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Fast topology optimization of phononic crystal‐based metastructures for vibration isolation by deep learning
A novel deep learning‐based optimization (DLBO) methodology is proposed for rapidly optimizing phononic crystal‐based metastructure topologies. DLBO eliminates the need for pre‐optimized data by leveraging the learned relation from metastructure features to bandgaps. It enables optimization based on qualitative/quantitative descriptions and forms a regular generalization domain to avoid misjudgments. DLBO achieves similar or better results to genetic algorithm (GA) and only requires 0.01% of the time GA costs. Metastructures with different periodic constants and filling fractions are also optimized, offering insights for balancing space, material, and vibration isolation. Based on a newly defined objective function, an economical metastructure is customized for subway‐induced vibrations; and its performance on vibration isolation is verified through a 3D finite element model. Additionally, the datasets and codes in this study are shared.
Fast topology optimization of phononic crystal‐based metastructures for vibration isolation by deep learning
A novel deep learning‐based optimization (DLBO) methodology is proposed for rapidly optimizing phononic crystal‐based metastructure topologies. DLBO eliminates the need for pre‐optimized data by leveraging the learned relation from metastructure features to bandgaps. It enables optimization based on qualitative/quantitative descriptions and forms a regular generalization domain to avoid misjudgments. DLBO achieves similar or better results to genetic algorithm (GA) and only requires 0.01% of the time GA costs. Metastructures with different periodic constants and filling fractions are also optimized, offering insights for balancing space, material, and vibration isolation. Based on a newly defined objective function, an economical metastructure is customized for subway‐induced vibrations; and its performance on vibration isolation is verified through a 3D finite element model. Additionally, the datasets and codes in this study are shared.
Fast topology optimization of phononic crystal‐based metastructures for vibration isolation by deep learning
Liu, Chen‐Xu (Autor:in) / Yu, Gui‐Lan (Autor:in) / Liu, Zhanli (Autor:in)
Computer‐Aided Civil and Infrastructure Engineering ; 39 ; 776-790
01.03.2024
15 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Topology optimization of phononic crystals with uncertainties
British Library Online Contents | 2017
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