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At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Jefferson Laboratory C100 Superconducting Radio-Frequency Cavity Fault Data, 2020

The dataset was created to train machine learning models for the task of identifying the (1) cavity and (2) fault type from C100-type cryomodules at the Thomas Jefferson National Accelerator Facility (Jefferson Lab), thereby replacing the time-consuming efforts of a subject matter expert. Superconducting radio-frequency (SRF) cavity trips represent a significant source of accelerator downtime. Real-time – rather than post-mortem – identification of the offending cavity and classification of the fault type would give control room operators valuable feedback for corrective action planning. The anticipated benefit is increased beam-on-target time for users and provides performance metrics that can be used to improve future cavity designs. A series of 17 RF signals are recorded for each of the 8 cavities in a C100 cryomodule every time a cavity trips. These time-series signals are written to file using a specially designed data acquisition system. The dataset represents fault events recorded during Continuous Electron Beam Accelerator Facility (CEBAF) beam operations between January 18, 2019 and March 9, 2020. The following filtering steps were applied to collected data; (1) only 4 of the 17 signals per cavity are retained (GMES, GASK, CRFP, DETA2) (2) only events with data from each of the eight cavities in the cryomodule are kept, (3) only events that were sampled at 5 kHz were kept, (4) events from cryomodule 0L04 were neglected, (5) events occurring between February 4, 3PM and February 5, 12PM were neglected. As a result of preprocessing, the dataset is comprised of 2,375 unique events. The full dataset is comprised of three files: features.csv, cavity_labels.csv, fault_labels.csv. Each instance in faults.csv includes a timestamp (“date_time”), a label for the cryomodule which experienced the trip (“zone_label”), and 192 features (“feature_1”, “feature_2”... “feature_192”). The features correspond to 6 autoregressive features for each of 4 signals per cavity for each of the 8 cavities (6 × 4 signals/cavity × 8 cavities/cryomodule = 192). To deal with the large variation of signal amplitudes, time-series standardization via the z-score (standard score) function was applied prior to computing the features. For each instance, there is an associated label for the (1) cavity which faulted first (cavity_labels.csv) and (2) the type of fault that caused the trip (fault_labels.csv). The cavity identification can take values of [0, 1, 2, 3, 4, 5, 6, 7, 8] and the fault type can take values of [‘Microphonics’, ‘Quench_100ms’, ‘Controls_Fault’, ‘E_Quench’, ‘Quench_3ms’, ‘Single_Cav_Turn_Off’ , ‘Heat_Riser_Choke’, ‘Multi_Cav_Turn_Off’].

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

INITIAL STUDIES OF CAVITY FAULT PREDICTION AT JEFFERSON LABORATORY

The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Laboratory is a CW recirculating linac that utilizes over 400 superconducting radio-frequency (SRF) cavities to accelerate electrons up to 12 GeV through 5-passes. Recent work has shown that, given RF signals from a cavity during a fault as input, machine learning approaches can accurately classify the fault type. In this paper we report on initial results of predicting a fault onset using only data prior to the failure event. A data set was constructed using time-series data immediately before a fault (’unstable’) and 1.5 seconds prior to a fault (’stable’) gathered from over 5,000 saved fault events. The data was used to train a binary classifier. The results gave key insights into the behavior of several fault types and provided motivation to investigate whether data prior to a failure event could also predict the type of fault. We discuss our method using a sliding window approach and report on initial results. Recent modifications to the low-level RF control system will provide access to streaming signals and we outline a path forward for leveraging deep learning on streaming data

Vidyaratne, Lasitha↗
DOE OSTI2024

SRF Cavity Instability Detection with Machine Learning at CEBAF

During the operation of the Continuous Electron Beam Accelerator Facility (CEBAF), one or more unstable superconducting radio-frequency (SRF) cavities often cause beam loss trips while the unstable cavities themselves do not necessarily trip off. The present RF controls for the legacy cavities report at only 1 Hz, which is too slow to detect fast transient instabilities during these trip events. These challenges make the identification of an unstable cavity out of the hundreds installed at CEBAF a difficult and time-consuming task. To tackle these issues, a fast data acquisition system (DAQ) for the legacy SRF cavities has been developed, which records the sample at 5 kHz. A Principal Component Analysis (PCA) approach is being developed to identify anomalous SRF cavity behavior. We will discuss the present status of the DAQ system and PCA model, along with initial performance metrics. Overall, our method offers a practical solution for identifying unstable SRF cavities, contributing to increased beam availability and machine reliability.

