Marcelo Magalhães

A NEW TECHNIQUE TO IDENTIFY ARBITRARILY SHAPED NOISE SOURCES

Acoustic intensity is one of the available tools for evaluating sound radiation from vibrating bodies. Active intensity may, in some situations, not give a faithful insight about how much energy is in fact carried into the far field. It was then proposed a new parameter, the supersonic acoustic intensity, which takes into account only the intensity generated by components having a smaller wavenumber than the acoustic one. However, the method is only efective for simple sources, such as plane plates, cylinders and spheres. This work presents a new technique, based on the Boundary Elements Method and the Singular Value Decomposition, to compute the supersonic acoustic intensity for arbitrarily shaped sources. The technique is based in the Kirchoff-Helmholtz equation in a discretized approach, leading to a radiation operator that relates the normal velocity on the sources surface mesh with the pressure at grid points located in the field. Then, the singular value decomposition technique is set to the radiation operator and a cutoff criterion is applied to remove non propagating components. Some numerical examples are presented.

Nonsmooth impedance profile identification using reflection data

Many works published about identification of acoustic properties of inhomogeneous media present methods based on the assumption that these properties vary smoothly along the propagation direction. This work presents a comparison between two time‐domain sequential inverse methods for evaluation of either smooth or nonsmooth impedance profiles of transversely infinite media excited by plane waves at normal incidence. Sequential methods, also known as layer stripping methods, despite having the disadvantage of being quite sensitive to noise in the measured signal, are faster than other approaches, such as those that use global optimization techniques. The first method in the comparison [R. A. Tenenbaum and M. Zindeluk, J. Acoust. Soc. Am. 92, 3364–3369 (1992)] assumes a smooth profile by using a transformation of variables, which neglects the refraction effects between two consecutive layers. The second one, more general, performs some more calculations since it takes these effects into account, paying the p...

Sequential versus iterative methods for recovering acoustical impedance profiles of inhomogeneous media

Optimization methods for identification of nonhomogeneous media are known to be very time consuming. On the other hand, sequential methods (layer stripping methods) are computationally very efficient and rather unstable under noisy data. In this work, some recent optimization techniques are applied to recover the impedance profile from the reflected data. A careful approach has to be used though, since the cost function is highly nonlinear and its evaluation is quite costly in a computational point of view. The optimization method chosen is a secant one, which, instead of using information about the second derivatives of the cost function, generates successive approximations of its Hessian matrix, based on information gathered as the procedure progresses. The sequential method lies in a new algorithm for computing the reflection response of a truly layered media for an arbitrary input, which can easily be inverted. The results of both methods are compared for some synthetic echoes. The behavior of the met...

An optimization technique applied to the recovery of acoustic impedance profiles

A method based on a global optimization technique is used to solve the inverse (identification) problem of one‐dimensional wave propagation in transversely infinite inhomogenous media. The scheme consists of minimizing a cost function given by the square distance (according to the Euclidean norm) between a measured and a synthetic signal, the latter produced by an arbitrary impedance profile. This cost function imposes several difficulties, since the model for the synthetic signal causes it to be polynomial whose degree is at least equal to the number of layers in which the profile is discretized. The optimization method used is a secant one, which, instead of making use of information about the second derivatives of the cost function, which is too time consuming, generates sucessive approximations of its Hessian matrix, based on information gathered as the procedure progresses. Moreover, in order to guarantee that the method follows a descent direction, that is, the cost function is always reduced, a BFG...