Multiphase turbulence mechanisms identification from consistent analysis of direct numerical simulation data
Direct Numerical Simulation (DNS) serves as an irreplaceable tool to probe the complexities of multiphase flow and identify turbulent mechanisms that elude conventional experimental measurement techniques. The insights unlocked via its careful analysis can be used to guide the formulation and develo...
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| Format: | Article |
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Elsevier BV
2018
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| Online Access: | http://hdl.handle.net/1721.1/116997 https://orcid.org/0000-0003-3054-7026 https://orcid.org/0000-0001-8720-9437 |
| _version_ | 1826211575555424256 |
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| author | Brown, Cameron Bolotnov, Igor A. Tryggvason, Gretar Lu, Jiacai Magolan, Benjamin Lawrence Baglietto, Emilio |
| author2 | Massachusetts Institute of Technology. Department of Nuclear Science and Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Nuclear Science and Engineering Brown, Cameron Bolotnov, Igor A. Tryggvason, Gretar Lu, Jiacai Magolan, Benjamin Lawrence Baglietto, Emilio |
| author_sort | Brown, Cameron |
| collection | MIT |
| description | Direct Numerical Simulation (DNS) serves as an irreplaceable tool to probe the complexities of multiphase flow and identify turbulent mechanisms that elude conventional experimental measurement techniques. The insights unlocked via its careful analysis can be used to guide the formulation and development of turbulence models used in multiphase computational fluid dynamics simulations of nuclear reactor applications. Here, we perform statistical analyses of DNS bubbly flow data generated by Bolotnov (Reτ= 400) and Lu–Tryggvason (Reτ= 150), examining single-point statistics of mean and turbulent liquid properties, turbulent kinetic energy budgets, and two-point correlations in space and time. Deformability of the bubble interface is shown to have a dramatic impact on the liquid turbulent stresses and energy budgets. A reduction in temporal and spatial correlations for the streamwise turbulent stress (uu) is also observed at wall-normal distances of y+= 15, y/δ = 0.5, and y/δ = 1.0. These observations motivate the need for adaptation of length and time scales for bubble-induced turbulence models and serve as guidelines for future analyses of DNS bubbly flow data. Keywords: Budget Equations, Bubble-Induced Turbulence, DNS, M&C2017, Multiphase CFD |
| first_indexed | 2024-09-23T15:08:09Z |
| format | Article |
| id | mit-1721.1/116997 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T15:08:09Z |
| publishDate | 2018 |
| publisher | Elsevier BV |
| record_format | dspace |
| spelling | mit-1721.1/1169972022-09-29T12:55:57Z Multiphase turbulence mechanisms identification from consistent analysis of direct numerical simulation data Brown, Cameron Bolotnov, Igor A. Tryggvason, Gretar Lu, Jiacai Magolan, Benjamin Lawrence Baglietto, Emilio Massachusetts Institute of Technology. Department of Nuclear Science and Engineering Massachusetts Institute of Technology. Laboratory for Nuclear Science Magolan, Benjamin Lawrence Baglietto, Emilio Direct Numerical Simulation (DNS) serves as an irreplaceable tool to probe the complexities of multiphase flow and identify turbulent mechanisms that elude conventional experimental measurement techniques. The insights unlocked via its careful analysis can be used to guide the formulation and development of turbulence models used in multiphase computational fluid dynamics simulations of nuclear reactor applications. Here, we perform statistical analyses of DNS bubbly flow data generated by Bolotnov (Reτ= 400) and Lu–Tryggvason (Reτ= 150), examining single-point statistics of mean and turbulent liquid properties, turbulent kinetic energy budgets, and two-point correlations in space and time. Deformability of the bubble interface is shown to have a dramatic impact on the liquid turbulent stresses and energy budgets. A reduction in temporal and spatial correlations for the streamwise turbulent stress (uu) is also observed at wall-normal distances of y+= 15, y/δ = 0.5, and y/δ = 1.0. These observations motivate the need for adaptation of length and time scales for bubble-induced turbulence models and serve as guidelines for future analyses of DNS bubbly flow data. Keywords: Budget Equations, Bubble-Induced Turbulence, DNS, M&C2017, Multiphase CFD United States. Department of Energy. Naval Reactors Division (Rickover Fellowship Program in Nuclear Engineering) 2018-07-19T13:44:07Z 2018-07-19T13:44:07Z 2017-08 2017-07 2018-07-16T14:07:12Z Article http://purl.org/eprint/type/JournalArticle 1738-5733 http://hdl.handle.net/1721.1/116997 Magolan, Ben, et al. “Multiphase Turbulence Mechanisms Identification from Consistent Analysis of Direct Numerical Simulation Data.” Nuclear Engineering and Technology, vol. 49, no. 6, Sept. 2017, pp. 1318–25. https://orcid.org/0000-0003-3054-7026 https://orcid.org/0000-0001-8720-9437 http://dx.doi.org/10.1016/J.NET.2017.08.001 Nuclear Engineering and Technology Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Elsevier BV Elsevier |
| spellingShingle | Brown, Cameron Bolotnov, Igor A. Tryggvason, Gretar Lu, Jiacai Magolan, Benjamin Lawrence Baglietto, Emilio Multiphase turbulence mechanisms identification from consistent analysis of direct numerical simulation data |
| title | Multiphase turbulence mechanisms identification from consistent analysis of direct numerical simulation data |
| title_full | Multiphase turbulence mechanisms identification from consistent analysis of direct numerical simulation data |
| title_fullStr | Multiphase turbulence mechanisms identification from consistent analysis of direct numerical simulation data |
| title_full_unstemmed | Multiphase turbulence mechanisms identification from consistent analysis of direct numerical simulation data |
| title_short | Multiphase turbulence mechanisms identification from consistent analysis of direct numerical simulation data |
| title_sort | multiphase turbulence mechanisms identification from consistent analysis of direct numerical simulation data |
| url | http://hdl.handle.net/1721.1/116997 https://orcid.org/0000-0003-3054-7026 https://orcid.org/0000-0001-8720-9437 |
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