Carpenter, A.↗
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 OSTI2021

INITIAL STUDIES OF CAVITY FAULT PREDICTION AT JEFFERSON LABORATORY

The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Laboratory is a CW recirculating linac that utilizes over 400 superconducting radio-frequency (SRF) cavities to accelerate electrons up to 12 GeV through 5-passes. Recent work has shown that, given RF signals from a cavity during a fault as input, machine learning approaches can accurately classify the fault type. In this paper we report on initial results of predicting a fault onset using only data prior to the failure event. A data set was constructed using time-series data immediately before a fault (’unstable’) and 1.5 seconds prior to a fault (’stable’) gathered from over 5,000 saved fault events. The data was used to train a binary classifier. The results gave key insights into the behavior of several fault types and provided motivation to investigate whether data prior to a failure event could also predict the type of fault. We discuss our method using a sliding window approach and report on initial results. Recent modifications to the low-level RF control system will provide access to streaming signals and we outline a path forward for leveraging deep learning on streaming data

Vidyaratne, Lasitha↗
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

SRF Cavity Instability Detection with Machine Learning at CEBAF

During the operation of the Continuous Electron Beam Accelerator Facility (CEBAF), one or more unstable superconducting radio-frequency (SRF) cavities often cause beam loss trips while the unstable cavities themselves do not necessarily trip off. The present RF controls for the legacy cavities report at only 1 Hz, which is too slow to detect fast transient instabilities during these trip events. These challenges make the identification of an unstable cavity out of the hundreds installed at CEBAF a difficult and time-consuming task. To tackle these issues, a fast data acquisition system (DAQ) for the legacy SRF cavities has been developed, which records the sample at 5 kHz. A Principal Component Analysis (PCA) approach is being developed to identify anomalous SRF cavity behavior. We will discuss the present status of the DAQ system and PCA model, along with initial performance metrics. Overall, our method offers a practical solution for identifying unstable SRF cavities, contributing to increased beam availability and machine reliability.

Carpenter, A.↗
DOE OSTI2025

SRF cavity instability detection with machine learning at CEBAF

During the operation of the Continuous Electron Beam Accelerator Facility (CEBAF), one or more unstable superconducting radio-frequency (SRF) cavities often cause beam loss trips while the unstable cavities themselves do not necessarily trip off. The present RF controls for the legacy cavities report at only 1 Hz, which is too slow to detectfast transient instabilities during these trip events. These challenges make the identification of an unstable cavity out of the hundreds installed at CEBAF a difficult and time-consuming task. To tackle these issues, a fast data acquisition system (DAQ) for the legacy SRF cavities has been developed, which records the sample at 5 kHz. An unsupervised learning framework has been developed to identify anomalous SRF cavity behavior. We will discuss the present status of the DAQ system and our framework, along with recent successes in detecting anomalous cavity behavior. Overall, our method offers a practical solution for identifying unstable SRF cavities, contributing to increased beam availability and machine reliability.

