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DOE OSTI2025

On resistive interchange, double tearing, and resonant and non-resonant infernal modes in spherical tokamak negative central shear discharges

We examine the linear and nonlinear stability of a sequence of reversed shear NSTX equilibria with the same toroidal current density and pressure profile but with different toroidal field strengths. All equilibria have two q = 2 surfaces and have q > 1 everywhere. For this sequence, which all have β P ∼ 0.5, as the minimum value of q decreases below about 1.4, the localized resistive interchange criterion is strongly violated, and the unstable mode with toroidal mode number n=1 changes its dominant poloidal mode number m from a (m,n)=(2,1) double tearing mode to a non-resonant (1,1) infernal mode. This (1,1) mode nonlinearly flattens both the current density and the pressure near the magnetic axis. The higher-n unstable modes are all resistive infernal modes localized around the surface where q has a minimum and the magnetic shear vanishes. They generally saturate nonlinearly at low amplitude but can cause magnetic surface breakup near the axis.

Spitzer resistivity↗

DOE OSTI2026

Data-Driven Voltage Regulation of Distribution Grid Using Nonlinear Autoregressive Model with Exogenous Inputs (NARX)

This article proposes data-driven control via a nonlinear autoregressive model with exogenous inputs (NARX) for real-time voltage regulation of a modified feeder using reactive power sources. Traditional voltage control strategies rely on rule-based heuristics or optimization techniques, which often require detailed system models and extensive computational resources. The NARX-based controller learns system dynamics from historical data and predicts optimal reactive power dispatch in real-time for voltage correction. The proposed approach is evaluated on a power system feeder model under varying load and network conditions. Simulation results demonstrate that the NARX-based controller achieves improved voltage regulation, offering higher adaptability to system fluctuations. This study highlights the potential of data-driven control for enhancing the reliability of power distribution networks.

Donge, Vrushabh [ORNL] (ORCID:0000000306062803)↗

DOE OSTI2026

Nonlinear reversal of photoexcitation on the attosecond time scale improves ultrafast X-ray diffraction images

The complex refractive index of a material governs its light-matter interactions, with intense light fields enabling tailored nonlinear optical responses. In the X-ray regime, rapid photoionization limits the potential of nonlinear techniques by inducing irreversible electronic damage. Here we demonstrate that intense, sub-femtosecond X-ray pulses, shorter than typical Auger decay times, can partially reverse photoexcitation via stimulated emission near atomic resonances. By analyzing thousands of coherent diffraction patterns and ion spectra from neon nanoparticles exposed to sub-fs and 15-fs pulses, we observe enhanced X-ray diffraction alongside reduced energy absorption for sub-fs pulses. Theoretical modeling attributes this to dynamics akin to Rabi flopping that prolong the lifetime of resonant states and suppress electronic bleaching. These findings suggest that ultrashort, intense X-ray pulses enable active control of X-ray refractive index and damage pathways, opening avenues for improved high-resolution imaging and nonlinear spectroscopy in complex nanoscale systems.

Ulmer, Anatoli [Universität Hamburg (Germany)] (OR↗

DOE OSTI2026

Field-driven ion pairing dynamics in concentrated electrolytes

Here, we investigate ion pairing dynamics in electrolytes driven far from equilibrium using molecular simulations and nonequilibrium rate theory. Focusing on 0.5M LiPF 6 in water and acetonitrile under uniform electric fields, we compute transition path theory observables, including reactive fluxes and mean first-passage times of ion pairing. Moreover, we introduce a dynamical proxy of free-ion population, where its field-induced change is strongly correlated with the nonlinear enhancement of conductivity, yielding an increase of 40% at 50 mV/Å in acetonitrile, compared to that of less than 10% in aqueous electrolytes. Further kinetic analysis elucidates that Onsager’s classical theory substantially overestimates field-induced enhancement of ion pair dissociation in molecular electrolytes. This discrepancy arises from solvent-mediated dynamical pathways and field-induced dielectric decrement that suppress ion pair dissociation within explicit solvents, highlighting that a faithful description of molecular details is essential. Our results provide a molecular interpretation of nonlinear electrolyte transport beyond continuum theories and establish a general framework for quantifying nonequilibrium reaction kinetics in condensed phase systems.

