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

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.