Large Eddy Simulation of Rotors: Filtered Actuator Line Modelling of Wind Turbines and Aeroacoustics of Propeller
2020-10-14 16:30 - 17:30 (Asia/Jerusalem) | https://technion.zoom.us/j/95290857526
Lecturer:Ronith Stanly
Faculty:Department of Aerospace Engineering
Institute:Technion – Israel Institute of Technology
Location:https://technion.zoom.us/j/95290857526
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Engineering devices employing rotors such as propellers on UAV’s, wind and tidal turbines play a vital role in both propulsion and energy extraction. These devices feature complex unsteady turbulent flowfields and have significant non-local effects related to downstream wake interactions and far-field noise. In the past decade, the use of advanced turbulence modeling approach within a computational fluid dynamics (CFD) approach, such as large eddy simulations (LES) have become more popular. LES offers higher fidelity but also increases computational cost. When dealing with multi-rotor devices such as quad-copters or wind farms the cost of blade-resolved LES becomes prohibitive. Combining LES with the family of lower-fidelity models, called actuator line models (ALM), has grown in popularity in the past decade. ALM models replace full blade resolution with an array of actuator points or lines parameterized by aerodynamic lift/drag polar plots along the blades. Body forces computed based on these actuator points are then projected onto the LES flow mesh mimicking the effect of rotating blades on the flow. In this seminar, an ALM model is implemented into an in-house high-fidelity LES code, called MIRACLES, and applied to (a) rotor wake flow and noise predictions, and (b) several benchmark single- and twin-wind turbine cases. Wherever possible comparisons are made to both measured data and previous simulation results to assess the performance of the combined LES/ALM approach for simulating rotor flows. Novel aspects of this study include the implementation of the Ffowcs-Williams Hawkings (FWH) noise model and the recent Filtered-ALM and its MPI parallelization. Simulations typically are run on several hundred CPU cores and take several days to a week to gather flow and statistical results.
