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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

HEPTAPOD: Orchestrating High Energy Physics Workflows Towards Autonomous Agency

Many workflows in high-energy-physics (HEP) stand to benefit from recent advances in transformer-based large language models (LLMs). While early applications of LLMs focused on text generation and code completion, modern LLMs now support orchestrated agency: the coordinated execution of complex, multi-step tasks through tool use, structured context, and iterative reasoning. We introduce the HEP Toolkit for Agentic Planning, Orchestration, and Deployment (HEPTAPOD), an orchestration framework designed to bring this emerging paradigm to HEP pipelines. The framework enables LLMs to interface with domain-specific tools, construct and manage simulation workflows, and assist in common utility and data analysis tasks through schema-validated operations and run-card-driven configuration. To demonstrate these capabilities, we consider a representative Beyond the Standard Model (BSM) Monte Carlo validation pipeline that spans model generation, event simulation, and downstream analysis within a unified, reproducible workflow. HEPTAPOD provides a structured and auditable layer between human researchers, LLMs, and computational infrastructure, establishing a foundation for transparent, human-in-the-loop systems.

Menzo, Tony [Alabama U.; Fermilab] (ORCID:00000002↗
DOE OSTI2022

Multitask Machine Learning of Collective Variables for Enhanced Sampling of Rare Events

Computing accurate reaction rates is a central challenge in computational chemistry and biology because of the high cost of free energy estimation with unbiased molecular dynamics. In this work, a data-driven machine learning algorithm is devised to learn collective variables with a multitask neural network, where a common upstream part reduces the high dimensionality of atomic configurations to a low dimensional latent space and separate downstream parts map the latent space to predictions of basin class labels and potential energies. Here, the resulting latent space is shown to be an effective low-dimensional representation, capturing the reaction progress and guiding effective umbrella sampling to obtain accurate free energy landscapes. This approach is successfully applied to model systems including a 5D Müller Brown model, a 5D three-well model, the alanine dipeptide in vacuum, and an Au(110) surface reconstruction unit reaction. It enables automated dimensionality reduction for energy controlled reactions in complex systems, offers a unified and data-efficient framework that can be trained with limited data, and outperforms single-task learning approaches, including autoencoders.

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

Evaluation of geophysical and anthropogenic sources of hydroacoustic noise in the Alaskan arctic

Quantifying the ocean soundscape is crucial for ocean-based seismoacoustic monitoring; it sets a baseline for the kinds and sizes of signals that can be detected above the background noise. As sea ice recedes, human activity in and around the Arctic Ocean is increasing, elevating sound levels and heightening the urgency of monitoring. Here, seven years of passive acoustic recordings from a National Oceanographic and Atmospheric Administration hydrophone in the Beaufort Sea are analyzed, focusing on frequencies between 1 and 125 Hz, a band of interest for detecting regional earthquakes and similar events. Sound is related to geophysical and anthropogenic sources, and seasonal and intraseasonal variations in the soundscape are examined. Sea ice emerges as a keystone feature of the Arctic Ocean acoustic environment, controlling or contributing to ambient sound levels at all frequencies studied. During low-ice months, sound levels are dominated by wind and sea surface waves, and in ice-covered months, wind-driven ice noise dominates. Seismic air gun surveys are prominent during low-ice periods, with sound levels decreasing with increasing distance and bathymetric complexity along the propagation path. The implications of this baseline soundscape for event detection in the Alaskan Arctic are discussed.

Niklasson, Siobhan [Los Alamos National Laboratory↗
DOE OSTI2024

Development of a Hardware-in-The-Loop Testbed for a Decentralized, Data-Driven Electric Vehicle Charging Control Algorithm

