Computational statistically optimized near-field acoustical holography (C-SONAH) for virtual noise source identification

Abstract

In this paper, a new methodology, computational statistically optimized near-field acoustic holography (C-SONAH), that can be applied to virtually identify aeroacoustic sources, is described. Sound pressure is first obtained by a combined computational fluid dynamics and computational aeroacoustics (CAA) method, then SONAH is used to locate the acoustic sources and predict the sound field. C-SONAH provides computational advantages over the direct CAA methods in simulating sound emitted in systems with rotating elements, because CAA analyzes the sources on the moving elements which makes the sound field calculation computationally expensive. However, with the help of a SONAH procedure, those rotating sources are converted into a series of equivalent stationary planar or cylindrical waves, which reduces not only the number of sources but also the time required to compute the sound field from each source. This methodology is demonstrated by characterizing the aerodynamic noise emitted from a bladeless fan. In this validation case, the predicted acoustic maps are compared with the results obtained from acoustic array measurements, revealing that the C-SONAH method can predict the noise sources generated by the airflow and rotating components inside the fan. Thus, it can be used as an effective tool to gain insights into the aeroacoustic noise generation mechanism and to guide the design optimization of similar products.