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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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At least 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

How do magnetized HED flows transition to collimated plasma jets? (Final Report)

We present results from a pulsed-power experiment designed to generate accretion-like flows in the laboratory that may be used to study the dynamics of astrophysical accretion-driven outflows. To emulate systems like Young Stellar Object (YSO) outflows, we create radially converging plasma flows in a short disk that transition into much longer bipolar outflows. Our experiment uses a ~1 MA pulsed-power driver to generate plasma from an array of thin aluminum wires mounted to a 3D-printed current path that controls the angular momentum and magnetic field topology. In this paper, we show that experimental data scale favorably to observed properties of YSO jets. This set of experiments tests the simplest version of our possible plasma dynamics, where outflows are generated without rotation or axial magnetic field present. The temperature, velocity and density of the disk and outflows are characterized using interferometry, gated optical and ultraviolet imaging, and Thomson scattering diagnostics. We discuss the Reynolds number, magnetic Reynolds number and Mach numbers calculated in three different stages of the experiment, and compare our results to 3D numerical simulations in the PERSEUS magnetohydrodynamics code. This report is a summary of the work done by the graduate students who were supported by the project.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Role of Dynamic Polarization Interactions in the Electrical Double Layer at Calcite (104) Interfaces with Aqueous Solutions

Reactions at mineral interfaces with aqueous solutions control many geochemical and biogeochemical processes in the Earth’s critical zone. At the molecular level, insights into important properties such as the structure of the electrical double layer (EDL) at specific mineral interfaces continue to improve due to the increasing fidelity of laboratory instrumentation and computational approaches. However, molecular simulation approaches suffer from limited reach into relevant scales of time, length, and system complexity. To span this gap, a novel hybrid approach that couples first principle plane-wave density functional theory (DFT) with classical DFT (cDFT) is demonstrated and applied to calcite (104) interfaces with various electrolytes. In this approach, a region of interest described using DFT interacts with the surrounding medium described using cDFT to arrive at a self-consistent ground state. Benchmarking against experimental observations and entirely first principle DFT simulations demonstrates that this hybrid model efficiently encompasses the key short-range and collective interactions in the EDL. Simulations of calcite (104)/solution interfaces reveal the key static and dynamic polarization interactions that give rise to structuring of ions and water. Ion hydration interactions have the strongest effect on the depth of the first minimum in the density distribution of counterions at the surface, and the position and width of the first density peak is largely determined by the strength of ion-correlation forces. Finer details of ion distributions are controlled by mutual polarization of the calcite surface and interfacial electrolyte. Finally, this new ability to efficiently and rigorously predict EDL structure at mineral surfaces in contact with complex solutions paves the way to accurately modeling sorption, nucleation, dissolution, and growth in realistic systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Entropy production in model colloidal suspensions under shear via the fluctuation theorem

Dissipative systems often exhibit novel and unexpected properties. This is, for instance, the case of simple liquids, which, when subjected to shear and after reaching a steady state, can exhibit a negative entropy production over finite length scales and timescales. This result, among others, is captured by nonequilibrium relations known as fluctuation theorems. Using nonequilibrium molecular dynamics simulations, we examine how, by fine-tuning the properties of the components of a complex fluid, we can steer the nonequilibrium response of the fluid. More specifically, we show how we control the nonequilibrium probability distribution for the shear stress and, in turn, how often states with a negative entropy production can occur. To achieve this, we start by characterizing how the size for the liquid matrix impacts the probability of observing negative entropy states, as well as the timescale over which these can be observed. We then measure how the addition of larger particles to this liquid matrix, i.e., simulating a model colloidal suspension, results in an increase in the occurrence of such states. As a result, this suggests how modifications in the composition of the mixture and in the properties of its components lead to an increase in the probability of observing states of negative entropy production and, thus, for the system to run in reverse.

