Artificial intelligence enhanced mathematical modeling on rotary triboelectric nanogenerators under various kinematic and geometric conditions

被引：34

作者：

Khorsand, Mohammad ^{[1
]}

Tavakoli, Javad ^{[2
]}

Guan, Haowen ^{[1
]}

Tang, Youhong ^{[1
]}

机构：

[1] Flinders Univ S Australia, Inst NanoScale Sci & Technol, Coll Sci & Engn, Bedford Pk, SA 5042, Australia

[2] Univ Technol Sydney, Sch Biomed Engn, Sydney, NSW 2007, Australia

来源：

NANO ENERGY | 2020年 / 75卷

关键词：

Energy harvesting; Rotary triboelectric nanogenerators; Nanoenergy; Optimality; High output power; ENERGY; CONTACT;

D O I：

10.1016/j.nanoen.2020.104993

中图分类号：

O64 [物理化学（理论化学）、化学物理学];

学科分类号：

070304 ; 081704 ;

摘要：

The triboelectric nanogenerator (TENG) has been introduced as a revolutionary technology in the renewable electrical energy generation at micro/nanoscale. In the current study, experimental and theoretical models for augmented rotary TENGs are presented. The power generated by TENGs is found to be a function of the number of segments, rotational speed, and tribo-surface spacing. Mathematical modeling combined with artificial intelligence is applied to characterize the TENG output under various kinematics and geometric conditions. Sensitivity analysis reveals that the generated energy and the matched resistance depend highly on segmentation and angular velocity rate. It is shown that the optimized harvested energy reaches 0.369 mJ at each cycle. The TENG dynamic outputs for various structural parameters are found and described. This study enhances understanding of rotation-induced periodic TENGs and reveals optimized characteristics for disk-shaped TENG energy harvesters.

引用

页数：12

共 48 条

[1] A review on energy harvesting approaches for renewable energies from ambient vibrations and acoustic waves using piezoelectricity [J].