30 September 2016Abstract:
Generating inflow fields for scale-resolving simulations of turbulent flow is crucial for a wide range of applications and is an active area of research. In this thesis, a method for inflow generation employing a precursor turbulent channel flow simulation is proposed. A procedure for determining the parameters of the precursor simulation based on the properties of the inflow is given. To evaluate the performance of the method, results from a simulation of a flat-plate zero-pressure-gradient turbulent boundary layer are analysed. The adaption length is quantified in terms of the development of integral quantities and the statistical moments of the velocity field. The performance is also compared with that of a state-of-the-art rescaling method for the generation of inflow data. It is shown that both approaches result in adaption lengths of comparable sizes, which makes the proposed method an attractive alternative due to its conceptual simplicity and robustness.
As part of the work on inflow generation, a Python package, eddylicious, was developed. The purpose of the package is to be a framework within which various generation methods can be implemented. The package is available online under an open-source license. An overview of the architecture and currently implemented functionality of the package is given in this thesis.
Furthermore, the results of a preparatory study on large-eddy simulation of wall-bounded turbulent flows are discussed. Fully-developed turbulent channel flow is used as a model problem, and the general-purpose computational fluid dynamics solver OpenFOAM is employed. The accuracy of the results with respect to the resolution of the computational mesh is analysed. Several modelling approaches for the subgrid scale stresses are considered.
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