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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Porting the Nonlinear Optimization Library HiOp to Accelerator-Based Hardware Architectures

While interior point method has been the centerpiece of nonlinear programming tools used in science and engineering, its reliance on linear solvers that can tackle sparse symmetric indefinite and highly ill-conditioned problems made it difficult to implement it effectively on hardware accelerators. HiOp optimization package attempts to provide an implementation of the interior point method suitable for hardware accelerators by compressing the original sparse problem to produce an underlying linear problem that is dense and of manageable size. Implementations of dense linear solvers are more mature and utilize hardware accelerators better than their sparse counterparts. There is a number of important domain problems, such as optimal power flow analysis for power grids, where the sparse problem can be effectively compressed and deploying dense linear solver within the interior point method can improve performance. Here we describe a portable implementation of HiOp optimization engine, which uses a linear solver from Magma library and runs entirely on hardware accelerators. To compress the problem, HiOp uses customized mixed dense-sparse linear algebra. All HiOp kernels are implemented using Umpire and RAJA portability libraries. We describe details of the implementation and discuss trade-offs between performance, portability and development cost.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Control Systems Design for STS Accelerator

The Second Target Station (STS) Project will expand the capabilities of the existing Spallation Neutron Source (SNS), with a suite of neutron instruments optimized for long wavelengths. A new accelerator transport line will be built to deliver one out of four SNS pulses to the new target station. The Integrated Control Systems (ICS) will provide remote control, monitoring, OPI, alarms, and archivers for the accelerator systems, such as magnets power supply, vacuum devices, and beam instrumentation. The ICS will upgrade the existing Linac LLRF controls to allow independent operation of the FTS and STS and support different power levels of the FTS and STS proton beam. The ICS accelerator controls are in the phase of preliminary design for the control systems of magnet power supply, vacuum, LLRF, Timing, Machine protection system (MPS), and computing and machine network. The accelerator control systems build upon the existing SNS Machine Control systems, use the SNS standard hardware and EPICS software, and take full advantage of the performance gains delivered by the PPU Project at SNS.

Yan, Jay↗
DOE OSTI2024

Errant Beam Prognostics with Machine Leaning at SNS Accelerator

Particle Accelerators are complex machine with many pieces of equipment running in synchronization to deliver required beam. However, faults in particle accelerators reduce the availability of the beam for experiments affecting the overall science output. To avoid these faults, we apply anomaly detection techniques to predict any unusual behavior and perform preemptive actions to improve the total availability. Many researchers have adopted semi-supervised Machine Learning (ML) methods such as auto-encoders and variational auto-encoders for such tasks. However, supervised ML techniques designed for similarity learning such as Siamese Neural Network (SNN) can outperform semi-supervised or unsupervised methods for anomaly prediction. One of the challenges associated with application of ML models to particle accelerators is the variability in observed data over time due to system configuration changes. We employ conditional models such as Conditional Siamese Neural Networks (CSNN), and Conditional-VAE (CVAE) to learn the variability in the data by using beam configuration parameters as conditional input. We apply these models for errant beam prediction at Spallation Neutron Source accelerator under different system configurations and compare their performance. We demonstrate that CSNN outperforms CVAE in our application. This talk will present the data source, collection, analysis, data-preparation, model development, hyper-parameter studies and the results.

Rajput, Kishansingh↗
DOE OSTI2023

Radioisotope replacement with compact electron linear accelerators

The replacement of radioactive sources with alternative technologies has been identified as a priority by international authorities, due to the risk of accidents and diversion by terrorists for use in Radiological Dispersal Devices. Many of these sources can be replaced with the X-rays produced by electron beams accelerated to MeV energies. However, the size, weight and costs of electron linacs must be significantly reduced to be considered for radioisotope replacement. RadiaBeam Technologies, LLC is developing a series of inexpensive compact electron accelerators in the 1–10 MeV range for radioisotope replacement such as Ir-192, Cs-137 and Co-60 for various applications. The dramatic level of miniaturization and cost-reduction was achieved thanks to the implementation of such innovative technologies as high-frequency magnetrons, split accelerating structure fabrication technology and solid-state Marx modulators. Here, in this paper, we overview RadiaBeam's compact linac developments, discuss the enabling technologies, and report on the current progress.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Electron pulse train accelerated by a linearly polarized Laguerre–Gaussian laser beam

