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<p>This study focuses on the optimal design of an Auxetic Re-entrant Honeycomb (ARH) metamaterial structure aimed at enhancing energy harvesting (ES) performance in doubly-curved sandwich shells (DCSS). To accomplish this, the geometric parameters of the ARH are optimized using a Multi-Objective Genetic Algorithm (MOGA) and considering geometric and physical constraints. Initially, the study provides an accurate calculation for the density of the ARH layer and compares it with previous models. Then, the governing electromechanical coupled equations are derived using Modified First-Order Shear Deformation Theory (MFSDT) and Hamilton’s principle, assuming simple support boundary conditions at all four edges. Utilizing Galerkin’s principle, the frequency response function (FRF) of the specific output power is analyzed. In addition to evaluating the free vibration results against authoritative references, the finite element simulation is also used to validate both the eigenfrequencies and FRFs data derived from the analytical solution. The optimization algorithm aims to maximize the specific power output while minimizing the resonant frequency of the structure. The results from optimizing the ARH core layer demonstrate that the new designs achieve ES capabilities that are 42.96 times greater than those with an isotropic core and 6.92 times greater than those of the conventional ARH. Additionally, the optimized structures show improved stiffness metrics compared to their pre-optimization counterparts. Thus, this design successfully enhances both ES and structural behavior, particularly in terms of stiffness.</p>

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Optimized doubly-curved ARH-based metastructure to augment specific vibrational energy conversion using a hybrid constraint multi-objective genetic algorithm

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月20日

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  • Reza Ansarian,
  • Mehrdad Motavasselolhagh,
  • Roohollah Talebitooti,
  • Davood Younesian

摘要

This study focuses on the optimal design of an Auxetic Re-entrant Honeycomb (ARH) metamaterial structure aimed at enhancing energy harvesting (ES) performance in doubly-curved sandwich shells (DCSS). To accomplish this, the geometric parameters of the ARH are optimized using a Multi-Objective Genetic Algorithm (MOGA) and considering geometric and physical constraints. Initially, the study provides an accurate calculation for the density of the ARH layer and compares it with previous models. Then, the governing electromechanical coupled equations are derived using Modified First-Order Shear Deformation Theory (MFSDT) and Hamilton’s principle, assuming simple support boundary conditions at all four edges. Utilizing Galerkin’s principle, the frequency response function (FRF) of the specific output power is analyzed. In addition to evaluating the free vibration results against authoritative references, the finite element simulation is also used to validate both the eigenfrequencies and FRFs data derived from the analytical solution. The optimization algorithm aims to maximize the specific power output while minimizing the resonant frequency of the structure. The results from optimizing the ARH core layer demonstrate that the new designs achieve ES capabilities that are 42.96 times greater than those with an isotropic core and 6.92 times greater than those of the conventional ARH. Additionally, the optimized structures show improved stiffness metrics compared to their pre-optimization counterparts. Thus, this design successfully enhances both ES and structural behavior, particularly in terms of stiffness.