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DOE OSTI2021

Adaptive Load Shedding for Grid Emergency Control via Deep Reinforcement Learning

Emergency control, typically such as under-voltage load shedding (UVLS), is broadly used to grapple with low voltage and voltage instability issues in real-world power systems under contingencies. However, existing emergency control schemes are rule-based and cannot be adaptively applied to uncertain and floating operating conditions. Here, we propose an adaptive UVLS algorithm for emergency control via deep reinforcement learning (DRL) and expert systems. We first construct dynamic components for picturing the power system operation as the environment. The transient voltage recovery criteria, which poses time-varying requirements to UVLS, is integrated into the states and reward function to advise the learning of deep neural networks. The proposed method has no tuning issue of coefficients in reward functions, and this issue was regarded as a deficiency in the existing DRL-based algorithms. Case studies illustrate that the proposed method outperforms the traditional UVLS relay in both the timeliness and efficacy for emergency control.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Towards robust PICOSEC Micromegas precise timing detectors

The PICOSEC Micromegas (MM) detector is a precise timing gaseous detector consisting of a Cherenkov radiator combined with a photocathode and a MM amplifying structure. A 100-channel PICOSEC MM prototype with 10 × 10 cm 2 active area equipped with a Cesium Iodide (CsI) photocathode demonstrated a time resolution below σ = 18 ps. The objective of this work is to improve the PICOSEC MM detector robustness aspects, i.e. integration of resistive MM and carbon-based photocathodes, while maintaining good time resolution. The PICOSEC MM prototypes have been tested in laboratory conditions and successfully characterised with 150 GeV/c muon beams at the CERN SPS H4 beam line. The excellent timing performance below σ = 20 ps for an individual pad obtained with the 10 × 10 cm 2 area resistive PICOSEC MM of 20 MΩ/$\square$ showed no significant time resolution degradation as a result of adding a resistive layer. A single-pad prototype equipped with a 12 nm thick Boron Carbide (B 4 C) photocathode presented a time resolution below σ = 35 ps, opening up new possibilities for detectors with robust photocathodes. The results made the concept more suitable for the experiments in need of robust detectors with good time resolution.

RETHGEM↗

DOE OSTI2022

Microfabricated Devices and Ion Trapping Capabilities

Next generation ion traps will likely need to support tens if not hundreds of ions in order to achieve several logical qubits. As we scale to those sizes, the same problems we face now – rf dissipation, control I/O, and optical access – will only grow and become more complicated. While many of these challenges can potentially be solved with technology integration, independently researching the feasibility of that integration and other solutions may help reduce the time and risk in scaling up to larger traps, by testing on smaller less complex devices. We should also consider other fabrication techniques that may help scale to larger devices, such as: through-substrate-vias (TSVs), different metal coatings, exotic rf routing, on chip laser sources, or even a secondary macroscopic trap to reload ions from. To have these technologies ready for full scale integration when we need them, ion traps with some of these capabilities need to be produced now. Developing the rigorous fabrication methods for producing reliable traps takes time and experimentation. We propose developing larger ion traps and reliable integrated technology in conjunction to make both available faster.

36 MATERIALS SCIENCE↗

DOE OSTI2025

On the lapse contour in the gravitational path integral

The gravitational path integral is usually implemented with a covariant action by analogy with other gauge field theories, but the gravitational case is different in important ways. A key difference is that the integrand has an essential singularity, which occurs at zero lapse where the spacetime metric degenerates. The lapse integration contour required to impose the local time reparametrization constraints must run from − ∞ to + ∞ , yet must not pass through zero. This raises the question: for an application—such as a partition function—where the constraints should be imposed, what is the correct integration contour, and why? We study that question by starting with the reduced phase space path integral, which involves no essential singularity. We observe that if the momenta are to be integrated before the lapse, to obtain a configuration space path integral, the lapse contour should pass below the origin in the complex lapse plane. This contour is also consistent with the requirement that quantum field fluctuation amplitudes have the usual short distance vacuum form, and with obtaining the Bekenstein-Hawking horizon entropy from a Lorentzian path integral. Published by the American Physical Society 2025

Banihashemi, Batoul (ORCID:0000000228679209)↗

DOE OSTI2024

Simple and General Unitarity Conserving Numerical Real-Time Propagators of the Time-Dependent Schrödinger Equation Based on Magnus Expansion

