Collaborative Research: Using Uncertainty Quantification and Validated Computational Models to Analyze Pumping Performance of Valveless, Tubular Hearts

合作研究：使用不确定性量化和经过验证的计算模型来分析无瓣管状心脏的泵血性能

• 批准号: 2152040
• 负责人: Yanyan He
• 金额: $ 24.95万
• 依托单位: University of North Texas
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2152040&HistoricalAwards=false
• 关键词: Collaborative; Research; Using; Uncertainty; Quantification

Valveless, tubular hearts drive many animals' circulatory flow through muscular contractions. This flow carries oxygen, nutrients, and waste around the body. It also drives the development of blood vessels and other organs in vertebrate embryos. So, understanding how these hearts produce flow is key to understanding the development of vertebrate embryos and the evolution of their circulatory systems. This project will develop a computational model of the most essential features of this system: the electrical activity of the heart, muscle contractions of the tube walls, and the fluid-structure interactions of the heart walls and blood within. This computational framework aims to be faithful to that of a real, model animal (tunicate, or sea squirt). The model will then be analyzed with mathematical tools to determine the physical limits of the pumping system. Results of this project will improve the understanding of human heart development at the earliest stages. Also, it will point to how the large, multi-chambered hearts of vertebrates could have evolved from smaller structures. The results will be released through university courses, scientific conferences and seminars, online repositories, and regular publications. This project will also provide interdisciplinary training to high school, undergraduate, and graduate students. An accurate computational model of flow produced by valveless tubular hearts will be developed and validated using observations and experiments on the solitary tunicate, Ciona savignyi. The Immersed Boundary Method with Adaptive Mesh Refinement and a Windkessel model will be used to mathematically model fluid flow within the system. A Mitchell-Schaeffer model of cardiac electrodynamics will be tuned using optical mapping of heart electrical activity. Measurements on live C. savignyi circulatory flow with micro-Particle Image Velocimetry will be used as model validation. With the completed model, uncertainty quantification (UQ) techniques based on physics-constrained generalized polynomial chaos (gPC) and Dempster-Shafer (DS) theory will be used to study the uncertainty and analyze the parameter sensitivity on the model. The physics-constrained gPC expansion can construct a computationally cheaper surrogate based on the fluid flow simulations at the properly chosen input points in the parameter space. The mixed types of uncertainty in the fluid flow quantities of interest will be determined using DS theory combined with the gPC method, and the sensitivity of parameters will be used to develop hypotheses regarding parameter evolution. The performance data corresponding to various parameter combinations in live animals will be collected and used to improve the 2D computational fluid dynamic model by utilizing model correction methods. The improved model can be used to provide more accurate predictions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

valveless，管状心脏驱动许多动物的循环流穿过肌肉收缩。该流动带有氧气，营养和浪费。它还驱动脊椎动物胚胎中血管和其他器官的发展。因此，了解这些心脏如何产生流程是了解脊椎动物胚胎的发展及其循环系统的演变的关键。该项目将开发该系统最重要的特征的计算模型：心脏的电活动，管壁的肌肉收缩以及心脏壁和血液的流体结构相互作用。这个计算框架的目的是忠于真实的模型动物（皮象或海喷）。然后，将使用数学工具对模型进行分析，以确定泵系统的物理极限。该项目的结果将在最早的阶段提高对人心脏发展的理解。同样，它将指出脊椎动物的大型多腔心脏如何从较小的结构中演变出来。结果将通过大学课程，科学会议和研讨会，在线存储库和常规出版物发布。该项目还将为高中，本科生和研究生提供跨学科的培训。通过观察和实验，将开发和验证由Valveles Tubular心脏产生的流动流动的准确计算模型。带有自适应网状细化和Windkessel模型的浸没边界方法将用于数学上对系统内的流体流进行建模。心脏电活动的光学映射将调整心脏电动力学的Mitchell-Schaeffer模型。用微粒子图像赛车测定法对实时C. savignyi循环流进行测量将用作模型验证。通过完整的模型，基于物理受限的广义多项式混乱（GPC）和Dempster-shafer（DS）理论的不确定性定量（UQ）技术将用于研究不确定性并分析模型上的参数敏感性。物理受限的GPC扩展可以根据参数空间中正确选择的输入点的流体流量模拟来构建一个较便宜的替代物。流体流量中的不确定性混合类型将使用与GPC方法结合的DS理论确定，并且参数的敏感性将用于开发有关参数进化的假设。通过使用模型校正方法，将收集与活动物中各种参数组合相对应的性能数据，以改善2D计算流体动力学模型。改进的模型可用于提供更准确的预测。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评论标准来评估值得支持的。