Engineering Papers⌕ Search

Engineering topics

Carpenter, A.

Publications and source records attributed to Carpenter, A..

29 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Upgrade of the gated laser entrance hole imager G-LEH-2 on the National Ignition Facility

A major upgrade has been implemented for the ns-gated laser entrance hole imager on the National Ignition Facility (NIF) to obtain high-quality data for Hohlraum physics study. In this upgrade, the single “Furi” hCMOS sensor (1024 × 448 pixel arrays with two-frame capability) is replaced with dual “Icarus” sensors (1024 × 512 pixel arrays with four-frame capability). Both types of sensors were developed by Sandia National Laboratories for high energy density physics experiments. With the new Icarus sensors, the new diagnostic provides twice the detection area with improved uniformity, wider temporal coverage, flexible timing setup, and greater sensitivity to soft x rays (<2 keV). These features, together with the fact that the diagnostic is radiation hardened and can be operated on the NIF for high neutron yield deuterium–triterium experiments, enable significantly greater return of data per experiment.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Machine Learning Based Cavity Fault Classification and Prediction

We report on the development of machine learning models for classifying C100 superconducting radio frequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. CEBAF is a continuous-wave recirculating linac utilizing 418 SRF cavities to accelerate electrons up to 12 GeV through 5-passes. Of these, 96 cavities (12 cryomodules) are designed with a digital low-level RF system configured such that a cavity fault triggers waveform recordings of 17 RF signals for each of the eight cavities in the cryomodule. Subject matter experts (SME) can analyze the collected time-series data, identify which of the eight cavities faulted first, and classify the type of fault. This information is used to find trends and strategically deploy mitigations to problematic cryomodules. However, manually labeling the data is laborious and time-consuming. By leveraging machine learning, near real-time – rather than post-mortem – identification of the offending cavity and classification of the fault type has been implemented. We discuss the performance of the ML models during a recent physics run.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Initial Implementation Of Machine Learning System for SRF Cavity Fault Classification at CEBAF

The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Laboratory is high power CW electron accelerator. It uses a mixture of two types of SRF modules, older, lower energy C20/C50 modules and newer higher energy C100 modules, arrayed in two anti-parallel linear accelerators. Accurately classifying the type of cavity faults is essential to maintaining and improving accelerator performance. Each C100 cryomodule contains eight 7-cell cavities. When a cavity fault occurs within a cryomodule, all eight cavities generate 17 waveforms each containing 8192 points. This data is exported from EPICS and saved for review. Analysis of these waveforms is time intensive and requires subject matter expertise (SME). SMEs examine the data from each event and label it according to one of several known cavity fault types. Multiple machine learning models have been developed on this labeled dataset with sufficient performance to warrant the creation of a limited machine learning software system for use by accelerator operations staff. This paper discusses the transition from model development to implementation of a prototype system.

Carpenter, A.↗
DOE OSTI2019

SRF Cavity Fault Classification Using Machine Learning At CEBAF

The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab is the first large high power CW recirculating electron accelerator to make use of SRF accelerating structures. The structures are configured in two anti-parallel linacs connected by arcs. Each linac consists of twenty C20/C50 cryomodules each containing eight 5-cell cavities and five C100 upgrade cryomodules each containing eight 7-cell cavities. Accurately classifying the source of cavity faults is critical for improving accelerator performance. A cavity fault triggers a waveform acquisition process where 17 waveform records sampled at 5 kHz are recorded for each of the 8 cavities in the affected cryomodule. The waveform record length is sufficiently long for transient microphonic effects to be observable. This data combined with archived signals sampled at 10 Hz are used to classify faults. Significant time is required for a subject matter expert to analyze and identify the intra-cavity signatures of imminent faults. This paper describes a path forward that utilizes machine learning for automatic fault classification. Post-training identification of the physical origins of faults are discussed, as are potential machine-trained model-free implementations of trip avoidance procedures. These methods should provide new insights into cavity fault mechanisms and facilitate intelligent optimization of cryomodule performance

