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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

PIP-II Particle Accelerator Project

The PIP-II project is an essential upgrade of Fermilab’s particle accelerator complex. Groundbreaking occurred in March 2019. The upgrade will enable Fermilab’s accelerators to generate an unprecedented stream of neutrinos—subtle, subatomic particles that could hold the key to understanding the universe’s evolution—by creating the world’s most intense high-energy neutrino beams. This capability positions Fermilab to be the world leader in accelerator-based neutrino research. It enables the scientific program for the international, Fermilab-hosted Deep Underground Neutrino Experiment (DUNE) and Long-Baseline Neutrino Facility (LBNF). The key to PIP-II is power. When the PIP-II project is complete, Fermilab will be able to generate proton beams greater than 1 megawatt—60 percent higher than current capabilities. These powerful beams of protons will in turn create intense beams of neutrinos. Future PIP-II upgrades will triple the lab’s current beam power. PIP-II’s high-intensity proton beams will provide a flexible platform for the long-term future of the Fermilab accelerator complex and the U.S. accelerator-based particle physics program.

Fermilab, Fermilab↗
DOE OSTI2026

Multiscale and multidimensional modeling of particle acceleration and transport in solar flares

Multi-messenger, multi-viewpoint, and time-resolved observations of solar flares are now providing unprecedented constraints on particle acceleration sites, energy conversion, and energy transport. The interpretation of current observations, including microwave imaging spectroscopy from EOVSA, hard x-ray (HXR) imaging from Solar Orbiter/STIX, gamma-ray diagnostics from Fermi, and in situ measurements from Parker Solar Probe and Solar Orbiter, collectively demands modeling frameworks that go beyond traditional spatially unresolved, one-zone models or single-mechanism descriptions. This review surveys multiscale and multidimensional modeling approaches, including kinetic, magnetohydrodynamic (MHD), and macroscopic particle models, that are being developed to meet the need. Kinetic simulations reveal that three-dimensional (3D) effects, including field-line chaos and self-generated turbulence, are essential for sustained power-law particle acceleration. MHD simulations now capture flux-rope eruptions, plasmoid-unstable current sheets, and turbulent flare regions in realistic magnetic topologies. Macroscopic models coupling MHD with energetic-particle models produce spatially resolved electron distributions and synthetic HXR and microwave emissions for direct comparison with observations. Despite these advances, outstanding challenges remain in bridging kinetic and global scales, improving MHD simulations and macroscopic particle models, and achieving quantitative model-observation closure.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Kinetic study of shock formation and particle acceleration in laser-driven quasi-parallel magnetized collisionless shocks

Quasi-parallel magnetized collisionless shocks are believed to be one of the most efficient accelerators in the universe. Compared to quasi-perpendicular shocks, quasi-parallel shocks are more difficult to form in the laboratory and to simulate because of their large spatial scales and long formation times. Our two-dimensional particle-in-cell simulations show that the early stages of quasi-parallel shock formation are achievable in experiments planned for the National Ignition Facility and that particles accelerated by diffusive shock acceleration (DSA) are expected to be observable in the experiment. Repetitive ion acceleration by crossings of the shock front, a key feature of DSA, is seen in the simulations. Other characteristic features of quasi-parallel shocks such as upstream wave excitation by energetic ions are also observed, and energy partition between the ions and the electrons in the downstream of the shock is briefly discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Plasma flow generation and particle acceleration from expanding magnetic bubbles

Impulsive plasma dynamics in the laboratory are often driven by rising electric currents, yet their quantitative plasma response has not been well established. By means of fully kinetic particle-in-cell simulations and laser-driven capacitor-coil experiments, we show that a rising current expels plasma, forming an expanding magnetic bubble and accelerating particles. The expansion front velocity scales with the Alfvén speed determined by the magnetic field at its inner edge and the plasma density at its outer edge. This mechanism establishes impulsive current drive as a fundamental way that generates plasma flows and accelerates particles in laboratory plasmas, with potential relevance to astrophysics.

