A cooling fan is one of the primary components affecting the cooling performance of an engine cooling system. In recent years, with the increase in electric vehicles (EVs) and hybrid vehicles (HVs), the cooling performance and noise level of the cooling fan have become very important. Thus, the development of a low-noise fan with the same cooling performance is urgently required. To address this issue, it is critical to find the relation between the performance of the fan and the flow structures generated around it, which is discussed in the present paper.
Specifically, a computational method is employed that uses unsteady Reynolds-averaged Navier-Stokes (URANS) coupling with a sliding mesh (SLM). Measurements of the P-Q (Pressure gain-Flow rate) characteristics are performed to validate the predictive accuracy of the simulation. Additionally, proper orthogonal decomposition (POD) is employed to analyze the unsteady data obtained by URANS calculations to reveal the typical flow structures. This technique makes it possible to classify the phenomena into several typical modes, which help to clarify the phenomena analytically. As a result, the impacts of the dominant flow structures on the fan performance are clarified quantitatively by calculating the contribution ratio obtained by the POD analysis.