A linearly polarized Laguerre–Gaussian (LP-LG) laser beam with a twist index $l$ = –1 has field structure that fundamentally differs from the field structure of a conventional linearly polarized Gaussian beam. Close to the axis of the LP-LG beam, the longitudinal electric and magnetic fields dominate over the transverse components. This structure offers an attractive opportunity to accelerate electrons in vacuum. It is shown, using three-dimensional particle-in-cell simulations, that this scenario can be realized by reflecting an LP-LG laser off a plasma with a sharp density gradient. The simulations indicate that a 600 TW LP-LG laser beam effectively injects electrons into the beam during the reflection. The electrons that are injected close to the laser axis experience a prolonged longitudinal acceleration by the longitudinal laser electric field. The electrons form distinct monoenergetic bunches with a small divergence angle. The energy in the most energetic bunch is 0.29 GeV. The bunch charge is 6 pC and its duration is approximately 270 as. The divergence angle is just 0.57° (10 mrad). By using a linearly polarized rather than a circularly polarized Laguerre–Gaussian beam, our scheme makes it easier to demonstrate the electron acceleration experimentally at a high-power laser facility.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Laboratory measurements of the physics of auroral electron acceleration by Alfvén waves

While the aurora has attracted attention for millennia, important questions remain unanswered. Foremost is how auroral electrons are accelerated before colliding with the ionosphere and producing auroral light. Powerful Alfvén waves are often found traveling Earthward above auroras with sufficient energy to generate auroras, but there has been no direct measurement of the processes by which Alfvén waves transfer their energy to auroral electrons. Here, we show laboratory measurements of the resonant transfer of energy from Alfvén waves to electrons under conditions relevant to the auroral zone. Experiments are performed by launching Alfvén waves and simultaneously recording the electron velocity distribution. Numerical simulations and analytical theory support that the measured energy transfer process produces accelerated electrons capable of reaching auroral energies. The experiments, theory, and simulations demonstrate a clear causal relationship between Alfvén waves and accelerated electrons that directly cause auroras.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Online multi-objective particle accelerator optimization of the AWAKE electron beam line for simultaneous emittance and orbit control

Multi-objective optimization is important for particle accelerators where various competing objectives must be satisfied routinely such as, for example, transverse emittance vs bunch length. We develop and demonstrate an online multi-time scale multi-objective optimization algorithm that performs real time feedback on particle accelerators. We demonstrate the ability to simultaneously minimize the emittance and maintain a reference trajectory of a beam in the electron beamline in CERN’s Advanced Proton Driven Plasma Wakefield Acceleration Experiment.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

A compact electron source for the dielectric laser accelerator

In this work, we design and demonstrate a compact electron source that combines an integrated silicon nanotip photoemitter with a compact silicon-based electrostatic lens. The lens simultaneously accelerates electrons to 30 keV and focuses the resulting beam to a 0.4 μm (RMS) beam diameter with 62 pm-rad normalized emittance at a distance of 20 mm from the cathode. The compact nature of this lens provides a compelling source for dielectric laser accelerator (DLA) beamlines, ultrafast electron diffraction, or ultrafast electron microscopy. Driven by a 220 fs, 1960 nm pulsed laser beam, electron currents up to 28 electrons/pulse at 100 kHz are demonstrated. The electron bunch length is 540 ± 50 fs for photocurrents of <1 electron/pulse, increasing to 700 ± 80 fs for 28 electrons/pulse, as measured by cross correlation with a 220 fs pulsed laser beam. The maximum 5D peak brightness is measured to be 6.8 × 10 13 A/(m 2 rad 2 ) at 28 electrons/pulse. These results represent a significant step toward developing practical benchtop-sized linear accelerators based on DLA technology or compact ultrafast electron microscopy and diffraction applications.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Advances in Nb 3 Sn superconducting radiofrequency cavities towards first practical accelerator applications

Abstract Nb 3 Sn is a promising next-generation material for superconducting radiofrequency cavities, with significant potential for both large scale and compact accelerator applications. However, so far, Nb 3 Sn cavities have been limited to continuous wave accelerating fields <18 MV m −1 . In this paper, new results are presented with significantly higher fields, as high as 24 MV m −1 in single cell cavities. Results are also presented from the first ever Nb 3 Sn-coated 1.3 GHz 9-cell cavity, a full-scale demonstration on the cavity type used in production for the European XFEL and LCLS-II. Results are presented together with heat dissipation curves to emphasize the potential for industrial accelerator applications using cryocooler-based cooling systems. The cavities studied have an atypical shiny visual appearance, and microscopy studies of witness samples reveal significantly reduced surface roughness and smaller film thickness compared to typical Nb 3 Sn films for superconducting cavities. Possible mechanisms for increased maximum field are discussed as well as implications for physics of RF superconductivity in the low coherence length regime. Outlook for continued development is presented.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Robust errant beam prognostics with conditional modeling for particle accelerators

