Journal article
Forward Computational Modeling of Respiratory Airflow
Applied sciences, Vol.14(24), 11591
01/01/2024
DOI: 10.3390/app142411591
PMCID: PMC12225619
PMID: 40620539
Abstract
The simulation of gas flow in the bronchial tree using computational fluid dynamics (CFD) has become a useful tool for the analysis of gas flow mechanics, structural deformation, ventilation, and particle deposition for drug delivery during spontaneous and assisted breathing. CFD allows for new hypotheses to be tested in silico, and detailed results generated without performing expensive experimental procedures that could be potentially harmful to patients. Such computational techniques are also useful for analyzing structure–function relationships in healthy and diseased lungs, assessing regional ventilation at various time points over the course of clinical treatment, or elucidating the changes in airflow patterns over the life span. CFD has also allowed for the development and use of image-based (i.e., patient-specific) models of three-dimensional (3D) airway trees with realistic boundary conditions to achieve more meaningful and personalized data that may be useful for planning effective treatment protocols. This focused review will present a summary of the techniques used in generating realistic 3D airway tree models, the limitations of such models, and the methodologies used for CFD airflow simulation. We will discuss mathematical and image-based geometric models, as well as the various boundary conditions that may be imposed on these geometric models. The results from simulations utilizing mathematical and image-based geometric models of the airway tree will also be discussed in terms of similarities to actual gas flow in the human lung.
Details
- Title: Subtitle
- Forward Computational Modeling of Respiratory Airflow
- Creators
- Emmanuel Akor - University of IowaBing Han - Morgan State UniversityMingchao Cai - Morgan State UniversityChing-Long Lin - University of Iowa, Mechanical EngineeringDavid Kaczka - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Applied sciences, Vol.14(24), 11591
- DOI
- 10.3390/app142411591
- PMID
- 40620539
- PMCID
- PMC12225619
- eISSN
- 2076-3417
- Publisher
- MDPI AG
- Grant note
- Office of the Assistant Secretary of Defense for Health Affairs: W81XWH-21-1-0507 Office of the Assistant Secretary of Defense for Health Affairs, Peer Reviewed Medical Research Program: T32 HL144461, R01 HL168116 NIH: W911NF-23-1-0004 Army Research Office award: 02232301 Morgan State University: ED P116S210005 Department of Education
Supported in part by the Office of the Assistant Secretary of Defense for Health Affairs, Peer Reviewed Medical Research Program award W81XWH-21-1-0507, NIH grants T32 HL144461 and R01 HL168116, Army Research Office award W911NF-23-1-0004 and affiliated project award from the Center for Equitable Artificial Intelligence and Machine Learning Systems (CEAMLS) at Morgan State University (project ID 02232301), as well as the Department of Education grant ED P116S210005. Opinions, interpretations, conclusions, and recommendations are those of the authors, and are not necessarily endorsed by the Department of Defense.
- Language
- English
- Date published
- 01/01/2024
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology; Anesthesia; Mechanical Engineering
- Record Identifier
- 9984759992802771
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