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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

200 MV Record Voltage Of vCM And LCLS-II-HE Cryomodules Production Start At Fermilab

The Linac Coherent Light Source (LCLS) is an X-ray science facility at SLAC National Accelerator Laboratory. The LCLS-II project (an upgrade to LCLS) is in the commissioning phase; the LCLS-II-HE (High Energy) project is another upgrade to the facility, enabling higher energy operation. An electron beam is accelerated using superconducting radio frequency (SRF) cavities built into cryomodules. It is planned to build 24 1.3 GHz standard cryomodules and one 1.3 GHz single-cavity Buncher Capture Cavity (BCC) cryomodule for the LCLS-II-HE project. Fourteen of these standard cryomodules and the BCC are planned to be assembled and tested at Fermilab. Procurements for standard cryomodule components are nearing completion. The first LCLS-II-HE cryomodule, referred to as the verification cryomodule (vCM) was assembled and tested at Fermilab. Fermilab has completed the assembly of the second cryomodule. This paper presents LCLS-II-HE cryomodule production status at Fermilab, emphasizing the changes done based on the successes, challenges, mitigations, and lessons learned from LCLS-II; validation of the changes with the excellent vCM results.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

High Power Superconducting Electron Linear Accelerators For Industrial Application

Fermilab is executing a technology development program to develop a compact yet powerful electron accelerator. We are leveraging R&D breakthroughs in SRF cavities, cost-effective radio-frequency sources, modern cryo-coolers, and high average current electron guns. We show that a single accelerator module can deliver average beam power as high as 1 MW through detailed thermal, RF, and particle simulations.

Thangaraj, Jayakar Tobin↗
DOE OSTI2024

Modelling of the electron cyclotron emission burst from a laboratory tokamak plasma with loss-cone maser instability

The maser instability associated with the loss-cone distribution has been widely invoked to explain the radio bursts observed in the astrophysical plasma environment, such as aurora and corona. In the laboratory plasma of a tokamak, events reminiscent of these radio bursts have also been frequently observed as an electron cyclotron emission (ECE) burst in the microwave range (~2f ce near the last closed flux surface) during transient magnetohydrodynamic events. These bursts have a short duration of ~10 μs and display a radiation spectrum corresponding to a radiation temperature T e,rad of over 30 keV while the edge thermal electron temperature T e is only in the range of 1 keV. Suprathermal electrons can be generated through magnetic reconnection, and a loss-cone distribution can be generated through open stochastic field lines in the magnetic mirror of the near-edge region of a tokamak plasma. Radiation modelling shows that a sharp distribution gradient ∂f/∂v ⊥ > 0 at the loss-cone boundary can cause a negative absorption of ECE radiation through the maser instability. The negative absorption then amplifies the radiation so that the microwave intensity is significantly stronger than the thermal value. The significant T e,rad from the simulations suggests the potential role of the loss-cone maser instability in generating the ECE burst in a tokamak.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Design considerations for an ultrahigh-bandwidth Phase Contrast Imaging system applied to fusion grade devices

Abstract The PCI diagnostic is an internal reference interferometer that creates an image of absolutely calibrated electron density fluctuations integrated along the line of sight of the probing light beam. While conventional PCI diagnostics installed on fusion experiments worldwide employ light of wavelength equal to 10.59 μm, the same system using light at 1.55 μm wavelength would extend the spectral response in wave-number and frequency by factors of seven and over one hundred, respectively, thereby potentially providing quantitative measurements of the internal structure of density perturbations induced by either turbulent or radio-frequency waves, simultaneously covering ion to electron gyro-radius scales up to the GHz frequency region. Based on a previously developed 1.55 μm PCI prototype system, constraints to the design for such a diagnostic in fusion grade devices are presented and compared to those faced with the conventional method.