Magnus expansion (ME) provides a general way to expand the real-time propagator of a time-dependent Hamiltonian within the exponential such that the unitarity is satisfied at any order. Here, we use this property and explicit integration of Lagrange interpolation formulas for the time-dependent Hamiltonian within each time interval and derive approximations that preserve unitarity for the differential time evolution operators of general time-dependent Hamiltonians. The resulting second-order approximation is the same as using the average of Hamiltonians for two end points of time. We identify three fourth-order approximations involving commutators of Hamiltonians at different times and also derive a sixth-order expression. A test of these approximations along with other available expressions for a two-state time-dependent Hamiltonian with sinusoidal time dependences provides information on the relative performance of these approximations and suggests that the derived expressions can serve as useful numerical tools for time evolution in time-resolved spectroscopy, quantum control, quantum sensing, real-time ab initio quantum dynamics, and open system quantum dynamics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Integration of Electrolysis Systems Into Isolated Microgrid Systems at Extreme Cold Climates

Isolated microgrid systems are generally used to power communities in remote locations where transmission line installations are cost prohibitive. These systems are usually powered by diesel generators, delivering expensive energy with high carbon emissions. However, most of these communities have special geographic access to renewable energy. These special conditions make it interesting to investigate the deployment of renewable energies and storage solutions. Within this scope, this paper analyses the integration of water electrolysis systems into off-grid communities, simulating the electrolyzer as well as the whole electrical grid in real time.

digital real-time simulation↗

DOE OSTI2024

Dynamic mode decomposition of nonequilibrium electron-phonon dynamics: accelerating the first-principles real-time Boltzmann equation

Abstract Nonequilibrium dynamics governed by electron–phonon ( e -ph) interactions plays a key role in electronic devices and spectroscopies and is central to understanding electronic excitations in materials. The real-time Boltzmann transport equation (rt-BTE) with collision processes computed from first principles can describe the coupled dynamics of electrons and atomic vibrations (phonons). Yet, a bottleneck of these simulations is the calculation of e –ph scattering integrals on dense momentum grids at each time step. Here we show a data-driven approach based on dynamic mode decomposition (DMD) that can accelerate the time propagation of the rt-BTE and identify dominant electronic processes. We apply this approach to two case studies, high-field charge transport and ultrafast excited electron relaxation. In both cases, simulating only a short time window of ~10% of the dynamics suffices to predict the dynamics from initial excitation to steady state using DMD extrapolation. Analysis of the momentum-space modes extracted from DMD sheds light on the microscopic mechanisms governing electron relaxation to a steady state or equilibrium. The combination of accuracy and efficiency makes our DMD-based method a valuable tool for investigating ultrafast dynamics in a wide range of materials.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Learning Forecasts of Rare Stratospheric Transitions from Short Simulations

Abstract Rare events arising in nonlinear atmospheric dynamics remain hard to predict and attribute. We address the problem of forecasting rare events in a prototypical example, sudden stratospheric warmings (SSWs). Approximately once every other winter, the boreal stratospheric polar vortex rapidly breaks down, shifting midlatitude surface weather patterns for months. We focus on two key quantities of interest: the probability of an SSW occurring, and the expected lead time if it does occur, as functions of initial condition. These optimal forecasts concretely measure the event’s progress. Direct numerical simulation can estimate them in principle but is prohibitively expensive in practice: each rare event requires a long integration to observe, and the cost of each integration grows with model complexity. We describe an alternative approach using integrations that are short compared to the time scale of the warming event. We compute the probability and lead time efficiently by solving equations involving the transition operator, which encodes all information about the dynamics. We relate these optimal forecasts to a small number of interpretable physical variables, suggesting optimal measurements for forecasting. We illustrate the methodology on a prototype SSW model developed by Holton and Mass and modified by stochastic forcing. While highly idealized, this model captures the essential nonlinear dynamics of SSWs and exhibits the key forecasting challenge: the dramatic separation in time scales between a single event and the return time between successive events. Our methodology is designed to fully exploit high-dimensional data from models and observations, and has the potential to identify detailed predictors of many complex rare events in meteorology.