Moon, Seokjin [University of California, Berkeley,↗

DOE OSTI2025

Ultrafast dynamics of a fermion chain in a terahertz field-driven optical cavity

In this article, we study the effect of a terahertz field-driven single cavity mode for ultrafast control of a fermion chain with dissipation-induced nonlinearity and quadratic coupling to an infrared-active phonon mode. Unlike the first-order phase transition in the nonequilibrium steady state of the system without a cavity or with a high-quality cavity, we find that a realistic dissipation in a low-quality driven cavity process prevents such a transition. Without realistic photon loss from the cavity, the transition only survives for the lower phonon-polariton branch with strong drive; a weak laser field fails to induce the phase transition and renders the polaritons symmetrical. The ability to control the phase transition is crucial for realizing strongly modulated steady states; we propose experimentally feasible regimes where this occurs.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2025

Shear Response of Ionizable Polymer Melts at the Crossover from Ionomers to Polyelectrolytes

Nonlinear shear response of polymers is affected by inherent barriers for diffusive motion, including entanglements and topology. In ionizable polymers, ionic clusters further constrain the intrinsic dynamics of the polymers, significantly enhancing their viscosity. Here, using fully atomistic molecular dynamics simulations, the nonlinear shear response of ionizable polymers is presented, across the transition from the ionomer regime where distinctive clusters dominate the structure to the polyelectrolyte regime where clusters percolate, in polystyrene randomly sulfonated with fractions of SO 3 − groups of f = 0.20 and 0.35, in pristine and tetrahydrofuran (THF) swollen polyelectrolyte melts. For f = 0.20, the ionic clusters first fracture into smaller clusters followed by splitting into individual ionic groups and eventually reform. At higher f, the clusters morph in shape but do not break under high shear. At very high shear rates, all of the chains stretch and recoil rapidly. As the shear rate is reduced, some chains stretch and recoil, while others remain largely unaffected by the shear. Macroscopically, for all systems, the shear viscosity displays initially an elastic response, followed by nonlinear shear stress overshoot and, eventually, a steady state. The evolution of viscosity with time and shear reflects that of the ionizable domains.

cluster chemistry↗

DOE OSTI2024

Operation of Grid Forming Converters as Self Excited Induction Generators Under Non-Ideal Loading Conditions

Self-excited induction generators offer a robust solution for power production for standalone as well as grid-connected systems. In general, self-excited induction generators require excitation capacitors which make use of the machine magnetization characteristics for voltage build up process as well as operation at a specific frequency. In this paper, a self-excited induction machine is modeled with both the electrical and mechanical dynamics. This modeled virtual machine's dynamics are utilized for voltage build up process for a standalone photovoltaic converter connected to a local load for a microgrid application. The modeled machine's parameters are used from the name plate rating from the manufacturer. However, in a microgrid the accommodation of unbalanced and/or nonlinear harmonic rich load is a necessity, therefore, in this work the virtual self-excitation capacitors of the modeled machine are varied based on the machine characteristics. With the objective of ensuring harmonic free point of common coupling voltage, the modeled virtual self-excitation capacitors are varied to accomplish change in terminal frequency and the virtual load torque is varied to obtain voltage magnitude change. To verify the efficacy, the overall system is modeled in MATLAB/Simulink and PLECS domain and most important case studies are presented.

grid forming converters (GFM)↗

DOE OSTI2026

Multibody for Everybody (M4E) - A Linearization Approach to Enable Frequency Domain Analysis, Time Integration and Control Co-Design

