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The present work proposes an optimized method for pressure reconstruction and far-field noise prediction for tandem cylinder flow based on time-resolved planar particle image velocimetry (PIV). Proper orthogonal decomposition (POD) low-order reconstruction is applied to mitigate the incoherent noise introduced during PIV measurement and processing and enhances the identification of dynamic characteristics of relative structures within the flow field. As a result, PIV-based pressure reconstruction through solving the Poisson equation encompasses fewer errors arising from contaminated boundary conditions and Poisson source terms, thereby mitigating the propagation of errors into pressure. The time-marching algorithm accelerates the convergence and stabilizes the iterative progress while solving the Poisson equation, leading a considerable computation savings for cases with multiple snapshots like aeroacoustics relative investigation. The reconstructed wall pressure is compared with the reference pressure fluctuation signal simultaneously measured, yielding good agreement. Finally, the far-field noise was predicted through Curle’s analogy based on reconstructed wall pressure, considering the spanwise correction derived through an additional spanwise planar PIV. The predicted far-field noise agrees well with the reference microphone measurement. The overall assessment demonstrates that the employment of POD low-order reconstruction and time-marching algorithm significantly improve the speed and accuracy for estimating pressure field and far-field noise.
Chen et al. (Mon,) studied this question.