Robust adaptive high-order RANS methods
Thesis: S.M., Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2014.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2015
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| Online Access: | http://hdl.handle.net/1721.1/95563 |
| _version_ | 1811086180394991616 |
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| author | Kudo, Jun, S.M. Massachusetts Institute of Technology |
| author2 | David L. Darmofal. |
| author_facet | David L. Darmofal. Kudo, Jun, S.M. Massachusetts Institute of Technology |
| author_sort | Kudo, Jun, S.M. Massachusetts Institute of Technology |
| collection | MIT |
| description | Thesis: S.M., Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2014. |
| first_indexed | 2024-09-23T13:22:04Z |
| format | Thesis |
| id | mit-1721.1/95563 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T13:22:04Z |
| publishDate | 2015 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/955632019-04-10T08:01:07Z Robust adaptive high-order RANS methods Robust adaptive high-order Reynolds-Averaged Navier-Stokes methods Kudo, Jun, S.M. Massachusetts Institute of Technology David L. Darmofal. Massachusetts Institute of Technology. Computation for Design and Optimization Program. Massachusetts Institute of Technology. Computation for Design and Optimization Program. Computation for Design and Optimization Program. Thesis: S.M., Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2014. Cataloged from PDF version of thesis. Includes bibliographical references (pages 89-94). The ability to achieve accurate predictions of turbulent flow over arbitrarily complex geometries proves critical in the advancement of aerospace design. However, quantitatively accurate results from modern Computational Fluid Dynamics (CFD) tools are often accompanied by intractably high computational expenses and are significantly hindered by the lack of automation. In particular, the generation of a suitable mesh for a given flow problem often requires significant amounts of human input. This process however encounters difficulties for turbulent flows which exhibit a wide range of length scales that must be spatially resolved for an accurate solution. Higher-order adaptive methods are attractive candidates for addressing these deficiencies by promising accurate solutions at a reduced cost in a highly automated fashion. However, these methods in general are still not robust enough for industrial applications and significant advances must be made before the true realization of robust automated three-dimensional turbulent CFD. This thesis presents steps towards this realization of a robust high-order adaptive Reynolds-Averaged Navier-Stokes (RANS) method for the analysis of turbulent flows. Specifically, a discontinuous Galerkin (DG) discretization of the RANS equations and an output-based error estimation with an associated mesh adaptation algorithm is demonstrated. To improve the robustness associated with the RANS discretization, modifications to the negative continuation of the Spalart-Allmaras turbulence model are reviewed and numerically demonstrated on a test case. An existing metric-based adaptation framework is adopted and modified to improve the procedure's global convergence behavior. The resulting discretization and modified adaptation procedure is then applied to two-dimensional and three-dimensional turbulent flows to demonstrate the overall capability of the method. by Jun Kudo. S.M. 2015-02-25T17:10:20Z 2015-02-25T17:10:20Z 2014 2014 Thesis http://hdl.handle.net/1721.1/95563 903536899 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 94 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Computation for Design and Optimization Program. Kudo, Jun, S.M. Massachusetts Institute of Technology Robust adaptive high-order RANS methods |
| title | Robust adaptive high-order RANS methods |
| title_full | Robust adaptive high-order RANS methods |
| title_fullStr | Robust adaptive high-order RANS methods |
| title_full_unstemmed | Robust adaptive high-order RANS methods |
| title_short | Robust adaptive high-order RANS methods |
| title_sort | robust adaptive high order rans methods |
| topic | Computation for Design and Optimization Program. |
| url | http://hdl.handle.net/1721.1/95563 |
| work_keys_str_mv | AT kudojunsmmassachusettsinstituteoftechnology robustadaptivehighorderransmethods AT kudojunsmmassachusettsinstituteoftechnology robustadaptivehighorderreynoldsaveragednavierstokesmethods |