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

Atomic cluster expansion potential for large scale simulations of hydrocarbons under shock compression

We present an Atomic Cluster Expansion (ACE) machine learned potential developed for high-fidelity atomistic simulations of hydrocarbons, targeting pressures and temperatures near and above supercritical fluid regimes for molecular fluids. A diverse set of stoichiometries were covered in training, including 1:0 (pure carbon), 1:4 (methane), and 1:1 (benzene), and rich bonding environments sampled at supercritical temperatures, hydrogen rich, reactive mixtures where metastable stoichiometries arise, including 1:2 (ethylene) and 1:3 (ethane). A high-fidelity training database was constructed by performing large-scale quantum molecular dynamic simulations [density functional theory (DFT) MD] of diamond, graphite, methane, and benzene. A novel approach to selecting structures from DFT MD is also presented, which allows for the rapid selection of unique DFT MD frames from complex trajectories. Comparisons to DFT and experimental data demonstrate that the presented ACE potential accurately reproduces isotherms, carbon melting curves, radial distribution functions, and shock Hugoniots for carbon and hydrocarbon systems for pressures up to 100 GPa and temperatures up to 6000 K for hydrocarbon systems and up to 9000 K for pure carbon systems. This work delivers a potential that can be used for accurate, large-scale simulations of shocked hydrocarbons and demonstrates a methodology for fitting and validating machine learning interatomic potentials to complex molecular environments, which can be applied to energetic materials in future works.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Neural-network quantum states for the nuclear many-body problem

A long-standing goal of nuclear theory is to explain how the structure and dynamics of atomic nuclei and neutron-star matter emerge from the underlying interactions among protons and neutrons. Achieving this goal requires solving the nuclear quantum many-body problem with high accuracy across a wide range of length scales and density regimes. In this review, we discuss how artificial neural network representations of the nuclear many-body wave function have significantly extended the capabilities of continuum quantum Monte Carlo methods. In particular, neural network quantum states enable calculations of larger systems than were previously accessible and provide a flexible framework for capturing phenomena that challenge conventional approaches, including the emergence of nuclear clusters and superfluid phases in dense matter. We highlight recent applications to finite nuclei, infinite nuclear and neutron matter, and dynamical processes relevant to lepton-nucleus and nucleus-nucleus scattering. We also discuss conceptual and methodological connections with condensed matter physics, emphasizing developments in neural network quantum states that bridge strongly correlated systems across disciplines. Together, these developments demonstrate how neural-network methods open new avenues toward unified and accurate descriptions of nuclear structure, matter, and reactions.

Lovato, Alessandro [Argonne; TIFPA-INFN, Trento; V↗
DOE OSTI2026

Quantum kinetic modeling of KEEN waves in a warm-dense regime

We report the first fully kinetic, quantum study of kinetic electrostatic electron nonlinear (KEEN) waves, showing that quantum diffraction systematically erodes the classical trapping mechanism, narrows harmonic locking to the fundamental, and hastens post-drive decay. Electrons are evolved with a second-order Strang-split 1D1V Wigner–Poisson solver that couples conservative semi-Lagrangian WENO advection to an analytic Fourier space update for the non-local Wigner term, while ions remain classical. We focus on collisionless dynamics in a weakly coupled regime, providing a controlled baseline before collisional extensions. Short, frequency-tuned ponderomotive pulses drive KEEN formation in a uniform Maxwellian plasma; as the dimensionless quantum parameter H rises from the classical limit to values relevant to warm-dense matter, doped semiconductors, and 2D electron systems, the drive threshold increases, higher harmonics are damped, trapped electron vortices diffuse, and the subplasma electrostatic energy relaxes to a lower stationary level, as confirmed by continuous wavelet analysis. These microscopic changes carry macroscopic weight. Ignition-scale capsules now compress matter to regimes where the electron de Broglie wavelength rivals the Debye length, making classical kinetic descriptions insufficient. By extending KEEN physics into this quantum domain, our results offer a potential diagnostic of non-equilibrium electron dynamics for next-generation inertial-confinement designs and high-energy-density platforms, indicating that predictive fusion modeling may benefit from the integration of kinetic fidelity with quantum effects.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Computation of the Thermal Expansion Coefficient of Graphene with Gaussian Approximation Potentials