This study presents the design of an electric vehicle (EV)-grid integration (EVGI) hardware test-bed to implement smart EV charging algorithms. Here, the proposed test-bed also allows to create different grid events via flexible integration of other power hardware (e.g., controllable loads and battery energy storage systems) and test their impacts on EV charging. The design uses a real-time digital simulator to realize a complex distribution grid model with primary and secondary networks. A grid simulator physically realizes the selected nodes of the simulated grid to power an actual EV, forming a hardware-in-the-loop (HIL) test setup. The EV-grid integration is demonstrated based on the custom hardware and software implementation of the J1772 charging protocol using dSPACE MicroLabBox, operating as a custom EV Supply Equipment (EVSE). The HIL test-bed features a novel testing platform for accurate implementation and analysis of scalable charging algorithms. To this end, a data-driven, decentralized, model-free charging controller based on the Additive Increase and Multiplicative Decrease (AIMD) algorithm is presented and validated on an EV using the HIL test-bed. We tested the proposed algorithm under various case studies, and presented a comparison study with an existing droop-based, decentralized charging solution. The results showed that the EV successfully performed charging commands generated by the EVSE and regulated its charging power to effectively reduce the system loading caused by high EV penetration.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2019

Modeling Load Dynamics to Support Resiliency-based Operations in Low-Inertia Microgrids

Microgrids have repeatedly demonstrated the ability to provide uninterrupted service to critical end-use loads during normal outages, severe weather events, and natural disasters. While their ability to provide critical services is well documented, microgrids present a more dynamic operational environment than grid-connected distribution systems. The dynamics of a microgrid are commonly driven by the high inertia of rotating generators, which are common in many microgrids. In such high-inertia systems, the impact of end-use load dynamics are often not examined. However, with the increased penetration of inverter-based generation with little or no inertia, it is necessary to consider the impact that the dynamics of the end-use loads have on the operations of microgrids, particularly for a resiliency-based operation. These operations include, but are not limited to, switching operations, loss of generating units, and the starting of induction motors. This paper examines the importance of including dynamic models of the end-use load when evaluating the operations of low inertia microgrids, and shows that by properly representing their behavior, it is possible to cost effectively size equipment while supporting resilient operations of critical end-use loads.

Tuffner, Francis K.↗
NASA NTRS2020

Object-Based Comparison of Data-Driven and Physics-Driven Satellite Estimates of Extreme Rainfall

The Global Precipitation Measurement (GPM) constellation of spaceborne sensors provides a variety of direct and indirect measurements of precipitation processes. Such observations can be employed to derive spatially and temporally consistent gridded precipitation estimates either via data-driven retrieval algorithms or by assimilation into physically based numerical weather models. We compare the data-driven Integrated Multisatellite Retrievals for GPM (IMERG) and the assimilation-enabled NASA-Unified Weather Research and Forecasting (NU-WRF) model against Stage IV reference precipitation for four major extreme rainfall events in the southeastern United States using an object-based analysis framework that decomposes gridded precipitation fields into storm objects. As an alternative to conventional ‘‘grid-by-grid analysis,’’ the object-based approach provides a promising way to diagnose spatial properties of storms, trace them through space and time, and connect their accuracy to storm types and input data sources. The evolution of two tropical cyclones are generally captured by IMERG and NU-WRF, while the less organized spatial patterns of two mesoscale convective systems pose challenges for both. NU-WRF rain rates are generally more accurate, while IMERG better captures storm location and shape. Both show higher skill in detecting large, intense storms compared to smaller, weaker storms. IMERG’s accuracy depends on the input microwave and infrared data sources; NU-WRF does not appear to exhibit this dependence. Findings highlight that an object-oriented view can provide deeper insights into satellite precipitation performance and that the satellite precipitation community should further explore the potential for ‘‘hybrid’’ data-driven and physics-driven estimates in order to make optimal usage of satellite observations.

extreme events↗
DOE OSTI2018

Importance sampling large deviations in nonequilibrium steady states. I

Large deviation functions hold information on the stability and response of systems driven into nonequilibrium steady states and in such a way are similar to free energies for systems at equilibrium. As with equilibrium free energies, evaluating large deviation functions numerically for all but the simplest systems is difficult because by construction they depend on exponentially rare events. In this first paper of a series, we assess different trajectory-based sampling methods capable of computing large deviation functions of time integrated observables within nonequilibrium steady states. We highlight some convergence criteria and best practices using a number of different models, including a biased Brownian walker, a driven lattice gas, and a model of self-assembly. We show how two popular methods for sampling trajectory ensembles, transition path sampling and diffusion Monte Carlo, suffer from exponentially diverging correlations in trajectory space as a function of the bias parameter when estimating large deviation functions. Improving the efficiencies of these algorithms requires introducing guiding functions for the trajectories.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2019

