Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Particle Accelerator”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Celeritas: GPU-accelerated particle transport for detector simulation in High Energy Physics experiments

Within the next decade, experimental High Energy Physics (HEP) will enter a new era of scientific discovery through a set of targeted programs recommended by the Particle Physics Project Prioritization Panel (P5), including the upcoming High Luminosity Large Hadron Collider (LHC) HL-LHC upgrade and the Deep Underground Neutrino Experiment (DUNE). These efforts in the Energy and Intensity Frontiers will require an unprecedented amount of computational capacity on many fronts including Monte Carlo (MC) detector simulation. In order to alleviate this impending computational bottleneck, the Celeritas MC particle transport code is designed to leverage the new generation of heterogeneous computer architectures, including the exascale computing power of U.S. Department of Energy (DOE) Leadership Computing Facilities (LCFs), to model targeted HEP detector problems at the full fidelity of Geant4. This paper presents the planned roadmap for Celeritas, including its proposed code architecture, physics capabilities, and strategies for integrating it with existing and future experimental HEP computing workflows.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Uncertainty Aware Deep Learning for Particle Accelerators

Standard deep learning models for classification and regression applications are ideal for capturing complex system dynamics. However, their predictions can be arbitrarily inaccurate when the input samples are not similar to the training data. Implementation of distance aware uncertainty estimation can be used to detect these scenarios and provide a level of confidence associated with their predictions. In this paper, we present results from using Deep Gaussian Process Approximation (DGPA) methods for errant beam prediction at Spallation Neutron Source (SNS) accelerator (classification) and we provide an uncertainty aware surrogate model for the Fermi National Accelerator Lab (FNAL) Booster Accelerator Complex (regression).

Rajput, Kishansingh↗
DOE OSTI2025

Virtual to Physical: Reinforcement Learning to Optimize SNS Particle Accelerator Controls

Complex accelerators must have control systems that can handle dynamic nonlinear environments. This makes traditional control methods unsuitable as they can struggle to adapt to these uncertainties. This provides an ideal environment for reinforcement learning algorithms as they are adaptable and generalizable. We present a reinforcement learning pipeline that can effectively handle the dynamics of a complex accelerator. We test and prove our pipelines capabilities on multiple environments including the Spallation Neutron Source (SNS) and the Beam Test Facility (BTF) at Oakridge National Lab (ORNL). Due to the limited time available to train an online algorithm like reinforcement learning on a real accelerator, we utilize a virtual twin accelerator (VIRAC) developed by ORNL to pretrain the policy and show its ability to converge in the virtual environment. We then test the adaptability of the pretrained RL model by applying it on the real accelerator and comparing the results. Utilizing our Scientific Optimization and Controls Toolkit (SOCT) and open-source standards such as Gymnasium we create and solve for a MEBT orbit correction problem in the SNS and an emittance maximization problem in the BTF. We show how Twin Delayed Deep Deterministic Policy Gradient (TD3) can solve this optimization environment in the virtual accelerator and transfer this policy onto the real accelerator for inference and model retraining. We show how reinforcement learning can be utilized as a control system for complex accelerators and provide a model pipeline for how an implementation performs and can be adapted to new accelerator control problems.

Kasparian, Armen [Thomas Jefferson National Accele↗
DOE OSTI2025

Digital Twin Industry Standards and Opportunities from the Particle Accelerator Community

Digital twins (DTs) are predictive models of a physical system that dynamically update to reflect any changes. This concept was first conceived as early as 1993 by David Gelernter in his speculative non-fiction Mirror Worlds. DTs were coined in 2002 by Michael Grieves and applied to Product Lifecycle Management for manufacturing. Since then, industry has been developing tools to simplify the creation, deployment, and use of digital twins for manufacturing, fleet management, and biological systems. We will recommend industry standard technology and interfaces that we should adopt in the accelerator community. We further identify gaps in this technology to which we can add new capabilities and solutions, which can be expanded for our use cases.

Miceli, Tia [Fermilab] (ORCID:0000000265577789)↗
DOE OSTI2022

MULTI-TECHNIQUE CHARACTERIZATION OF SUPERCONDUCTING MATERIALS FOR PARTICLE ACCELERATOR APPLICATIONS

We investigated the performance limitations of superconducting radio-frequency (SRF) cavities and materials using multiple experimental techniques. In particular, this study focuses on understanding the surface properties of nitrogen-doped Nb cavities and super?conducting thin films with higher Tc such as Nb3Sn. The main goal of this work is to use different techniques to better understand each aspect of the complex loss mechanism in superconductors to further improve the already highly efficient SRF cavities. Nitrogen doping applied to a Nb SRF cavity significantly improves the quality factor Q0 compared to a conventional Nb cavity, at an expense of reduced maximum accelerating gradient. The early quench mechanism was analyzed by using temperature maps before and during the quenching. The temperature maps revealed insignificant heating before the quench, and we concluded that nitrogen doping reduces critical magnetic fields in local regions, leading to premature quenching. To understand the origin of the increasing Q0 with the rf field, the density of states (DOS) of cold spots from nitrogen-doped and standard cavities were measured and analyzed using scanning tunneling microscopy. The results suggested that nitrogen doping reduces the spatial inhomogeneity of superconducting properties and shrinks the metallic suboxide layers, which tunes the DOS in such a way as to produce the field-induced reduction in the surface resistance. To characterize SRF thin films, an experimental setup for measuring a coplanar waveguide (CPW) resonator was developed and tested. A surface impedance measurement of the Nb film showed good agreement with the BCS calculation. The preliminary results from measurements of Nb3Sn and NbTiN films are also presented here. The nonlinear Meissner effect was investigated in Nb3Sn film CPW resonators by mea?suring the resonant frequency as a function of a parallel magnetic field. Contrary to a conventional quadratic dependence of the penetration depth ?(B) on the applied magnetic field B, as expected in s-wave superconductors, nearly a linear increase of ?(B) with B was observed. It was concluded that this behavior of ?(B) is due to weakly linked grain bound?aries on the polycrystalline Nb3Sn films, which can mimic the NLME expected in a clean d-wave superconductor.