1.1 Background/Objectives: Marine energy represents a promising yet underexploited source of power. To increase the harvested power, significant efforts have been made to improve wave energy converter (WEC) modeling capabilities and optimize power take-off (PTO) performance; however, these efforts have often treated WEC dynamics, PTO design, and controller development sequentially. In contrast, control co-design (CCD) is emerging as a promising strategy to address these issues directly, creating a growing need for fast analysis tools suitable for repeated simulation and parametric studies [1]. To support this need, this work presents the Multibody for Everybody (M4E) [2] linearization module, which employs a symbolic toolbox to provide deeper insight of WEC design parameters. The objective is to demonstrate that a minimal-coordinate linearization of articulated WEC dynamics can provide accurate wave response predictions and substantial computational savings relative to nonlinear time-domain simulation, while preserving compatibility with broader wave-energy analysis workflows, enabling CCD. 1.2 Approach/Activities: The proposed approach linearizes the equations of motion, generated by M4E, in minimal coordinates about a selected operating point and combines the resulting system with frequencydomain hydrodynamic terms to incorporate the reduced mass, damping, stiffness, and forcing operators. The linearized model is used for both impedance-based response amplitude operator (RAO) prediction and rapid regular-wave time integration. The methodology is demonstrated on a single-flap device and a FOSWEC configuration, with linearized M4E responses compared against the corresponding nonlinear M4E simulations and WEC-Sim results. Regular-wave time histories, RAO trends, and runtime differences are assessed. The framework is also compatible with broader wave-energy workflows, including coupling to WecOptTool, although that capability is not the focus of this work [3]. 1.3 Results/Lessons: The linearized M4E model reproduces key regularwave response characteristics such as integration and Response Amplitude over multiple frequencies. This module matches nonlinear M4E and WEC-Sim results while substantially reducing integration cost. Thus, the proposed framework can serve as a rapid analysis layer for articulated WEC design, parameter studies, and controls-oriented workflows. The analysis is most appropriate in the near-equilibrium regime, about the linearization point.

16 TIDAL AND WAVE POWER↗

DOE OSTI2025

Spontaneous magnon decays from nonrelativistic time-reversal symmetry breaking in altermagnets

Quasiparticles are central to condensed matter physics, but their stability can be undermined by quantum many-body interactions. Magnons, i.e., quasiparticles in quantum magnets, are particularly intriguing because their properties are governed by both real and spin space. While crystal symmetries may be low, spin interactions often remain approximately isotropic, limiting spontaneous magnon decay. Textbook wisdom holds that collinear Heisenberg magnets follow a dichotomy: ferromagnets host stable magnons, while antiferromagnetic magnons may decay depending on dispersion curvature. Up to now, relativistic spin-orbit coupling and noncollinear order that connect spin space to real space were shown to introduce more complex magnon instability mechanisms. Here, we show that even in nonrelativistic isotropic collinear systems, this conventional dichotomy is disrupted in altermagnets. Altermagnets, a newly identified class of collinear magnets, exhibit compensated spin order with nonrelativistic time-reversal symmetry breaking and even-parity band splitting. Using kinematic analysis, nonlinear spin-wave theory, and quantum simulations, we reveal that even weak band splitting opens a decay phase space, driving quasiparticle breakdown. Additionally, 𝑑-wave altermagnets form a rare “island of stability” at the Brillouin-zone center. Furthermore, our findings establish a quasiparticle stability trichotomy in collinear Heisenberg magnets and position altermagnets as a promising platform for unconventional spin dynamics.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2025

Dynamic Carrier Modulation via Nonlinear Acoustoelectric Transport in van der Waals Heterostructures

Dynamically manipulating carriers in van der Waals heterostructures could enable solid-state quantum simulators with tunable lattice parameters. A key requirement is the formation of deep potential wells to reliably trap excitations. Here, we report the observation of nonlinear acoustoelectric transport and dynamic carrier modulation in boron nitride-encapsulated graphene devices coupled to intense surface acoustic waves (SAWs) on LiNbO 3 substrates. SAWs generate strong acoustoelectric current densities ( J AE ), transitioning from linear to nonlinear regimes with increasing SAW intensity. In the nonlinear regime, periodic carrier (electrons, holes, or their mixtures) stripes emerge. Using counter-propagating SAWs, we create standing SAWs (SSAWs) to dynamically manipulate charge distributions without static gates. The saturation of J AE , attenuation transitions, and tunable resistance peaks confirms strong carrier localization. Finally, these results establish SAWs as a powerful tool for controlling carrier dynamics in two-dimensional (2D) materials, paving the way for the development of time-dependent quantum systems and acoustic lattices for quantum simulation.

