Dual‐Phase‐Lag Bioheat Analysis of Non‐Fourier Thermal Wave Propagation in Multilayer Ocular Tissues

ABSTRACT This study presents an advanced analytical framework for predicting thermal wave propagation in a multilayer ocular structure using the dual‐phase‐lag (DPL) bioheat formulation. The results confirm that non‐Fourier thermal transport mechanisms are essential for accurately capturing transient heat behavior in biological tissues, particularly under external thermal exposure. Compared with classical Fourier and Lord–Shulman models, the DPL model predicts smoother temperature gradients and lower peak thermal loads, thereby providing more physiologically realistic temperature distributions. The model validity regime analysis demonstrates clear operational boundaries where classical diffusion‐based formulations fail and non‐Fourier effects dominate thermal response. Sensitivity analysis reveals that ambient temperature and evaporation primarily control anterior ocular thermal behavior, while tissue porosity and blood perfusion significantly influence deeper layers such as the retina and sclera. Transient thermal comparisons confirm that classical models overpredict early‐time heating due to the absence of relaxation effects. Multi‐parameter response surface and thermal safety mapping highlight strong nonlinear coupling between environmental and physiological transport mechanisms, enabling quantitative identification of safe exposure limits. Additionally, surrogate modeling demonstrates high prediction accuracy relative to full DPL solutions while significantly reducing computational cost, enabling real‐time thermal prediction and parametric optimization. Overall, the proposed hybrid analytical–computational framework establishes a robust platform for ocular thermal safety assessment, biomedical treatment planning, and environmental exposure risk evaluation. The findings also provide a generalized foundation for studying non‐Fourier heat transport in layered porous biological media and support the development of next‐generation predictive thermal modeling tools.