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2022-08-26
Gomez, Matthew R., Slutz, S.A., Jennings, C.A., Weis, M.R., Lamppa, D.C., Harvey-Thompson, A.J., Geissel, M., Awe, T.J., Chandler, G.A., Crabtree, J.A. et al..  2021.  Developing a Platform to Enable Parameter Scaling Studies in Magnetized Liner Inertial Fusion Experiments. 2021 IEEE International Conference on Plasma Science (ICOPS). :1—1.
Magnetized Liner Inertial Fusion (MagLIF) is a magneto-inertial fusion concept that relies on fuel magnetization, laser preheat, and a magnetically driven implosion to produce fusion conditions. In MagLIF, the target is a roughly 10 mm long, 5 mm diameter, 0.5 mm thick, cylindrical beryllium shell containing 1 mg/cm 3 D 2 gas. An axial magnetic field on the order of 10 T is applied to the target, and several kJ of laser energy is deposited into the fuel. Up to 20 MA of current is driven axially through the beryllium target, causing it to implode over approximately 100 ns. The implosion produces a 100-μm diameter, 8-mm tall fuel column with a burn-averaged ion temperature of several keV, that generates 10 11 -10 13 DD neutrons.
Lewis, William E., Knapp, Patrick F., Slutz, Stephen A., Schmit, Paul F., Chandler, Gordon A., Gomez, Matthew R., Harvey-Thompson, Adam J., Mangan, Michael A., Ampleford, David J., Beckwith, Kristian.  2021.  Deep Learning Enabled Assessment of Magnetic Confinement in Magnetized Liner Inertial Fusion. 2021 IEEE International Conference on Plasma Science (ICOPS). :1—1.
Magnetized Liner Inertial Fusion (MagLIF) is a magneto-inertial fusion (MIF) concept being studied on the Z-machine at Sandia National Laboratories. MagLIF relies on quasi-adiabatic heating of a gaseous deuterium (DD) fuel and flux compression of a background axially oriented magnetic field to achieve fusion relevant plasma conditions. The magnetic flux per fuel radial extent determines the confinement of charged fusion products and is thus of fundamental interest in understanding MagLIF performance. It was recently shown that secondary DT neutron spectra and yields are sensitive to the magnetic field conditions within the fuel, and thus provide a means by which to characterize the magnetic confinement properties of the fuel. 1 , 2 , 3 We utilize an artificial neural network to surrogate the physics model of Refs. [1] , [2] , enabling Bayesian inference of the magnetic confinement parameter for a series of MagLIF experiments that systematically vary the laser preheat energy deposited in the target. This constitutes the first ever systematic experimental study of the magnetic confinement properties as a function of fundamental inputs on any neutron-producing MIF platform. We demonstrate that the fuel magnetization decreases with deposited preheat energy in a fashion consistent with Nernst advection of the magnetic field out of the hot fuel and diffusion into the target liner.
2018-01-16
Benjamin, B., Coffman, J., Esiely-Barrera, H., Farr, K., Fichter, D., Genin, D., Glendenning, L., Hamilton, P., Harshavardhana, S., Hom, R. et al..  2017.  Data Protection in OpenStack. 2017 IEEE 10th International Conference on Cloud Computing (CLOUD). :560–567.

As cloud computing becomes increasingly pervasive, it is critical for cloud providers to support basic security controls. Although major cloud providers tout such features, relatively little is known in many cases about their design and implementation. In this paper, we describe several security features in OpenStack, a widely-used, open source cloud computing platform. Our contributions to OpenStack range from key management and storage encryption to guaranteeing the integrity of virtual machine (VM) images prior to boot. We describe the design and implementation of these features in detail and provide a security analysis that enumerates the threats that each mitigates. Our performance evaluation shows that these security features have an acceptable cost-in some cases, within the measurement error observed in an operational cloud deployment. Finally, we highlight lessons learned from our real-world development experiences from contributing these features to OpenStack as a way to encourage others to transition their research into practice.