Pulsed Power Systems For The Darht Accelerators

Downing, J.N.; Parsons, W.M.; Earley, L.M.; Mefton, J.G.; Moir, D.C.; Carlson, R. R.; Barnes, G.A.; Builta, L.A.; Eversole, S.; Keel, G.; Rader, D.C.; Romero, Jesús; Shurter, R. B.

doi:10.1109/ppc.1991.733441

Cited by 18 publications

(8 citation statements)

References 8 publications

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“…The injector consists of four major parts: a Prime Power Tank (PPT), Blumlein, transmission line, and diode. The PPT consists of a capacitor bank DC charged nominally to 112 kV [15,16]. This is switched through a Stangenes transformer to pulse charge a glycol Blumlein to 1.5 MV.…”

Section: Axis-imentioning

confidence: 99%

“…The Blumleins are pulse charged to a nominal 250 kV in a similar fashion to the injector Blumlein. The voltage is fed out to four parallel 44 Ω coaxial cables (two for each individual cell) in a 70-ns pulse [19,20]. See Figure 1.4 for representative photos of the accelerator and pulsed power halls.…”

Section: Axis-imentioning

confidence: 99%

“…5. 16 Long wave UV spectrometer measurement of a pulsed hollow cathode discharge operating at 2 Torr and a −3.5 kV charge under an argon gas fill. Lines are identified and labeled.…”

mentioning

confidence: 99%

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Diagnosing the DARHT electron beam-target interaction and hydrodynamic expansion [Slides]

Ramey

2022

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The purpose of this dissertation is to provide a roadmap for spectral diagnostic development and modeling of the electron beam-target interaction at the Dual-Axis Radiographic Hydrodynamic Test (DARHT) facility. This is motivated by the current lack of detailed knowledge of the beamtarget interaction and the warm dense matter (WDM) generated during this process, along with the direct relevance to DARHT's main purpose in fulfilling its Stockpile Stewardship mission. Fully characterizing the beam-target interaction will improve the radiographic performance of the DARHT accelerators and can lead to a more advanced and optimized target design for minimizing the radiographic spot size. The phase space trajectory from cold metal to expanded plasma plume in an electron beam driven target is not known. Complicating this issue is the lack of validation of equation-of-state models in the WDM regime and under the vapor dome. The plasmas generated at DARHT span both regimes. Fully diagnosing this beam-target interaction can provide answers to many of these present unknowns. A critical part of solving these problems includes a full-scale, spectroscopic-quality radiation transport model to interpret the resulting spectroscopic measurements. This has been developed by linking together several codes, including atomic physics, radiation hydrodynamics, and radiation transport, and will continue to mature into the future.

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Section: Axis-imentioning

confidence: 99%

Section: Axis-imentioning

confidence: 99%

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Diagnosing the DARHT electron beam-target interaction and hydrodynamic expansion [Slides]

Ramey

2022

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“…The AIRIX facility consists of an injector (Merle 1998) that delivers a 2^3.5 kA=60 ns=4 MeV electron beam and 64 induction cells powered by 32 high voltage generators (Downing et al 1991) (Figure 1). …”

Section: The Airix Installationmentioning

confidence: 99%

Smart Diagnosis on the AIRIX Induction Accelerator

Ribes

Delaunay

Merle³

et al. 2003

International Journal of Smart Engineering System Design

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The AIRIX facility is a high-current linear accelerator (2^3.5 kA) used for flash-radiography at the CEA of Moronvilliers (France). The general background of this study is the predictive maintenance of the AIRIX components by smart diagnosis. We are interested in analyzing the performances of the high voltage (HV) generators, which furnish the energy for accelerating the beam, and the fault detection on the whole machine. We present a tool for fault diagnosis based on pattern recognition using a neural network. We use, among other things, statistical models to define an error vector, which must be classified. To reduce the redundancy of this information and the computation time, we study two algorithms, the principal components analysis and the curvilinear components analysis. A three-layer Radial Basis Function (RBF) neural network realizes the decision rule. We propose an original algorithm to construct the network with unsupervised data. In order to take into account new states or unknown failures, we propose a strategy to adapt the network in a continuous learning. Finally, we present the first results obtained with this tool on the installation.

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“…Modifications to FXR are also proposed that will enable the accelerator to generate two beam pulses with a variable pulse spacing [3]. At LANL, construction is underway on the Dual-Axis Radiographic HydroTest Facility (DARHT) that will house two 16-MeV induction accelerators that face the object at a 90-degree included angle [4]. The DARHT Facility will obtain two radiographic snapshots of the same hydrodynamic event at different times or a single tomographic image of the object at a fixed time.…”

Section: Introductionmentioning

confidence: 99%