Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Singhal, Abhinav; Ghosh, Soumi; Saeed, Abdulkafi Mohammed; Abass, Kasim Sakran; Hundekari, Sheela; Seema; Chaudhary, Anjali Title A name given to the resource Analytical investigation of heat transfer in multilayer human eye based on dual-phase-lag thermoelastic theory Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Archive of Applied Mechanics;Volume;96;Issue;5;Article No.;97; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s00419-026-03075-8" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s00419-026-03075-8</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037430329?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037430329?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Singhal A., Christ University, Bengaluru, 560029, India; Ghosh S., Maharaja Agrasen Institute of Technology, Delhi, India; Saeed A.M., Department of Mathematics, College of Science, Qassim University, Buraydah, 51452, Saudi Arabia; Abass K.S., University of Kirkuk, Kirkuk, 36001, Iraq; Hundekari S., School of Computer Applications, SOET Pimpri Chinchwad University Sate Maval, Pune, India; Seema, Christ University, Bengaluru, 560029, India; Chaudhary A., Department of Management, College of Business Administration, Princess Nourah Bint Abdulrahman University, P.O.Box 84428, Riyadh, 11671, Saudi Arabia Description An account of the resource 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 biomedicalfacilities 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 LordShulman and classical Fourier models reveal important differences in predicted temperature behavior under non-Fourier heat transfer. Additional analysesincluding thermal safety mapping, sensitivity assessment, and response surface visualizationprovide 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 analyticalmachine 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. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2026. Subject The topic of the resource Dual-phase-lag bioheat equation; Multilayer biological media; Non-Fourier heat transfer; Ocular thermodynamics; Parametric sensitivity; Thermal wave propagation; Thermomechanical safety assessment Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 9391533; CODEN: AAMEE Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online