Accelerator Physics↗
DOE OSTI2023

SURFACE ROUGHNESS REDUCTION AND PERFORMANCE OF Nb3Sn FILM FOR SRF APPLICATIONS

Nb3Sn offers better RF performance (Q and Eacc) than Nb at any given temperature because of its superior superconducting properties. The potential deployment of Nb3Sn-coated SRF cavities at 4 K, delivering similar performance to that of Nb cavities at 2 K, will be a transformational technology to enable new classes of SRF accelerator applications. A clean and smooth surface can enhance the performance of the Nb3Sn-coated cavity, typically, the attainable acceleration gradient. The reduction of surface roughness is often linked with a correlative reduction in average coating thickness and grain size. A new approach to enhance the nucleation centers using the pre-deposition of Sn on Nb is introduced for surface roughness reduction. Besides Sn supply's careful tuning, the temperature profiles were varied to engineer the coating surface with reduced roughness as low as ~40 nm in 20 µm × 20 µm AFM scans, one-third that of the typical coating. Electropolishing of coated sample shows surface a clear surface smoothening. A few sets of coating parameters were used to coat 1.3 GHz sin-gle-cell cavities to understand the effects of roughness variation on the RF performance. This presentation discusses ways to reduce surface roughness with results from a systematic analysis of the samples and Nb3Sn-coated single-cell cavities.

Pudasaini, Uttar↗
DOE OSTI2023

SURFACE ROUGHNESS REDUCTION AND PERFORMANCE OF Nb3Sn FILM FOR SRF APPLICATIONS

Nb3Sn offers better RF performance (Q and Eacc) than Nb at any given temperature because of its superior superconducting properties. The potential deployment of Nb3Sn-coated SRF cavities at 4 K, delivering similar performance to that of Nb cavities at 2 K, will be a transformational technology to enable new classes of SRF accelerator applications. A clean and smooth surface can enhance the performance of the Nb3Sn-coated cavity, typically, the attainable acceleration gradient. The reduction of surface roughness is often linked with a correlative reduction in average coating thickness and grain size. A new approach to enhance the nucleation centers using the pre-deposition of Sn on Nb is introduced for surface roughness reduction. Besides Sn supply's careful tuning, the temperature profiles were varied to engineer the coating surface with reduced roughness as low as ~40 nm in 20 µm × 20 µm AFM scans, one-third that of the typical coating. Electropolishing of coated sample shows surface a clear surface smoothening. A few sets of coating parameters were used to coat 1.3 GHz sin-gle-cell cavities to understand the effects of roughness variation on the RF performance. This presentation discusses ways to reduce surface roughness with results from a systematic analysis of the samples and Nb3Sn-coated single-cell cavities.

Pudasaini, Uttar↗
DOE OSTI2023

Surface properties and RF performance of Vapor Diffused Nb3Sn on Nb after sequential anneals below 1000 °C

Nb3Sn is a next-generation superconducting material that can be used for future superconducting radiofrequency (SRF) accelerator cavities, promising better performance, cost reduction, and higher operating temperature than Nb. The Sn vapor diffusion method is currently the most preferred and successful technique to coat niobium cavi-ties with Nb3Sn. Among post-coating treatments to opti-mize the coating quality, higher temperature annealing without Sn is known to degrade Nb3Sn because of Sn loss. We have investigated Nb3Sn/Nb samples briefly annealed at 800-1000 °C for 10 and 20 minutes to poten-tially improve the surface to enhance the performance of Nb3Sn-coated cavities. Following the sample studies, a coated single-cell cavity was sequentially annealed at 900 °C and tested its performance each time, improving the cavity's quality factor relatively. This paper summarizes the sample studies and discusses the RF test results from sequentially annealed SRF Nb3Sn/Nb cavity.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Surface Properties and RF Performance of Vapor Diffused Nb3Sn on NB After Sequential Anneals Below 1000°C

Nb3Sn is a next-generation superconducting material that can be used for future superconducting radiofrequency (SRF) accelerator cavities, promising better performance, cost reduction, and higher operating temperature than Nb. The Sn vapor diffusion method is currently the most preferred and successful technique to coat niobium cavi-ties with Nb3Sn. Among post-coating treatments to opti-mize the coating quality, higher temperature annealing without Sn is known to degrade Nb3Sn because of Sn loss. We have investigated Nb3Sn/Nb samples briefly annealed at 800-1000°C for 10 and 20 minutes to poten-tially improve the surface to enhance the performance of Nb3Sn-coated cavities. Following the sample studies, a coated single-cell cavity was sequentially annealed at 900°C and tested its performance each time, improving the cavity's quality factor relatively. This paper summarizes the sample studies and discusses the RF test results from sequentially annealed SRF Nb3Sn/Nb cavity.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Pathway to tune sputter coated Nb$_3$Sn with Zr