Direct experimental measurement of thermal expansion coefficient without substrate effects is a challenging task for two-dimensional (2D) materials, and its accurate estimation with large-scale ab initio molecular dynamics is computationally very expensive. Machine learning-based interatomic potentials trained with ab initio data have been successfully used in molecular dynamics simulations to decrease the computational cost without compromising the accuracy. In this work, we investigated using Gaussian approximation potentials to reproduce the density functional theory-level accuracy for graphene within both lattice dynamical and molecular dynamical methods, and to extend their applicability to larger length and time scales. Two such potentials are considered, GAP17 and GAP20. GAP17, which was trained with pristine graphene structures, is found to give closer results to density functional theory calculations at different scales. Further vibrational and structural analyses verify that the same conclusions can be deduced with density functional theory level in terms of the reasoning of the thermal expansion behavior, and the negative thermal expansion behavior is associated with long-range out-of-plane phonon vibrations. Thus, it is argued that the enabled larger system sizes by machine learning potentials may even enhance the accuracy compared to small-size-limited ab initio molecular dynamics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Enhancing Adaptive Physics Refinement Simulations Through the Addition of Realistic Red Blood Cell Counts

Simulations of cancer cell transport require accurately modeling mm-scale and longer trajectories through a circulatory system containing trillions of deformable red blood cells, whose intercellular interactions require submicron fidelity. Using a hybrid CPU-GPU approach, we extend the advanced physics refinement (APR) method to couple a finely-resolved region of explicitly-modeled red blood cells to a coarsely-resolved bulk fluid domain. We further develop algorithms that: capture the dynamics at the interface of differing viscosities, maintain hematocrit within the cell-filled volume, and move the finely-resolved region and encapsulated cells while tracking an individual cancer cell. Comparison to a fully-resolved fluid-structure interaction model is presented for verification. Finally, we use the advanced APR method to simulate cancer cell transport over a mm-scale distance while maintaining a local region of RBCs, using a fraction of the computational power required to run a fully-resolved model.

Roychowdhury, Sayan↗
DOE OSTI2020

Half-Integer Quantized Topological Response in Quasiperiodically Driven Quantum Systems

A spin strongly driven by two harmonic incommensurate drives can pump energy from one drive to the other at a quantized average rate, in close analogy with the quantum Hall effect. The pumping rate is a nonzero integer in the topological regime, while the trivial regime does not pump. The dynamical transition between the regimes is sharp in the zero-frequency limit and is characterized by a Dirac point in a synthetic band structure. We show that the pumping rate is half-integer quantized at the transition and present universal Kibble-Zurek scaling functions for energy transfer processes. Finally, our results adapt ideas from quantum phase transitions, quantum information, and topological band theory to nonequilibrium dynamics, and identify qubit experiments to observe the universal linear and nonlinear response of a Dirac point in synthetic dimensions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Dynamical Signatures of Thermotoga maritima Maltose-Binding Proteins Affected by Ligand Binding

Functional segregation among protein isoforms depends on the interplay of their overall structures and the molecular dynamics of these structures. Thermotoga maritima maltose-binding protein (tmMBP) isoforms show size-dependent differential binding of maltose and malto-oligomers while maintaining remarkable fold conservation. This differential behavior needs detailed characterization in native-like aqueous conditions to understand the effects of protein dynamics on ligand binding and recognition. Small-angle neutron scattering (SANS), neutron spin echo (NSE) spectroscopy, and dynamic light scattering (DLS) were used in conjunction with previously published computational molecular dynamics (MD) simulations to understand the dynamic behavior of tmMBPs experimentally. SANS provided information on the overall structure of the molecules, while NSE was used to determine the dynamics in the nanosecond time scale. Both tmMBP2 and tmMBP3 have a bidomain architecture linked with a flexible hinge, with the binding pocket sitting in the cleft between the two domains. tmMBP2 and tmMBP3 showed different solution dynamics, with the translational and rotational components dominating the dynamics of both systems, resulting in a clear differentiation of their diffusion pattern. A faster dynamics component was also observed and was attributed to segmental dynamics. Differences observed between the ligand-free (apo) and ligand-bound (holo) states of the two proteins are attributed to conformational entropy. Our results highlight the intricacies of how structure and dynamics can together shape binding to a repertoire of substrates in structurally similar proteins.