Instruments & Instrumentation↗
DOE OSTI2025

SF6 / Acetone Separation & Purification

Sulfur hexafluoride (SF₆) is recognized as the most potent greenhouse gas used in the power transmission and semiconductor sectors. In the past decade, its global emissions have increased sharply, largely due to the absence of effective disposal and recovery pathways. This concern was formally addressed under the Kyoto Protocol, which urged participating nations to adopt measures to limit SF₆ emissions and mitigate its role in climate change. In recent years, various abatement strategies have been explored, including non-thermal plasma (NTP) technologies such as radio frequency, microwave, dielectric barrier discharge, and electron beam systems. While these methods can decompose SF₆, they also produce hazardous by-products like sulfur oxyfluorides, sulfur dioxide, hydrofluoric acid, and fluorine gas. Because these compounds pose both environmental and health risks, replacing traditional disposal practices with modern, more effective treatment methods has become an urgent priority.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Machine Learning for Anomaly Detection in Neural Network Security and SRF Cavities

This dissertation explores the development and deployment of machine learning approaches to address critical challenges in anomaly detection across two distinct domains: neural network security in federated learning settings and cavity behavior analysis in particle accelerator operations at Jefferson Lab in Newport News, Virginia. Anomaly detection identifies deviations from expected patterns, safeguarding systems in cybersecurity, industry, and research against malicious activities and failures. This dissertation demonstrates how our machine learning approaches enhance detection accuracy and efficiency in both neural network security and industrial applications. First, we investigate vulnerabilities in deep neural networks deployed in federated learning. Although federated learning preserves user privacy by training models locally, it remains vulnerable to backdoor attacks, in which malicious participants embed hidden triggers that induce targeted misbehavior. We propose a self-supervised contrastive learning framework to detect and mitigate such backdoor attacks. In our experiments, this method achieves higher detection accuracy and lower false positive rates than existing defenses, while operating without access to local model updates or original training data and thus preserving the privacy guarantees of the federated setting. Second, we address the operational reliability of superconducting radio-frequency (SRF) cavities at the Continuous Electron Beam Accelerator Facility (CEBAF). Our research leverages an unsupervised learning approach, combined with Principal Component Analysis (PCA) and k-means clustering, to identify anomalous behaviors in SRF cavities. Our method detects subtle anomalous behavior by analyzing SRF signal data. This knowledge allows for the early detection and resolution of potential faults, significantly improving the efficiency and reliability of operations. Third, we extend these insights to time-series anomaly detection more broadly. We design a contrastive-learning based model tailored to increasingly dynamic environments and academic research. This model improves detection accuracy in settings that require real-time monitoring and predictive maintenance. Our research underscores the broader applicability and impact of advanced machine learning techniques in anomaly detection. By extracting meaningful patterns from complex data, machine learning can significantly enhance security in distributed neural networks and improve the efficiency of particle accelerator operations. This dissertation serves as a stepping stone for future investigations into the vast possibilities of anomaly detection, inspiring further exploration and development of machine learning techniques in this field.

Ferguson, Hal [Old Dominion University]↗
DOE OSTI2023

Superconducting radio frequency photoinjectors for CW-XFEL

A dependable and consistent electron source is a crucial requirement for the achievement of high-power free electron lasers (FELs). Over the past two decades, it has been demonstrated that photoinjectors based on SRF technology (SRF guns) are suitable for continuous wave (CW) beam generation. SRF guns possess both the high accelerating field gradients of normal conducting RF photoinjectors and the low power dissipation thanks to mature SRF cavity technology, and therefore have the potential to provide the high-brightness, high-current beams required for CW-XFELs. After the demonstration of the first SRF gun in Dresden-Rossendorf, several SRF gun programs based on different approaches have achieved promising progress and even succeeded in routine operation. SRF guns are expected to play an important role in XFEL facilities in the near future. In this paper, we give an overview of design concepts, important parameters and development status of the worldwide SRF gun projects.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Calculation of rf-induced temporal jitter in ultrafast electron diffraction

A significant contribution to the temporal resolution of an ultrafast electron diffraction (UED) instrument is arrival time jitter caused by amplitude and phase variation of radio-frequency (rf) cavities. In this paper, we present a semianalytical approach for calculating rf-induced temporal jitter from klystron and rf cavity parameters. Our approach allows fast estimation of temporal jitter for MeV-UED beamlines and can serve as a virtual timing tool when shot-to-shot measurements of rf amplitude and phase jitters are available. A simulation study for the SLAC MeV-UED instrument is presented, and the temporal resolution of several beamline configurations is compared.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Photoemission characterization of N-polar III-nitride photocathodes as candidate bright electron beam sources for accelerator applications

