Computational design of a large dataset of viscoelastic metastructures for inverse design in low-frequency vibration attenuation

In the context of acoustic stealth, the attenuation of low-frequency vibrations generated by submarine rotating machinery remains a major challenge. Existing passive and active solutions still have limitations to effectively mitigate vibrations across varying operating conditions. The research work introduces a computational design approach that integrates generative design and finite element analysis to build a comprehensive dataset of viscoelastic meta-structures, characterized by their transmissibility. This dataset will serve as a foundation for future machine learning-enabled inverse design, paving the way for optimized vibration attenuation strategies.