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Coupling Computationally Expensive Radiative Hydrodynamic Simulations with Machine Learning for Graded Inner Shell Design Optimization in Double Shell Capsules

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dc.contributor.authorVazirani, Nomita Nirmalen
dc.contributor.committeechairScales, Wayne A.en
dc.contributor.committeechairEngland, Scott L.en
dc.contributor.committeememberGrosskopf, Michaelen
dc.contributor.committeememberBailey, Scott M.en
dc.contributor.departmentAerospace and Ocean Engineeringen
dc.date.accessioned2022-12-30T09:00:08Zen
dc.date.available2022-12-30T09:00:08Zen
dc.date.issued2022-12-29en
dc.description.abstractHigh energy density experiments rely heavily on predictive physics simulations in the design process. Specifically in inertial confinement fusion (ICF), predictive physics simulations, such as in the radiation-hydrodynamics code xRAGE, are computationally expensive, limiting the design process and ability to find an optimal design. Machine learning provides a mechanism to leverage expensive simulation data and alleviate limitations on computational time and resources in the search for an optimal design. Machine learning efficiently identifies regions of design space with high predicted performance as well as regions with high uncertainty to focus simulations, which may lead to unexpected designs with great potential. This dissertation focuses on the application of Bayesian optimization to design optimization for ICF experiments conducted by the double shell campaign at Los Alamos National Lab (LANL). The double shell campaign is interested in implementing graded inner shell layers to their capsule geometry. Graded inner shell layers are expected to improve stability in the implosions with fewer sharp density jumps, but at the cost of lower yields, in comparison to the nominal bilayer inner shell targets. This work explores minimizing hydrodynamic instability and maximizing yield for the graded inner shell targets by building and coupling a multi-fidelity Bayesian optimization framework with multi-dimensional xRAGE simulations for an improved design process.en
dc.description.abstractgeneralInertial confinement fusion (ICF) is an active field of research in which a fuel is compressed to extreme temperatures and densities to achieve thermonuclear ignition. Ignition is achieved when the fuel can continuously heat itself and sustain its reactions. These fusion reactions would produce large amounts of energy. Power plants using fusion could solve many of the world's energy concerns with far less pollution than current energy sources. Although ignition has not been achieved in the lab, ICF researchers are actively working towards this goal. At Los Alamos National Lab (LANL), ICF researchers are focused on studying ignition-relevant conditions for "double shell" targets through experiments at laser facilities, such at the National Ignition Facility (NIF). These experiments are extremely expensive to field, design, and analyze. To obtain the maximum information from each experiment, researchers rely on predictive physics simulations, which are computationally intensive, making it difficult to find optimal target designs. In this dissertation, better use of simulations is made by focusing on using machine learning along with simulation data to find optimal target designs. Machine learning allows for efficient use of limited computational time and resources on simulations, such that an optimal target design can be found in a reasonable amount of time before an ICF experiment. This dissertation specifically looks at using Bayesian optimization for design optimization of LANL's double shell capsules with graded material inner shells. Several Bayesian optimization frameworks are presented, along with a discussion of optimal designs and physics mechanisms that lead to high performing capsule designs. The work from this dissertation will create an improved design process for the LANL double shell (and other) campaigns, providing high fidelity optimization of ICF targets.en
dc.description.degreeDoctor of Philosophyen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:35968en
dc.identifier.urihttp://hdl.handle.net/10919/113004en
dc.language.isoenen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectinertial confinement fusionen
dc.subjectmachine learningen
dc.titleCoupling Computationally Expensive Radiative Hydrodynamic Simulations with Machine Learning for Graded Inner Shell Design Optimization in Double Shell Capsulesen
dc.typeDissertationen
thesis.degree.disciplineAerospace Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.nameDoctor of Philosophyen

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