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Turbulence modelling using dynamic parameterization with data assimilation
This research assesses the application of a novel approach to parameterization of turbulence models. Dynamic parameterization is used to improve performance of two turbulence schemes incorporated in a coastal hydrodynamic model code: the Prandtl mixing length (PML) model and the k- model. The 3D variational data assimilation scheme is used to assess model skill and facilitate optimization of the turbulence schemes. Neither the PML nor the k- models are particularly suitable for recirculating flows of complex turbulence structure when default empirical constants are used. Static parameterization improves model predictions but the degree of improvement varies across the flow. Dynamic parameterization is superior to static parameterization due to its general solution for a range of flows and the self-updating process does not require costly pre-processed determination of turbulence constants. When using dynamic parameterization, the PML model exhibits comparable levels of accuracy to the k- model while retaining its computational efficiency and ease of application.
Turbulence modelling using dynamic parameterization with data assimilation
This research assesses the application of a novel approach to parameterization of turbulence models. Dynamic parameterization is used to improve performance of two turbulence schemes incorporated in a coastal hydrodynamic model code: the Prandtl mixing length (PML) model and the k- model. The 3D variational data assimilation scheme is used to assess model skill and facilitate optimization of the turbulence schemes. Neither the PML nor the k- models are particularly suitable for recirculating flows of complex turbulence structure when default empirical constants are used. Static parameterization improves model predictions but the degree of improvement varies across the flow. Dynamic parameterization is superior to static parameterization due to its general solution for a range of flows and the self-updating process does not require costly pre-processed determination of turbulence constants. When using dynamic parameterization, the PML model exhibits comparable levels of accuracy to the k- model while retaining its computational efficiency and ease of application.
Turbulence modelling using dynamic parameterization with data assimilation
Olbert, Agnieszka Indiana (Autor:in) / Ragnoli, Emanuele (Autor:in) / Nash, Stephen (Autor:in) / Hartnett, Michael (Autor:in)
Journal of Hydraulic Research ; 55 ; 376-391
04.05.2017
16 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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