionality are compared with a simple MTM dispersion relation. At the experimental points considered, MTM growth rates can either increase or decrease with collision frequency depending on the parameters thus defying any simple characterization of collisionality dependence.

Physics↗

DOE OSTI2021

Flexocaloric effect near a ferroelastic transition

A Ginzburg-Landau model embedded into a vibrational model is used to study the flexocaloric effect in a beam near a ferroelastic transition. The caloric response upon bending is characterized by the isothermal entropy change and the adiabatic temperature change of the beam. Here, we obtain a larger response relative to the strength of the applied forces at temperatures slightly above the transition temperature. It is also obtained that the maximum caloric response is almost linear with the bending angle of the beam, whereas the relation between the bending angle and the applied forces is highly nonlinear. Small hysteresis associated with the phase transition is obtained for sufficiently large bending forces due to the existence of a critical point in the temperature-stress phase diagram of the ferroelastic material. Finally, the microstructure changes with bending in the beam are consistent with previous experimental observations.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2022

Applying Two-Dimensional Correlation Spectroscopy and Principal Component Analysis to Understand How Temperature Affects the Neptunium(V) Absorption Spectrum

The visible-near infrared (Vis-NIR) electronic absorption spectrum of neptunium(V) (NpO 2 + ) comprises numerous f-f electronic transitions with mostly undocumented temperature dependencies. The effect of temperature on the absorption spectrum of the pentavalent neptunyl dioxocation (NpO 2 + ) is an important factor to consider with spectrophotometric applications but has often been overlooked. Optical Vis-NIR absorption spectra (400–1700 nm) of Np(V) (0.017–0.89 M) in 1 M nitric acid were evaluated with varying temperatures (T = 10–80 °C). The intensity, position, and overall shape of the bands were sensitive to interactions with the solvent and coordination environment. Numerous temperature-induced isosbestic points were identified resulting from dynamic, overlapping peak shifts. Spectral variations were characterized using principal component analysis (PCA) and 2D correlation spectroscopy (COS). 2D-COS revealed that the absorption band near 1095 nm likely consists of two bands centered near 1087 and 1096 nm, which cannot be explained by current computational methods. 2D-COS analysis also provided an unambiguous assignment of unresolved peaks in the visible region for comparison with computational predictions. PCA was used to identify nonlinearity in the spectral response at elevated Np(V) concentrations ≥ 0.5 M. This unique experimental data and interpretation will foster a deeper understanding of the absorption spectra for complex actinyl ions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Frustrated Magnetism in FeGe 3 O 4 with a Chiral Trillium Network

The discovery of new magnetic ground states in geometrically frustrated lattices remains a central challenge in materials science. Here, we report the synthesis, structural characterization, and frustrated magnetic properties of FeGe 3 O 4 , a newly identified compound that crystallizes in the noncentrosymmetric cubic space group P 2 1 3. In this structure, Fe atoms form an intricate double-trillium lattice with nearest-neighbor Fe−Fe distances of ∼4.2 Å, while Ge 2+ ions mediate magnetic interactions through Fe− Ge−Fe pathways. Field-dependent magnetization at 2 K shows a pronounced nonlinearity, reaching a maximum moment of 2.55(3) μ B /Fe 2+ at 70 kOe without evidence of saturation. Magnetic susceptibility, heat capacity, and neutron scattering collectively reveal the onset of short-range magnetic interactions near 5 K, with no longrange ordering detected down to 0.06 K. Specific heat measurements demonstrate strong frustration: only ∼34% of the expected magnetic entropy is recovered at 2.4 K. Taken together, these results establish FeGe 3 O 4 as a rare example of a geometrically frustrated trillium lattice magnet, offering a promising platform for exploring exotic quantum magnetic phenomena.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Solvated Ionic Liquid-based Electrolytes: Experimental Characterization, Atomistic Modeling and Applications in Energy Storage

This study aimed on understanding the molecular structure and transport mechanisms of high-concentration lithium electrolytes, with a particular focus on solvate ionic liquids (SILs) based on glyme solvents, for use in next-generation, high-voltage energy storage systems. The hypothesis is that the composition of the solvents in these electrolytes can be designed and adjusted to control cation solvation, enabling charge transport via an ion-hopping mechanism. This mechanism resembles Grotthuss conduction of protons in water and is independent of electrolyte viscosity. The experimental studies employed linear and nonlinear infrared spectroscopy (FTIR and 2D IR), electrochemical measurements, and vibrational probes.

