Machine learning enabled quantification of stochastic active metadamping in acoustic metamaterials

Abstract

System uncertainties often lead to deviation from the anticipated behaviour of acoustic metamaterials (AMs) and thus, make their intended design ineffective. Therefore, in this study, the effect of stochastic parameters on a recently developed phenomenon in AMs, known as active metadamping is quantified using machine learning (ML). The enhancement of damping in an active feedback-controlled metamaterial over an equivalent uncontrolled counterpart is defined as active metadamping. Metadamping enables a metamaterial to dissipate energy faster in addition to its inherent bandgap characteristics. The ML technique, Gaussian process is used as the surrogate model to capture the stochastic dynamics of the active AM. A trade-off in the spatial attenuation and temporal energy dissipation characteristics is observed while velocity-feedback control is applied within the resonating units of the metamaterial. The overall dissipation and the decay ratio increase around five times in the controlled case; whereas, the attenuation bandwidth reduces or even vanishes. Most importantly, the results elucidate that metadamping is robust to the uncertainty of the system parameters, unlike the energy and the decay amplitude for the controlled case. Additionally, Gaussian process is able to capture the behaviour of active AMs by using only 1% computational cost as compared to that of the Monte Carlo simulations.