Acceleration of charged particles to extremely large energies by a sub-Dreicer electric field

Acceleration of a fraction of initially low-energy electrons in a cold, collisional plasma to energies orders of magnitude larger than thermal is shown to be possible with a sub-Dreicer electric field. Because such an electric field does not satisfy the runaway condition, any acceleration will be statistical. Random scattering collisions are probabilistic such that there is 63% chance that a particle collides after traveling one mean free path and a 37% chance of not colliding. If one considers only the electrons that do not collide on traversing a mean free path and also considers that the collisional mean free path scales quadratically with particle kinetic energy, one realizes that there will be a small fraction of electrons that never collide and are accelerated to increasingly high energy. Because the mean free path scales quadratically with kinetic energy, after each successfully traveled mean free path, continued acceleration becomes more likely. This model is applied to an MHD-driven plasma jet experiment at Caltech and it is shown that electrons are accelerated by an electric field associated with a fast magnetic reconnection event occurring as the jet breaks apart. This statistical acceleration model indicates that a fraction ~1.3 × 10 -7 of electrons with initial energy distributed according to a Maxwellian with T = 2 eV will be accelerated to 6 keV in the Caltech experiment and then collide to produce the observed X-ray signal. It is demonstrated that the statistical acceleration model provides a credible explanation for the production of solar energetic electrons.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

Toward adaptive decision support for assessing infrastructure system resilience using hidden performance measures

The understanding of resilience is an emerging topic within the study of risks affecting distributed transportation infrastructure systems. Although recent studies have explored the quantification of system resilience, there has been limited research aimed at understanding the role of multiple performance measures, geographies, and modeling uncertainties within the assessment of resilience and associated decision-making. Under real-world conditions, there is an increased burden on analysts for translating observed system data (from human and electronic sensor observations) into the estimation of system performance that may not be directly observable. This paper addresses these issues using a scenario-based risk modeling approach to understand: 1) resilience of complex systems, often in cases of hidden (not readily observable) measures of performance, 2) resilience sensitivity to modeling uncertainties in event and system characteristics, and 3) resilience sensitivity to the measurement of performance across multiple dimensions. The methods in this paper integrate uncertainty-driven risk and probabilistic modeling within a multi-state Markov approach. This study contributes to the state-of-the-art by developing methodologies for assessing community perceptions of infrastructure system resilience using observable factors and inferring possibly hidden performance measures for facilitating adaptive decision-support. The methods are demonstrated with hypothetical spatiotemporal data for the Washington, District of Columbia and Baltimore, Maryland urban regions in the United States against evacuation-causing and shelter-in-place disruptive scenarios across multiple system performance dimensions. The analysis results may be useful for risk managers, supply chain analysts, emergency decision-makers, and the general public.

Infrastructure System Resilience, Risk Management,↗
DOE OSTI2025

Impurity radiation seeding of neoclassical tearing mode growth

Abstract The physics of neoclassical tearing modes (NTMs) is of great concern to tokamak plasma stability and performance, especially in the burning plasma regime. Whereas in many situations the seed events can be clearly identified, such as sawteeth and edge localized modes, the potential seeding mechanism of NTMs due to the resistive tearing instability driven by the impurity radiation cooling still needs more study. Recent NIMROD simulations have demonstrated that local impurity radiation cooling can drive the seed island growth and trigger the subsequent onset of NTM instability. The seed island is mainly driven by the local helical perturbation of the diamagnetic current induced by the perturbed pressure gradient as a result of the impurity radiative cooling on the rational surface. A heuristic closure for the neoclassical viscosity is adopted, and the seed island is further driven by the perturbed bootstrap current induced from the neoclassical electron viscous stress in the extended Ohm’s law. The growth rate of the NTM in simulations is found proportional to the electron neoclassical viscosity, and a theoretical neoclassical driving term is adopted to account for the nonlinear neoclassical island growth in the simulations.

