ML-Driven optimization of two-phase microfluidic cooling using acoustofluidic bubble actuation and nanoarray-coated micropin structures.

This study presents a novel two-phase microfluidic cooling strategy integrating acoustofluidic bubble activation, nanoarray-coated micropin structures, and machine learning-guided optimization. Unlike conventional passive cooling, ultrasound-driven bubble actuation stabilizes boiling, prevents drying, and ensures uniform heat distribution, while nanoarray-coated micropins enhance liquid refilling via capillary forces and improve surface wettability. Experimental data were analyzed using deep neural networks (DNN), long short-term memory (LSTM) models, and statistical correlation methods (Spearman and Kendall), with interpretability provided by SHAP (DeepSHAP) and partial dependence plots (PDP). A comparison of model performance revealed that the LSTM achieved lower prediction errors than the DNN across all evaluated metrics (MAE 0.0055, SMAPE (0.8), and RMSE 0.0072), indicating its outperforming performance. SHAP and statistical analyses identified initial temperature as the most influential factor affecting heat transfer coefficient (HTC), followed by chipset material (S30-120 and stainless steel). Secondary factors include chipset configuration (SS, S30-120, S-nanorod, S-nanosheet) and nanoparticle type (SiO₂, ZnO), which significantly modulate bubble dynamics and thermal performance. PDP results highlight acoustofluidic excitation as the primary positive contributor, while flow rate and nanostructured surfaces provide moderate enhancements.