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  • 1.
    Hoffman, Johan
    et al.
    KTH, School of Computer Science and Communication (CSC), High Performance Computing and Visualization (HPCViz).
    Jansson, Johan
    KTH, School of Computer Science and Communication (CSC), High Performance Computing and Visualization (HPCViz). Basque Center for Applied Mathematics, Spain .
    Jansson, Niclas
    RIKEN Advanced Institute for Computational Science, Japan .
    Vilela De Abrea, Rodrigo
    KTH, School of Computer Science and Communication (CSC), High Performance Computing and Visualization (HPCViz). KTH, School of Engineering Sciences (SCI), Centres, Linné Flow Center, FLOW.
    Time-resolved adaptive FEM simulation of the DLR-F11 aircraft model at high Reynolds number2014In: 52nd AIAA Aerospace Sciences Meeting - AIAA Science and Technology Forum and Exposition, SciTech 2014, 2014Conference paper (Other academic)
    Abstract [en]

    We present a time-resolved, adaptive finite element method for aerodynamics, together with the results from the HiLiftPW-2 workshop, where this method is used to compute the flow past a DLR-F11 aircraft model at realistic Reynolds number. The mesh is automatically constructed by the method as part of the computation, and no explicit turbulence model is needed. The effect of unresolved turbulent boundary layers is modeled by a simple parametrization of the wall shear stress in terms of the skin friction. In the extreme case of very high Reynolds numbers we approximate the small skin friction by zero skin friction, corresponding to a free slip boundary condition, which results in a computational model without any model parameter that needs tuning. Thus, the simulation methodology by- passes the main challenges posed by high Reynolds number CFD: the design of an optimal computational mesh, turbulence (or subgrid) modeling, and the cost of boundary layer res- olution. The results from HiLiftPW-2 presented in this report show good agreement with experimental data for a range of different angles of attack, while using orders of magnitude fewer degrees of freedom than what is needed in state of the art methods such as RANS. 

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