Simulation of 3-D blood flow in the full systemic arterial tree and computational frameworks for efficient parameter estimation
Abstract/Contents
- Abstract
- Computational methods for simulating blood flow have become powerful tools to gain insight into the physical behavior of the cardiovascular system in health and disease. This work is aimed at developing computational tools and models for the investigation of age-related arterial stiffening and its relationship to the underlying hemodynamics. Due to the complex geometry and spatially-varying material properties of the central arteries, and the hemodynamics therein, large-scale three-dimensional computational models of arterial mechanics can improve our ability to interpret current clinical hemodynamic metrics and to advance our fundamental understanding of the mechanisms of disease progression. We have built a novel computational model of fully three-dimensional and unsteady hemodynamics within the primary large arteries in the human systemic circulation from head to legs. We demonstrated that this virtual full body systemic arterial tree is able to reproduce many of the key local and global hemodynamic features of the human arterial circulation and is a promising first step toward further computational analyses of the relationship between blood flow and arterial stiffening. As part of this work, we have tested and implemented an important boundary condition for the arterial fluid-solid interaction problem that mimics the tethering effect of the external tissues and stabilizes simulations in large networks of vessels. The task of efficiently fitting large-scale 3-D models to patient-specific measurements is challenging due to the computational effort required for a single simulation and the number of model parameters involved. We implemented two different computational frameworks for parameter estimation: the first is based on computationally inexpensive one-dimensional analogues of the full 3-D system. The second method is based on sequential data-assimilation techniques, specifically, nonlinear Kalman filtering. We demonstrate that these frameworks may be used to rapidly estimate the parameters of large-scale 3-D models based on clinical measurements of blood flow, pressure, and vessel wall distensibility.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2014 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Xiao, Nan |
|---|---|
| Associated with | Stanford University, Department of Bioengineering. |
| Primary advisor | Covert, Markus |
| Primary advisor | Figueroa Alvarez, Carlos |
| Thesis advisor | Covert, Markus |
| Thesis advisor | Figueroa Alvarez, Carlos |
| Thesis advisor | Kuhl, Ellen, 1971- |
| Advisor | Kuhl, Ellen, 1971- |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Nan Xiao. |
|---|---|
| Note | Submitted to the Department of Bioengineering. |
| Thesis | Ph.D. Stanford University 2014 |
| Location | electronic resource |
Access conditions
- Copyright
- © 2014 by Nan Xiao
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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