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of 47
pro vyhledávání: '"Ian L. Tregillis"'
We present a study of using machine learning to enhance hohlraum design for opacity measurement experiments. For opacity experiments we desire a hohlraum that, when its interior walls are illuminated by theNational Ignition Facility (NIF) lasers, wil
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d699e463c067edb37dc5161d2bff31ce
http://arxiv.org/abs/2009.03269
http://arxiv.org/abs/2009.03269
Autor:
Aaron C. Koskelo, Ian L. Tregillis
Publikováno v:
Journal of Applied Physics. 130:144501
We present a mathematical framework for describing the dynamical evolution of an ejecta cloud generated by a generic ejecta source model. We consider a piezoelectric sensor fielded in the path of an ejecta cloud, for experimental configurations in wh
Publikováno v:
Journal of Applied Physics. 130:124504
We consider the trajectory of an Asay foil ejecta diagnostic for scenarios where ejecta are produced at a singly shocked planar surface and fly ballistically through a perfect vacuum to the sensor. We do so by building upon a previously established m
Autor:
Aaron C. Koskelo, Ian L. Tregillis
Publikováno v:
ASME 2019 Verification and Validation Symposium.
Computational physicists are commonly faced with the task of resolving discrepancies between the predictions of a complex, integrated multi-physics numerical simulation and corresponding experimental datasets. Such efforts commonly require a slow ite
Autor:
John J. L. Morton, Jonathan Workman, K. Mussack, A. Hungerford, A. S. Moore, Chris Fontes, Chris L. Fryer, T. M. Guymer, Ian L. Tregillis, Evan Dodd, John Kline, Wesley Even, J. Benstead, Carl Greeff
Publikováno v:
High Energy Density Physics. 18:45-54
Although the fundamental physics behind radiation and matter flow is understood, many uncertainties remain in the exact behavior of macroscopic fluids in systems ranging from pure turbulence to coupled radiation hydrodynamics. Laboratory experiments