We report on the growth and characterization of a new class of photocathode structures for use as electron sources to produce high brightness electron beams for accelerator applications. The sources are realized using III-nitride materials and are designed to leverage the strong polarization field, which is characteristic of this class of materials when grown in their wurtzite crystal structure, to produce a negative electron affinity condition without the use of Cs, possibly allowing these materials to be operated in radio frequency guns. A Quantum Efficiency (QE) of about 1×10−3 and an emitted electrons’ Mean Transverse Energy (MTE) of about 100 meV are measured at a wavelength of 265 nm. In a vacuum level of 3×10−10 Torr, the QE does not decrease after more than 24 h of continuous operation. The lowest MTE of about 50 meV is measured at 300 nm along with a QE of 1.5×10−5. Surface characterizations reveal a possible contribution to the MTE from surface morphology, calling for more detailed studies.

Cultrera, L. (ORCID:0000000235606059)↗
DOE OSTI2022

A MeerKAT-meets-LOFAR study of MS 1455.0 + 2232: a 590 kiloparsec ‘mini’-halo in a sloshing cool-core cluster

ABSTRACT Radio mini-haloes are poorly understood, moderately extended diffuse radio sources that trace the presence of magnetic fields and relativistic electrons on scales of hundreds of kiloparsecs, predominantly in relaxed clusters. With relatively few confirmed detections to-date, many questions remain unanswered. This paper presents new radio observations of the galaxy cluster MS 1455.0+2232 performed with MeerKAT (covering the frequency range 872−1712 MHz) and LOFAR (covering 120−168 MHz), the first results from a homogeneously selected mini-halo census. We find that this mini-halo extends for ∼590 kpc at 1283 MHz, significantly larger than previously believed, and has a flatter spectral index (α = −0.97 ± 0.05) than typically expected. Our X-ray analysis clearly reveals a large-scale (254 kpc) sloshing spiral in the intracluster medium. We perform a point-to-point analysis, finding a tight single correlation between radio and X-ray surface brightness with a super-linear slope of $b_{\rm 1283\, MHz} = 1.16^{+0.06}_{-0.07}$ and $b_{\rm 145\, MHz} = 1.15^{+0.09}_{-0.08}$; this indicates a strong link between the thermal and non-thermal components of the intracluster medium. Conversely, in the spectral index/X-ray surface brightness plane, we find that regions inside and outside the sloshing spiral follow different correlations. We find compelling evidence for multiple sub-components in this mini-halo for the first time. While both the turbulent (re-)acceleration and hadronic scenarios are able to explain some observed properties of the mini-halo in MS 1455.0+2232, neither scenario is able to account for all the evidence presented by our analysis.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Grain-boundary structure and segregation in Nb 3 Sn coatings on Nb for high-performance superconducting radiofrequency cavity applications

Here, we report on atomic-scale analyses of grain boundary (GB) structures and segregation in Nb 3 Sn coatings on Nb, prepared by the vapor-diffusion process, for superconducting radiofrequency (SRF) cavity applications, utilizing atom-probe tomography, high-resolution scanning transmission electron-microscopy and first-principles calculations. We demonstrate that the chemical composition of Nb 3 Sn GBs is correlated strongly with the diffusion of Sn and Nb at GBs during the coating process. In a sample coated with a relatively large Sn flux, we observe an interfacial width of Sn segregation at a GB of ~3 nm, with a maximum concentration of ~35 at.%. After post-annealing at 1100 °C for 3 h, the Sn segregated at GBs disappears and Nb segregation is observed subsequently at GBs, indicating that Nb diffused into the Nb3Sn GBs from the Nb substrate. It is also demonstrated that the amount of Sn segregation in a Nb 3 Sn coating can be controlled by: (i) Sn flux; and (ii) the temperatures of the Nb substrates and Sn source, which may affect the overall kinetics including GB diffusion of Sn and Nb. An investigation of the correlation between the chemical compositions of GBs and Nb 3 Sn SRF cavity performance reveals that the Nb 3 Sn SRF cavities with the best performance (high-quality factors at high accelerating electric-field gradients) do not exhibit Sn segregation at GBs. Our results suggest that the chemical compositions of GBs in Nb 3 Sn coatings for SRF cavities can be controlled by GB engineering and can be utilized to optimize fabrication of high-quality Nb 3 Sn coatings for SRF cavities.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Deep Learning Based Superconducting Radio-Frequency Cavity Fault Classification at Jefferson Laboratory

