@Article{ee.2026.088854,
AUTHOR = {Jianwei Gong},
TITLE = {A Physics-Driven Surrogate Modeling Framework for the Inverse Design and Optimization of Dielectric Elastomer Tube Energy Harvesters},
JOURNAL = {Energy Engineering},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/energy/online/detail/28135},
ISSN = {1546-0118},
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<sup>2</sup> of 0.992, while the ANN reaches an R<sup>2</sup> 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.},
DOI = {10.32604/ee.2026.088854}
}