期刊
INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES
卷 214, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ijmecsci.2021.106878
关键词
Aperiodic metastructure; Parametric optimization; Genetic algorithm; Broad bandgap; Vibration tests
资金
- National Natural Science Foundation of China [11632003, 11925205, 51921003]
This paper presents an integrated analytical, numerical, and experimental study on optimizing mass-in-mass unit cells in a one-dimensional finite aperiodic metastructure using genetic algorithm to achieve successful broadband wave attenuation. The optimized structure shows a 90% increase in maximal attenuation bandwidth compared to conventional approaches, without any additional mass.
The paper presents an integrated analytical, numerical and experimental study on a kind of one-dimensional finite aperiodic metastructure, with mass-in-mass unit cells optimized for the broadband wave attenuation via genetic algorithm. The study begins with the analytic wave solution of the aperiodic structure and numerically validates the correctness of the wave solution in both frequency domain and time domain. Then, the paper outlines the optimization scheme in order to connect multiple separated narrow bandgaps into a wider continuous one, including the objective function of the wave attenuation, the design variables of inner masses and their constraints. The numerical studies show the successful broadband wave attenuation with a low vibration transmissibility in one direction and two opposite directions, respectively, based on the genetic algorithm method. The maximal attenuation bandwidth of the optimized aperiodic structure increases about 90% compared with the conventional repetitive local resonance, without any mass increase. Finally, the paper gives experimental studies of a 3D-printed lattice structure with an adjustable inner mass in each cell. The measured vibrations of the fabricated structure experimentally validate the optimized aperiodic structure with the broadband wave attenuation, as well as the effectiveness of proposed optimization method.
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