Abstract Particle accelerators are complex and comprise thousands of components, with many pieces of equipment running at their peak power. Consequently, they can fault and abort operations for numerous reasons, lowering efficiency and science output. To avoid these faults, we apply anomaly detection techniques to predict unusual behavior and perform preemptive actions to improve the total availability. Supervised machine learning (ML) techniques such as siamese neural network models can outperform the often-used unsupervised or semi-supervised approaches for anomaly detection by leveraging the label information. One of the challenges specific to anomaly detection for particle accelerators is the data’s variability due to accelerator configuration changes within a production run of several months. ML models fail at providing accurate predictions when data changes due to changes in the configuration. To address this challenge, we include the configuration settings into our models and training to improve the results. Beam configurations are used as a conditional input for the model to learn any cross-correlation between the data from different conditions and retain its performance. We employ conditional siamese neural network (CSNN) models and conditional variational auto encoder (CVAE) models to predict errant beam pulses at the spallation neutron source under different system configurations and compare their performance. We demonstrate that CSNNs outperform CVAEs in our application.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Accelerating cavity fault prediction using deep learning at Jefferson Laboratory

Abstract Accelerating cavities are an integral part of the continuous electron beam accelerator facility (CEBAF) at Jefferson Laboratory. When any of the over 400 cavities in CEBAF experiences a fault, it disrupts beam delivery to experimental user halls. In this study, we propose the use of a deep learning model to predict slowly developing cavity faults. By utilizing pre-fault signals, we train a long short-term memory-convolutional neural network binary classifier to distinguish between radio-frequency (RF) signals during normal operation and RF signals indicative of impending faults. We optimize the model by adjusting the fault confidence threshold and implementing a multiple consecutive window criterion to identify fault events, ensuring a low false positive rate. Results obtained from analysis of a real dataset collected from the accelerating cavities simulating a deployed scenario demonstrate the model’s ability to identify normal signals with 99.99% accuracy and correctly predict 80% of slowly developing faults. Notably, these achievements were achieved in the context of a highly imbalanced dataset, and fault predictions were made several hundred milliseconds before the onset of the fault. Anticipating faults enables preemptive measures to improve operational efficiency by preventing or mitigating their occurrence.

43 PARTICLE ACCELERATORS↗
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 OSTI2024

High-gradient performance of a prototype accelerator cavity for a 3 GeV proton radiography booster

This paper reports the design, fabrication, and results of the high-gradient conditioning and testing for a two-cell, π -mode, standing wave normal-conducting prototype booster cavity for the proposed 3 GeV proton linac upgrade at Los Alamos Neutron Science Center. Increasing the energy of proton beam from the existing 800 MeV to 3 GeV will improve resolution of the proton radiography by up to 10 times. The proposed energy boost can be achieved with a compact normal-conducting high-gradient radio-frequency (rf) linac section. The C-band section of the booster linac was designed with optimized-shaped copper accelerator cavities with distributed rf coupling. A short two-cell test prototype structure was designed for the frequency of 5.712 GHz, fabricated, and tested at the C-band Engineering Research Test Facility in New Mexico (CERF-NM) at Los Alamos National Laboratory. The maximum klystron power coupled into the test structure was 8.3 MW with 1 μ s pulse length and 100 Hz repetition rate. The breakdown probabilities were recorded as functions of the accelerating gradient and peak surface fields. Operation of the test cavity at accelerating gradients of up to 100 MV / m was demonstrated. Published by the American Physical Society 2024

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Efficient Nonthermal Ion and Electron Acceleration Enabled by the Flux-Rope Kink Instability in 3D Nonrelativistic Magnetic Reconnection

The relaxation of field-line tension during magnetic reconnection gives rise to a universal Fermi acceleration process involving the curvature drift of particles. However, the efficiency of this mechanism is limited by the trapping of energetic particles within flux ropes. Using 3D fully kinetic simulations, we demonstrate that the flux-rope kink instability leads to strong field-line chaos in weak-guide-field regimes where the Fermi mechanism is most efficient, thus allowing particles to transport out of flux ropes and undergo further acceleration. As a consequence, both ions and electrons develop clear power-law energy spectra that contain a significant fraction of the released energy. The low-energy bounds are determined by the injection physics, while the high-energy cutoffs are limited only by the system size. Furthermore, these results have strong relevance to observations of nonthermal particle acceleration in space and astrophysics.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Quantum Nature of Dielectric Laser Accelerators

