Enhanced sound insulation in double wall structures via MAMs: An RBF-based optimisation approach - Jie Zhang; Dan Yao; Wang Peng; Jiang Li; Shaoyun Guo (Materials & Design)
Double walls are widely used as they provide a higher level of sound insulation without compromising on mass. However, due to the coupling resonance between the structure and the cavity, additional sound insulation valleys occur, which are not beneficial for noise control. This paper reveals the mechanism by which membrane-type acoustic metamaterials (MAMs) improve the sound insulation valley of double-wall structures, proposes an optimisation design method based on radial basis functions (RBF), and investigates the newly generated valley and the influence of geometric dimensions. First, sound insulation prediction models for both the double wall structure and MAMs were established. The formation mechanism of sound insulation valleys of the double wall structure, and the vibro-acoustic behaviours of MAMs and the sandwich construction containing the MAMs were analysed. Second, an optimisation design method using an RBF neural network model was proposed, optimising the sound insulation peak frequency of the MAMs to align with the specific valley frequency of the double wall structure. Third, the origins of newly induced sound insulation valleys caused by the incorporation of MAMs into the double-wall structure are analysed, and a comparative analysis is conducted on the sound insulation characteristics of finite-sized structures and their periodic infinite counterparts with different unit cell arrays. The results show that the use of MAMs as core layers can effectively improve the sound insulation valleys by 40.3 dB and 14.1 dB of the double wall. The addition of sound absorption materials to the cavity of the sandwich construction can further enhance the new sound insulation valleys by about 4–6 dB. The optimisation design method proposed in this paper is applicable to both finite-sized and infinite-sized structures.