Finkel, Justin↗

DOE OSTI2024

Reverse Time Migration of Explosion-generated Infrasound and Ground-Coupled Airwaves

Accurately locating sources of seismic and infrasonic energy is integral to global monitoring of earthquakes and explosions. Infrasound arrivals times can be used to calculate the origins of events that generate acoustic energy. Picking times of emergent infrasound arrivals, however, can be difficult and prone to uncertainty. Reverse time migration (RTM) is a waveform based location method that does not rely on picked arrival times. Here we use RTM to locate a known chemical explosion that generated acoustic and acoustic-to-seismic signals on 26 and 108 receivers, respectively. All location predictions are less than 24 km from the known location with time errors of less than three minutes. We find strong overall agreement between our results and those of existing RTM and arrival time based methods. Our initial results suggest that RTM is a promising method of event location using acoustic arrivals recorded on both infrasound and seismic instrumentation.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2022

Inclusive, prompt and non-prompt J/ψ production at midrapidity in p-Pb collisions at $ \sqrt{s_{\mathrm{NN}}} $ = 5.02 TeV

A measurement of inclusive, prompt, and non-prompt J/ψ production in p-Pb collisions at a nucleon-nucleon centre-of-mass energy √ s NN = 5.02 TeV is presented. The inclusive J/ψ mesons are reconstructed in the dielectron decay channel at midrapidity down to a transverse momentum p T = o. The inclusive J/ψ nuclear modification factor R pPb is calculated by comparing the new results in p-Pb collisions to a recently measured proton-proton reference at the same centre-of-mass energy. Non-prompt J/ψ mesons, which originate from the decay of beauty hadrons, are separated from promptly produced J/ψ on a statistical basis for p T larger than 1.0 GeV/c. These results are based on the data sample collected by the ALICE detector during the 2016 LHC p-Pb run, corresponding to an integrated luminosity L int = 292 ± 11 μb –1 , which is six times larger than the previous publications. The total uncertainty on the p T -integrated inclusive J/ψ and non-prompt J/ψ cross section are reduced by a factor 1.7 and 2.2, respectively. The measured cross sections and R pPb are compared with theoretical models that include various combinations of cold nuclear matter effects. From the non-prompt J/ψ production cross section, the bb¯ production cross section at midrapidity, dσ bb¯ /dy, and the total cross section extrapolated over full phase space, σ bb¯ , are derived.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Quantum Hamilton-Jacobi theory, spectral path integrals, and exact WKB analysis

We propose a new way to perform path integrals in quantum mechanics by using a quantum version of Hamilton-Jacobi (HJ) theory. In classical mechanics, Hamilton-Jacobi theory is a powerful formalism, however, its utility is not explored in quantum theory beyond approximation schemes. The canonical transformation enables one to set the new Hamiltonian to constant or zero, but keeps the information about solution in Hamilton’s characteristic function. To benefit from this in quantum theory, one must work with a formulation in which classical Hamiltonian is used. This uniquely points to phase space path integral. However, the main variable in HJ formalism is energy, not time. Thus, we are led to consider the Fourier transform of the path integral, the spectral path integral Z ˜ ( E ) . The evaluation of path integrals reduces to determining the quantum Hamilton characteristic functions (which can be achieved via an asymptotic analysis) and a discrete sum over the quantum period lattice, generalizing Gutzwiller’s sum. Published by the American Physical Society 2025

Türe, Mustafa (ORCID:0009000975968618)↗

DOE OSTI2024

Unveiling the Potential of MeshGraphNets for Predicting Subsurface Evolution in Carbon Storage Projects

This is the conference paper accompanying an oral presentation “Unveiling the Potential of MeshGraphNets for Predicting Subsurface Evolution in Carbon Storage Projects” at the 17th International Conference on Greenhouse Gas Control Technologies GHGT-17 held in Calgary, Canada, October 20-24 , 2024. Carbon capture and storage (CCS) technology is critical for mitigating climate change but requires effective subsurface reservoir management to ensure safe containment of injected CO2. Accurate predictions of reservoir pressure and saturation are essential for assessing long-term CCS performance. Traditional numerical simulations, while effective, are computationally intensive, time-consuming, and constrained by data discretization. Previous work has shown the effectiveness of MeshGraphNets (MGN), a graph-based machine learning framework, as an innovative alternative for predicting reservoir behavior. MGN leverages graph neural networks (GNNs) and mesh representations to model complex geological formations, offering superior adaptability across different discretizations and reservoir configurations. Classic MGN implementations utilize an autoregressive technique to predict future behavior based on current predictions, but this technique is hampered by error accumulation over time. To enhance the model accuracy in time-series predictions, this study implemented a multi-step rollout strategy that integrates autoregressive predictions during training to stabilize prediction of saturation over time. Using the Illinois Basin – Decatur Project (IBDP) dataset, comprising 100 simulations of CO2 injection, pressure, and saturation changes, the framework demonstrated its ability to learn spatial dependencies and temporal dynamics. With inputs including permeabilities, porosities, and injection rates, MGN accurately predicted CO2 plume evolution over time, even with limited training data. Moreover, the addition of a multi-step rollout procedure during training improved the ability of MGN to predict stably over time by ~15%. This research positions MGN, enhanced with multi-step rollout capabilities, as a robust and efficient tool for CCS applications. It advances the field by enabling precise, computationally efficient predictions of reservoir behavior, providing a foundation for the broader adoption of machine learning frameworks in CCS and other geoscience domains.

