Model order reduction and sensitivity analysis for complex heat transfer simulations inside the human eyeball
Heat transfer in the human eyeball, a complex organ, is significantly influenced by various pathophysiological and external parameters. Particularly, heat transfer critically affects fluid behavior within the eye and ocular drug delivery processes. Overcoming the challenges of experimental analysis, this study introduces a comprehensive three-dimensional mathematical and computational model to simulate the heat transfer in a realistic geometry. Our work includes an extensive sensitivity analysis to address uncertainties and delineate the impact of different variables on heat distribution in ocular tissues. To manage the model's complexity, we employed a very fast model reduction technique with certified sharp error bounds, ensuring computational efficiency without compromising accuracy. Our results demonstrate remarkable consistency with experimental observations and align closely with existing numerical findings in the literature. Crucially, our findings underscore the significant role of blood flow and environmental conditions, particularly in the eye's internal tissues. Clinically, this model offers a promising tool for examining the temperature-related effects of various therapeutic interventions on the eye. Such insights are invaluable for optimizing treatment strategies in ophthalmology.
Authors
- Christophe Prud'Homme (ORCID: https://orcid.org/0000-0003-2287-2961)
- Marcela Szopos (ORCID: https://orcid.org/0000-0002-7300-3267)
- Thomas Saigre (ORCID: https://orcid.org/0009-0009-5763-4956)
Institutions
- Centre National de la Recherche Scientifique (FR)
- Université Paris Cité (FR)
- Sorbonne Paris Cité (FR)
- Institut de Recherche Mathématique Avancée (FR)
- Mathématiques Appliquées à Paris 5 (FR)
- Université de Strasbourg (FR)
Publication Details
- Journal
- International Journal for Numerical Methods in Biomedical Engineering
- Published
- 2024-09-09
- DOI
- https://doi.org/10.1002/cnm.3864
- Citations
- 1
- Primary Topic
- Model Reduction and Neural Networks
- Type
- article
- Field-Weighted Citation Impact
- 0.28
Funders
- Agence Nationale de la Recherche
- Université de Strasbourg
- Ministère de l'Enseignement supérieur, de la Recherche et de l'Innovation