Zeng, Shiyong (ORCID:000000017098818X)↗
DOE OSTI2020

DeepGrid: Robust Deep Reinforcement Learning-based Contingency Management

Increasing uncertainty raised by the integration of renewable energy resources requires an enormous number of simulations to be carried out for the security assessment of the power grid. However, it is challenging to assess the steady-state and dynamic security indices for different system contingency events by doing an exhaustive analysis in real-time due to the computational and communication constraints. One promising solution is using data-driven techniques along with the system models to train an intelligent contingency management framework to better handle the contingencies in real-time. Nevertheless, implementing a data-driven technique to obtain the best remedial actions necessitates to account for the effect of the measurement noise on the performance of the contingency management. To tackle these challenges, we leverage a robust deep reinforcement learning (DRL) algorithm called Double Deep Q-Network (DDQN) to design a recommender system capable of prescribing optimal control actions with the help of the real-time digital simulator (RTDS). The use of RTDS system in combination with the advanced DRL algorithm allows to explore a wide variety of system contingencies in order to derive better remedial actions. The performance of the proposed algorithm is evaluated in IEEE 9-bus system under different loading conditions, and different network configurations in presence of noisy measurements.

Ghasemkhani, Amir↗
DOE OSTI2022

Assessment of the Imaging Properties of Plastic Scintillators at FP60R for Neutron Imaging Applications [Slides]

4mm thick Stilbene gives the brightest signal but at the cost of resolution. 1 and 2 cm BC412 gave the highest resolution images. The HI-LY scintillator is better suited for DD neutrons. Future work includes: measurements this summer using an event-mode imaging camera which promises sub-pixel resolution through center-of-mass techniques, opportunities for DD neutron tests during laser-driven z-pinch experiments at UNR, and assess scatter within scintillator.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

A Framework for Characterizing the Risk of Ice Fall and Ice Throw from Small Wind Turbines

Small wind turbines rated up through 100 kW in size are typically deployed as distributed energy resources. Their proximity to populations, buildings, and other infrastructure can generate safety concerns regarding ice throw (ice detaching from operational turbines) and ice fall (ice detaching from a turbine during standstill or idling) even when the turbines are not installed in cold climates. This paper presents a data-driven approach to characterize and mitigate the potential risk from icing on small wind turbines. By identifying how likely it is that an icing event will occur each year, estimating the distances at which ice could throw or fall from the turbine, defining the risk context, and establishing risk management practices, small wind turbine developers and installers can help address communities’ safety concerns around ice throw and ice fall.

17 WIND ENERGY↗
DOE OSTI2024

Transmission Value in 2023: Market Data Shows the Value of Transmission Remained High in Certain Locations Despite Overall Low Wholesale Electricity Prices

In 2023 additional electricity transmission would have provided the most value for links that crossed between grid interconnection regions in the United States (the Western Interconnection, the Eastern Interconnection, the Texas Interconnection) or crossed between system operator regions within the same interconnection. Many multi-interconnection or multi-region links had values of greater than $\$20$/MWh, or up to $\$175$ million/yr per 1 GW expanded transmission (subject to limits to the depth of the market at each side of the link). In contrast, many links within regions, or between regions in the northeast, had relatively low values in 2023, following the overall decline in wholesale electricity prices in 2023 compared with 2021-2022. The most valuable link in 2023, at $\$61$ /MWh, was between Texas and the Southwest. Multiple events in 2023 (high natural gas prices in the western U.S., and high summer temperatures in Texas and the Southwest) were observed to have driven this high value. Of particular note, high prices in Texas occurred at a largely distinct set of hours from high prices in the Southwest, helping to drive up the value of transmission in total and demonstrating significant value to both regions. This example demonstrates the unique value of transmission (compared to other solutions, such as building local generation resources) in delivering benefits to multiple regions given its ability to connect areas of the country that inevitably face differing circumstances.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Optimizing Solar PV Deployment in Manufacturing: A Morphological Matrix and Fuzzy TOPSIS Approach

The growing energy demand of the industrial sector and the need for sustainable solutions highlight the importance of efficient decision making in solar photovoltaic (PV) implementation. Selecting optimal PV configuration is complex due to the interdependent technical, economic, environmental, and social factors involved. This study introduces an integrated decision-making method combining a morphological matrix and fuzzy TOPSIS to systematically select and rank optimal PV system configurations for manufacturing firms. While the morphological matrix exhaustively examines possible design solutions based on sensing, smart, sustainable, and social (S4) attributes, the fuzzy TOPSIS method ranks the alternatives by handling uncertainty in decision making. A case study conducted in a Mexican manufacturing company validates the methodology’s effectiveness. The optimal PV configuration identified comprehensively addresses operational and sustainability criteria, covering all lifecycle stages. This approach demonstrates quantitative superiority and greater robustness compared to existing fuzzy TOPSIS-based methods for solar PV applications. The findings highlight the practical value of data-driven, multi-criteria decision making for industrial solar energy adoption, enhancing project feasibility, cost efficiency, and environmental compliance. Future research will incorporate discrete event simulation (DES) to further refine energy consumption strategies in manufacturing.