2D materials↗

DOE OSTI2024

Classical-quantum simulation of non-equilibrium Marshak waves

In the radiation hydrodynamic simulations used to design inertial confinement fusion (ICF) and pulsed power experiments, nonlinear radiation diffusion tends to dominate CPU time. This raises the interesting question of whether a quantum algorithm can be found for nonlinear radiation diffusion which provides a quantum speedup. Recently, such a quantum algorithm was introduced based on a quantum algorithm for solving systems of nonlinear partial differential equations (PDEs) which provides a quadratic quantum speedup. Here, we apply this quantum PDE (QPDE) algorithm to the problem of a non-equilibrium Marshak wave propagating through a cold, semi-infinite, optically thick target, where the radiation and matter fields are not assumed to be in local thermodynamic equilibrium. The dynamics is governed by a coupled pair of nonlinear PDEs which are solved using the QPDE algorithm, as well as two standard PDE solvers: (i) Python's py-pde solver; and (ii) the KULL ICF simulation code developed at Lawrence-Livermore National Laboratory. We compare the simulation results obtained using the QPDE algorithm and the standard PDE solvers and find excellent agreement.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Principles of two-dimensional terahertz spectroscopy of collective excitations: The case of Josephson plasmons in layered superconductors

Two-dimensional terahertz spectroscopy (2DTS), a terahertz analog of nuclear magnetic resonance, is a new technique poised to address many open questions in complex condensed matter systems. The conventional theoretical framework used ubiquitously for interpreting multidimensional spectra of discrete quantum level systems is, however, insufficient for the continua of collective excitations in strongly correlated materials. Here, in this work, we develop a theory for 2DTS of a model collective excitation, the Josephson plasma resonance in layered superconductors. Starting from a mean-field approach at temperatures well below the superconducting phase transition, we obtain expressions for the multidimensional nonlinear responses that are amenable to intuition derived from the conventional single-mode scenario. We then consider temperatures near the superconducting critical temperature T c , where dynamics beyond mean-field become important and conventional intuition fails. As fluctuations proliferate near T c , the dominant contribution to nonlinear response comes from an optical parametric drive of counterpropagating Josephson plasmons, which gives rise to 2D spectra that are qualitatively different from the mean-field predictions. As such, and in contrast to one-dimensional spectroscopy techniques, such as third harmonic generation, 2DTS can be used to directly probe thermally excited finite-momentum plasmons and their interactions. Our theory can readily be tested in cuprates, and we discuss implications beyond the present context of Josephson plasmons.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2026

Use of Digital Real-Time Simulation and Optimization to Identify Maximum Real Power Injection on the Banshee Distribution Network: Preprint

This study investigates the hosting capacity of the Banshee Distribution Network by optimizing the real power injection at carefully selected Distributed Energy Resource (DER) locations. The analysis is conducted within the framework of power system operational constraints, including bus voltage ranges, thermal line ratings, and transformer loading limits. A Python-based Genetic Algorithm (GA), implemented using the PyGAD library, is employed to iteratively identify the optimal power injection configuration that maximizes network utilization while preserving system reliability. The methodology integrates a real-time simulation environment using the Real-Time Digital Simulator (RTDS), allowing high-fidelity evaluation of power flow and voltage behavior under each proposed injection scenario. By coupling the optimization algorithm with real-time simulation feedback, this approach ensures that both static and dynamic constraints are enforced during the evaluation process. The GA leverages evolutionary operators such as selection, crossover, and mutation to navigate the nonlinear search space efficiently. The results of the study delineate the feasible hosting capacity at three targeted buses, reflecting maximum real power levels that can be injected without causing voltage violations, transformer overloading, or line congestion. These findings provide a decision- support tool for distribution planners and utilities aiming to integrate higher penetrations of DERs in existing infrastructure. Additionally, the work lays the foundation for extending such optimization techniques to multi-objective formulations, including economic dispatch and reactive power coordination, in future studies.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

A neural-network-enhanced parameter-varying framework for multi-objective model predictive control applied to buildings

Management of the electrical grid is becoming more complex due to the increased penetration of alternative energy generation technologies and a broadening diversity of electric loads. This complexity creates challenges in balancing demand and generation that can increase the potential for grid instabilities. One effective way to address this issue is to leverage previously unexploited demand flexibility through advanced control strategies. In this work, we propose an advanced control method, called adaptive neural parameter-varying model predictive control (ANPV-MPC), to control the temperature and energy consumption of a building via its Heating, Ventilation, and Air Conditioning system. ANPV-MPC combines key ideas in parameter-varying control, adaptive control, and online learning strategies to bridge the gap between computationally efficient linear model predictive control and more accurate nonlinear model predictive control. The novelty in ANPV-MPC is the use of a physics-inspired Bayesian neural network to estimate the coefficients of the parameter-varying linear control model. The Bayesian neural network additionally provides uncertainty estimates, triggering online training to capture evolving building system conditions. We show that ANPV-MPC can approximate the building system dynamics with a 28.39% higher accuracy than traditional linear model predictive control, resulting in 36.23% better control performance without increasing complexity of the optimal control problem. ANPV-MPC also adapts in real time to previously unseen conditions using online learning, further improving its performance.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Surrogate Modeling of Landau Damping with Deep Operator Networks

