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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Ka-band linearizer for the Ultra-Compact X-ray free-electron laser at UCLA

There is a strong demand for accelerating structures able to achieve higher gradients and more compact dimensions for the next generation of linear accelerators for research, industrial and medical applications. Notably innovative technologies will permit compact and affordable advanced accelerators as the linear collider and X-ray free-electron lasers (XFELs) with accelerating gradients over twice the value achieved with current technologies. In particular XFELs are able to produce coherent X-ray pulses with peak brightness 10 orders of magnitude greater than preceding approaches, which has revolutionized numerous research fields through imaging of the nanoscopic world at the time and length scale of atom-based systems, that is of femtosecond and Angstrom. There is a strong interest for combining these two fields, to form a proper tool with the goal of producing a very compact XFEL in order to investigate multi-disciplinary topics in chemistry, biology, materials science, medicine and physics. In the framework of the Ultra-Compact XFEL project under study at the University of California, Los Angeles, a high gradient radio-frequency accelerating structure for the longitudinal phase-space linearization with an integrated voltage of at least 15 MV working on 6th harmonic of the main Linac frequency is required. We here present the electromagnetic design of a cryogenic normal-conducting 8 cm long Ka-band standing-wave linearizer working on π mode with a target accelerating gradient beyond 100 MV/m. The studies have been performed analytically and numerically to investigate the beam dynamics and electromagnetic issues.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Demonstration of sub-GV/m accelerating field in a photoemission electron gun powered by nanosecond 𝑋-band radio-frequency pulses

Radio-frequency (rf) photoemission electron guns operating at higher accelerating gradients offer a pathway to produce brighter electron bunches. Brighter beams are expected to revolutionize many areas of science: they could form the backbone of next-generation compact x-ray free-electron lasers or provide coherent ultrafast quantum electron probes. We report on the experimental demonstration of an rf photoemission electron source supporting an accelerating field close to 400 MV/m at the photocathode surface without major rf breakdown or significant dark current over a 3-week experimental run. This unprecedented gradient was achieved by operating the gun in a new regime with short rf pulses, ~9ns X-band (11.7 GHz). The demonstrated paradigm provides a viable path to form relativistic electron beams with unprecedented brightness.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

C$^3$ Demonstration Research and Development Plan

C$^3$ is an opportunity to realize an e$^+$e$^-$ collider for the study of the Higgs boson at $\sqrt{s} = 250$ GeV, with a well defined upgrade path to 550 GeV while staying on the same short facility footprint. C$^3$ is based on a fundamentally new approach to normal conducting linear accelerators that achieves both high gradient and high efficiency at relatively low cost. Given the advanced state of linear collider designs, the key system that requires technical maturation for C$^3$ is the main linac. This white paper presents the staged approach towards a facility to demonstrate C$^3$ technology with both Direct (source and main linac) and Parallel (beam delivery, damping ring, ancillary component) R&D. The white paper also includes discussion on the approach for technology industrialization, related HEP R&D activities that are enabled by C$^3$ R&D, infrastructure requirements and siting options.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Controlling Oxidation of Nb in Oxygen Abundant Environments.

Superconducting Radio Frequency (SRF) cavities, the core of modern particle accelerators, rely on high-purity niobium (Nb) to achieve high quality factors and accelerating gradients. However, native oxidation in ambient conditions introduces a complex oxide layer with increased surface defects and contamination, particularly from carbon. This research explores a controlled oxidation processes in an oxygen-abundant environment to try to control this complex oxide stack, with the goal of improving surface purity and uniformity. Using standard surface preparation techniques—including electropolishing, vacuum baking, and sonication—followed by a custom oxidation protocol, we compared structural and chemical changes through Confocal Microscopy, Scanning Electron Microscopy (SEM), and X-Ray Photoelectron Spectroscopy (XPS). Results show that iterative oxidation cycles can significantly reduce carbon-related defects, even without baking. Aditionally, baked samples with a specific oxidation technique demonstrated a measurable 25\% decrease in carbide formation.