Diffusion↗
DOE OSTI2023

Editorial: Hydrology, ecology, and nutrient biogeochemistry at the terrestrial-aquatic interface

Terrestrial-aquatic interfaces (TAI) play important roles in mediating the exchange of water and chemicals between land, surface and subsurface water systems, which is tightly coupled with biogeochemical transformations that influence water quality and ecosystem health (Harvey and Gooseff, 2015; Harvey et al., 2019). Understanding and predicting the interactions between the hydrologic, ecologic, and biogeochemical processes at those interfaces is crucial for the sustainable management of water resources and promotion of healthy ecosystems under different environmental stresses and disturbances, including climate change, human activities, and more frequent occurrence of extreme events. Both process-based and data-driven approaches have emerged to address critical challenges at TAIs as integral parts of the Earth system (e.g., Ward and Packman, 2019; Chen et al., 2021; Dwivedi et al., 2022). In this Research Topic, we sought research that advances the understanding of coupled hydrologic, ecologic, and biogeochemical processes along various TAIs from the summit to sea, e.g., river corridors and coastal TAI systems. We invited observational, experimental, theoretical, analytical, numerical, and data-driven research that aims to understand hydro-biogeochemical processes such as the redox dynamics and biogeochemical transformations of carbon, nutrients, and metals occurring at the TAIs and address their heterogeneity and scaling challenges.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

FOCAL Campaign II/III: Applying Active Hull Controls Using Tuned-mass Dampers/Hull Flexibility and Internal Loads

Campaign II and III of the Floating Offshore-wind Controls Advanced Laboratory Experimental Program (FOCAL) aimed to generate a dataset enabling the validation of the performance and loads of a scaled hull, with and without structural hull control. The floating platform was subjected to a variety of wave environments and controlled using tuned-mass dampers (TMDs) tuned to two of the systems natural frequencies (Platform Pitch and Tower-bending). The floating platform is fully instrumented to record a variety of parameters in real time such as platform dynamics, accelerations, and loads at different points in the structure. The test data considered was generated at the University of Maine's Harold Alfond Wind and Wave (W2) testing facility. This testing was focused only on validation of wave loading, and wind conditions were not considered. As such the platform does not support a working turbine, and instead supports a structure designed to have the same mass properties as the 1:70 IEA 15MW Reference turbine. The Load Cases (LC) considered in this testing campaign are as follows: LC 1.X - Platform Static Offset (TMD off); LC 2.X - Platform Free-decays (TMD off); LC 3.X - Wave Cases (Regular Wave, Irregular Wave, Pink Noise Wave) with and without TMDs active. Detailed properties on the model system are found in the following reference: Lenfest E., Floating Offshore-wind Controls Advanced Laboratory (FOCAL) Experimental Program - Campaigns 2 and 3: 1:70 Model-scale Testing of the IEA-Wind 15MW Reference Turbine and the VolturnUS-S Hull. UMaine ASCC Report Number 23-56-1183.

17 WIND ENERGY↗
DOE OSTI2024

Advanced Compressors for CO2-Based Power Cycles and Energy

Pumped thermal energy storage (PTES) is a cost-effective method for grid-scale, long-duration electrical energy storage (LDES). PTES uses a heat pump cycle to transfer thermal energy from a low temperature reservoir (LTR) to a high temperature reservoir (HTR), later using a heat engine cycle to reverse the process. A key component of the PTES system, the heat pump compressor, represents a significant driver to the cost, performance and operating characteristics of the PTES system. For grid-scale charging (>100 MW), traditional compressor scaling charts indicate that the operating conditions needed would be best served by a multistage axial compressor. While frame gas turbine compressors at these power ratings exist and operate at higher pressure ratios than needed for the CO2 PTES system, the inlet pressure and fluid density of this application exceed experience values. A conceptual design of a large-scale CO2 axial compressor was completed, including mean-line estimates of the compressor performance at full power conditions. The results of the conceptual design were used to refine the PTES cycle design, and updated operating conditions provided for further aero design optimization. A roughly one third scale of the first three stages of the 100 MW compressor was designed, built, and tested at relevant PTES charging conditions. Results of this test program are compared against high-resolution computational fluid dynamics models.

