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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Reconnection-driven Flares in M87*: Proton–Synchrotron-powered GeV Emission

Magnetic reconnection in current layers that form intermittently in radiatively inefficient accretion flows onto black holes is a promising mechanism for particle acceleration and high-energy emission. It has been recently proposed that such layers, arising during flux eruption events, can power the rapid TeV flares observed from the core of M87. In this scenario, inverse-Compton scattering of soft radiation from the accretion flow by energetic electron–positron pairs produced near the reconnection layer was suggested as the primary emission mechanism. However, detailed calculations show that radiation from pairs alone cannot account for the GeV emission detected by the Fermi observatory. In this work, we combine analytic estimates with 3D radiative particle-in-cell simulations of pair–proton plasmas to show that the GeV emission can be naturally explained by synchrotron radiation from protons accelerated in the current sheet. Although the exact proton content of the layer is uncertain, our model remains robust across a broad range of proton-to-pair number density ratios. While protons are subdominant in number compared to pairs, our simulations demonstrate that they can be accelerated more efficiently, leading to a self-regulated steady state in which protons dominate the energy budget. Ultimately, proton synchrotron emission accounts for approximately 5%–20% of the total dissipation power. The majority is radiated as MeV photons via pair synchrotron emission, with a smaller fraction emitted as TeV photons through inverse-Compton scattering.

Active galactic nuclei↗
DOE OSTI2025

Optimization of simulated high-field side lower hybrid current drive coupling using machine learning predictions of scrape-off layer density

Lower hybrid current drive (LHCD) is a potential source of non-inductive off-axis current drive (CD) for tokamaks. Although LHCD has been successfully deployed on a number of tokamaks, it is highly sensitive to the scrape-off layer (SOL) conditions local to the LHCD launcher. Large gaps between the launcher and plasma core, SOL turbulence, or edge density perturbations due to edge-localized modes can hamper CD or cause large reflected power. These coupling issues in part motivated the installation of an LHCD launcher on the high-field side (HFS) of DIII-D. On the HFS, the SOL is less turbulent and more controllable compared to the low-field side. This quiescence may result in more predictable edge conditions and thus a more predictable CD. Here, in this work, HFS SOL reflectometry measurements are predicted from global plasma parameters using machine learning models. The SOL predictions coupled with the full-wave simulation of the LHCD launcher allow for the prediction of reflected power, directivity, and arcing risk before the discharge. Launcher performance is then optimized using multi-objective Bayesian optimization, finding the shot parameters that result in an optimal SOL density that maximizes CD while minimizing the risk of arcing. The predictions and optimizations of LHCD performance are then accelerated using a surrogate model of the full-wave LHCD simulation.

Bayesian optimization↗
DOE OSTI2024

Ensemble Simulations on Leadership Computing Systems

Scientific productivity can be enhanced through workflow management tools, relieving large High Performance Computing (HPC) system users from the tedious tasks of scheduling and designing the complex computational execution of scientific applications. This paper presents a study on the usage of ensemble workflow tools to accelerate science using the Summit and Frontier supercomputing systems. The research aims to connect science domain simulations using Oak Ridge Leadership Computing Facility (OLCF) supercomputing platforms with ensemble workflow methods in order to accelerate HPC-enabled discovery and boost scientific impact. We present the coupling, porting and optimization of Radical-Cybertools on three applications: Chroma, NAMD and LAMMPS. The tools augment traditional HPC monolithic runs with a pilot scheduler. Lessons-learned are discussed for physics, biology and materials science applications. We discuss intrinsic limitations of coupling and porting ensemble workflow tools to applications that run on large HPC systems. The origins of technical challenges and their solutions developed during the implementation process are discussed. Data management strategies, OLCF’s policies for ensembles, and natively supported workflow tools are also summarized.