Romero, Juan [Fermilab]↗
DOE OSTI2022

Field Emission Mitigation in CEBAF SRF Cavities Using Deep Learning

The Continuous Electron Beam Accelerator Facility (CEBAF) operates hundreds of superconducting radio frequency (SRF) cavities in its two main linear accelerators. Field emission can occur when the cavities are set to high operating RF gradients and is an ongoing operational challenge. This is especially true in newer, higher gradient SRF cavities. Field emission results in damage to accelerator hardware, generates high levels of neutron and gamma radiation, and has deleterious effects on CEBAF operations. So, field emission reduction is imperative for the reliable, high gradient operation of CEBAF that is required by experimenters. Here we explore the use of deep learning architectures via multilayer perceptron to simultaneously model radiation measurements at multiple detectors in response to arbitrary gradient distributions. These models are trained on collected data and could be used to minimize the radiation production through gradient redistribution. This work builds on previous efforts in developing machine learning (ML) models, and is able to produce similar model performance as our previous ML model without requiring knowledge of the field emission onset for each cavity.

Ahammed, K.↗
DOE OSTI2022

Field Emission Mitigation in CEBAF SRF Cavities Using Deep Learning

The Continuous Electron Beam Accelerator Facility (CEBAF) operates hundreds of superconducting radio frequency (SRF) cavities in its two main linear accelerators. Field emission can occur when the cavities are set to high operating RF gradients and is an ongoing operational challenge. This is especially true in newer, higher gradient SRF cavities. Field emission results in damage to accelerator hardware, generates high levels of neutron and gamma radiation, and has deleterious effects on CEBAF operations. So, field emission reduction is imperative for the reliable, high gradient operation of CEBAF that is required by experimenters. Here we explore the use of deep learning architectures via multilayer perceptron to simultaneously model radiation measurements at multiple detectors in response to arbitrary gradient distributions. These models are trained on collected data and could be used to minimize the radiation production through gradient redistribution. This work builds on previous efforts in developing machine learning (ML) models, and is able to produce similar model performance as our previous ML model without requiring knowledge of the field emission onset for each cavity.

Ahammed, K.↗
DOE OSTI2024

A Broadband Mechanically Tuned Superconducting Cavity Design Suitable for the Fermilab Main Injector

Radio Frequency superconductivity has been a mainstay of accelerator science for decades. However, its benefits have yet to be applied to proton synchrotrons with demanding tuning requirements. For example, the Main Injector (MI), Fermilab's high-energy proton synchrotron, currently utilizes 20+ ferrite-loaded cavities for a targeted 1.2 s acceleration cycle. Harnessing the extremely high gradients associated with superconductivity, the required number of cavities could be reduced by an order of magnitude, dramatically lowering operational power requirements even with cryogenic considerations. Additionally, the current plans for the Fermilab accelerator complex evolution initiative involve almost doubling the number of cavities in MI if the same designs are to be used, further highlighting the potential benefits of superconductivity. These advantages are attractive, but to date, no tunable superconducting cavity suitable for MI has been proposed due to the incompatibility of conventional broadband tuning methods with superconductivity. Here, we present a tunable superconducting cavity concept capable of record-breaking performance. Tuning will be accomplished by using high-speed linear actuators to vary the insertion depth of metallic plungers into the cavity volume. This tuning concept is theoretically viable with currently available technology and will be fully compatible with a superconducting cavity.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

A Mechanically Tuned Superconducting Main Injector Cavity

Radio Frequency (RF) superconductivity has been a mainstay of accelerator science for decades. However, its benefits have yet to be applied to proton synchrotrons with demanding tuning requirements. For example, the Main Injector (MI), Fermilab's high-energy proton synchrotron, currently utilizes 20+ ferrite-loaded cavities for a targeted 1.2 s acceleration cycle. Harnessing the extremely high gradients associated with superconductivity, the required number of cavities could be reduced by an order of magnitude, dramatically lowering operational power requirements even with cryogenic considerations. Additionally, the current plans for the Fermilab Accelerator Complex Evolution (ACE) initiative involve almost doubling the number of cavities in MI if the same designs are to be used, further highlighting the potential benefits of superconductivity. These advantages are attractive, but to date, no tunable superconducting cavity suitable for MI has been proposed due to the incompatibility of conventional broadband tuning methods with superconductivity. Here, we present a tunable superconducting cavity concept capable of record-breaking performance. Tuning will be accomplished by using high-speed linear actuators to vary the insertion depth of metallic plungers into the cavity volume. This tuning concept is theoretically viable with currently available technology and will be fully compatible with a superconducting cavity.

