Low speed, small scale turbomachinery operates at low Reynolds number with transition phenomena occurring. In small consumer product applications, high efficiency and low noise are key performance metrics. Transition behaviour will partly determine the state of the boundary layer at the trailing edge; whether it is laminar, turbulent or separated impacts aerodynamic and acoustic performance. This study aimed to evaluate a commercially available CFD transition model on a low Reynolds number Eppler E387 airfoil and identify whether it was able to correctly model the boundary layer transition, and at what expense.
CFD was carried out utilising the ANSYS Shear Stress Transport (SST) k-ω γ-Reθ transition model. The CFD progressed from 2D in Fluent v150, through to single cell thickness 3D (pseudo 2D) in CFX v172. An Eppler E387 low Reynolds number airfoil, for which experimental data was readily available from literature at Re = 200,000 was used as the validation case for the CFD, with results computed at numerous incidence angles and mesh densities.
Additionally, experimental surface oil flow visualisation was undertaken in a wind tunnel using a scaled E387 airfoil for the zero incidence case at Re = 50,000. The flow visualisation exhibited the expected key features of transition in the breakdown of the boundary layer from laminar to turbulent, and was used as a validation case for the CFD transition model.
The comparison between the results from the CFD transition model and the experimental data from literature suggested varying levels of agreement based on the mesh density and CFD solver in the starting location of the laminar separation bubble, with higher disparity for the position of the reattachment point. Whether 2D or 3D, the prediction accuracy was seen to worsen at high incidence angles. Finally, the location of the laminar separation bubble between CFD and oil flow visualisation had good agreement and a set of guidelines on the mesh parameters which can be applied to low Reynolds number turbomachinery simulations was determined.
Multi-Objective Optimization of Low Reynolds Number Airfoil Using Convolutional Neural Network and Non-Dominated Sorting Genetic Algorithm
