{"@context":"http://iiif.io/api/presentation/2/context.json","@id":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/manifest.json","@type":"sc:Manifest","label":"Uncertainty Quantification For Physical and Numerical Diffusion Models In Inertial Confinement Fusion Simulations","metadata":[{"label":"dc.description.sponsorship","value":"This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree."},{"label":"dc.format","value":"Monograph"},{"label":"dc.format.medium","value":"Electronic Resource"},{"label":"dc.identifier.uri","value":"http://hdl.handle.net/11401/77277"},{"label":"dc.language.iso","value":"en_US"},{"label":"dc.publisher","value":"The Graduate School, Stony Brook University: Stony Brook, NY."},{"label":"dcterms.abstract","value":"This thesis concerns simulations of Inertial Confinement Fusion. Inertial confinement is carried out in a large scale facility at National Ignition Facility. The experiments have failed to reproduce design calculations, and so uncertainty quantification of calculations is an important asset. Uncertainties can be classified as aleatoric or epistemic. This thesis is concerned with aleatoric uncertainty quantification. Among the many uncertain aspects that affect the simulations, we have narrowed our study of possible uncertainties. The first source of uncertainty we present is the amount of pre-heating of the fuel done by hot electrons. The second source of uncertainty we consider is the effect of the algorithmic and physical transport diffusion and their effect on the hot spot thermodynamics. Physical transport mechanisms play an important role for the entire duration of the ICF capsule, so modeling them correctly becomes extremely vital. In addition, codes that simulate material mixing introduce numerical (algorithmically) generated transport across the material interfaces. This adds another layer of uncertainty in the solution through the artificially added diffusion. The third source of uncertainty we consider is physical model uncertainty. The fourth source of uncertainty we focus on a single localized surface perturbation (a divot) which creates a perturbation to the solution that can potentially enter the hot spot to diminish the thermonuclear environment. Jets of ablator material are hypothesized to enter the hot spot and cool the core, contributing to the observed lower reactions than predicted levels. A plasma transport package, Transport for Inertial Confinement Fusion (TICF) has been implemented into the Radiation Hydrodynamics code FLASH, from the University of Chicago. TICF has thermal, viscous and mass diffusion models that span the entire ICF implosion regime. We introduced a Quantum Molecular Dynamics calibrated thermal conduction model due to Hu for thermal transport. The numerical approximation uncertainties are introduced by the choice of a hydrodynamic solver for a particular flow. Solvers tend to be diffusive at material interfaces and the Front Tracking (FT) algorithm, which is an already available software code in the form of an API, helps to ameliorate such effects. The FT algorithm has also been implemented in FLASH and we use this to study the effect that divots can have on the hot spot properties."},{"label":"dcterms.available","value":"2017-09-20T16:52:20Z"},{"label":"dcterms.contributor","value":"Samulyak, Roman"},{"label":"dcterms.creator","value":"Rana, Verinder S."},{"label":"dcterms.dateAccepted","value":"2017-09-20T16:52:20Z"},{"label":"dcterms.dateSubmitted","value":"2017-09-20T16:52:20Z"},{"label":"dcterms.description","value":"Department of Applied Mathematics and Statistics"},{"label":"dcterms.extent","value":"119 pg."},{"label":"dcterms.format","value":"Monograph"},{"label":"dcterms.identifier","value":"http://hdl.handle.net/11401/77277"},{"label":"dcterms.issued","value":"2016-12-01"},{"label":"dcterms.language","value":"en_US"},{"label":"dcterms.provenance","value":"Made available in DSpace on 2017-09-20T16:52:20Z (GMT). No. of bitstreams: 1\nRana_grad.sunysb_0771E_13130.pdf: 10312607 bytes, checksum: dc23a69100c08a0edfff41e1dc9e7880 (MD5)\n  Previous issue date:    1"},{"label":"dcterms.publisher","value":"The Graduate School, Stony Brook University: Stony Brook, NY."},{"label":"dcterms.subject","value":"Front Tracking, High Performance Computiong, Inertial Confinement Fusion, Physical Diffusion Models, Thermodynamics, Uncertainty Quantification"},{"label":"dcterms.title","value":"Uncertainty Quantification For Physical and Numerical Diffusion Models In Inertial Confinement Fusion Simulations"},{"label":"dcterms.type","value":"Dissertation"},{"label":"dc.type","value":"Dissertation"}],"description":"This manifest was generated dynamically","viewingDirection":"left-to-right","sequences":[{"@type":"sc:Sequence","canvases":[{"@id":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/canvas/page-1.json","@type":"sc:Canvas","label":"Page 1","height":1650,"width":1275,"images":[{"@type":"oa:Annotation","motivation":"sc:painting","resource":{"@id":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/63%2F92%2F54%2F63925471227219077972784299472511042144/full/full/0/default.jpg","@type":"dctypes:Image","format":"image/jpeg","height":1650,"width":1275,"service":{"@context":"http://iiif.io/api/image/2/context.json","@id":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/63%2F92%2F54%2F63925471227219077972784299472511042144","profile":"http://iiif.io/api/image/2/level2.json"}},"on":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/canvas/page-1.json"}]}]}]}