Superconducting radio frequency (SRF) cavities are essential elements of many modern-day particle accelerators such as, European XFEL (DESY), LCLS II-HE (SLAC) and PIP II (Fermilab). Nb$_3$Sn superconducting material promises significant potential to exceed the performance of conventional niobium based SRF cavities. We aim to advance the ongoing R$&$D efforts to achieve Nb$_3$Sn coated cavities with high material quality. In this work, we have developed to achieve a route for effectively reducing the intrinsic defects in magnetron sputter coated Nb$_3$Sn SRF cavities and eventually improve the overall RF performance. We have observed that Zr is mainly being incorporated as ZrO$_2$ precipitates of average dimensions ranging from 20-100nm. By increasing the concentration of Zr, we have three major effects in the physical properties. Firstly, we noted that the density of the surface and bulk voids as well as their average dimensions are dramatically reduced on increasing the Zr content in Nb$_3$Sn. These surface voids are believed to be detrimental to cavity applications due to increase the local dissipation of electric and magnetic fields. These results indicate that inclusion of Zr in Nb$_3$Sn sputtered coating will improve the material quality and might help to reduce the overall RF power dissipation. Secondly, we observed that increasing Zr concentration from 0 to 2.4% substantially improves both superconducting transition temperature (Tc) and upper critical magnetic field (Hc2). However, on further increasing the Zr concentration to 24%, we noticed a small decrement in the Hc2 value. These results infer that the increase in Zr concentration up to an optimal concentration will be beneficial to enhance the superconducting properties. Increase in Tc promises lesser RF losses and improvement in accelerating gradient, whereas the increase in Hc2 suggests more efficient flux pinning which could give rise to high quality factors in presence of multi-Tesla magnetic field. Additionally, we also observed that increasing Zr concentration possibly prevents the oxygen diffusion and leads to thinner formation of primary surface oxide (Nb$_2$O$_5$ and SnO). This study gives a route to tune Zr fraction in Nb3Sn to modify the physical properties that control the high field performance of Nb3Sn RF cavities.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

The mapping & avoidance of high order cavity modes

In this paper a technique developed in 2016 is presented for mapping the multidimensional, multi-mode high order mode landscape of a beam driven super conducting RF cavity. The initial use case for this technique was intended to allow longitudinal gradient control of a beam driven super conducting cavity while simultaneously avoiding many dangerous high order modes and excessive component stresses. This paper will focus on the stability and component stress considerations of the 56MHz superconducting cavity, located in the 4 o’clock sector interaction region of the Relativistic Heavy Ion Collider (RHIC) at Brookhaven National Laboratory. The 56MHz cavity is intended to work in conjunction with the Stochastic Cooling system to improve the longitudinal confinement in the central 197MHz RF bucket and thus increase the integrated luminosity during heavy ion beam runs at RHIC.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

A 100 kW 1.3 GHz Magnetron System with Amplitude and Phase Control

Calabazas Creek Research, Inc., Fermilab, and Communications & Power Industries, LLC, developed a 100 kW peak, 10 kW average, 1.3 GHz, magnetron-based, RF system for driving accelerators. Efficiency varied between 81% and 87%. Phase locking uses a novel approach that provides fast amplitude and phase control when coupled into a superconducting accelerator cavity [1]. The system was successfully tested at Fermilab and produced 100 kW in 1.5 ms pulses at a repetition rate of 2 pps. A locking bandwidth of 0.9 MHz was achieved with a drive signal of 269 W injected through a 4 port circulator. The phase locking signal was 25 dB below the magnetron output power. The spectrum of the phase locked magnetron was suitable for driving accelerator cavities. Phase modulation was demonstrated to 50 kHz (the limit of the available driver source). The average power was limited by available conditioning time. Scaling indicates 42 kW of average power should be achievable. Estimated cost is less than $1/Watt of delivered RF power, exclusive of power supplies or modulators. System design and performance measurements will be presented.

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↗