Compressor Heat↗
DOE OSTI2025

Direct measurement of thermal Knudsen forces in rarefied gas environments

At micro- and nanoscales, momentum transfer between surfaces is influenced by various physical mechanisms, including quantum fluctuations, electromagnetic interactions, electric charges, and the dynamics of (rarefied) gases. Under non-isothermal conditions, rarefied gases give rise to thermal Knudsen forces whose magnitudes strongly depend on the gas species and surface characteristics. Knudsen forces are particularly relevant in nanotechnology, optical manipulation, and aerospace systems, where gas rarefaction occurs due to highly confined geometries, sub-micrometer length scales, and reduced particle densities. Despite their significance, predictive modeling of Knudsen forces is limited by a lack of comprehensive experimental data across diverse materials and surface morphologies. Here, in this work, we present a highly sensitive and adaptable measurement platform capable of directly quantifying Knudsen forces using a suspended, interchangeable micro-cantilever within controlled rarefied helium and nitrogen environments. The system integrates optical fiber interferometry to precisely capture out-of-plane displacements at sub-micrometer resolution, driven by Knudsen forces. From the empirical data, we derive a robust correlation linking the magnitudes of Knudsen forces to energy accommodation coefficients, offering deeper insights into the underlying gas–surface interaction mechanisms.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

NGEE Arctic Authorship Guidelines

Authorship Guidelines were developed to help facilitate trust among team members as we span multiple institutions, scientific disciplines, and career stages. NGEE Arctic was built on a foundation of open science, data sharing, and collaboration. In Phase 4 of the project, it was particularly important to keep this foundation in mind as we develop new collaborations across the Arctic. Included in this package is one *.pdf. The Next-Generation Ecosystem Experiments in the Arctic (NGEE Arctic) project is a research effort to reduce uncertainty in the Department of Energy’s Energy Exascale Earth System Model (E3SM) by developing a predictive understanding of Arctic tundra ecosystems underlain by permafrost and to quantify feedbacks from the Arctic tundra to the Earth system. NGEE Arctic is supported by the Department of Energy's Office of Biological and Environmental Research. Over Phases 1–3, observations made by the NGEE Arctic team across a gradient of permafrost landscapes in Arctic Alaska improved the representation of tundra processes in the land surface component of E3SM (the E3SM Land Model, ELM). Model improvements emphasized unique aspects of permafrost environments and explored reductions in model complexity while retaining predictive power. The Arctic-informed ELM developed by NGEE Arctic has been used to make novel predictions on processes ranging from permafrost thaw to soil biogeochemical cycling to Earth system feedbacks associated with the unique characteristics of tundra plants. In Phase 4, the NGEE Arctic team is evaluating our new predictive understanding under novel conditions across the Arctic domain. In collaboration with partners at long-term pan-Arctic research sites we are examining whether an Arctic-informed ELM can faithfully simulate interactions among surface and subsurface processes at site, regional, and pan-Arctic scales. In turn, we are using variety of tools to dynamically extend and evaluate ELM inference, with an emphasis on data synthesis and pan-Arctic model evaluation, reintegration of code with an evolving E3SM, scaling across heterogeneous Arctic landscapes, and the appropriate representation of the impacts of increasingly frequent Arctic disturbances.