Makita, Junki↗
DOE OSTI2026

Ultra-High Vacuum Outgassing Characterization of Thermally Processed Low-Carbon Steel for Advanced Particle Accelerator and Gravitational Wave Detector Applications

This dissertation investigated AISI 1020 low-carbon steel as an alternative vacuum chamber material to conventional stainless steel for ultra-high vacuum (UHV) and extreme-high vacuum (XHV) applications. After a 400 °C/48 h bake, AISI 1020 tube chambers achieved a hydrogen outgassing rate of 2.4 × 10¿¹6 Torr·L·s¿¹·cm¿², approximately 2,300 times lower than the prebaked 316L stainless-steel comparator, among the lowest hydrogen outgassing rates ever reported for an uncoated metallic vacuum chamber. Bare and magnetite-coated AISI 1020 chambers were then compared using throughput and rate-of-rise methods. The magnetite coating yielded 5× lower water outgassing at room temperature, but this advantage disappeared after 80 °C baking. After full thermal conditioning (400 °C/48 h prebake followed by 150 °C/96 h and 200 °C/110 h), bare steel achieved 25× lower hydrogen outgassing than the magnetite-coated chamber (9.6 × 10¿¹6 Torr·L·s¿¹·cm¿²) and >99% H2 purity with carbon species below RGA detection. Monte Carlo molecular flow simulations of a CEBAF photogun beamline (96 scenarios) showed that replacing 304L stainless steel with AISI 1020 reduces equilibrium H2 pressure by a factor of 833; a single 304L electrode contributes 98.8% of the gas load despite occupying only 9.1% of the internal surface area. A 500-m Einstein Telescope beampipe screening showed that corrugated bellows contribute 18% of the gas load from only 0.7% of the surface area. A five-model adsorption isotherm framework applied to 22 pumpdown datasets (164 fits with AR(1)-GLS correction) established that the experimental protocol, not the material, controls isotherm identifiability: Dubinin–Radushkevich wins isothermal pipe pumpdowns; Langmuir wins thermally dominated chamber bakes. Cross-dataset joint fitting of the AISI 1020 pipe pumpdowns yielded an H2 diffusion activation energy Ed = 7.24 ± 1.28 kcal·mol¿¹, consistent with trap dominated diffusion in commercial low-carbon steels. Two companion innovations were developed: a Variable Conductance Device (VCD, patent pending IDF-00723) for XHV outgassing measurement, and VacuumDesignerPro (VDP), a MATLAB-based design tool validated against LIGO benchmarks.

Al-Allaq, Aiman H [Old Dominion University]↗
DOE OSTI2022

Application of Convolutional and Feedforward Neural Networks for Fault Detection in Particle Accelerator Power Systems

High voltage converter modulators (HVCM) provide power to the accelerating cavities of the spallation neutron source (SNS) facility. HVCM experience catastrophic failures, which increase the downtime of the SNS and reduce beam time. The faults may occur due to different reasons including failures of the resonant capacitor, core saturation due to the magnetic flux, insulated-gate bipolar transistor (IGBT) failures, and others. We recently have setup a HVCM test stand to develop and test machine learning models for anomaly detection and fault prognostics. In this work, we propose binary classifiers and autoencoder architectures based on convolutional (CNN) and feedforward neural networks (FNN) to facilitate distinguishing normal from faulty waveforms coming from the HVCM during operation. The results indicate that the CNN binary classifier is the best model among the four showing very stable performance in the training and testing sets with impressive metrics of precision and recall reaching up to 99\% with a very small uncertainty. The FNN classifier shows the least performance with a large uncertainty in its metrics. The performances of the two autoencoders based on CNN and FNN were in between, showing very good performance nonetheless.

Radaideh, Majdi↗
DOE OSTI2025

Bridging the Gap Between Modern UX Design and Particle Accelerator Control Room Interfaces

Accelerator control systems often represent relatively complex and safety-sensitive human-machine interfaces within process control industries. These systems are technically robust and reflect the cumulative integration of solutions built and adapted across decades. One of the regular, unfortunate casualties of provisional accelerator control system updates is their human-system interfaces (HSIs) which often lag behind modern usability and design standards. An additional challenge is that although there is a multitude of established human factors (HF), and user experience (UX) principles for everyday digital applications, there are very few (if any) established principles for complex and safety-critical applications for an accelerator. This paper argues for the importance of established HF and UX principles (herein referred to as human-centered design principles) into the development of accelerator HSIs, emphasizing the need for clarity, consistency, responsiveness, and cognitive accessibility. Drawing from HF/UX best practices and human-centered design, this paper discusses how these approaches can enhance operator performance, reduce human error, and improve accelerator personnel collaboration. Case studies from Accelerator Control Operations Research Network (ACORN) at Fermilab are explored to demonstrate how interfaces built with human-centered design principles can scale with system complexity while remaining intuitive and efficient for diverse user roles including operators, machine experts, and engineers. By bridging the gap between traditional control system design and modern human-centered design methods, this paper provides a roadmap for evolving accelerator HSIs into more usable, maintainable, and effective tools.

Hill, Rachael [Idaho Natl. Lab.]↗