43 PARTICLE ACCELERATORS↗
NASA NTRS1974

Evolution of a moderately short turbulent boundary layer in a severe pressure gradient

The early and intermediate development of a highly accelerated (or decelerated) turbulent boundary layer is analyzed. For sufficiently large accelerations (or pressure gradients) and for total normal strains which are not excessive, the equation for the Reynolds shear stress simplifies to give a stress that remains approximately constant as it is convected along streamlines. The theoretical results for the evolution of the mean velocity in favourable and adverse pressure gradients agree well with experiment for the cases considered. A calculation which includes mass injection at the wall is also given.

Deissler, R. G.↗
DOE OSTI2025

Controlling Oxidation of Nb in Oxygen Abundant Environments

Modern particle accelerators depend on Superconducting Radio Frequency (SRF) cavities made from high-purity niobium (Nb) to achieve optimal performance, including high quality factors and strong accelerating gradients. However, when exposed to air, niobium naturally forms a complex oxide layer that can introduce surface imperfections and carbon contamination. This work examines an alternative oxidation strategy under oxygen-rich conditions to better regulate the oxide formation process. The ultimate objective is to improve surface uniformity and cleanliness, thereby reducing defect density and enhancing performance. We used Confocal Microscopy, Scanning Electron Microscopy (SEM), and X-Ray Photoelectron Spectroscopy (XPS) to analyze surface changes. We used standard metrics, like Arithmetic Average Roughness (Ra) and Root Mean Square (Rq). Three oxidation methods were applied: short, extended, and a repeated HF and H₂O₂ oxidation process. Preliminary results show promising reductions in carbon contamination and surface defects.

Romero, Juan [Fermilab]↗
DOE OSTI2025

Improved laser-plasma accelerator stability via high-bandwidth longitudinal focal position stabilization of a 100 TW-class laser system

Laser-plasma accelerators (LPAs) offer an attractive alternative to conventional accelerators for the development of compact electron sources and next-generation light sources. Due to orders-of-magnitude larger accelerating gradients, LPAs enable the acceleration of high-brightness electron beams to ultrarelativistic energies in millimeter- to centimeter-scale distances. However, LPA stability is limited by shot-to-shot fluctuations of the driving laser system. Specifically, fluctuations in the final-focus longitudinal position result in correlated instability in LPA electron beam qualities, including total beam charge, average beam energy, and energy spread. We demonstrate active stabilization of the longitudinal focal position for a 100 TW-class laser system. This repetition-rate scalable stabilization system leverages noninvasive wave front monitoring of a copropagating, unamplified kHz pulse train to guide corrective adjustments to the focal position of a 1 Hz amplified drive laser via an upstream telescope on millisecond timescales. In this demonstration, the approach limits standard deviation fluctuations of the amplified drive beam’s longitudinal focal position to ±0.22⁢ mm (representing at least a 53% reduction), achieved with a correction bandwidth that reaches the Nyquist frequency limit. Consequently, we observe marked improvements in both long-term and shot-to-shot LPA stability in terms of charge and spectrum.

Beam control↗
DOE OSTI2024

TRANSFER LEARNING FOR FIELD EMISSION MITIGATION IN CEBAF SRF CAVITIES

The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab operates hundreds of super-conducting radio frequency (SRF) cavities in its two linear accelerators (linacs). Field emission (FE) is an ongoing operational challenge in higher gradient SRF cavities. FE generates high levels of neutron and gamma radiation leading to damaged accelerator hardware and a radiation hazard environment. During machine development periods, we performed gradient scans to record data capturing the relationship between cavity gradients and radiation levels measured throughout the linacs. However, the field emission environment at CEBAF varies considerably over time as the configuration of the radio frequency (RF) gradients changes and due to the changing behaviour of field emitters. An artificial intelligence/machine learning (AI/ML) approach with transfer learning could be a valuable tool to mitigate FE and lower the radiation levels. In this work, we mainly focus on leveraging the RF trip data gathered during CEBAF operations. We develop a transfer learning-based surrogate model for radiation detector readings given RF cavity gradients to track the CEBAF?s changing configuration and environment. Then, we could use the developed model as an optimization process for redistributing the RF gradients within a linac to minimize radiation levels.