Zhang, Yang [Princeton University, NJ (United Stat↗
DOE OSTI2021

Magnetic Energy Release, Plasma Dynamics, and Particle Acceleration in Relativistic Turbulent Magnetic Reconnection

In strongly magnetized astrophysical plasma systems, magnetic reconnection is believed to be the primary process during which explosive energy release and particle acceleration occur, leading to significant high-energy emission. Past years have witnessed active development of kinetic modeling of relativistic magnetic reconnection, supporting this magnetically dominated scenario. A much less explored issue in studies of relativistic reconnection is the consequence of three-dimensional dynamics, where turbulent structures are naturally generated as various types of instabilities develop. This paper presents a series of three-dimensional, fully kinetic simulations of relativistic turbulent magnetic reconnection (RTMR) in positron–electron plasmas with system domains much larger than kinetic scales. Our simulations start from a force-free current sheet with several different modes of long-wavelength magnetic field perturbations, which drive additional turbulence in the reconnection region. Because of this, the current layer breaks up and the reconnection region quickly evolves into a turbulent layer filled with coherent structures such as flux ropes and current sheets. Additionally, we find that plasma dynamics in RTMR is vastly different from its 2D counterpart in many aspects. The flux ropes evolve rapidly after their generation, and can be completely disrupted by the secondary kink instability. This turbulent evolution leads to superdiffusive behavior of magnetic field lines as seen in MHD studies of turbulent reconnection. Meanwhile, nonthermal particle acceleration and the timescale for energy release can be very fast and do not depend strongly on the turbulence amplitude. Additionally, the main acceleration mechanism is a Fermi-like acceleration process supported by the motional electric field, whereas the nonideal electric field acceleration plays a subdominant role. We also discuss possible observational implications of three-dimensional RTMR in high-energy astrophysics.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Agentic artificial intelligence for multistage physics experiments at a large-scale user facility particle accelerator

We present a language-model-driven agentic artificial intelligence (AI) system to autonomously execute multistage physics experiments on a production synchrotron light source. Implemented at the Advanced Light Source particle accelerator, the system translates natural language user prompts into structured execution plans that combine archive data retrieval, control-system channel resolution, automated script generation, controlled machine interaction, and analysis. In a representative machine physics task, we show that preparation time was reduced by 2 orders of magnitude relative to manual scripting even for a system expert, while operator-standard safety constraints were strictly upheld. Core architectural features, plan-first orchestration, bounded tool access, and dynamic capability selection, enable transparent, auditable execution with fully reproducible artifacts. These results establish a blueprint for the safe integration of agentic AI into accelerator experiments and demanding machine physics studies, as well as routine operations, with direct portability across accelerators worldwide and, more broadly, to other large-scale scientific infrastructures.

Accelerator/storage ring control systems↗
DOE OSTI2026

Microsecond-latency feedback at a particle accelerator by online reinforcement learning on hardware

The commissioning and operation of future large-scale scientific experiments will challenge current tuning and control methods. Reinforcement learning (RL) algorithms are a promising solution due to their ability to dynamically adapt to changing environments and consider delayed consequences. In many real-world applications, RL policies must produce actions in real time, often within microseconds to milliseconds, imposing significant constraints on system latency and computational overhead that conventional machine learning libraries are not designed to handle. To control phenomena in real time at these timescales, RL needs to be deployed on-the-edge, namely on dedicated hardware located near the system it controls, without relying on a host CPU or cloud-based inference. In this work we present the design and deployment of an experience accumulator system in a particle accelerator. In this system, deep-RL algorithms run using hardware acceleration and act within a few microseconds, enabling the use of RL for control of phenomena like beam instabilities. The training uses the collected data offline to reduce the number of operations carried out on the acceleration hardware. The proposed architecture was tested in real experimental conditions at the Karlsruhe research accelerator, a synchrotron light source, where the system was used to control artificially induced horizontal betatron oscillations in real-time, with a control loop period of just 2.7 μs. The results showed a performance comparable to the commercial feedback system available at the accelerator, demonstrating the viability and potential of this approach. Due to the self-learning and reconfiguration capability of this implementation, a seamless application to other control problems is possible. Applications range from particle accelerators to large-scale research and industrial facilities.

FPGA↗
DOE OSTI2022

Kinetic Simulations of Instabilities and Particle Acceleration in Cylindrical Magnetized Relativistic Jets

Relativistic magnetized jets, such as those from AGN, GRBs, and XRBs, are susceptible to current- and pressure-driven MHD instabilities that can lead to particle acceleration and nonthermal radiation. Here, we investigate the development of these instabilities through 3D kinetic simulations of cylindrically symmetric equilibria involving toroidal magnetic fields with electron–positron pair plasma. Generalizing recent treatments by Alves et al. and Davelaar et al., we consider a range of initial structures in which the force due to toroidal magnetic field is balanced by a combination of forces due to axial magnetic field and gas pressure. We argue that the particle energy limit identified by Alves et al. is due to the finite duration of the fast magnetic dissipation phase. We find a rather minor role of electric fields parallel to the local magnetic fields in particle acceleration. In all investigated cases, a kink mode arises in the central core region with a growth timescale consistent with the predictions of linearized MHD models. In the case of a gas-pressure-balanced (Z-pinch) profile, we identify a weak local pinch mode well outside the jet core. We argue that pressure-driven modes are important for relativistic jets, in regions where sufficient gas pressure is produced by other dissipation mechanisms.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Collaboration for Advanced Modeling of Particle Accelerators

