This work introduces the source localization application using a phononic crystal (PC) array. The PC band structure and the eigen-modes are analyzed and utilized for detecting the angle of arrival. The eigen-modes, as the basis functions of the scattering wave, possess strong angle-dependent features, naturally suitable for developing source localization algorithms. An artificial neural network is trained with randomly weighted eigen-modes to achieve deep learning of the modal features and angle dependence. The trained neural network can then accurately identify the incident angle of an unknown scattering signal, with minimal side lobe levels and suppressed main lobe width.
In this paper we use level set topology optimization to reveal novel phononic crystal topologies which give rise to metamaterial properties including negative and singular effective properties. The level set formalism has been developed on the basis of polynomial functions, the locations of whose zeros control the distribution of material phases. This significantly reduces the number of design variables involved and allows us to search very large design spaces using global optimization techniques. Optimization process reveals that a 2-phase unit cell in which one of the phases is simultaneously lighter and stiffer than the other results in dynamic behavior which has all the attendant characteristics of a locally resonant composite. This behavior is further explored through the use of mode shape analysis. Results presented in this paper are also an example of how purely computational techniques can illuminate novel physical phenomenon.
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