通过强大的正确直角分解来实现减少顺序模型,以捕捉个性化的大动脉血动力学
Chotirawee Chatpattanasiri1, Gaia Franzetti1, Mirko Bonfanti1
1Department of Mechanical Engineering, University College London, London, UK.
Journal of biomechanics
|September 1, 2023
概括
强大的正确直角分解 (RPOD) 增强了粒子图像速度测量数据,用于大动脉血流的减少顺序建模. 该方法使用更少的模式准确地捕捉流动动力学,简化了临床应用的模拟.
科学领域:
- 流体动力学 流体动力学
- 生物医学工程 生物医学工程
- 计算建模计算建模
背景情况:
- 减少订单模型 (ROM) 为复杂的血液动力学模拟提供了有效的替代方案.
- 特定于患者的大动脉剖析模型带来了计算挑战.
- 准确的速度场重建对于理解血流动力学至关重要.
研究的目的:
- 为了比较来自强大的正确直角分解 (RPOD) 的减少顺序模型 (ROM) 与传统的正确直角分解 (POD) 进行大动脉血流.
- 评估RPOD在从实验和计算流体动力学数据中重建速度场的有效性.
- 在临床血液动力学分析中证明ROM的潜力.
主要方法:
- 应用POD和RPOD在体外粒子图像速度测量 (PIV) 数据的患者特异性剖析的大动脉.
- 从PIV和计算流体动力学 (CFD) 数据中比较了分解的流量场.
- 使用不同数量的POD模式重建了流域,并将其与全订单模型 (FOM) 进行了比较.
主要成果:
- 使用强大的主要组件分析 (RPCA) 的 RPOD 提高了 PIV 数据质量,使 POD 可以在更少的模式下捕获动能.
- 在整个心脏循环中,流量重建的准确性各不相同,而扩张期需要更多的模式.
- 前10个POD模式通常证明足以表示大动脉流域.
- 大动脉剖析中的连贯流结构被最初的POD模式描述得很好.
结论:
- 在低维空间中,ROM可以有效地表示大动脉流的宏观行为.
- RPOD提高了数据质量,并减少了准确的流体重建所需的模式数量.
- 简化,计算效率高的模拟和预测是可能的,这有助于血液动力学模型的临床翻译.
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