Dielectric elastomer(DE)is an electroactive polymer with the characteristics of high energy output,great flexibility,light-weight,mechanical compliance,and low cost,which are particularly suitable for DE energy genera...Dielectric elastomer(DE)is an electroactive polymer with the characteristics of high energy output,great flexibility,light-weight,mechanical compliance,and low cost,which are particularly suitable for DE energy generators.Energy harvesting efficiency is a key index to evaluate the performance of the energy generator,which depends on the structural configuration and the mechanical and dielectric properties of the DE material.This paper proposes a fractional viscoelastic polarization(FVP)model by combining the fractional viscoelasticity model and the polarization-based lumped parameter model.A dynamical model of a cone dielectric energy generator(CDEG)considering stretch-dependent electrostriction and nonlinear viscoelasticity is established.Additionally,a deep neural network(DNN)model is developed to explore the relationships between various parameters and the output energy of CDEGs to efficiently and accurately predict the energy output of CDEGs.Based on the DNN model,optimal parameter designs for CDEGs are obtained by using non-dominated sorting genetic algorithm II(NSGA-II).The experiments verified that the FVP model predicts accurately the output energy of CDEG and the established optimal design framework can accurately provide the optimal design parameters of CDEG,which offers deep insights for the design and fabrication of a high-efficiency dielectric energy generator.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.12372027 and 12402109)the Natural Science Foundation of Shandong Province(Grant No.ZR2022MA086)the Basic Scientific Research Expenses of Central Government Universities(Grant No.23CX03010A).
文摘Dielectric elastomer(DE)is an electroactive polymer with the characteristics of high energy output,great flexibility,light-weight,mechanical compliance,and low cost,which are particularly suitable for DE energy generators.Energy harvesting efficiency is a key index to evaluate the performance of the energy generator,which depends on the structural configuration and the mechanical and dielectric properties of the DE material.This paper proposes a fractional viscoelastic polarization(FVP)model by combining the fractional viscoelasticity model and the polarization-based lumped parameter model.A dynamical model of a cone dielectric energy generator(CDEG)considering stretch-dependent electrostriction and nonlinear viscoelasticity is established.Additionally,a deep neural network(DNN)model is developed to explore the relationships between various parameters and the output energy of CDEGs to efficiently and accurately predict the energy output of CDEGs.Based on the DNN model,optimal parameter designs for CDEGs are obtained by using non-dominated sorting genetic algorithm II(NSGA-II).The experiments verified that the FVP model predicts accurately the output energy of CDEG and the established optimal design framework can accurately provide the optimal design parameters of CDEG,which offers deep insights for the design and fabrication of a high-efficiency dielectric energy generator.
