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<p>Thermal damage to ocular tissues is a critical medical concern because even small temperature elevations can impair corneal endothelial function, accelerate cataract formation, and disrupt retinal metabolism. This issue is particularly relevant in regions with intense thermal environments, such as Saudi Arabia, where preventive health care and advanced biomedical&#xa0;facilities are required. This study develops a predictive framework for estimating temperature distributions within the human eye under external heat exposure. A dual-phase-lag (DPL) bioheat transfer model incorporating two thermal relaxation times is formulated to capture finite speed thermal wave propagation in the multilayer structure of the eye, and closed-form analytical solutions are obtained using a normal mode approach. A mechanics-informed machine learning surrogate model is then constructed using data generated from the analytical DPL solutions, enabling rapid prediction of intraocular temperature across the parameter space. Parametric investigations examine the effects of ambient temperature, evaporation rate, tissue porosity, and blood perfusion on the thermal response of the six ocular layers. Comparisons with the Lord–Shulman and classical Fourier models reveal important differences in predicted temperature behavior under non-Fourier heat transfer. Additional analyses—including thermal safety mapping, sensitivity assessment, and response surface visualization—provide further insight into the combined influence of environmental and physiological parameters. The results show that non-Fourier thermal effects significantly influence peak intraocular temperature, while ambient temperature and evaporation dominate anterior eye heating and perfusion primarily affects deeper tissues. The present model assumes axisymmetric geometry and temperature-independent material properties, which may be extended in future studies using three-dimensional or patient-specific models. Overall, the proposed hybrid analytical–machine learning framework provides an efficient tool for ocular thermal risk assessment and supports the development of preventive strategies for populations exposed to extreme thermal environments.</p>

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Analytical investigation of heat transfer in multilayer human eye based on dual-phase-lag thermoelastic theory

  • 期刊论文

  • 发表日期：

    2026年4月25日

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  • Abhinav Singhal,
  • Soumi Ghosh,
  • Abdulkafi Mohammed Saeed,
  • Kasim Sakran Abass,
  • Sheela Hundekari,
  • Seema,
  • Anjali Chaudhary

摘要

Thermal damage to ocular tissues is a critical medical concern because even small temperature elevations can impair corneal endothelial function, accelerate cataract formation, and disrupt retinal metabolism. This issue is particularly relevant in regions with intense thermal environments, such as Saudi Arabia, where preventive health care and advanced biomedical facilities are required. This study develops a predictive framework for estimating temperature distributions within the human eye under external heat exposure. A dual-phase-lag (DPL) bioheat transfer model incorporating two thermal relaxation times is formulated to capture finite speed thermal wave propagation in the multilayer structure of the eye, and closed-form analytical solutions are obtained using a normal mode approach. A mechanics-informed machine learning surrogate model is then constructed using data generated from the analytical DPL solutions, enabling rapid prediction of intraocular temperature across the parameter space. Parametric investigations examine the effects of ambient temperature, evaporation rate, tissue porosity, and blood perfusion on the thermal response of the six ocular layers. Comparisons with the Lord–Shulman and classical Fourier models reveal important differences in predicted temperature behavior under non-Fourier heat transfer. Additional analyses—including thermal safety mapping, sensitivity assessment, and response surface visualization—provide further insight into the combined influence of environmental and physiological parameters. The results show that non-Fourier thermal effects significantly influence peak intraocular temperature, while ambient temperature and evaporation dominate anterior eye heating and perfusion primarily affects deeper tissues. The present model assumes axisymmetric geometry and temperature-independent material properties, which may be extended in future studies using three-dimensional or patient-specific models. Overall, the proposed hybrid analytical–machine learning framework provides an efficient tool for ocular thermal risk assessment and supports the development of preventive strategies for populations exposed to extreme thermal environments.