Aeroacoustic Source Localization

TL;DR

This paper compares beamforming and DAMAS algorithms for aeroacoustic source localization, demonstrating DAMAS's superior accuracy in simulations and real wind turbine measurements by effectively reducing sidelobes and misconceptions.

Contribution

It introduces the DAMAS algorithm principles and applies it to both simulated and real aeroacoustic source localization, highlighting its advantages over traditional beamforming methods.

Findings

DAMAS reduces sidelobes and misconceptions in source localization.

DAMAS provides more accurate source position and intensity estimates.

Experimental results confirm DAMAS's effectiveness in wind turbine noise localization.

Abstract

The deconvolutional DAMAS algorithm can effectively eliminate the misconceptions in the usually-used beamforming localization algorithm, allowing for more accurate calculation of the source location as well as the intensity. When solving a linear system of equations, the DAMAS algorithm takes into account the mutual influence of different locations, reducing or even eliminating sidelobes and producing more accurate results. This work first introduces the principles of the DAMAS algorithm. Then it applies both the beamforming algorithm and the DAMAS algorithm to simulate the localization of a single-frequency source from a 1.5 MW wind turbine, a complex line source with the text "UCAS" and a line source downstream of an airfoil trailing edge. Finally, the work presents experimental localization results of the source of a 1.5 MW wind turbine using both the beamforming algorithm and the DAMAS algorithm.