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Dense plasma focus (DPF) Z-pinch devices are sources of copious high energy electrons and ions, x-rays, and neutrons. Megajoule-scale DPFs can generate 10 12 neutrons per pulse in deuterium gas through a combination of thermonuclear and beam-target fusion. However, the details of the neutron production are not fully understood and past optimization efforts of these devices have been largely empirical. Previously, we reported on the first fully kinetic simulations of a kilojoule-scale DPF and demonstrated that both kinetic ions and kinetic electrons are needed to reproduce experimentally observed features, such as charged-particle beam formation and anomalous resistivity. Here, we present the first fully kinetic simulation of a MegaJoule DPF, with predicted ion and neutron spectra, neutron anisotropy, neutron spot size, and time history of neutron production. The total yield predicted by the simulation is in agreement with measured values, validating the kinetic model in a second energy regime. V C 2014 AIP Publishing LLC. INTRODUCTIONWe describe here the first fully kinetic simulation of a MegaJoule-scale dense plasma focus (DPF). 1-4 This simulation is appreciably more computationally intensive than kinetic modeling of kilojoule-scale devices, 5,6 due to the greater spatial scales involved and the smaller time-step needed to resolve the higher electron cyclotron frequencies associated with the higher plasma current. The neutron yield predicted by this simulation is consistent with measured neutron yields, now validating this kinetic model in a second energy/current regime.A DPF consists of two coaxially located electrodes with a high-voltage source at one end ( Figure 1). In the presence of a low-pressure gas, the high-voltage source induces a surface flashover and the formation of a current-conducting plasma sheath across an insulator at the upstream end of the DPF. During the "run-down" phase, the current sheath is accelerated down the length of the electrodes by magnetic pressure, ionizing, and sweeping up neutral gas as it accelerates. When the plasma sheath reaches the end of the inner electrode, a portion is pushed radially inward during the "run-in" phase. When the leading-edge of the current sheath reaches the axis, it "pinches" the plasma to create a hot, dense region that emits high-energy electron and ion beams, x-rays, and (in the presence of D or D-T) neutrons. 4 In addition to fluid modeling, previous work has included a non-self-consistent test particle approach to access kinetic effects, 7-11 kinetic simulations of a Z-pinch with scaled ion-electron mass ratio, 12 and kinetic simulations of a conventional gas-puff Z-pinch. 13 We previously reported on the first fully kinetic model of a kJ-scale DPF Z-pinch device, including electrode boundaries, and demonstrated self-consistent production of high-energy charged particles and neutron production 5 as well as a detailed benchmark of the model with experiments. 6 SIMULATION AND EXPERIMENTAL SET UPThe simulation set-up is briefly summarized here: calc...

A Dense Plasma Focus (DPF) is a pulsed-power machine that electromagnetically accelerates and cylindrically compresses a shocked plasma in a Z-pinch. The pinch results in a brief (∼100 nanosecond) pulse of X-rays, and, for some working gases, also a pulse of neutrons. A great deal of experimental research has been done into the physics of DPF reactions, and there exist mathematical models describing its behavior during the different time phases of the reaction. Two of the phases, known as the inverse pinch and the rundown, are approximately governed by magnetohydrodynamics, and there are a number of well-established codes for simulating these phases in two dimensions or in three dimensions under the assumption of axial symmetry. There has been little success, however, in developing fully three-dimensional simulations. In this work we present three-dimensional simulations of DPF reactions and demonstrate that 3D simulations predict qualitatively and quantitatively different behavior than their 2D counterparts. One of the most important quantities to predict is the time duration between the formation of the gas shock and Z-pinch, and the 3D simulations more faithfully represent experimental results for this time duration and are essential for accurate prediction of future experiments.

A supervised machine learning algorithm, called locally adaptive discriminant analysis (LADA), has been developed to locate boundaries between identifiable image features that have varying intensities. LADA is an adaptation of image segmentation, which includes techniques that find the positions of image features (classes) using statistical intensity distributions for each class in the image. In order to place a pixel in the proper class, LADA considers the intensity at that pixel and the distribution of intensities in local (nearby) pixels. This paper presents the use of LADA to provide, with statistical uncertainties, the positions and shapes of features within ultrafast images of shock waves. We demonstrate the ability to locate image features including crystals, density changes associated with shock waves, and material jetting caused by shock waves. This algorithm can analyze images that exhibit a wide range of physical phenomena because it does not rely on comparison to a model. LADA enables analysis of images from shock physics with statistical rigor independent of underlying models or simulations.

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