The pverarching purpose is to accelerate and expand the scope of discoveries from high energy physics (HEP) particle accelerators by enabling the design of accelerators that are significantly more compact and cheaper to build and run. This will be realized through (i) developing high-performance computing (HPC) accelerator and beam modeling capabilities to design the full range of systems required (ii) developing community simulation ecosystems that seamlessly integrate accelerator elements to facilitate the design and control of next-generation accelerators.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Nb$_3$Sn Superconducting Radiofrequency Cavities: a Maturing Technology for Particle Accelerators and Detectors

Nb3Sn superconducting radiofrequency (SRF) cavities have substantial potential for enabling new performance capabilities for particle accelerators for high energy physics (HEP), as well as for RF cavity-based detectors for dark matter, gravitational waves, and other quantum sensing applications. Outside of HEP, Nb3Sn SRF cavities can also benefit accelerators for nuclear physics, basic energy sciences, and the industry. In this contribution to Snowmass 2021, we overview the potential, status, and outlook of Nb3Sn SRF cavities.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Reconnection-driven Particle Acceleration in Relativistic Shear Flows

Particle energization in shear flows is invoked to explain nonthermal emission from the boundaries of relativistic astrophysical jets. Yet the physics of particle injection, i.e., the mechanism that allows thermal particles to participate in shear-driven acceleration, remains unknown. With particle-in-cell simulations, we study the development of Kelvin–Helmholtz (KH) instabilities seeded by the velocity shear between a relativistic magnetically dominated electron–positron jet and a weakly magnetized electron–ion ambient plasma. Here, we show that, in their nonlinear stages, KH vortices generate kinetic-scale reconnection layers, which efficiently energize the jet particles, thus providing a first-principles mechanism for particle injection into shear-driven acceleration. Our work lends support to spine-sheath models of jet emission—with a fast core/spine surrounded by a slower sheath —and can explain the origin of radio-emitting electrons at the boundaries of relativistic jets.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Double-power-law Feature of Energetic Particles Accelerated at Coronal Shocks

Recent observations have shown that in many large solar energetic particle (SEP) events the event-integrated differential spectra resemble double power laws. We perform numerical modeling of particle acceleration at coronal shocks propagating through a streamer-like magnetic field by solving the Parker transport equation, including protons and heavier ions. We find that for all ion species the energy spectra integrated over the simulation domain can be described by a double power law, and the break energy depends on the ion charge-to-mass ratio as E B ~ (Q/A) α , with α varying from 0.16 to 1.2 by considering different turbulence spectral indices. We suggest that the double-power-law distribution may emerge as a result of the superposition of energetic particles from different source regions where the acceleration rates differ significantly due to particle diffusion. The diffusion and mixing of energetic particles could also provide an explanation for the increase of Fe/O at high energies as observed in some SEP events. Although further mixing processes may occur, our simulations indicate that either a power-law break or rollover can occur near the Sun and predict that the spectral forms vary significantly along the shock front, which may be examined by upcoming near-Sun SEP measurements from the Parker Solar Probe and Solar Orbiter.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

9.3 Microns: Toward a Next-Generation CO2 Laser for Particle Accelerators

We present realistic theoretical predictions of the performance of the next-generation long-wave infrared (LWIR) laser for advanced particle acceleration research at the Accelerator Test Facility (ATF) of Brookhaven National Laboratory. Two upgrades are planned for the present ATF CO2 laser, which currently produces up to 5 TW peak power in 2 ps pulses at 9.2 μm. For the first upgrade, the deployment of a ten-millijoule solid-state seed laser at 9.3 μm will reduce the pulse duration 4 times, to 500 fs, and increase the peak power to 15 TW. The second upgrade will be the implementation of the postcompression of this pulse. This will reduce the pulse duration to three optical cycles (100 fs) and increase the usable peak power to 25 TW.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Effects of Coronal Magnetic Field Configuration on Particle Acceleration and Release during the Ground Level Enhancement Events in Solar Cycle 24