25 ENERGY STORAGE↗

DOE OSTI2023

First-Principles Investigation of Electrides Derived from Sodalites

Recently, electride materials, with excess anionic electrons confined in their empty space, have received growing attention due to their promising applications in catalysis, nonlinear optics, and spin electronics. However, the utilization of electride materials is limited by their thermal instability. Here, we introduce an alternative way to achieve the localized anionic electron states via the removal of highly symmetric Wyckoff sites of anions from the existing sodalite compounds. Using four halide sodalites as the parental structures, our simulation reveals that the materials after the removal of anionic halide sites exhibit typical electride behaviors that are characterized by the existence of localized electronic states near the Fermi level. Compared to most previously studied electrides, these materials are expected to be more thermally stable due to the complex structural framework and thus promising for practical applications. Among them, Na 4 (AlSiO 4 ) 3 manifests magnetic electronic structure. We demonstrate that this magnetism originates from a highly localized excess electron state surrounded by electropositive alkaline cations. Here, our results suggest Na 4 (AlSiO 4 ) 3 could be a promising spintronics component, thus encouraging further experimental study.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Extracting dark-matter velocities from halo masses: A reconstruction conjecture

Increasing attention has recently focused on nontraditional dark-matter production mechanisms which result in primordial dark-matter velocity distributions with highly nonthermal shapes. Here, in this paper, we undertake an assessment of how the detailed shape of a general dark-matter velocity distribution impacts structure formation in the nonlinear regime. In particular, we investigate the impact on the halo-mass and subhalo-mass functions, as well as on astrophysical observables such as satellite and cluster-number counts. We find that many of the standard expectations no longer hold in situations in which this velocity distribution takes a highly nontrivial, even multimodal shape. For example, we find that the nominal free-streaming scale alone becomes insufficient to characterize the effect of free-streaming on structure formation. In addition, we propose a simple one-line conjecture which can be used to “reconstruct” the primordial dark-matter velocity distribution directly from the shape of the halo-mass function. Although our conjecture is completely heuristic, we show that it successfully reproduces the salient features of the underlying dark-matter velocity distribution even for nontrivial distributions which are highly nonthermal and/or multimodal, such as might occur for nonminimal dark sectors. Moreover, since our approach relies only on the halo-mass function, our conjecture provides a method of probing dark-matter properties even for scenarios in which the dark and visible sectors interact only gravitationally.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Estimating Leaf Area Index in Row Crops Using Wheel-Based and Airborne Discrete Return Light Detection and Ranging Data

Leaf area index (LAI) is an important variable for characterizing plant canopy in crop models. It is traditionally defined as the total one-sided leaf area per unit ground area and is estimated by both direct and indirect methods. This paper explores the effectiveness of using light detection and ranging (LiDAR) data to estimate LAI for sorghum and maize with different treatments at multiple times during the growing season from both a wheeled vehicle and Unmanned Aerial Vehicles. Linear and nonlinear regression models are investigated for prediction utilizing statistical and plant structure-based features extracted from the LiDAR point cloud data with ground reference obtained from an in-field plant canopy analyzer (indirect method). Results based on the value of the coefficient of determination ( R 2 ) and root mean squared error for predictive models ranged from ∼0.4 in the early season to ∼0.6 for sorghum and ∼0.5 to 0.80 for maize from 40 Days after Sowing to harvest.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Extreme Scale Infrasound Inversion and Prediction for Weather Characterization and Acute Event Detection

Accurate and timely weather predictions are critical to many aspects of society with a profound impact on our economy, general well-being, and national security. In particular, our ability to forecast severe weather systems is necessary to avoid injuries and fatalities, but also important to minimize infrastructure damage and maximize mitigation strategies. The weather community has developed a range of sophisticated numerical models that are executed at various spatial and temporal scales in an attempt to issue global, regional, and local forecasts in pseudo real time. The accuracy however depends on the time period of the forecast, the nonlinearities of the dynamics, and the target spatial resolution. Significant uncertainties plague these predictions including errors in initial conditions, material properties, data, and model approximations. To address these shortcomings, a continuous data collection occurs at an effort level that is even larger than the modeling process. It has been demonstrated that the accuracy of the predictions depends on the quality of the data and is independent to a certain extent on the sophistication of the numerical models. Data assimilation has become one of the more critical steps in the overall weather prediction business and consequently substantial improvements in the quality of the data would have transformational benefits. This paper describes the use of infrasound inversion technology, enabled through exascale computing, that could potentially achieve orders of magnitude improvement in data quality and therefore transform weather predictions with significant impact on many aspects of our society.