This work investigates the efficacy of deep learning (DL) for classifying C100 superconducting radio-frequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. CEBAF is a large, high-power continuous wave recirculating linac that utilizes 418 SRF cavities to accelerate electrons up to 12 GeV. Recent upgrades to CEBAF include installation of 11 new cryomodules (88 cavities) equipped with a low-level RF system that records RF time-series data from each cavity at the onset of an RF failure. Typically, subject matter experts (SME) analyze this data to determine the fault type and identify the cavity of origin. This information is subsequently utilized to identify failure trends and to implement corrective measures on the offending cavity. Manual inspection of large-scale, time-series data, generated by frequent system failures is tedious and time consuming, and thereby motivates the use of machine learning (ML) to automate the task. This study extends work on a previously developed system based on traditional ML methods (Tennant and Carpenter and Powers and Shabalina Solopova and Vidyaratne and Iftekharuddin, Phys. Rev. Accel. Beams, 2020, 23, 114601), and investigates the effectiveness of deep learning approaches. The transition to a DL model is driven by the goal of developing a system with sufficiently fast inference that it could be used to predict a fault event and take actionable information before the onset (on the order of a few hundred milliseconds). Because features are learned, rather than explicitly computed, DL offers a potential advantage over traditional ML. Specifically, two seminal DL architecture types are explored: deep recurrent neural networks (RNN) and deep convolutional neural networks (CNN). We provide a detailed analysis on the performance of individual models using an RF waveform dataset built from past operational runs of CEBAF. In particular, the performance of RNN models incorporating long short-term memory (LSTM) are analyzed along with the CNN performance. Furthermore, comparing these DL models with a state-of-the-art fault ML model shows that DL architectures obtain similar performance for cavity identification, do not perform quite as well for fault classification, but provide an advantage in inference speed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

High-current conduction cooled superconducting radio-frequency cryomodule

A high-current, compact, conduction cooled superconducting radio-frequency cryomodule for particle accelerators. The cryomodule will accelerate an electron beam of average current up to 1 ampere in continuous wave (CW) mode or at high duty factor. The cryomodule consists of a single-cell superconducting radio-frequency cavity made of high-purity niobium, with an inner coating of Nb3Sn and an outer coating of pure copper. Conduction cooling is achieved by using multiple closed-cycle refrigerators. Power is fed into the cavity by two coaxial couplers. Damping of the high-order modes is achieved by a warm beam-pipe ferrite damper.

Ciovati, Gianluigi↗
DOE OSTI2024

Parasitic conduction loss of lithium niobate on insulator platform

Thin-film lithium niobate on insulator (LNOI) has recently emerged as an exciting platform for nonlinear integrated photonic devices, drawing parallels with the impact of silicon on insulator (SOI) in the electronic device realm. While the influence of interfacial states of SOI on electronic device performance, particularly their contribution to parasitic radio frequency (RF) losses, is well-documented, this aspect remains relatively unexplored in LNOI, despite their shared silicon/oxide interface structure. Here, we investigate the RF loss arising from mobile charges at the silicon/oxide interface of the LNOI platform. We show that this parasitic conduction can be exacerbated, especially during plasma etching, and leads to substantial RF losses. Through strategic incorporation of a trap-rich poly-silicon layer at this interface, we effectively eliminate this parasitic conduction, resulting in the suppression of RF losses. This work establishes crucial guidelines for harnessing LNOI in applications such as high-frequency electro-mechanical devices, high-speed electro-optic modulators, and high-efficiency microwave-to-optics converters.

