Multi-scale modeling of multiphase flows and fluid-structure interactions in the lung

CBET -1706801 PI：Gaver，Donald P.机械通气是严重肺部疾病的常见治疗方法。 虽然它可以帮助维持肺功能，但机械通气也可以通过对脆弱的肺组织施加大的机械应力来诱导肺损伤。因此，开发优化呼吸机性能同时最小化损伤风险的方案可能使许多接受机械通气的患者受益。 该奖项将支持计算和实验技术的发展，以分析肺中单个通道的空气和液体流动，并将这些研究结果纳入预测机械通气期间整体肺性能的模型中。 该项目中开发的预测工具可以为安全机械通气的个性化医疗方法提供基础。 此外，该项目还将为K-12学生提供教育活动，并为其他学生提供机会，特别是来自代表性不足的群体的学生，在所有学术水平上参与研究。各种肺部疾病涉及通过肺部通道的液体异常。 将开发一个计算效率高、降维、多尺度模型，通过结合多相流和液体与组织成分之间的流体-结构相互作用，动态模拟异质肺充气和放气。 该模型将模拟肺的解剖学模型，包括组织和液体成分。 将进行新的实验来分析流体内衬通道中的多相流动，包括气道关闭、粘性流体塞运动和传播以及气道重新开放等现象，包括动态表面张力和其他物理化学相互作用的影响。 来自这些实验和以前的实验的结果将与理论模型结合成肺的器官水平模型。 将对正常和疾病状态下肺部的多尺度和宏观尺度现象进行大规模模拟，以生成可与完善的生理测量进行比较的预测。 该项目的结果不仅将提供一种预测呼吸机性能的工具，还可能导致对肺功能的新的预测见解。

CBET - 1706801PI: Gaver, Donald P.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.