Briceño, Citlaly Pérez↗
NASA NTRS1977

Computer simulation of earthquakes

In a computer simulation study of earthquakes a seismically active strike slip fault is represented by coupled mechanical blocks which are driven by a moving plate and which slide on a friction surface. Elastic forces and time independent friction are used to generate main shock events, while viscoelastic forces and time dependent friction add aftershock features. The study reveals that the size, length, and time and place of event occurrence are strongly influenced by the magnitude and degree of homogeneity in the elastic, viscous, and friction parameters of the fault region. For example, periodically reoccurring similar events are observed in simulations with near-homogeneous parameters along the fault, whereas seismic gaps are a common feature of simulations employing large variations in the fault parameters. The study also reveals correlations between strain energy release and fault length and average displacement and between main shock and aftershock displacements.

Cohen, S. C.↗
NASA NTRS2006

Over-and-Out Coronal Mass Ejections: Blowouts of Magnetic Arches by Ejective Flares in One Foot

Streamer puffs from compact ejective flares in the foot of an outer loop of the magnetic arcade under a streamer were recently identified as a new variety of coronal mass ejection (CME) (Bemporad, Sterling, Moore, & Poletto 2006, ApJ Letters, in press). In the reported examples, the compact flares produced only weak to moderate soft X-ray bursts having peak intensities no stronger than GOES class C3. Here, we present two examples of this type of CME in which the compact flare in the flank of the steamer base is much stronger (one M-class, the other X-class in GOES X-rays) and the resulting streamer puff is wider and brighter than in the discovery examples. Coronal dimming observed in SOHOBIT Fe XII images in the launching of each of these two CMEs M e r supports the view that these CMEs are produced by a high loop of the steamer arcade being blown out by magnetoplasma ejecta exploding up the leg of the loop from the flare. In addition, we present evidence that this same type of CME occurs on larger scales than in the above examples. We examine a sequence of flare eruptions seated on the north side of AR 8210 as it rotated across the southern hemisphere in late April and early May 1998. Each flare occurs in synchrony with the launching of a large CME centered on the equator. Coronal dimming in EIT Fe XII images shows the trans-equatorial footprints of these CMEs extending north from the flare site. The set of flare-with-CME events includes the trans-equatorial loop eruptions reported by Khan & Hudson (1998, GRL, 27, 1083). Our observations indicate that these CMEs were not driven by the self-eruption of the transequatorial loops, but that these loops were part of a trans-equatorial magnetic arch that was blown open by ejecta from the flares on the north side of AR 8210. Thus, a relatively compact ejective flare can be the driver of a CME that is much larger in lateral extent than the flare and is laterally far offset from the flare. It has previously been thought that such spatial disparities between the flare and the CME prohibited the flare explosion from being the driver of the CME (e.g., Kahler 1992, ARA&A, 30, 113).

Moore, Ronald L.↗
NASA NTRS2010

A Multi-Spacecraft View of Solar-Energetic-Particle Onsets in the 1977 November 22 Event

We examine the onset timing of solar energetic particles (SEPs) in the large ground-level event (GLE) of 1977 November 22 as observed from six spacecraft at four distinct solar longitudes. In most cases, it was possible to use velocity dispersion of the energetic protons to fix the solar particle release (SPR) time and the path-length traveled by the initial particle burst from each solar longitude. We find that the SPR times do depend upon solar longitude, being earliest for spacecraft that are magnetically well-connected to the source region, and later for longitudes on the flanks of the outward driven shock wave. The earliest SPR time occurs well after peak photon emission from the associated Ha flare. These observations are consistent with conclusions derived from single-longitude observations of different GLE events.

Reames, Donald V.↗