| dc.identifier.uri | http://hdl.handle.net/11401/77654 | |
| dc.description.sponsorship | This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree. | en_US |
| dc.format | Monograph | |
| dc.format.medium | Electronic Resource | en_US |
| dc.language.iso | en_US | |
| dc.publisher | The Graduate School, Stony Brook University: Stony Brook, NY. | |
| dc.type | Dissertation | |
| dcterms.abstract | Mix is critical to the modeling of chemical or nuclear reaction processes in fluids. We simulate Rayleigh-Taylor unstable pre-turbulent and transitionally turbulent fluid mixing regimes. We model experiments generated by the flow of hot and cold water over a splitter plate into an observation channel. Three statistical second moments of the flow were measured. Our simulations achieve excellent agreement with two of these and partial agreement with the third. We draw two broader lessons from this study. The first is that numerical algorithms do matter. We compare our simulations to one obtained using Miranda, a 10th-order compact stencil turbulence code. However, this code lacks front tracking, an important aspect of our simulation algorithm. The Miranda simulation misses data error bars for the measure of mix over most of the experimental time, although it is nearly DNS and uses mesh two, four and eight times finer than what we report here. The second broader lesson is that details of data analysis matter. The velocity statistics are generated by two laser sheets in rapid succession, to track particles seeded into the flow and to generate the resulting velocity statistics. This methodology suppresses particles and fluid elements with motion normal to the laser sheet, a result that biases the second moments as reported. Consequently, the experimentally reported second moments are not suitable for direct use in the calibration of RANS simulation codes. Rather, as reported here, LES simulations, validated against the biased statistics, can be used to construct unbiased statistics, and these are suitable for setting RANS parameters. | |
| dcterms.available | 2017-09-20T16:53:12Z | |
| dcterms.contributor | Jiao, Xiangmin | en_US |
| dcterms.contributor | Glimm, James | en_US |
| dcterms.contributor | Samulyak, Roman | en_US |
| dcterms.contributor | Zingale, Michael. | en_US |
| dcterms.creator | Hu, Wenlin | |
| dcterms.dateAccepted | 2017-09-20T16:53:12Z | |
| dcterms.dateSubmitted | 2017-09-20T16:53:12Z | |
| dcterms.description | Department of Applied Mathematics and Statistics. | en_US |
| dcterms.extent | 82 pg. | en_US |
| dcterms.format | Application/PDF | en_US |
| dcterms.format | Monograph | |
| dcterms.identifier | http://hdl.handle.net/11401/77654 | |
| dcterms.issued | 2015-12-01 | |
| dcterms.language | en_US | |
| dcterms.provenance | Made available in DSpace on 2017-09-20T16:53:12Z (GMT). No. of bitstreams: 0
Previous issue date: 1 | en |
| dcterms.publisher | The Graduate School, Stony Brook University: Stony Brook, NY. | |
| dcterms.subject | Applied mathematics | |
| dcterms.subject | Computational Fluids Dynamics, Hydrodynamics, Large Eddy Simulation, Mixing | |
| dcterms.title | Statistical Moments in Variable-Density Incompressible Mixing Flows | |
| dcterms.type | Dissertation | |