Ahammed, K.↗
DOE OSTI2024

TRANSFER LEARNING FOR FIELD EMISSION MITIGATION IN CEBAF SRF CAVITIES

The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab operates hundreds of super-conducting radio frequency (SRF) cavities in its two linear accelerators (linacs). Field emission (FE) is an ongoing operational challenge in higher gradient SRF cavities. FE generates high levels of neutron and gamma radiation leading to damaged accelerator hardware and a radiation hazard environment. During machine development periods, we performed gradient scans to record data capturing the relationship between cavity gradients and radiation levels measured throughout the linacs. However, the field emission environment at CEBAF varies considerably over time as the configuration of the radio frequency (RF) gradients changes and due to the changing behaviour of field emitters. An artificial intelligence/machine learning (AI/ML) approach with transfer learning could be a valuable tool to mitigate FE and lower the radiation levels. In this work, we mainly focus on leveraging the RF trip data gathered during CEBAF operations. We develop a transfer learning-based surrogate model for radiation detector readings given RF cavity gradients to track the CEBAF?s changing configuration and environment. Then, we could use the developed model as an optimization process for redistributing the RF gradients within a linac to minimize radiation levels.

Ahammed, K.↗
DOE OSTI2023

High gradient research and facilities at Los Alamos National Laboratory [Slides]

A particle accelerator is a scientific instrument that produces a directional stream of electrically charged particles, usually electrons or protons. The accelerator propels particles to high speeds, close to the speed of light. Historically, accelerators play a crucial role in scientific discovery. Nowadays, accelerators produce particle beams for biomedical and material research, manufacturing, energy technology, and Homeland security.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Microscopic understanding of the effects of impurities in low RRR SRF cavities

The SRF community has shown that introducing certain impurities into high-purity niobium can improve quality factors and accelerating gradients. We question why some impurities improve RF performance while others hinder it. The purpose of this study is to characterize the impurities of niobium coupons with a low residual resistance ratio (RRR) and correlate these impurities with the RF performance of low RRR cavities so that the mechanism of impurity-based improvements can be better understood and improved upon. The combination of RF testing, temperature mapping, frequency vs temperature analysis, and materials studies reveals a microscopic picture of why low RRR cavities experience low BCS resistance behavior more prominently than their high RRR counterparts. We evaluate how differences in the mean free path, grain structure, and impurity profile affect RF performance. The results of this study have the potential to unlock a new understanding on SRF materials and enable the next generation of high Q/high gradient surface treatments.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

SurFE-XD (Surface curvature-driven Finite Elements model for Diffusion under eXtreme conditions)

SurFE-XD is a mesoscale finite element framework to model surface diffusion under mutliphysics environments. The code uses legacy C++ library dolphin wrapped with python in a FEniCS driven unified form language and just-in-time (JIT) compilation setting. The purpose of the release is to attract wide-ranging usage of the code along with publication supplementation to support reproducibility of scientific data. SurFE-XD has been originally conceived under the LDRD-DR funding for “High-Gradient (C-BAND) Breakdown tolerant accelerator materials project. Currently SrFE-XD support electrostatics and Thermo-elasticity driven surface diffusion kernels. Releasing the code will also enable to include contributions from other physical regimes e.g., plasticity and electrodynamics etc as well portability to GPU-based platforms.

Bagchi, Soumendu↗
DOE OSTI2020

DPM: A deep learning PDE augmentation method with application to large-eddy simulation

A framework is introduced that leverages known physics to reduce overfitting in machine learning for scientific applications. The partial differential equation (PDE) that expresses the physics is augmented with a neural network that uses available data to learn a description of the corresponding unknown or unrepresented physics. Training within this combined system corrects for missing, unknown, or erroneously represented physics, including discretization errors associated with the PDE's numerical solution. For optimization of the network within the PDE, an adjoint PDE is solved to provide high-dimensional gradients, and a stochastic adjoint method (SAM) further accelerates training. Additionally, the approach is demonstrated for large-eddy simulation (LES) of turbulence. High-fidelity direct numerical simulations (DNS) of decaying isotropic turbulence provide the training data used to learn sub-filter-scale closures for the filtered Navier–Stokes equations. Out-of-sample comparisons show that the deep learning PDE method outperforms widely-used models, even for filter sizes so large that they become qualitatively incorrect. It also significantly outperforms the same neural network when a priori trained based on simple data mismatch, not accounting for the full PDE. Measures of discretization errors, which are well-known to be consequential in LES, point to the importance of the unified training formulation's design, which without modification corrects for them. For comparable accuracy, simulation runtime is significantly reduced. A relaxation of the typical discrete enforcement of the divergence-free constraint in the solver is also successful, instead allowing the DPM to approximately enforce incompressibility physics. Since the training loss function is not restricted to correspond directly to the closure to be learned, training can incorporate diverse data, including experimental data.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