58 GEOSCIENCES↗

DOE OSTI2024

Using Knowledge-Guided Machine Learning To Assess Patterns of Areal Change in Waterbodies across the Contiguous United States

Lake and reservoir surface areas are an important proxy for freshwater availability. Advancements in machine learning (ML) techniques and increased accessibility of remote sensing data products have enabled the analysis of waterbody surface area dynamics on broad spatial scales. However, interpreting the ML results remains a challenge. While ML provides important tools for identifying patterns, the resultant models do not include mechanisms. Thus, the “black-box” nature of ML techniques often lacks ecological meaning. Using ML, we characterized temporal patterns in lake and reservoir surface area change from 1984 to 2016 for 103,930 waterbodies in the contiguous United States. We then employed knowledge-guided machine learning (KGML) to classify all waterbodies into seven ecologically interpretable groups representing distinct patterns of surface area change over time. Many waterbodies were classified as having “no change” (43%), whereas the remaining 57% of waterbodies fell into other groups representing both linear and nonlinear patterns. This analysis demonstrates the potential of KGML not only for identifying ecologically relevant patterns of change across time but also for unraveling complex processes that underpin those changes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Using Knowledge-Guided Machine Learning To Assess Patterns of Areal Change in Waterbodies across the Contiguous United States

58 GEOSCIENCES↗

DOE OSTI2020

An adaptive space-time phase field formulation for dynamic fracture of brittle shells based on LR NURBS

Here, we present an adaptive space-time phase field formulation for dynamic fracture of brittle shells. Their deformation is characterized by the Kirchhoff–Love thin shell theory using a curvilinear surface description. All kinematical objects are defined on the shell’s mid-plane. The evolution equation for the phase field is determined by the minimization of an energy functional based on Griffith’s theory of brittle fracture. Membrane and bending contributions to the fracture process are modeled separately and a thickness integration is established for the latter. The coupled system consists of two nonlinear fourth-order PDEs and all quantities are defined on an evolving two-dimensional manifold. Since the weak form requires C 1 -continuity, isogeometric shape functions are used. The mesh is adaptively refined based on the phase field using Locally Refinable (LR) NURBS. Time is discretized based on a generalized-α method using adaptive time-stepping, and the discretized coupled system is solved with a monolithic Newton–Raphson scheme. Finally, the interaction between surface deformation and crack evolution is demonstrated by several numerical examples showing dynamic crack propagation and branching.

42 ENGINEERING↗

DOE OSTI2025

Recent Widespread Deceleration of Global Surface Urban Heat Islands Unveiled by Satellites

Tracking the temporal dynamics of urban heat island (UHI) is critical for urban heat adaptation and mitigation strategies. However, whether UHI trends have shifted recently and their underlying drivers remain unknown. Here we investigate the variabilities in surface UHI trends and their associated determinants in 2,104 cities worldwide from 2000 to 2022. Our findings reveal that approximately half of the world's cities have experienced notable shifts in surface UHI trends, predominantly characterized by UHI deceleration. These shifts can be primarily attributed to alterations in vegetation trends during the day and to modifications in surface albedo and local warming trends at night. Our study challenges the conventional linear models commonly employed to estimate surface UHI trends, suggesting potential biases in such estimates. Our findings underscore the need for nuanced policies to curtail UHI growth by considering changes in urban underlying surfaces and background climate, particularly from a nonlinear perspective.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Interpreting radial correlation Doppler reflectometry using gyrokinetic simulations

A linear response, local model for the DBS amplitude applied to gyrokinetic simulations shows that radial correlation Doppler reflectometry measurements (RCDR, Schirmer et al 2007 Plasma Phys. Control. Fusion 49 1019) are not sensitive to the average turbulence radial correlation length, but to a correlation length that depends on the binormal wavenumber $k_{\perp}$ selected by the Doppler backscattering (DBS) signal. Nonlinear gyrokinetic simulations show that the turbulence naturally exhibits a nonseparable power law spectrum in wavenumber space, leading to a power law dependence of the radial correlation length with binormal wavenumber $l_r \sim C k_{\perp}^{-\alpha} (\alpha \approx 1)$ which agrees with the inverse proportionality relationship between the measured $l_r$ and $k_{\perp} $ observed in experiments (Fernández-Marina et al 2014 Nucl. Fusion 54 072001). This new insight indicates that RCDR characterizes the eddy aspect ratio in the perpendicular plane to the magnetic field. It also motivates future use of a nonseparable turbulent spectrum to quantitatively interpret RCDR and potentially other turbulence diagnostics. The radial correlation length is only measurable when the radial resolution at the cutoff location $W_\text n$ satisfies $W_\text n \ll l_r$, while the measurement becomes dominated by $W_\text n$ for $W_\text n \gg l_r$. This suggests that $l_r$ is likely to be inaccessible for electron-scale DBS measurements (${k_{\perp}}{\rho_{\text s}} \gt 1$). The effect of $W_\text n$ on ion-scale radial correlation lengths could be nonnegligible.

radial correlation Doppler reflectometry↗

DOE OSTI2025

Deep Learning–Assisted Multiobjective Optimization of Geological CO 2 Storage Performance under Geomechanical Risks

