Enhancing acoustic metamaterials by leveraging nonlinear dynamics

• Grant number: 24/22736-3
• Support Opportunities: Scholarships in Brazil - Doctorate (Direct)
• Start date: May 01, 2025
• End date: February 28, 2030
• Field of knowledge: Engineering - Mechanical Engineering - Mechanics of Solids
• Principal Investigator: Leopoldo Pisanelli Rodrigues de Oliveira
• Grantee: Lucas José Dantas Alcântara
• Host Institution: Escola de Engenharia de São Carlos (EESC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
• Associated research grant: 18/15894-0 - Periodic structure design and optimization for enhanced vibroacoustic performance: ENVIBRO, AP.TEM

Abstract

Acoustic metamaterials have been widely used in engineering applications due to their ability to control mechanical waves, leveraging periodic elements to exploit phenomena such as Bragg-scattering and internal resonances. These systems are designed to create frequency ranges where wave propagation is attenuated, known as bandgaps. However, in linear time-invariant systems, bandgap properties are fixed, which limits their performance under varying environmental or load conditions. To overcome these limitations, nonlinear metamaterials are noteworthy for their dynamic properties, enabling amplitude-dependent and tunable bandgaps. Furthermore, the use of smart materials allows for the creation of reconfigurable metamaterials by adjusting system parameters and enhancing the obtained bandgap. The modeling of metamaterials traditionally employs linear solution techniques, such as the finite element method and dispersion diagram analyses. However, these methods face limitations when applied to nonlinear metamaterials due to the complexity of their dynamic interactions and amplitude dependencies. In this context, the use of Machine Learning emerges as a complementary approach to modeling these systems, providing tools capable of predicting their behavior and characterizing the inherent nonlinearities. Thus, this project aims to develop nonlinear acoustic metamaterials combined with smart materials to adjust system parameters and enhance the obtained bandgap. The analysis will cover the study of the unit cell to the behavior of the complete system through the development of analytical models, their simulation, and experimental validation of the obtained responses. In addition, Machine Learning techniques will be integrated to complement the modeling and performance analysis of these structures, enabling the identification of dynamic parameters and the prediction of nonlinear behavior under varying conditions.