研究目的
To investigate the dynamics, performance, and optimization of a harmonically excited primary system combined with an absorber and a piezoelectric energy harvester, focusing on simultaneous vibration absorption and energy harvesting.
研究成果
The optimization technique using genetic algorithm and response surface methodology effectively finds optimal parameters for vibration absorption and energy harvesting. Nonlinear systems outperform linear counterparts in bandwidth and performance. The method is validated against analytical solutions and can be applied to multifunctional systems.
研究不足
The study is computational and does not involve physical experiments. Challenges include attaining both objectives (vibration absorption and energy harvesting) simultaneously in practical cases, and the performance depends on excitation amplitude and parameter selection.
1：Experimental Design and Method Selection:
The study uses a generalized two-degree-of-freedom nonlinear system modeled with equations of motion. Multi-harmonic balance method (MHBM) with arc length continuation is employed for frequency response analysis, and genetic algorithm combined with response surface methodology is used for optimization.
2：Sample Selection and Data Sources:
The system parameters are varied based on predefined cases (e.g., linear/nonlinear primary and secondary systems). Data is generated numerically through simulations.
3：List of Experimental Equipment and Materials:
No specific physical equipment is mentioned; the study is computational, involving mathematical models and software tools like Minitab for response surface methodology.
4：Experimental Procedures and Operational Workflow:
Equations of motion are derived and solved using MHBM. Frequency response plots are generated for different parameter values. Optimization is performed to minimize primary system amplitude and maximize energy harvesting.
5：Data Analysis Methods:
Fourier series and discrete Fourier transform are used in MHBM. Response surface methodology builds empirical models, and genetic algorithm optimizes parameters based on fitness functions.
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