Mechanical ventilation is a common therapy in serious lung disorders. While it can help sustain pulmonary function, mechanical ventilation can also induce lung injury by imposing large mechanical stresses on fragile pulmonary tissues. Therefore, developing protocols that optimize ventilator performance while minimizing the risk of injury could benefit many patients who undergo mechanical ventilation. This award will support the development of computational and experimental techniques to analyze the flow of air and liquid on the scale of individual passageways in the lung and incorporate results of those studies into models that predict overall lung performance during mechanical ventilation. The predictive tools developed in this project could provide a foundation for a personalized medical approach toward safe mechanical ventilation. In addition, the project will provide educational activities for K-12 students and opportunities for other students, especially those from underrepresented groups, at all academic levels to participate in the research.
A variety of lung ailments involve abnormalities in the fluid that lines passageways through the lung. A computationally efficient, reduced-dimension, multi-scale model will be developed to dynamically simulate heterogeneous lung inflation and deflation by incorporating multiphase flows and fluid-structure interactions between liquid and tissue components. The model will simulate an anatomically based model of the lung, including tissue and liquid components. New experiments will be conducted to analyze multiphase flow in fluid-lined channels including such phenomena as airway closure, viscous fluid plug motion and propagation, and airway reopening, including effects of dynamic surface tension and other physicochemical interactions. Results from these experiments and previous ones will be combined with theoretical models into an organ-level model of the lung. Large-scale simulations of multi-scale and macro-scale phenomena in the lung under normal and disease states will be conducted to generate predictions that can be compared with well-established physiological measurements. Results of the project will not only provide a tool to predict ventilator performance, but also could lead to new predictive insights into lung function.
