Year: 2015
Type: dissertation
Abstract: Obtaining reliable CFD predictions of the bronchial flow that reflects the actual flow\nwithin a living lung requires the development of a deforming airways model, and the\nimposition of physiological subject-specific boundary conditions. This thesis addresses\nthese two issues by the development of dynamic CFD models of the bronchial airways\nusing a dynamic CT data set covering the breathing cycle of a laboratory animal. A\ndeformation algorithm is proposed that matches the CFD mesh of the subsequent\nairway geometries generated from the dynamic CT data set. In addition, a novel\nnonlinear dynamic airway model generated from a pair of CT images is introduced. The\nproposed non-linear deforming model is capable of successfully capturing the non-linear\nmotion characteristics of the bronchial airways based on the clinical measurements of\nthe lung volume change. Furthermore, a technique to drive physiological subject specific\nboundary conditions for the terminal surfaces of the CFD models of the\nbronchial airways is introduced. The proposed technique depends on approximating the\nlung volume associated to each terminal surface over several time points over the\nbreathing cycle based on the mechanical coupling between the bronchial airways and\nthe vascular tree. The computed dynamic subject-specific boundary conditions were\nimposed on the terminal surfaces of the deforming airway model and the effect of wall\nmotion on the flow features during tidal breathing is investigated for the first time.\nThe outcome of this thesis is expected to improve the fidelity of the CFD predictions of\nthe bronchial flow compared to the actual flow within a living lung. In addition, the\navailability of a new non-linear dynamic model of the bronchial airways that requires\none pair of CT images as input, which complies with the radiation dosage restrictions for\nhumans will facilitate the development of well-resolved CFD models of the human\nbronchial airways.
Source: Figshare
Author: Gihad Ibrahim
Language: English
Subfield: Pulmonary and Respiratory Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W580157163