Georgiadou, Antigoni [ORNL] (ORCID:000000020977631↗
DOE OSTI2024

GEANT4 Simulations on Faraday Cup Design for PIP-II Laser Wire Scanner System

The PIP-II accelerator upgrade at Fermilab represents a groundbreaking leap forward in high-energy physics research. This ambitious initiative involves enhancing Fermilab's accelerator complex by replacing the current linear accelerator with a warm front end (WFE) capable of accelerating H- beams up to 2.1 MeV. Subsequently, a superconducting linac further accelerates these beams up to 800 MeV. To precisely measure the transverse beam profile, a combination of traditional wire scanners at the WFE section and Laser wire scanners along the superconducting linac are planned for implementation. This investigation centers on refining the Faraday cup design for the PIP-II Laser wire scanners by utilizing GEANT4, a Monte Carlo simulation toolkit. Leveraging this method enables a comprehensive analysis of particle trajectories, energy deposition, secondary electron emission, backscattering, etc., facilitating optimization through adjustments to cup geometries, materials, and placement to maximize its efficiency.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Latent diffusion can map beam loss to two-dimensional phase-space projections

Beam loss monitors (BLMs) and beam current monitors (BCMs) are ubiquitous at particle accelerators around the world. These simple devices provide noninvasive high-level beam measurements but give no insight into the detailed 6D (𝑥,𝑦,𝑧,𝑝 𝑥 ,𝑝 𝑦 ,𝑝 𝑧 ) beam phase-space distributions or dynamics. We show that generative conditional latent diffusion models can learn intricate patterns to solve the extreme inverse problem of mapping waveforms of tens of BLMs or BCMs along an accelerator to detailed 2D projections of a charged particle beam’s 6D phase-space density. This transformational method can be used at any particle accelerator to transform simple noninvasive devices into detailed beam phase-space diagnostics. We demonstrate this concept via multiparticle simulations of the high-intensity beam in the kilometer-long Los Alamos Neutron Science Center linear proton accelerator.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Beam correction for multi-pass arcs in FFA@CEBAF: status update

This work examines the multi-pass steering of six electron beams in an FFA arc ranging from approximately 10.5 GeV to 22 GeV. Shown here is an algorithm based on singular value decomposition (SVD) to successfully steer all six beams through the arc given precise knowledge of all beam positions at each of one hundred and one diagnostic locations with one hundred individual corrector magnets: that is successive application of SVD to different 100 × 101 response matrices—one for each beam energy. Further, a machine learning scheme is developed which only requires knowledge of the energy-averaged beam position at each location to provide equivalent steering. Extension of this scheme to other beam optics quantities as well as transverse and longitudinal coupling is explored.

Accelerator Physics↗
DOE OSTI2024

A Cellular Automaton Simulation for Predicting Phase Evolution in Solid-State Reactions

New computational tools for solid-state synthesis recipe design are needed in order to accelerate the experimental realization of novel functional materials proposed by high-throughput materials discovery workflows. This work contributes a cellular automaton simulation framework for predicting the time-dependent evolution of intermediate and product phases during solid-state reactions as a function of precursor choice and amount, reaction atmosphere, and heating profile. The simulation captures the effects of reactant particle spatial distribution, particle melting, and reaction atmosphere. Reaction rates based on rudimentary kinetics are estimated using density functional theory data from the Materials Project and machine learning estimators for the melting point and the vibrational entropy component of the Gibbs free energy. The resulting simulation framework allows for the prediction of the likely outcome of a reaction recipe before any experiments are performed. We analyze five experimental solid-state recipes for BaTiO 3 , CaZrN 2 , and YMnO 3 found in the literature to illustrate the performance of the model in capturing reaction selectivity and reaction pathways as a function of temperature and precursor choice. This simulation framework offers an easier way to optimize existing recipes, aid in the identification of intermediates, and design effective recipes for yet unrealized inorganic solids in silico .