Holcomb, Paul↗

DOE OSTI2024

System for controller area network payload decoding

A system for decoding an unknown automotive controller area network (“CAN”) message definitions. CAN data vehicle signal mappings are typically held in secret and varied by automotive model and year. Without knowledge of the mappings, the wealth of real-time vehicle data hidden in the automotive CAN packets is uninterpretable—impeding research, after-market tuning, efficiency and performance monitoring, fault diagnosis, and privacy-related technologies. This system can ascertain the CAN signals' boundaries (start bit and length), endianness (byte ordering), signedness (binary-to-integer encoding) from raw CAN data. This allows conversion of CAN data to time series. Interpreting the translated CAN data's physical meaning and finding a linear mapping to standard units (e.g., knowing the signal is speed and scaling values to represent units of miles per hour) can be achieved for many signals by leveraging diagnostic standards to obtain real-time measurements of in-vehicle systems. The system can be integrated into lightweight hardware enabling an OBD-II plugin for real-time in-vehicle CAN decoding or run on standard computers. The system can output a standard DBC file with the signal definition information.

Verma, Kiren E.↗

DOE OSTI2020

NESSi : The N on- E quilibrium S ystems S imulation package

The nonequilibrium dynamics of correlated many-particle systems is of interest in connection with pump–probe experiments on molecular systems and solids, as well as theoretical investigations of transport properties and relaxation processes. Nonequilibrium Green’s functions are a powerful tool to study interaction effects in quantum many-particle systems out of equilibrium, and to extract physically relevant information for the interpretation of experiments. Here, we present the open-source software package NESSi (The Non-Equilibrium Systems Simulation package) which allows to perform many-body dynamics simulations based on Green’s functions on the L-shaped Kadanoff–Baym contour. NESSi contains the library libcntr which implements tools for basic operations on these nonequilibrium Green’s functions, for constructing Feynman diagrams, and for the solution of integral and integro-differential equations involving contour Green’s functions. The library employs a discretization of the Kadanoff–Baym contour into time points and a high-order implementation of integration routines. The total integrated error scales up to $\mathcal{O}(N^{-7})$, which is important since the numerical effort increases at least cubically with the simulation time. A distributed-memory parallelization over reciprocal space allows large-scale simulations of lattice systems. We provide a collection of example programs ranging from dynamics in simple two-level systems to problems relevant in contemporary condensed matter physics, including Hubbard clusters and Hubbard or Holstein lattice models. The libcntr library is the basis of a follow-up software package for nonequilibrium dynamical mean-field theory calculations based on strong-coupling perturbative impurity solvers.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2020

Minimizing total energy cost and tardiness penalty for a scheduling-layout problem in a flexible job shop system: A comparison of four metaheuristic algorithms

Job scheduling and machine layout are interrelated in improving energy consumption (EC) and productivity measures such as tardiness and represent two important decisions that must be made by manufacturers. This interdependency can be explained by transportation time, which connects scheduling and layout. Scheduling and layout, however, have not been thoroughly studied in conjunction using an integrated model in the context of sustainable manufacturing. Hence, we propose an energy-aware optimization model in which scheduling is integrated with layout in a single-level framework. More specifically, a single objective function is defined to minimize the facility energy cost and the job tardiness penalty, which control EC and tardiness respectively in a flexible job shop system. In order to model machine EC more accurately, we also consider three different machine states: a processing state and two idle states. Our case studies show that the integrated model exhibits better performance in controlling manufacturing EC and job tardiness than a non-integrated model in which machine locations are uncontrollable and transportation times between machines are unchangeable. To deal with large-sized problems, we also introduce four new metaheuristics. The performances of these new metaheuristics are compared in terms of objective function values and CPU times using various case studies. The results indicate that a hybrid ant colony optimization and simulated annealing (ACO-SA) algorithm provides better performance than the other algorithms. Specifically, our case studies show that the integrated model using ACO-SA can improve the objective function value by around 5% when compared to the non-integrated model.

