Multiscale Acoustic Design of Wood-Based Sound-Absorbing Materials: From Hierarchical Porous Structures to Metamaterials and Data-Driven Optimization

Wood and wood-based materials represent low-carbon sustainable alternatives to petroleum sound absorbers, yet their baseline sound absorption coefficient varies drastically with wood species, anatomical cutting orientation and pore connectivity due to strong structural anisotropy. This review systematically integrates multiscale structural regulation, porous acoustic theories and data-driven optimization into a unified framework, revealing that broadband high sound absorption relies on the synergistic coordination of impedance matching, thermo-viscous dissipation and low-frequency resonant mechanisms, rather than simply maximizing porosity. We quantitatively compare state-of-the-art wood absorbers: directionally frozen wood aerogels achieve near-perfect absorption (α = 0.95–1.00, NRC = 0.82) across 520–6300 Hz, marking the current performance benchmark, while multifunctional superhydrophobic wood aerogels deliver moderate absorption (α ≈ 0.40) but stand out as all-biomass weather-resistant composites. Rigid-frame JCA/JCAL and poroelastic Biot models are clarified for wood’s distinct stiffness characteristics, and existing data-driven approaches are categorized, highlighting that most neural surrogates rely solely on FEM simulation without physical impedance-tube validation. Critical unresolved challenges including poor moisture/fire durability, insufficient industrial scalability and incomplete material databases are summarized, and targeted research priorities covering gradient manufacturing, hybrid physics–machine learning models and lifecycle environmental evaluation are proposed.