In geological CO 2 storage, designing the optimal well control strategy for CO 2 injection to maximize CO 2 storage while minimizing the associated geomechanical risks is not trivial. This challenge arises due to pressure buildup, CO 2 plume migration, the highly nonlinear nature of geomechanical responses to rock-fluid interaction, and the high computational cost associated with coupled flow and geomechanics simulations. In this paper, we introduce a novel optimization framework to address these challenges. The optimization problem is formulated as follows: maximize total CO 2 storage while minimizing geomechanical risks by adjusting the injection schedules within bounded constraints. The geomechanical risks are primarily driven by injection-induced pressure build-up, which is characterized by ground displacement and the induced microseismicity. We used the Fourier neural operator (FNO)-based deep learning model to construct surrogate models, replacing the time-consuming coupled flow and geomechanics simulations for evaluating the aforementioned objective functions. The developed surrogate models have been incorporated into a multiobjective optimization framework through a genetic algorithm to reduce the computational burden. The proposed optimization framework reduces the computational cost from approximately 2,400 hours, when using objective function evaluations based on physics-based simulations, to around 20 minutes. A set of Pareto-optimal solutions of the proposed workflow yields nontrivial optimal decisions, reducing the microseismicity potential and the vertical displacement. This Pareto front highlights the optimal trade-offs between CO 2 storage amount, safety, and ground displacement, emphasizing the need for careful optimization and management of injection strategies to achieve a balanced outcome. The novelty of this work is twofold. First, we demonstrate the importance of incorporating the minimization of the geomechanical risks as objective functions into the CO 2 storage optimization workflow to mitigate the potential risk of induced microseismicity and ground displacement. Second, we leverage the FNO-based surrogate models to optimize a real-field CO 2 storage operation.

42 ENGINEERING↗

DOE OSTI2023

Microbial Models for Simulating Soil Carbon Dynamics: A Review

Abstract Soils store the largest amount of carbon (C) in the biosphere, and the C pool in soil is critical to the global C balance. Numerous microbial models have been developed over the last few decades to represent microbial processes that regulate the responses of soil organic carbon (SOC) to climate change. However, the representation of microbial processes varies, and how microbial processes are incorporated into SOC models has not been well explored. Here, we reviewed 71 microbial models to characterize the microbial processes incorporated into SOC models and analyzed variations in mechanistic complexity. We revealed that (a) four processes (microbial‐mediated decomposition, mineral interaction, microbial necromass recycling, and active and dormant microbial dynamics) are commonly incorporated in microbial models, (b) ∼48% of models simulate only one microbial process (i.e., microbial‐mediated decomposition) and 35% of models simulate two microbial processes: for example, microbial‐mediated decomposition and mineral interaction, (c) more than 80% microbial models use nonlinear equations, such as forward Michaelis‐Menten kinetics, to represent SOC decomposition, (d) the concept of persistence of SOC due to its intrinsic properties has been replaced by organo‐mineral interaction (∼39% of microbial models) that protects SOC from decomposition, and (e) various temperature and moisture modifiers and pH effects have been used to explain the environmental effect on microbial processes. In the future, to realistically incorporate microbial processes into Earth System Models, it is imperative to identify experimental evidence on rate limitation processes and firmly ground model structure on the field and laboratory data.

Environmental Sciences & Ecology↗

DOE OSTI2024

Apparently superluminal superfluids

We consider the superfluid phase of a specific renormalizable relativistic quantum field theory. We prove that, within the regime of validity of perturbation theory and of the superfluid effective theory, there are consistent and regular vortex solutions where the superfluid’s velocity field as traditionally defined smoothly interpolates between zero and arbitrarily large superluminal values. We show that this solution is free of instabilities and of superluminal excitations. We show that, in contrast, a generic vortex solution for an ordinary fluid does develop an instability if the velocity field becomes superluminal. All this questions the characterization of a superfluid velocity field as the actual velocity of “something”.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

Nonlinear ion transport mediated by induced charge in ultrathin nanoporous membranes

Ultrathin membranes with nanoporous conduits show promise for ionic separations and desalination applications, but the mechanisms underlying the nonlinear ionic transport observed in these systems are not well understood. Here, in this work, we demonstrate how induced charge at membrane interfaces can lead to nonlinear ionic transport and voltage-dependent conductance through such channels. The application of an electric field on a polarizable membrane leads to induced charges at the membrane interfaces. The induced charges in turn are screened by diffuse charges in the electrolyte, which are acted upon by the electric field. For extremely thin membranes, the induced charge effect can be significant even for moderate applied voltages commonly used in experiments. We apply a continuum Poisson-Nernst-Planck model to characterize the current-voltage behavior of ultrathin membranes over a wide parameter space. The predictions of the model are compared to recent experiments on graphene and MoS 2 membranes in an electric field. We expect the role of induced charge to be especially pronounced in the limit of atomically thin membranes.

36 MATERIALS SCIENCE↗