Surrogate-based multi-objective design optimization of tree-shaped fins with uniform branch end distribution for latent heat thermal energy storage

The enhancement of latent heat thermal energy storage (LHTES) systems through fin geometry optimization remains a critical challenge for leveraging the full potential of renewable energy sources. This study focuses on optimizing the geometries of tree-shaped fins to enhance power and energy densities in LHTES systems. The goal is to find branch designs with high energy and power density through a novel surrogate model-based optimization strategy that explores a broad design space. The surrogate models applied, including linear regression, principal component analysis-based linear regression, artificial neural networks, and random forest, are evaluated for their predictive performance. The random forest model demonstrates superior accuracy in predicting targets. The optimization process results in a Pareto-optimal design with a volume fraction of 33.9%. This optimal design substantially enhances the system's power density by 61.6% compared to conventional plate fins at an equivalent energy density. This optimized design improves energy and power density, achieving a uniform end-to-branch distribution, which is a pivotal factor for consistent temperature distribution and improved thermal efficiency. By integrating surrogate-based optimization with broad ranges of the tree-shaped fin design, this research has significantly improved the operational efficiency of LHTES systems. This research promises more effective thermal management and provides a methodological framework for design innovation in thermal energy storage.