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Descripción
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| Turbulent flow fields contain a wide range of spatio-temporal scales. Direct Numerical Simulation techniques provide an accurate description of turbulent flow fields. This accuracy comes at the expense of high computational cost and memory footprint. In this context, feature detection algorithms are useful tools that help to identify relevant flow structures, their interactions and ulterior evolution. In this contribution, we employ feature detection algorithms to analyze canonical and controlled turbulent channel flows. Specifically, we will apply both Proper Orthogonal Decomposition and Dynamic Mode Decomposition to DNS-generated turbulent channel flow data-bases. The ultimate goal is to reveal whether flow features linked to drag reduction exist and, if that is the case, learn how those structures could be enhanced, thus leading to more efficient drag reduction strategies. | |
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Internacional
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Si |
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Nombre congreso
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7th European Conference on Computational Fluid Dynamics (ECFD 7) |
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Tipo de participación
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960 |
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Lugar del congreso
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Glasgow |
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Revisores
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No |
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ISBN o ISSN
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978-84-947311-6-7 |
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DOI
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Fecha inicio congreso
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11/06/2018 |
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Fecha fin congreso
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15/06/2018 |
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Desde la página
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775 |
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Hasta la página
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784 |
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Título de las actas
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Proceedings of the 6th. European Conference on Computational Mechanics (Solids, Structures and Coupled Problems). ECCM 6, 7th. European Conference on Computational Fluid Dynamics ECFD 7 |