Validation of Different Fan Modelling Techniques in Computational Fluid Dynamics
Paper i proceeding, 2018
The accuracy of predicting the engine bay flow field with computational fluid dynamics (CFD) is crucial for designing efficient cooling systems for heat sensitive components. The engine cooling fan is the main driving component in cases of high thermal load, such as uphill driving with a trailer, or high speed driving on a highway, when the ram air itself is no longer sufficient for cooling purposes.
The most widely used fan modelling method is the Moving Reference Frame (MRF). This method can be used in steady and unsteady simulations, but has the drawback of using a fan geometry that is fixed in the global reference frame and, therefore, causing non-physical low velocity regions in the wake of
the blades. The Rigid Body Motion (RBM or ”sliding mesh”) approach is a more accurate, but also more expensive approach, since it uses an unsteady solver. This study looks closely at the prediction of the flow field in the wake of an axial fan for different freestream velocities and fan speeds using the traditional MRF and RBM approach. In addition, a method that uses the average of flow field data for multiple MRF simulations with different fan positions is presented. Thereby the shadow of the fan blades is removed from the wake and the flow field becomes more uniform without the need of performing unsteady simulations. As a reference, measurements are performed on a vehicle fan with a 2D Laser Doppler Anemometry set-up in a small scale wind tunnel.
The results show good agreement between the measurements and the RBM simulations. As expected, the MRF simulations show a distinct blade pattern in the wake flow field. This was successfully removed by the proposed averaged MRF method. Even though there are still some differences between this method and the experimental results, the average MRF method has shown to be applicable as it improves the flow field results at a relatively low computational cost.
Computational Fluid Dynamics