A Physics-Driven Surrogate Modeling Framework for the Inverse Design and Optimization of Dielectric Elastomer Tube Energy Harvesters
Jianwei Gong*
Department of Physics, City University of Hong Kong, Hong Kong, China
* Corresponding Author: Jianwei Gong. Email: 
Energy Engineering https://doi.org/10.32604/ee.2026.088854
Received 10 July 2026; Accepted 20 August 2026; Published online 31 August 2026
Abstract
Dielectric elastomer tube energy harvesters have complex nonlinear electromechanical coupling, making parameter optimisation challenging when relying solely on experiments or finite element simulations. In this study, we present a physics-driven machine learning framework for parameter optimisation of such devices. A vectorised numerical solver based on hyperelastic constitutive relations, Maxwell stress, and gas adiabatic expansion is implemented to generate a synthetic dataset of 5000 physically consistent samples. Using four key geometric and operational parameters as inputs, several machine learning models are trained to predict energy conversion efficiency. A stacking ensemble model achieves a five-fold cross-validation R
2 of 0.992, while the ANN reaches an R
2 of 0.997 on the independent test set. Bayesian optimisation is then applied for single-objective efficiency maximisation and multi-objective trade-off between efficiency and wall thickness. SHAP analysis is used to interpret the contribution of each input parameter. The proposed framework provides a digital design workflow that can accelerate parameter exploration for dielectric elastomer tube harvesters without requiring extensive experimental data.
Keywords
Dielectric elastomer; tube energy harvester; machine learning surrogate model; Bayesian optimisation; parameter optimisation; physics-informed modelling
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