Behavior Prediction and Inverse Design for Self-Rotating Skipping Ropes Based on Random Forest and Neural Network

Abstract

Self-oscillatory systems have great utility in energy harvesting, engines, and actuators due to their ability to convert ambient energy directly into mechanical work. This characteristic makes their design and implementation highly valuable. Due to the complexity of the motion process and the simultaneous influence of multiple parameters, computing self-oscillatory systems proves to be challenging, especially when conducting inverse parameter design. To simplify the computational process, a combined approach o0f Random Forest (RF) and Backpropagation Neural Network (BPNN) algorithms is employed. The example used is a self-rotating skipping rope made of liquid crystal elastomer (LCE) fiber and a mass block under illumination. Numerically solving the governing equations yields precise solutions for the rotation frequency of the LCE skipping rope under various system parameters. A database containing 138,240 sets of parameter conditions and their corresponding rotation frequencies is constructed to train the RF and BPNN models. The training outcomes indicate that RF and BPNN can accurately predict the self-rotating skipping rope frequency under various parameters, demonstrating high stability and computational efficiency. This approach allows us to discover the influences of distinct parameters on the rotation frequency as well. Moreover, it is capable of inverse design, meaning it can derive the corresponding desired parameter combination from a given rotation frequency. Through this study, a deeper understanding of the dynamic behavior of self-oscillatory systems is achieved, offering a new approach and theoretical foundation for their implementation and construction.

Mathematical Modeling of the Displacement of a Light-Fuel Self-Moving Automobile with an On-Board Liquid Crystal Elastomer Propulsion Device

“…Various self-oscillating systems have been effectively created using responsive materials, such as liquid crystal elastomers (LCEs) [21][22][23], dielectric elastomers [24], ionic gels [25], environmentally sensitive hydrogels [26], and other similar materials. In addition, many attempts have been made to create multiple self-sustained motion modes, including vibration [27][28][29] and bending [30,31], rolling [12,32,33], self-rotation [34][35][36], torsion [37,38], self-fluttering [39], self-oscillation of auxetic metamaterials [40,41], eversion or inversion [42], swimming [43,44], buckling [45,46], jumping [47][48][49], and chaos [50,51], and they even achieve synchronized motion of multiple coupled self-oscillators [52,53]. Self-oscillating systems can balance the damping dissipation during motion by absorbing energy from the environment [14].…”

Theoretical Analysis of Light-Actuated Self-Sliding Mass on a Circular Track Facilitated by a Liquid Crystal Elastomer Fiber

“…Compared with other active materials categories, these materials have distinctive advantages, such as wireless non-contact driving, a lightweight structural design, and a reduced environmental impact [ 45 , 46 ]. Considering the advantageous characteristics of light, there is a wide range of self-vibrating systems enabled by light-actuated liquid crystal elastomers (LCEs), encompassing actions like bending [ 47 , 48 ], synchronization [ 49 ], rolling [ 50 , 51 ], shuttling [ 52 ], jumping [ 31 ], flying [ 53 ], floating [ 16 ], swimming [ 22 ], spinning [ 54 ], chaos [ 55 , 56 ], and various other self-vibration mechanisms. Light-responsive LCEs exhibit extensive applicability and broad prospects in the fields of micro robots [ 57 , 58 , 59 ], biomimetic soft robots [ 60 , 61 , 62 , 63 ], biomedicine [ 64 ], and energy harvesting [ 65 ], due to their reversible contraction [ 66 ] and relaxation properties [ 67 , 68 ] under light stimuli.…”