Ground level enhancements (GLEs) are extreme solar energetic particle (SEP) events that are of particular importance in space weather. In solar cycle 24, two GLEs were recorded on 2012 May 17 (GLE 71) and 2017 September 10 (GLE 72), respectively, using a range of advanced modern instruments. Here we conduct a comparative analysis of the two events by focusing on the effects of large-scale magnetic field configuration near active regions on particle acceleration and release. Although the active regions are both located near the western limb, temporal variations of SEP intensities and energy spectra measured in situ display different behaviors at early stages. By combining a potential field model, we find the coronal mass ejection (CME) in GLE 71 originated below the streamer belt, while in GLE 72 it originated near the edge of the streamer belt. We reconstruct the CME shock fronts with an ellipsoid model based on nearly simultaneous coronagraph images from multiple viewpoints and further derive the 3D shock geometry at the GLE onset. The highest-energy particles are primarily accelerated in the shock–streamer interaction regions, i.e., likely at the nose of the shock in GLE 71 and the eastern flank in GLE 72, due to quasi-perpendicular shock geometry and confinement of closed fields. Subsequently, they are released to the field lines connecting to near-Earth spacecraft when the shocks move through the streamer cusp region. This suggests that magnetic structures in the corona, especially shock–streamer interactions, may have played an important role in the acceleration and release of the highest-energy particles in the two events.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Distributions of Particles Accelerated by Strong Alfvénic Turbulence

This work presents a model for generating nonthermal power-law tails of particles’ energy probability density functions in turbulent collisionless plasmas, applicable to both nonrelativistic and relativistic scenarios. We propose that strong Alfvénic turbulence energizes plasma particles through curvature acceleration, particularly for particles with Larmor radii comparable to the scales of turbulence. When the energy density of the energized particles increases, the efficiency of the energy exchange process diminishes. As a result, the acceleration process saturates, leading to power-law distributions of particle momentum and energy. In the nonrelativistic case, the momentum probability density function scales as f(p)dp ∝ p −3 dp, while in the ultrarelativistic case, the energy probability density function scales as f(γ)dγ ∝ γ −3 dγ, where γ is the Lorentz factor. This model provides a unified framework for understanding particle acceleration in both energy regimes, complementing existing analytical approaches. The predicted scalings are consistent with available observations of energetic ion distributions in the heliosphere and with the findings from numerical simulations of ultrarelativistic particle acceleration in magnetically dominated plasma turbulence.

Alfven waves↗
DOE OSTI2026

The Memory Scaling of Reverse-Mode Differentiation in Particle Accelerator Simulations with Space Charge

The recent development of differentiable simulation codes for particle accelerators has enabled gradient-based workflows that promise finer control and more realistic modeling of accelerator facilities. However, when using reverse-mode automatic differentiation, the memory usage continuously increases during the simulation, and can potentially exceed the available hardware memory - especially when costly space charge computation is included. To study the memory requirements for differentiable simulations, we have implemented space charge in Cheetah, a PyTorch-based beam tracking code that supports reverse-mode differentiation. We find that the memory usage for reverse-mode differentiation grows linearly with the number of macroparticles and cells, and that it is proportional to the number of space charge kicks involved in the simulation. This general scaling can be used to evaluate whether a given differentiable simulation is feasible given hardware memory constraints.

Dhamrait, Arjun↗
DOE OSTI2021

Adaptive Machine Learning for Robust Diagnostics and Control of Time-Varying Particle Accelerator Components and Beams

Machine learning (ML) is growing in popularity for various particle accelerator applications including anomaly detection such as faulty beam position monitor or RF fault identification, for non-invasive diagnostics, and for creating surrogate models. ML methods such as neural networks (NN) are useful because they can learn input-output relationships in large complex systems based on large data sets. Once they are trained, methods such as NNs give instant predictions of complex phenomenon, which makes their use as surrogate models especially appealing for speeding up large parameter space searches which otherwise require computationally expensive simulations. However, quickly time varying systems are challenging for ML-based approaches because the actual system dynamics quickly drifts away from the description provided by any fixed data set, degrading the predictive power of any ML method, and limits their applicability for real time feedback control of quickly time-varying accelerator components and beams. In contrast to ML methods, adaptive model-independent feedback algorithms are by design robust to un-modeled changes and disturbances in dynamic systems, but are usually local in nature and susceptible to local extrema. In this work, we propose that the combination of adaptive feedback and machine learning, adaptive machine learning (AML), is a way to combine the global feature learning power of ML methods such as deep neural networks with the robustness of model-independent control. We present an overview of several ML and adaptive control methods, their strengths and limitations, and an overview of AML approaches.

97 MATHEMATICS AND COMPUTING↗