Affiliation:
1. Centre Internacional de Mètodes Numèrics en Enginyeria (CIMNE) Barcelona Spain
2. Escola Superior d'Enginyeries Industrial Aeroespacial i Audiovisuals de Terrassa (ESEIAAT) Universitat Politècnica de Catalunya–BarcelonaTech (UPC) Terrassa Spain
3. Escola Tècnica Superior d'Enginyers de Camins, Canals i Ports de Barcelona (ETSECCPB) Universitat Politècnica de Catalunya–BarcelonaTech (UPC) Barcelona Spain
Abstract
AbstractMachine learning (ML) and Deep learning (DL) are increasingly pivotal in the design of advanced metamaterials, seamlessly integrated with material or topology optimization. Their intrinsic capability to predict and interconnect material properties across vast design spaces, often computationally prohibitive for conventional methods, has led to groundbreaking possibilities. This paper introduces an innovative machine learning approach for the optimization of acoustic metamaterials, focusing on Multiresonant Layered Acoustic Metamaterial (MLAM), designed for targeted noise attenuation at low frequencies (below 1000 Hz). This method leverages ML to create a continuous model of the Representative Volume Element (RVE) effective properties essential for evaluating sound transmission loss (STL), and subsequently used to optimize the overall topology configuration for maximum sound attenuation using a Genetic Algorithm (GA). The significance of this methodology lies in its ability to deliver rapid results without compromising accuracy, significantly reducing the computational overhead of complete topology optimization by several orders of magnitude. To demonstrate the versatility and scalability of this approach, it is extended to a more intricate RVE model, characterized by a higher number of parameters, and is optimized using the same strategy. In addition, to underscore the potential of ML techniques in synergy with traditional topology optimization, a comparative analysis is conducted, comparing the outcomes of the proposed method with those obtained through direct numerical simulation (DNS) of the corresponding full 3D MLAM model. This comparative analysis highlights the transformative potential of this combination, particularly when addressing complex topological challenges with significant computational demands, ushering in a new era of metamaterial and component design.
Funder
Ministerio de Ciencia e Innovación
Cited by
1 articles.
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