42 ENGINEERING↗

DOE OSTI2023

Microelectrode Arrays Measure Blocking of Voltage-Gated Calcium Ion Channels on Supported Lipid Bilayers Derived from Primary Neurons

Drug studies targeting neuronal ion channels are crucial to understand neuronal function and develop therapies for neurological diseases. The traditional method to study neuronal ion-channel activities heavily relies on the whole-cell patch clamp as the industry standard. However, this technique is both technically challenging and labour-intensive, while involving the complexity of keeping cells alive with low throughput. Therefore, the shortcomings are limiting the efficiency of ion-channel-related neuroscience research and drug testing. Here, this work reports a new system of integrating neuron membranes with organic microelectrode arrays (OMEAs) for ion-channel-related drug studies. This work demonstrates that the supported lipid bilayers (SLBs) derived from both neuron-like (neuroblastoma) cells and primary neurons are integrated with OMEAs for the first time. The increased expression of voltage-gated calcium (CaV) ion channels on differentiated SH-SY5Y SLBs compared to non-differentiated ones is sensed electrically. Also, dose-response of the CaV ion-channel blocking effect on primary cortical neuronal SLBs from rats is monitored. The dose range causing ion channel blocking is comparable to literature. This system overcomes the major challenges from traditional methods (e.g., patch clamp) and showcases an easy-to-test, rapid, ultra-sensitive, cell-free, and high-throughput platform to monitor dose-dependent ion-channel blocking effects on native neuronal membranes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Temporal entanglement entropy as a probe of renormalization group flow

The recently introduced concept of timelike entanglement entropy has sparked a lot of interest. Unlike the traditional spacelike entanglement entropy, timelike entanglement entropy involves tracing over a timelike subsystem. In this work, we propose an extension of timelike entanglement entropy to Euclidean space (“temporal entanglement entropy”), and relate it to the renormalization group (RG) flow. Specifically, we show that tracing over a period of Euclidean time corresponds to coarse-graining the system and can be connected to momentum space entanglement. We employ Holography, a framework naturally embedding RG flow, to illustrate our proposal. Within cutoff holography, we establish a direct link between the UV cutoff and the smallest resolvable time interval within the effective theory through the irrelevant $T\bar{T}$ deformation. Increasing the UV cutoff results in an enhanced capability to resolve finer time intervals, while reducing it has the opposite effect. Moreover, we show that tracing over a larger Euclidean time interval is formally equivalent to integrating out more UV degrees of freedom (or lowering the temperature). As an application, we point out that the temporal entanglement entropy can detect the critical Lifshitz exponent z in non-relativistic theories which is not accessible from spatial entanglement at zero temperature and density.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2026

Thermal property characterization of phase change materials in building applications: A systematic review of fundamentals, recent progress, and future directions

Phase change materials (PCMs) can reduce building peak loads and enable demand-responsive thermal energy storage (TES), but their deployment depends on reliable measurement and interpretation of thermal properties across laboratory, intermediate, and application scales. Here, this review systematically examines characterization methods, testing protocols, and recent advances for neat PCMs and PCM composites, emphasizing thermal conductivity, enthalpy-related properties (phase change temperature, latent heat, specific heat), and cycling stability. For thermal conductivity, we compare steady-state and transient techniques and note limitations when phase transition and contact resistance affect measurements. For enthalpy–temperature characterization, we discuss differential scanning calorimetry together with intermediate- and bulk-scale methods, including T-history, heat flow meter testing, and three-layer calorimetry (3LC), to generate application-relevant enthalpy–temperature profiles. Cycling stability is organized into four experimental families: thermoelectric–air, fully thermoelectric, water-bath, and in situ chamber approaches, with attention to separating reversible supercooling from true degradation such as phase segregation. We highlight emerging noncontact diagnostics, including infrared thermography and embedded sensing, for spatially resolved validation and multiscale interpretation. Finally, we review the growing use of AI and machine learning for property prediction, inverse characterization from experimental signals, and real-time state estimation in building-integrated TES. Key needs include harmonized protocols, interlaboratory benchmarking, uncertainty reporting, and metadata-rich datasets to accelerate reproducible PCM qualification for grid-flexible buildings.

AI↗