Iversen, Colleen [ORNL] (ORCID:0000000182933450)↗
DOE OSTI2025

A foundation model for atomistic materials chemistry

Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much of our understanding of chemistry and materials science. Over the last decade or so, machine-learned force fields have transformed atomistic modeling by enabling simulations of ab initio quality over unprecedented time and length scales. However, early machine-learning (ML) force fields have largely been limited by (i) the substantial computational and human effort required to develop and validate potentials for each particular system of interest and (ii) a general lack of transferability from one chemical system to the next. Here, we show that it is possible to create a general-purpose atomistic ML model, trained on a public dataset of moderate size, that is capable of running stable molecular dynamics for a wide range of molecules and materials. We demonstrate the power of the MACE-MP-0 model-and its qualitative and at times quantitative accuracy-on a diverse set of problems in the physical sciences, including properties of solids, liquids, gases, chemical reactions, interfaces, and even the dynamics of a small protein. The model can be applied out of the box as a starting or "foundation" model for any atomistic system of interest and, when desired, can be fine-tuned on just a handful of application-specific data points to reach ab initio accuracy. Establishing that a stable force-field model can cover almost all materials changes atomistic modeling in a fundamental way: experienced users obtain reliable results much faster, and beginners face a lower barrier to entry. Foundation models thus represent a step toward democratizing the revolution in atomic-scale modeling that has been brought about by ML force fields.

Batatia, Ilyes↗
DOE OSTI2024

The inviscid incompressible limit of Kelvin–Helmholtz instability for plasmas

The Kelvin–Helmholtz Instability (KHI) is an interface instability that develops between two fluids or plasmas flowing with a common shear layer. KHI occurs in astrophysical jets, solar atmosphere, solar flows, cometary tails, planetary magnetospheres. Two applications of interest, encompassing both space and fusion applications, drive this study: KHI formation at the outer flanks of the Earth’s magnetosphere and KHI growth from non-uniform laser heating in magnetized direct-drive implosion experiments. Here, we study 2D KHI with or without a magnetic field parallel to the flow. We use both the GAMERA code, which solves the compressible Euler equations, and the STRATOSPEC code, which solves the Navier-Stokes equations under the Boussinesq approximation, coupled with the magnetic field dynamics. GAMERA is a global three-dimensional MHD code with high-order reconstruction in arbitrary nonorthogonal curvilinear coordinates, which is developed for a large range of astrophysical applications. STRATOSPEC is a three-dimensional pseudo-spectral code with an accuracy of infinite order (no numerical diffusion). Magnetized KHI is a canonical case for benchmarking hydrocode simulations with extended MHD options. An objective is to assess whether or not, and under which conditions, the incompressibility hypothesis allows to describe a dynamic compressible system. For comparing both codes, we reach the inviscid incompressible regime, by decreasing the Mach number in GAMERA, and viscosity and diffusion in STRATOSPEC. Here, we specifically investigate both single-mode and multi-mode initial perturbations, either with or without magnetic field parallel to the flow. The method relies on comparisons of the density fields, 1D profiles of physical quantities averaged along the flow direction, and scale-by-scale spectral densities. We also address the triggering, formation and damping of filamentary structures under varying Mach number or Atwood number, with or without a parallel magnetic field. Comparisons show very satisfactory results between the two codes. The vortices dynamics is well reproduced, along with the breaking or damping of small-scale structures. We end with the extraction of growth rates of magnetized KHI from the compressible regime to the incompressible limit in the linear regime assessing the effects of compressibility under increasing magnetic field. The observed differences between the two codes are explained either from diffusion or non-Boussinesq effects.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Adaptive Spatially Aware I/O for Multiresolution Particle Data Layouts

Large-scale simulations on nonuniform particle distributions that evolve over time are widely used in cosmology, molecular dynamics, and engineering. Such data are often saved in an unstructured format that neither preserves spatial locality nor provides metadata for accelerating spatial or attribute subset queries, leading to poor performance of visualization tasks. Furthermore, the parallel I/O strategy used typically writes a file per process or a single shared file, neither of which is portable or scalable across different HPC systems. We present a portable technique for scalable, spatially aware adaptive aggregation that preserves spatial locality in the output. We evaluate our approach on two supercomputers, Stampede2 and Summit, and demonstrate that it outperforms prior approaches at scale, achieving up to 2.5× faster writes and reads for nonuniform distributions. Furthermore, the layout written by our method is directly suitable for visual analytics, supporting low-latency reads and attribute-based filtering with little overhead.

Usher, Will↗