Additive manufacturing enables the creation of architected porous materials, such as Triply Periodic Minimal Surfaces (TPMS), offering unprecedented design freedom for acoustic liners. However, navigating the vast design space to optimize broadband performance remains a significant challenge, often limited to intuitive design choices or single-parameter sweeps. This work explores the development of a computationally driven framework for the inverse design of multi-layered, anisotropic TPMS-based liners. The study investigates leveraging geometric anisotropy as a powerful design vector to tune acoustic properties, potentially offering performance gains without the weight penalties associated with traditional methods. The proposed methodology involves the experimental characterization of anisotropic material samples to build a comprehensive database of their effective acoustic transport parameters. This database will serve as a material library for a computationally efficient Transfer Matrix Method (TMM) forward model, enabling the rapid evaluation of countless layered configurations. A global optimization algorithm will then be used to systematically search the high-dimensional design space, identifying novel liner architectures with enhanced broadband sound absorption. The goal is to establish a simplified pathway for designing next-generation, lightweight acoustic liners tailored for demanding aerospace applications.
Godakawela et al. (Wed,) studied this question.