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

Diffusion model approach to simulating electron-proton scattering events

Generative artificial intelligence is a fast-growing area of research offering various avenues for exploration in high-energy nuclear physics. In this work, we explore the use of generative models for simulating electron-proton collisions relevant to experiments like the Continuous Electron Beam Accelerator Facility and the future Electron-Ion Collider (EIC). These experiments play a critical role in advancing our understanding of nucleons and nuclei in terms of quark and gluon degrees of freedom. The use of generative models for simulating collider events faces several challenges such as the sparsity of the data, the presence of global or eventwide constraints, and steeply falling particle distributions. In this work, we focus on the implementation of diffusion models for the simulation of electron-proton scattering events at EIC energies. Our results demonstrate that diffusion models can reproduce relevant observables such as momentum distributions and correlations of particles, momentum sum rules, and the leading electron kinematics, all of which are of particular interest in electron-proton collisions. Although the sampling process is relatively slow compared to other machine-learning architectures, we find diffusion models can generate high-quality samples. We foresee various applications of our work including inference for nuclear structure, interpretable generative machine learning, and searches of physics beyond the Standard Model. Published by the American Physical Society 2024

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Elucidating texture and grain morphology contributions to the micromechanical response of additively manufactured Inconel 625

Microstructural variation of additively manufactured (AM) metal components in comparison to wrought counterparts makes certification for critical applications a challenge. Microscale simulations leveraging modern computational tools may be used to supplement testing of AM microstructures, thus accelerating certification by reducing the number of experiments needed. However, as micromechanical response is closely tied to critical properties like fatigue-life and fracture, utilization of these simulations with macroscale experimental data alone is insufficient. One means to attain microscale experimental data is in situ diffraction data collected from synchrotron X-ray sources. In this work, such data were collected during in situ compression of AM Inconel 625 superalloy. Interpretation of experimental results was assisted by massive (8M element) complementary micromechanical simulations performed on sets of virtual microstructures generated using cellular automata. Together, micromechanical data from diffraction experiments and simulations were used to probe the effects of textured “track” microstructures generated during laser powder bed fusion and directional strength-to-stiffness on micromechanical response. Though fiber-averaged directional strength-to-stiffness ratios were expected to dominate given the high elastic anisotropy of the material, the combination of small variations in texture and specific grain configurations unique to AM microstructures lead to significant variability in micromechanical response after yield. The findings emphasize the importance of high-fidelity microstructural representation that captures key texture components and AM-specific morphology for property prediction of AM metals.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Extending JuTrack’s capabilities to the FRIB accelerator to enhance online modeling

JuTrack is a Julia-based accelerator modeling and tracking package that utilizes compiler-level automatic differentiation (AD) to enable fast and accurate derivative calculations. While JuTrack provides a solid foundation for beam dynamics simulations, its capabilities must be extended to support the Facility for Rare Isotopes (FRIB) linac. This includes modeling heavy-ion linac accelerator components such as the liquid-lithium charge stripper, which facilitates efficient acceleration by remove electrons from heavy isotopes, and incorporating multi-charge state acceleration tracking, which allows for charge-dependent beam dynamics. These extensions address challenges such as the beam matching and optimization of multi charge state through various accelerating structures and beam-material interaction modeling while maintaining the auto differentiation capability. This work focuses on adapting JuTrack to incorporate these elements, enhancing its online modeling abilities. We present modifications to JuTrack’s framework and demonstrate their performance in FRIB simulations.

Accelerator Physics↗
DOE OSTI2025

Similarity Metric for Data Optimization and Efficient Training of Reactive Machine Learning Force Fields for Hydrocarbon Radiolysis

Radiolysis is a common approach to sterilize polymers, chemically modify them for upcycling, and accelerate their decomposition for recycling purposes. Reactive molecular dynamics (MD) simulations provide a powerful tool to generate atomic-level trajectories of the reactive processes and quantify radiolytic chemical degradation pathways. For this, machine learning (ML) surrogate models for reactive force fields with quantum mechanical accuracy are now widely used, which require ML training data sets that can provide information on atomic environments for target chemical systems. However, radiolysis chemistry can be highly complex and diverse, which poses significant challenges for generating training data to parametrize ML models. In this regard, we developed a method for optimizing the training data set using a cosine similarity metric to help guide training set selection for radiolysis of polyethylene, a model hydrocarbon polymer, as well as to enhance the transferability of our reactive ML force field (MLFF) to a variety of molecular and polymeric systems. Our approach performs atom-by-atom comparisons between local atomic environments to pinpoint important data points associated with rare and localized events, such as radiolysis damage within structures. We apply this approach to train the Chebyshev Interaction Model for Efficient Simulation (ChIMES) MLFF model, which expresses the atomic interaction potentials in terms of linear combinations of many-body Chebyshev polynomials. We first show that our method can reduce our training set size by ∼70% while improving overall accuracy compared to more standard MD model fitting approaches. We then validate our optimum model against diverse hydrocarbon simulation data, including simple alkanes and systems with unsaturated carbon bonds, over a wide range of thermodynamic conditions. Finally, we use our ChIMES model to perform MD simulations of radiolytic damage with large-scale systems that help avoid system size effects. Overall, our approach yields an MD force field that retains most of the accuracy of the underlying quantum method while yielding many orders of improvement in computational efficiency. In conclusion, our efforts will have impact on future hydrocarbon polymer radiolysis studies, where the chemical details of the polymer–radiation interactions can have a strong effect on the resulting products observed in experiments.

