The applications of metamaterials for sound and vibration absorption have been limited due to their resonant mechanism which resulted in narrow bandwidth. The design of metamaterial should guarantee a minimum thickness as well as a broad bandwidth to expedite their practical application for noise mitigation in dynamic structures. Periodic array of resonators has been employed to enhance absorption bandwidth at the cost of increased thickness, which is related by optimal causality constraints.
In Electromagnetic wave, a unique configuration of antennas (frequency independent antennas), theoretically that have invariant material property independent of the frequency, has been exploited for their ultra-broadband behavior. They have long been associated with geometric criteria based upon construction from angles and self-complementarity.
This research project investigates acoustic equivalence of such antennas that can yield a broadband absorption without the need for periodic array of resonators. Achieving material properties, such as impedance, independent of the frequency is interesting and fascinating concept and will provide a new insight into the design of broadband sound absorbing structures. Deep learning algorithms will be employed to find a possible combination of materials that can yield the desired property. This project is supported by the Alexander Von Humboldt (AvH) fellowship.