Control and optimization strategies for noise transmission reduction in sonic crystal acoustic barriers

Acoustic metamaterials, such as Sonic Crystals (SC), are periodic artificial structures designed to manipulate sound waves in ways that natural materials cannot. These materials consist of arranged lattices of rigid scatterers embedded in air and exhibit unique properties, such as negative refraction and Band Gaps (BG), which can be leveraged to create highly effective noise control solutions. These Band Gaps prevent transmission of certain frequency ranges depending on the angle of incidence the sound waves relative to the crystal, making them ideal for applications in noise barriers and other acoustic devices. This research investigates the potential of these advanced materials to enhance the performance of noise reduction devices through innovative design and optimization strategies aimed at minimizing unwanted sound, thereby improving the acoustic environment in various settings. Specifically, among noise reduction devices such as barriers, absorbers, and diffusers, this thesis focuses on Sonic Crystal Noise Barriers (SCNB). The design and optimization of these devices require a deep understanding of acoustic principles and the ability to predict how sound interacts with different materials and structures. This is achieved through the use of simulations that forecast the behavior and performance of these devices, thus saving time and resources by avoiding the need to manufacture and experimentally test multiple prototypes before arriving at the optimal solution for each specific case. In line with this, the thesis explores the potential of Active Noise Control (ANC) systems enhanced with Reinforcement Learning (RL) techniques as a complement to improve SCNBs. By integrating reinforcement learning, the study aims to develop ANC systems that continuously learn and adapt to changing environmental conditions, providing a dynamic and adaptive approach to noise control, particularly at low frequencies, where active control is most effective, and where SCNBs tend to be less efficient. In addition to ANC, Helmholtz Resonators (HR) are incorporated to improve these barriers. By combining numerical methods and optimization algorithms, not only is the performance of SCNBs found in the literature improved, but a deeper understanding of the physical principles governing them and their interaction with HR is also achieved. 3D printing technology, used in architectural engineering, combined with parametric modeling, greatly increases the versatility in the creation of prototypes resulting from optimizations with HRs. This thesis also addresses the insulation capabilities of SCNBs made from cylindrical scatterers with multiple Helmholtz resonators, proposing a design that incorporates two Helmholtz resonators per scatterer. This design shows a significant increase in Insertion Loss (IL) compared to conventional barriers. Additionally,examining the interaction between the BGs of HRs and Bragg-BGs, this thesis proposes new transmission applications for multiresonant SCs beyond noise barriers. The ability to control transmission through an active metamaterial by rotating the scatterers provides an advantage over conventional passive metamaterials. Furthermore, the thesis explores, both numerically and experimentally, wave incidence measurements to further refine noise control strategies. Prototypes designed to mitigate tonal noise, such as that produced by train braking, are experimentally tested under both normal incidence and diffuse incidence of sound waves on the SCNB. In summary, this doctoral thesis provides a comprehensive exploration of SCNBs, from design and optimization to practical applications. It delves into the phenomenology of the interactions between these periodic materials, ANC, HRs, and 3D printing. By integrating advanced optimization techniques and reinforcement learning, the study aims to improve current noise control solutions.