Algorithmic localisation of noise sources in the tip region of a low-speed axial flow fan

Abstract

An objective and algorithmised methodology is proposed to analyse beamform data obtained for axial fans. Its application is demonstrated in a case study regarding the tip region of a low-speed cooling fan. First, beamforming is carried out in a co-rotating frame of reference. Then, a distribution of source strength is extracted along the circumference of the rotor at the blade tip radius in each analysed third-octave band. The circumferential distributions are expanded into Fourier series, which allows for filtering out the effects of perturbations, on the basis of an objective criterion. The remaining Fourier components are then considered as base sources to determine the blade-passage-periodic flow mechanisms responsible for the broadband noise. Based on their frequency and angular location, the base sources are grouped together. This is done using the fuzzy c-means clustering method to allow the overlap of the source mechanisms. The number of clusters is determined in a validity analysis. Finally, the obtained clusters are assigned to source mechanisms based on the literature. Thus, turbulent boundary layer-trailing edge interaction noise, tip leakage flow noise, and double leakage flow noise are identified.

Comparison of the Rotating Source Identifier and the Virtual Rotating Array Method

“…The applied weight vector was chosen to conform with previous investigations reported in literature [5,20,21]. One element of this vector is shown in Eq.…”

“…One source was investigated, its location being representative of the fan tip radius, placed at the coordinates x = 1, y = 0.2 m. The reason for investigating just one source is the high computational demand of ROSI. This was deemed acceptable, as the source represents a point on the tip radius, often of interest in an axial fan [21]. Furthermore, the angular location has little relevance, if the number of microphones M is large enough, being a further reason for investigating only one source position.…”

A fuzzy clustering method for periodic data, applied for processing turbomachinery beamforming maps

“…To investigate and analyse turbomachinery noise, a novel algorithmic approach was proposed in [18,19]. It aimed at identifying broadband noise generation mechanisms in an axial fan, in an objective manner.…”

“…To do that, a novel algorithmic processing method was proposed in [18,19]. It aimed at the identification of noise generation mechanisms through a data reduction and clustering procedure.…”

“…The main contribution of the present paper is to introduce a method for fuzzy clustering, applicable when one of the dimensions of the data set has periodic attributes. This is applied for the analysis of turbomachinery beamform maps as part of the aforementioned algorithm [19], aimed at objective and reproducible identification of noise source mechanisms. The extended algorithm is applied onto the beamform maps of an axial fan, to identify the dominant noise generating mechanisms.…”