Abstract
Triboelectric nanogenerators (TENGs) are extensively applied in the energy harvesting field owing to their advantages of low cost, diversified structural design, and superior conversion efficiency from low-frequency mechanical energy. Among them, the grating/disk-structured TENG is one of the most promising types because it can continuously provide a high output. However, owing to its complex structure, modeling simulations and structural optimization of grating-structured TENGs in freestanding mode have not been performed previously. Moreover, the influence of nonideal structural parameters such as height and parasitic capacitance on the performance is unknown. Furthermore, more advanced numerical data processing methods are expected to be proposed. In this work, we obtained the optimal grating number n and optimal average power of cylindrical grating-structured TENGs using the support vector regression algorithm and other numerical analysis methods during simulation. Subsequently, we studied the influence of the gap h between the rotor and the stator and the parasitic capacitance Cp on the performance of the grating-structured TENGs. This work can complement the previous structural optimization and simulation works on grating-structured TENGs, providing a reference for the application of machine learning to the structural optimization of TENGs.
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