Kavli Affiliate: Laura Schaefer
| First 5 Authors: Lichang Zhu, Lichang Zhu, , ,
| Summary:
Predicting and interpreting thermal performance under oscillating flow in
porous structures remains a critical challenge due to the complex coupling
between fluid dynamics and geometric features. This study introduces a
data-driven wGAN-LBM-Nested_CV framework that integrates generative deep
learning, numerical simulation based on the lattice Boltzmann method (LBM), and
interpretable machine learning to predict and explain the thermal behavior in
such systems. A wide range of porous structures with diverse topologies were
synthesized using a Wasserstein generative adversarial network with gradient
penalty (wGAN-GP), significantly expanding the design space. High-fidelity
thermal data were then generated through LBM simulations across various
Reynolds (Re) and Strouhal numbers (St). Among several machine learning models
evaluated via nested cross-validation and Bayesian optimization, XGBoost
achieved the best predictive performance for the average Nusselt number (Nu)
(R^2=0.9981). Model interpretation using SHAP identified the Reynolds number,
Strouhal number, porosity, specific surface area, and pore size dispersion as
the most influential predictors, while also revealing synergistic interactions
among them. Threshold-based insights, including Re > 75 and porosity > 0.6256,
provide practical guidance for enhancing convective heat transfer. This
integrated approach delivers both quantitative predictive accuracy and physical
interpretability, offering actionable guidelines for designing porous media
with improved thermal performance under oscillatory flow conditions.
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