Kinetic simulations excel at capturing microscale plasma physics phenomena with high accuracy, but their computational demands make them impractical for modeling large-scale space and astrophysical systems. In this context, we build a surrogate model, using Deep Operator Networks (DeepONets), based upon the Vlasov–Poisson simulation data to model the dynamical evolution of plasmas, focusing on the Landau damping process—a fundamental kinetic phenomenon in space and astrophysical plasmas. The trained DeepONets are able to capture the evolution of electric field energy in both linear and nonlinear regimes under various conditions. Extensive validation highlights DeepONets’ robust performance in reproducing complex plasma behaviors with high accuracy, paving the way for large-scale modeling of space and astrophysical plasmas.

plasma astrophysics↗

DOE OSTI2024

Effect of causality constraints on Bayesian analyses of heavy-ion collisions

There have long been questions about the limits to the validity of relativistic fluid dynamics and whether it is being used outside its regime of validity in modern simulations of relativistic heavy-ion collisions. An important new tool for answering this question is a causality analysis in the nonlinear regime—if the solutions of the evolution equations do not respect relativistic causality, then they are not a faithful representation of the underlying relativistic theory (in this case, quantum chromodynamics). Using this nonlinear criterion, it has recently been shown that hydrodynamics is indeed being used outside its regime of validity in simulations, at least sometimes. Here we explore the phenomenological implications, particularly the quantitative effects of demanding limits on acausality in modern Bayesian parameter estimation. We find that, while typically only a small fraction of the system's energy is initially in an acausal regime, placing strict limits on the allowed energy fraction significantly changes the preferred properties of the initial condition, which in turn alters the extracted medium properties such as bulk viscosity, where large values are no longer favored. Furthermore, these findings highlight the importance of developing better theoretical descriptions of the early-time, out-of-equilibrium dynamics of relativistic heavy-ion collisions.

Bayesian methods↗

DOE OSTI2026

Use of Digital Real-Time Simulation and Optimization to Identify Maximum Real Power Injection on Banshee Distribution Network

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Impact of dynamic Jahn-Teller effect on magnetic excitations, lattice vibration, and thermal conductivity in U 𝑥 ⁢T⁢h 1−𝑥 ⁡O 2 system

Vibrational and magnetic properties of single-crystal uranium-thorium dioxide (U 𝑥 ⁢T⁢h 1−𝑥⁡ O 2 ) with a full range of 0 < 𝑥 < 1 are investigated. Thorium dioxide is a diamagnet whose thermal properties are governed by lattice vibration. The addition of paramagnetic uranium ions leads to the emergence of magnetic effects that alter the thermophysical properties noticeably even at room temperature. The interaction of phonons with magnetic moments of uranium 5⁢𝑓 electrons mediated by magnetoelastic coupling results in an anomalous low-temperature thermal conductivity profile. Analysis of the magnetic susceptibility measurements indicates a departure from the Curie-Weiss relationship characteristic of noninteracting paramagnetic ions, previously associated with the dynamic Jahn-Teller (DJT) effect characterized by coupling between spin and the oxygen sublattice. The T 2⁢g Raman peak position follows a nonlinear trend as a function of uranium concentration and hints that these Raman active optical modes play a role in either DJT or in mediating quadrupole-quadrupole interactions. A first-principle-based thermal transport model is implemented to explain the low-temperature transport measurements, where the anomalous reduction is attributed to phonon-spin resonant scattering. The interplay between spins and phonons is also captured using high-resolution inelastic x-ray scattering (IXS) measurements of phonon linewidths. Our results provide insights into the phonon interactions with the magnetic excitations governing DJT effect and impacting the low-temperature thermal transport processes in this material system. Furthermore, these findings have implications for understanding low-temperature thermal transport and magnetic properties in advanced materials for information processing and energy applications.

36 - MATERIALS SCIENCE↗