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<p>This study presents a novel phase-change cooling strategy that synergistically integrates acoustofluidic bubble dynamics with nanoarray-coated micropin fins to enhance thermal performance. A multidisciplinary framework combines experimental measurements–heat transfer coefficient (HTC), heat flux, velocity fields, and pressure drop–across SS, S30–120, and S-nanosheet architectures with machine-learning models, including LASSO, Random Forest, and Deep Neural Networks. Statistical correlation analyses (Spearman rank and Kendall tau) and interpretability techniques (SHAP, Partial Dependence Plots, Symbolic Metamodeling, Double Machine Learning, and TCAV) provide both predictive accuracy and physical insight. Nanosheet-coated surfaces deliver substantial performance gains, achieving a 71.2% increase in critical heat flux and a 160.9% improvement in HTC compared with smooth surfaces, validated by a highly accurate DNN model (R² = 0.99, MAE = 0.01). SHAP analysis identifies heat flux as the dominant factor governing bubble dynamics, while nanoarrays enhance nucleation and optimize pressure drop to improve efficiency. Symbolic Metamodeling and Double Machine Learning confirm heat flux as the primary driver of heat transfer, with secondary contributions from velocity, which influences bubble residence time, and nanoarray density. Feature-importance results further show that S-nanorod structures and pressure drop strongly regulate nucleation and flow behavior, whereas microreactor parameters have minimal influence.</p>

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Hydrodynamic investigation of acoustic bubble–surface interactions on nanoarray-coated fins for microelectronic cooling with machine learning-based analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月28日

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  • Seyed Hamed Godasiaei,
  • Pouyan Talebizadehsardari,
  • Amir Keshmiri

摘要

This study presents a novel phase-change cooling strategy that synergistically integrates acoustofluidic bubble dynamics with nanoarray-coated micropin fins to enhance thermal performance. A multidisciplinary framework combines experimental measurements–heat transfer coefficient (HTC), heat flux, velocity fields, and pressure drop–across SS, S30–120, and S-nanosheet architectures with machine-learning models, including LASSO, Random Forest, and Deep Neural Networks. Statistical correlation analyses (Spearman rank and Kendall tau) and interpretability techniques (SHAP, Partial Dependence Plots, Symbolic Metamodeling, Double Machine Learning, and TCAV) provide both predictive accuracy and physical insight. Nanosheet-coated surfaces deliver substantial performance gains, achieving a 71.2% increase in critical heat flux and a 160.9% improvement in HTC compared with smooth surfaces, validated by a highly accurate DNN model (R² = 0.99, MAE = 0.01). SHAP analysis identifies heat flux as the dominant factor governing bubble dynamics, while nanoarrays enhance nucleation and optimize pressure drop to improve efficiency. Symbolic Metamodeling and Double Machine Learning confirm heat flux as the primary driver of heat transfer, with secondary contributions from velocity, which influences bubble residence time, and nanoarray density. Feature-importance results further show that S-nanorod structures and pressure drop strongly regulate nucleation and flow behavior, whereas microreactor parameters have minimal influence.