Abstract
Needs for low noise turbo machineries have been increased. To extract the pressure fluctuation dominant to the noise, proper orthogonal decomposition (POD) was applied to unsteady computational fluid dynamics (CFD) results of a centrifugal blower in a vacuum cleaner. We extracted a time series of static pressure distribution in the diffuser, applied the POD to these data and compared the results of a measurement. The result was that the first six POD modes made a 99.4% contribution in terms of the L_2 norm. In the scope of this research, the first six modes were revealed to surrogate the pressure fluctuation sufficiently. Also, the data storage was reduced to less than 2.0% of one of the original unsteady results. Next, frequency spectra were obtained by applying discrete Fourier transform (DFT) to the expansion coefficients. The spectra of the expansion coefficients had a peak near whole-number multiples of the BPF. The noise, the frequency of which is BPF, causes the majority of the noise that occurs in the diffuser. Therefore, we found using both the POD and DFT that we could both reduce the dramatic data storage and extract the pressure fluctuation dominant to the noise.
