The use of an acoustic quiet zone as an active acoustic cloaking system

Abstract

The use of active control to acoustically cloak an object has been demonstrated previously, and is effective if the scattered component of the pressure field can be measured and directly minimised. In practice, this is non-trivial as a pressure sensor in the sound-field will detect the superposition of the incident and scattered pressures. An alternative approach is presented here which uses the control sources to generate a zone of quiet around the scattering object, with a constraint on their exterior radiation. This reduces the incident acoustic pressure on the object and thus the acoustic scattering. In this case, real-time measurements of scattered pressure or detailed geometrical knowledge of the scattering object are not required. The performance of the proposed acoustic cloaking strategy is assessed using simulated data of a rigid spherical scattering object, using a practical arrangement of control sources and error sensors. The same arrangement of sources and sensors is also used to directly minimise the scattered sound field

An experimental investigation into active structural acoustic cloaking of a flexible cylinder

“…Although this method provides a measure of the scattered acoustic field that can be utilised to implement an active acoustic cloaking system in a stationary sound field, it is clearly not a practical solution for the implementation of a real-time controller in the presence of changes in the primary disturbance or the acoustic environment. Although a number of previous publications have investigated methods of estimating the scattered acoustic field in real-time [10,38,31,30], these also rely on knowledge of the acoustic field with and without the scattering body present. This means that these strategies cannot yet be readily applied to practical active cloaking strategies where the acoustic environment is non-stationary.…”

“…This means that the scattered pressure must first be estimated before being utilised as the error signal in an active control system. This estimation can potentially be achieved in a number of ways, including the use of a Virtual Sensing strategy [30], by using analytical or numerical modelling [31], or by using a wave decomposition approach [10].…”