Dielectric laser accelerators (DLAs) hold great promise for producing economic and compact on-chip radiation sources. On-chip DLAs benefit from fabrication capabilities of the silicon industry and from breakthroughs in silicon-photonic nanostructures to enhance the interaction between particles and laser fields. Seemingly unrelated recent advances in the quantum interactions of electrons and light have raised interest in the underlying classical-quantum correspondence principle at the foundations of electron acceleration. Here, we present the observation of the underlying quantum nature of DLAs: observing quantized peaks in the electron-energy spectra. Our findings demonstrate quasi-phase-matching between an electron wave function and a light wave, which also demonstrates the role of the quantum wave function in the inverse Smith-Purcell effect. We harness the capabilities of an ultrafast transmission electron microscope (UTEM) to maintain a long electron-light interaction length extending over hundreds of periods of the laser pulse, mediated by a silicon-photonic nanograting DLA. The UTEM is shown as a new platform for characterization of future DLA concepts. The results raise fundamental questions regarding the role of quantum mechanics in DLA design, and more generally about the prospects of manipulating particles’ quantum wave functions in accelerator physics.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Multi-GeV Electron Bunches from an All-Optical Laser Wakefield Accelerator

We present the first demonstration of multi-GeV laser wakefield acceleration in a fully optically formed plasma waveguide, with an acceleration gradient as high as 25 GeV/m . The guide was formed via self-waveguiding of < 15 J, 45 fs (< ~ 300 TW) pulses over 20 cm in a low-density hydrogen gas jet, with accelerated electron bunches driven up to 5 GeV in quasimonoenergetic peaks of relative energy width as narrow as ~ 15 % , with divergence down to ~ 1mrad and charge up to tens of picocoulombs. Energy gain is inversely correlated with on-axis waveguide density in the range Ne0 = (1.3 – 3.2) x 10 17 cm –3 . We find that shot-to-shot stability of bunch spectra and charge are strongly dependent on the pointing of the injected laser pulse and gas jet uniformity. We also observe evidence of pump depletion-induced dephasing, a consequence of the long optical guiding distance.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

GPU-accelerated multitiered iterative phasing algorithm for fluctuation X-ray scattering

The multitiered iterative phasing (MTIP) algorithm is used to determine the biological structures of macromolecules from fluctuation scattering data. It is an iterative algorithm that reconstructs the electron density of the sample by matching the computed fluctuation X-ray scattering data to the external observations, and by simultaneously enforcing constraints in real and Fourier space. This paper presents the first ever MTIP algorithm acceleration efforts on contemporary graphics processing units (GPUs). The Compute Unified Device Architecture (CUDA) programming model is used to accelerate the MTIP algorithm on NVIDIA GPUs. The computational performance of the CUDA-based MTIP algorithm implementation outperforms the CPU-based version by an order of magnitude. Furthermore, the Heterogeneous-Compute Interface for Portability (HIP) runtime APIs are used to demonstrate portability by accelerating the MTIP algorithm across NVIDIA and AMD GPUs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Ionizing radiation effects in SONOS-based neuromorphic inference accelerators

Here, we evaluate the sensitivity of neuromorphic inference accelerators based on Silicon-Oxide-Nitride-Oxide-Silicon (SONOS) charge trap memory arrays to total ionizing dose (TID) effects. Data retention statistics were collected for 16 Mbit of 40 nm SONOS digital memory exposed to ionizing radiation from a Co-60 source, showing good retention of the bits up to the maximum dose of 500 krad(Si). Using this data, we formulate a rate-equation-based model for the TID response of trapped charge carriers in the ONO stack, and predict the effect of TID on intermediate device states between ‘program’ and ‘erase’. This model is then used to simulate arrays of low-power, analog SONOS devices that store 8-bit neural network weights and support in situ matrix-vector multiplication. We evaluate the accuracy of the irradiated SONOS-based inference accelerator on two image recognition tasks – CIFAR-10 and the challenging ImageNet dataset – using state-of-the-art convolutional neural networks, such as ResNet-50. We find that across the datasets and neural networks evaluated, the accelerator tolerates a maximum TID between 10 krad(Si) and 100 krad(Si), with deeper networks being more susceptible to accuracy losses due to TID.

43 PARTICLE ACCELERATORS↗