Physics↗
DOE OSTI2025

Detecting anomalous SRF cavity behavior with unsupervised learning

We present an unsupervised learning framework for detecting anomalous superconducting radio-frequency (SRF) cavity behavior at the Continuous Electron Beam Accelerator Facility (CEBAF), emphasizing its initial performance and effectiveness. Key to the system’s success was the development of data acquisition systems (DAQs) that capture fast-sampled, information-rich signals, essential for detecting transient effects. The approach involves creating daily cavity-specific models using principal component analysis to handle variations in rf signal behavior and mitigate performance degradation from data drift. This unsupervised method eliminates the need for expensive labeling by continuously updating models with recent data. Deployed and operational for 3 months before a scheduled shutdown, the system successfully identified several issues with DAQ signals, confirming its effectiveness. Despite access to only a fraction of CEBAF’s SRF cavity signals, the framework efficiently detected several instances requiring intervention, demonstrating a significant improvement over traditional, labor-intensive methods of manual plot inspection. Published by the American Physical Society 2025

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

High-Temperature RF Transmission Loss Characteristics of Platinum–Inconel 600 and Platinum–304 Steel Interconnects

Radio frequency (RF) transmission losses that occur at electronic interconnects is currently an important limitation of wireless sensors and other electronic device packages that must operate in high-temperature environments. Herein, in-situ RF transmission losses were measured for two types of interconnects: platinum (Pt) wire bonded to an Inconel 600 coaxial cable; and Pt-wire bonded to a block of 304 steel. Heating the Pt wire/Inconel interconnects at 800 °C for up to 200 hours were found to be very stable with no measurable transmission losses. In contrast, Pt wire/304 steel block interconnects examined over similar test periods at 800 °C exhibited gradual increases in transmission losses leveling off at 10, 38 and 85 mdB at 400 MHz, 700 MHz, and 1 GHz, respectively. Increasing the test temperature to 900 °C caused further elevated transmission losses of 30, 70 and 108 mdB at the same reported frequencies. By comparison, temperature cycling of the Pt wire/304 steel interconnects between room temperature and 900 °C was found to cause more significant degradation. In particular, four thermal cycles from room temperature to 900 °C resulted in losses of 75, 271, and 482 mdB at the same reported frequencies. Parabolic oxidation relative to the RF skin depth in the 304 steel block at the tested frequencies are discussed as possible reasons for the transmission losses during the isothermal soaks, whereas interconnect degradation due to differences in thermal expansion between Pt and 304 steel are primarily responsible for transmission losses caused by high temperature cycling.

304 steel↗
DOE OSTI2024

SRF CAVITY FAULT PREDICTION USING DEEP LEARNING AT JEFFERSON LAB

In this study, we present a deep learning-based pipeline for predicting superconducting radio-frequency (SRF) cavity faults in the Continuous Electron Beam Accelera-tor Facility (CEBAF) at Jefferson Lab. We leverage pre-fault RF signals from C100-type cavities and employ deep learning to predict faults in advance of their onset. We train a binary classifier model to distinguish between stable and impending fault signals. Test results show accuracies exceeding 99% for distinguishing between normal signals and pre-fault signals from a class of more slowly developing fault types, such as microphonics. We describe results from a proof-of-principle demonstration on a realistic, imbalanced data set and report performance metrics. Encouraging results suggest that future SRF systems could leverage this framework and implement measures to mitigate the onset in more slowly developing fault types.

Rahman, M.↗
DOE OSTI2026

PARTICULATE STUDY OF NEG PUMPS IRRADIATED IN THE CEBAF TUNNEL

Non-evaporable getter (NEG) pumps are being used to maintain ultra-high vacuum in the beamline of superconducting radio-frequency (SRF) accelerators, such as the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. Because of the sensitivity of the performance of SRF cavities to particulate contamination, it is important to evaluate the integrity of the NEG material after exposure to high radiation during beam operation. The particulate from two NEG pumps based on ZAO® getter alloy was measured with a particle counter in a clean-room. The pumps were assembled onto a hermetically sealed setup which was placed in the CEBAF tunnel. The setup was exposed to 24 h beam operation for ~ 230 days. The total gamma-rays dose measured on the test setup was ~ 1.6 Mrad. The total neutron dose measured at ~ 75 cm of the test setup was ~ 11 krad. The particulate count from the two pumps was measured again in the clean-room after irradiation. Whereas an increase of particulate counts was measured, compared to before irradiation, subsequent measurements indicate the absence of systematically loose particulate. The pumping speed of one of the irradiated pumps was also measured to be consistent with that of a non-irradiated one, corroborating the absence of significant damage to the ZAO® NEG material due to irradiation.

Ciovati, G. [Thomas Jefferson National Accelerator↗