Solopova, A.↗
DOE OSTI2019

SRF Cavity Fault Classification Using Machine Learning At CEBAF

The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab is the first large high power CW recirculating electron accelerator to make use of SRF accelerating structures. The structures are configured in two anti-parallel linacs connected by arcs. Each linac consists of twenty C20/C50 cryomodules each containing eight 5-cell cavities and five C100 upgrade cryomodules each containing eight 7-cell cavities. Accurately classifying the source of cavity faults is critical for improving accelerator performance. A cavity fault triggers a waveform acquisition process where 17 waveform records sampled at 5 kHz are recorded for each of the 8 cavities in the affected cryomodule. The waveform record length is sufficiently long for transient microphonic effects to be observable. This data combined with archived signals sampled at 10 Hz are used to classify faults. Significant time is required for a subject matter expert to analyze and identify the intra-cavity signatures of imminent faults. This paper describes a path forward that utilizes machine learning for automatic fault classification. Post-training identification of the physical origins of faults are discussed, as are potential machine-trained model-free implementations of trip avoidance procedures. These methods should provide new insights into cavity fault mechanisms and facilitate intelligent optimization of cryomodule performance

Solopova, A.↗
DOE OSTI2016

Next Generation Gamma-Ray Cherenkov Detectors for the National Ignition Facility

The newest generation of Gas Cherenkov Detector (GCD-3) employed in Inertial Confinement Fusion experiments at the Omega Laser Facility has provided improved performance over previous generations. Comparison of reaction histories measured using two different deuterium-tritium fusion products, namely gamma rays using GCD and neutrons using Neutron Temporal Diagnostic (NTD), have provided added credibility to both techniques. GCD-3 is now being brought to the National Ignition Facility (NIF) to supplement the existing Gamma Reaction History (GRH-6m) located 6 m from target chamber center (TCC). Initially it will be located in a reentrant well located 3.9 m from TCC. Data from GCD-3 will inform the design of a heavily-shielded “Super” GCD to be located as close as 20 cm from TCC. In conclusion, it will also provide a test-bed for faster optical detectors, potentially lowering the temporal resolution from the current ~100 ps state-of-the-art photomultiplier tubes (PMT) to ~10 ps Pulse Dilation PMT technology currently under development.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2013

Mach-Zehnder Modulator Performance on the NIF South Pole Bang Time Diagnostic

We present performance data for Mach-Zehnder optical modulators fielded on the National Ignition Facility (NIF) as a potential signal path upgrade for the South Pole Bang Time diagnostic. A single channel demonstration system has been deployed utilizing two modulators operating in a 90-degree In phase and Quadrature (I/Q) configuration. X-ray target emission signals are split and fed into two recording systems: a reference CRT based oscilloscope, Greenfield FTD10000, and the dual Mach-Zehnder system. Results of X-ray implosion time (bang time) determination from these two recording systems are compared and presented.

Mach-Zehnder, NIF Diagnostics↗
DOE OSTI2012

Neutron spectrometry-An essential tool for diagnosing implosions at the National Ignition Facility (invited)

DT neutron yield (Y{sub n}), ion temperature (T{sub i}), and down-scatter ratio (dsr) determined from measured neutron spectra are essential metrics for diagnosing the performance of inertial confinement fusion (ICF) implosions at the National Ignition Facility (NIF). A suite of neutron-time-of-flight (nTOF) spectrometers and a magnetic recoil spectrometer (MRS) have been implemented in different locations around the NIF target chamber, providing good implosion coverage and the complementarity required for reliable measurements of Y{sub n}, T{sub i}, and dsr. From the measured dsr value, an areal density ({rho}R) is determined through the relationship {rho}R{sub tot} (g/cm{sup 2}) = (20.4 {+-} 0.6) Multiplication-Sign dsr{sub 10-12MeV}. The proportionality constant is determined considering implosion geometry, neutron attenuation, and energy range used for the dsr measurement. To ensure high accuracy in the measurements, a series of commissioning experiments using exploding pushers have been used for in situ calibration of the as-built spectrometers, which are now performing to the required accuracy. Recent data obtained with the MRS and nTOFs indicate that the implosion performance of cryogenically layered DT implosions, characterized by the experimental ignition threshold factor (ITFx), which is a function of dsr (or fuel {rho}R) and Y{sub n}, has improved almost two orders of magnitude since the first shot in September, 2010.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1991

New aspects relating to the behaviour of composites and adhesives in space

Some of the specialized testing procedures performed at the JPL Molecular Contamination Investigation Facility for the WideField Planetary Camera II (WFPC II) program for the screening of polymeric materials for outgassing properties are described. For WFPC II, a science performance goal of 1-percent photometric accuracy at 1470 A over an extended time (at least 30 days) has been established. Utilization of the newest technology using CCD detectors poses even more stringent requirements. Test results are presented, and data reduction and modeling techniques are discussed.

Carpenter, A.↗