Bionic vascular fin latent heat system synergistically enhances heat Transfer: Parameter optimization and prediction of pulsating flow and Nanofluid based on machine learning

This study develops a data-driven framework to optimize heat transfer in phase change thermal energy storage systems by synergistically integrating bioinspired alveolar-vascular fins, pulsating nanofluid flow, and machine learning. The proposed system employs fractal fins mimicking pulmonary vasculature to enhance heat conduction, while copper nanofluids and pulsating flow collectively improve thermal conductivity and boundary layer disruption. Through coupled computational fluid dynamics simulations and machine learning modeling, we establish a multi-parameter optimization framework evaluating five key operational parameters: flow velocity, pulsation amplitude, period, nanoparticle concentration, and temperature. The optimal configuration achieves remarkable performance metrics: 280 kJ storage capacity, 311.72 kJ/kg energy density, and 0.0485 kJ/s charging rate. RSM analysis identifies fluid velocity as the dominant factor influencing melting time and heat storage capacity, while nanoparticle concentrations exceeding 0.03 % cause substantial pressure drop increases. Among various machine learning models, the decision tree algorithm exhibits superior predictive accuracy, with SHAP analysis providing quantitative insights into parameter sensitivity. NSGA-II multi-objective optimization yields an optimal solution with 203.50 s melting time, 9.17 kJ heat storage, and 18.85 Pa pressure drop, corresponding to operational parameters of 0.13 m/s flow velocity, 0.06 m/s pulsation amplitude, 32.76 s period, 0.7 % nanoparticle concentration, and 373 K fluid temperature. This research provides a novel, data-driven optimization paradigm for advanced TES system design, with particular relevance for renewable energy storage and industrial waste heat recovery applications.