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.

无瓣膜的管状心脏通过肌肉收缩驱动许多动物的循环流动。这种流动将氧气，营养物质和废物带到身体周围。它还驱动脊椎动物胚胎中血管和其他器官的发育。因此，了解这些心脏如何产生流动是了解脊椎动物胚胎发育及其循环系统进化的关键。该项目将开发该系统最基本特征的计算模型：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。这个计算框架旨在忠实于真实的模型动物（被囊动物或海鞘）。然后将使用数学工具分析该模型，以确定泵送系统的物理极限。该项目的结果将提高对人类心脏早期发育的理解。此外，它还将指出脊椎动物的大型多室心脏是如何从较小的结构进化而来的。研究结果将通过大学课程、科学会议和研讨会、在线知识库和定期出版物发布。该项目还将为高中生、本科生和研究生提供跨学科培训。一个精确的计算模型所产生的无瓣膜管状心脏的流量将开发和验证使用的观察和实验上的孤立被囊，玻璃海鞘。将使用具有自适应网格细化的浸没边界法和Windkessel模型对系统内的流体流动进行数学建模。心脏电动力学的Mitchell-Schaeffer模型将使用心脏电活动的光学标测进行调整。在Live C上进行测量。savignyi循环流与微粒子图像测速法将被用作模型验证。利用基于物理约束的广义多项式混沌（gPC）和Dempster-Shafer（DS）理论的不确定性量化（UQ）技术，研究了模型的不确定性，分析了模型参数的敏感性。物理约束的gPC扩展可以基于在参数空间中适当选择的输入点处的流体流动模拟来构建计算上更便宜的替代。将使用DS理论结合gPC方法来确定感兴趣的流体流量中的混合类型的不确定性，并且将使用参数的灵敏度来开发关于参数演变的假设。将收集与活体动物中的各种参数组合相对应的性能数据，并利用模型校正方法用于改进2D计算流体动力学模型。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。