3D Off-Grid Localization for Adjacent Cavitation Noise Sources Using Bayesian Inference

3D Off-Grid Localization for Adjacent Cavitation Noise Sources Using Bayesian Inference

Blog Article

The propeller tip vortex cavitation (TVC) localization problem involves the separation of noise sources in proximity.This work describes a sparse localization method for off-grid cavitations to estimates their precise locations while keeping reasonable computational efficiency.It adopts two different grid (pairwise off-grid) sets with a moderate grid interval and provides L-Desk redundant representations for adjacent noise sources.To estimate the position of the off-grid cavitations, a block-sparse Bayesian learning-based method is adopted for the pairwise off-grid scheme (pairwise off-grid BSBL), which iteratively updates the grid points using Bayesian inference.Subsequently, simulation and experimental results demonstrate that the proposed method achieves the separation of adjacent off-grid cavitations with reduced computational cost, while the other scheme suffers from a heavy computational burden; for the separation of adjacent off-grid cavitations, the pairwise off-grid BSBL took significantly less time Rocker Panel (29 s) compared with the time taken by the conventional off-grid BSBL (2923 s).