Hydrocarbons↗
DOE OSTI2024

Extending GPU-accelerated Gaussian integrals in the TeraChem software package to f type orbitals: Implementation and applications

Here, the increasing availability of graphics processing units (GPUs) for scientific computing has prompted interest in accelerating quantum chemical calculations through their use. However, the complexity of integral kernels for high angular momentum basis functions often limits the utility of GPU implementations with large basis sets or for metal containing systems. In this work, we report the implementation of f function support in the GPU-accelerated TeraChem software package through the development of efficient kernels for the evaluation of Hamiltonian integrals. The high efficiency of the resulting code is demonstrated through density functional theory (DFT) calculations on increasingly large organic molecules and transition metal complexes, as well as coupled cluster singles and doubles calculations on water clusters. Preliminary investigations into Ni(I) catalysis with DFT and the photochemistry of MnH(CH 3 ) with complete active space self-consistent field are also carried out. Overall, our GPU-accelerated software appears to be well-suited for fast simulation of large transition metal containing systems, as well as organic molecules.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Experimental and computational study of phase space dynamics in strongly coupled plasmas with steep density gradients

Understanding how plasmas thermalize when density gradients are steep remains a fundamental challenge in plasma physics, with direct implications for fusion experiments and astrophysical phenomena. Standard hydrodynamic models break down in these regimes, and kinetic theories make predictions that have never been directly tested. Here, we present the first detailed phase-space measurements of a strongly coupled plasma as it evolves from sharp density gradients to thermal equilibrium. Using laser-induced fluorescence imaging of an ultracold calcium plasma, we track the complete ion distribution function f(x,v,t). We discover that commonly used kinetic models (Bhatnagar–Gross–Krook and Lenard–Bernstein) overpredict thermalization rates, even while correctly capturing the initial counterstreaming plasma formation. Our measurements reveal that the initial ion acceleration response scales linearly with electron temperature, and that the simulations underpredict the initial ion response. In our geometry we demonstrate the formation of well-controlled counterpropagating plasma beams. This experimental platform enables precision tests of kinetic theories and opens new possibilities for studying plasma stopping power and flow-induced instabilities in strongly coupled systems.

Bergeson, Scott (ORCID:0000000231249226)↗
DOE OSTI2025

Analysis of the Blowout Plasma Wakefields Produced by Drive Beams with Elliptical Symmetry

In the underdense, or blowout regime, of plasma wakefield acceleration, the particle beam is denser than the plasma. In this scenario, the plasma electrons are nearly completely rarefied from the beam channel, leaving only a nominally uniform ion-filled “bubble.” Extensive investigations of this interaction assuming axisymmetry have been undertaken. However, the blowout produced by a transversely asymmetric (flat) driver, which would be present in linear collider “afterburner” schemes, possesses quite different characteristics. Such beams create an asymmetric plasma bubble which leads to unequal focusing in the two transverse dimensions, accompanied by a nonuniform accelerating gradient. The asymmetric blowout cross section is found through simulation to be elliptical, and treating it as such permits a simple extension of the symmetric theory. In particular, focusing fields linear in both transverse directions inside the bubble are found. The form of the wake potential and the associated beam-matching conditions in this elliptical cavity are discussed. Here, we also examine blowout boundary estimation in the long driver limit and applications of the salient asymmetric features of the wakefield.

