Modeling thermal systems with fractional models: human bronchus application - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Nonlinear Dynamics Année : 2022

Modeling thermal systems with fractional models: human bronchus application

Résumé

System thermal modeling allows heat and temperature simulations for many applications, such as refrigeration design, heat dissipation in power electronics, melting processes and bio-heat transfers. Sufficiently accurate models are especially needed in open-heart surgery where lung thermal modeling will prevent pulmonary cell dying. For simplicity purposes, simple RC circuits are often used, but such models are too simple and lack of precision in dynamical terms. A more complete description of conductive heat transfer can be obtained from the heat equation by means of a two-port network. The analytical expressions obtained from such circuit models are complex and nonlinear in the frequency ω. This complexity in Laplace domain is difficult to handle when it comes to control applications and more specifically during surgery, as heat transfer and temperature control of a tissue may help in reducing necrosis and preserving a greater amount of a given organ. Therefore, a frequency-domain analysis of the series and shunt impedances will be presented and different techniques of approximations will be explored in order to obtain simple but sufficiently precise linear fractional transfer function models. Several approximations are proposed to model heat transfers of a human middle bronchus and will be quantified by the absolute errors.
Fichier principal
Vignette du fichier
IMS_NLD_Duhe_2022.pdf (1.17 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03721264 , version 1 (12-07-2022)

Licence

Paternité

Identifiants

Citer

Jean-François Duhé, Stéphane Victor, Pierre Melchior, Youssef Abdelmounen, François Roubertie. Modeling thermal systems with fractional models: human bronchus application. Nonlinear Dynamics, 2022, 108 (1), pp.579-595. ⟨10.1007/s11071-022-07239-3⟩. ⟨hal-03721264⟩
34 Consultations
33 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More