Manwani, P. [University of California, Los Angeles↗
DOE OSTI2025

New particle pusher with hadronic interactions for modeling multimessenger emission from compact objects

We propose novel numerical schemes based on the Boris method in curved spacetime, incorporating both hadronic and radiative interactions for the first time. Once the proton has lost significant energy due to radiative and hadronic losses, and its gyroradius has decreased below typical scales on which the electromagnetic field varies, we apply a guiding center approximation (GCA). We fundamentally simulate collision processes either with a Monte-Carlo method or, where applicable, as a continuous energy loss, contingent on the local optical depth. To test our algorithm for the first time combining the effects of electromagnetic, gravitational, and radiation fields including hadronic interactions, we simulate highly relativistic protons traveling through various electromagnetic fields and proton backgrounds. We provide unit tests in various spatially dependent electromagnetic and gravitational fields and background photon and proton distributions, comparing the trajectory against analytic results. We propose that our method can be used to analyze hadronic interactions in black hole accretion disks, jets, and coronae to study the neutrino abundance from active galactic nuclei.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Searching for Strongly Coupled Dark Sectors with Unsupervised and Generative Learning

Recipient of the URA Early Career Award for groundbreaking searches for dark matter arising from strongly coupled dark sectors with the CMS detector, pioneering work in ML-based model-independent anomaly detection for collider and astrophysics experiments, and leadership in the development of new AI/ML techniques to improve event reconstruction and detector simulation in particle physics, as well as novel strategies to accelerate AI inference and throughput with heterogeneous computing using coprocessors as a service.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Realizing Steady-State Microbunching with Optical Stochastic Crystallization

Optical Stochastic Cooling (OSC) is a state-of-the-art beam cooling technology first demonstrated in 2021 at the IOTA storage ring at Fermilab's FAST facility. A second phase of the research program is planned to run in 2026 and will incorporate an optical amplifier to enable significantly increased cooling rates and greater operational flexibility. In addition to beam cooling, an OSC system can be configured to enable advanced control over the phase space of the beam. An example operational mode could enable crystallization, where the particles in a bunch are locked into a self-reinforcing, regular microstructure at the OSC fundamental wavelength; we refer to this as Optical Stochastic Crystallization (OSX). OSX represents a new path toward Steady-State Microbunching (SSMB), which may enable light sources combining the high brightness of a free-electron laser with the high repetition rate of a storage ring. Such a source has applications from the terahertz to the extreme ultraviolet (EUV), including high-power EUV generation for semiconductor lithography. This contribution will discuss the integration of OSX development as part of the OSC program at IOTA. The design of an accelerator lattice to enable the mechanism and associated high fidelity simulations will be shown, and a path to realizing an experimental demonstration will be discussed.

Wallbank, Michael James [Fermilab]↗
DOE OSTI2025

Steady-State Microbunching using Optical Stochastic Cooling

Optical Stochastic Cooling (OSC) is a state-of-the-art beam cooling technology first demonstrated in 2021 at the IOTA storage ring at Fermilab's FAST facility. A second phase of the research program is planned to run in 2026 and will incorporate an optical amplifier to enable significantly increased cooling rates and greater operational flexibility. In addition to beam cooling, an OSC system can be configured to enable advanced control over the phase space of the beam. An example operational mode could enable crystallization, where the particles in a bunch are locked into a self-reinforcing, regular microstructure at the OSC fundamental wavelength; we refer to this as Optical Stochastic Crystallization (OSX). OSX represents a new path toward Steady-State Microbunching (SSMB), which may enable light sources combining the high brightness of a free-electron laser with the high repetition rate of a storage ring. Such a source has applications from the terahertz to the extreme ultraviolet (EUV), including high-power EUV generation for semiconductor lithography. This contribution will discuss the integration of OSX development as part of the OSC program at IOTA. The design of an accelerator lattice to enable the mechanism and associated high fidelity simulations demonstrating the beam dynamics will be shown, and a path to realizing an experimental demonstration will be discussed.

Wallbank, Michael James [Fermilab]↗