一种混合的减少顺序模型,以在计算血动力学中强制执行外流压力边界条件
Pierfrancesco Siena1, Pasquale Claudio Africa1, Michele Girfoglio2
1Mathematics Area, mathLab, SISSA, International School for Advanced Studies, via Bonomea 265, I-34136, Trieste, Italy.
Biomechanics and modeling in mechanobiology
|September 15, 2025
概括
这项研究引入了一种混合减少顺序模型 (ROM),用于模拟心血管系统中不稳定的血液流动. 这种新的方法准确地重建复杂的流动模式,同时显著降低计算成本.
科学领域:
- 计算流体动力学的流体动力学.
- 生物医学工程 生物医学工程
- 心血管模型的建模.
背景情况:
- 精确模拟不稳定的血流对于心血管应用至关重要.
- 传统的全订单模型 (FOM) 在计算上昂贵.
- 在心血管模拟中,处理非均的输出压力边界条件是一个重大挑战.
研究的目的:
- 开发一种高效的减少顺序模型 (ROM) 用于在心血管应用中重建不稳定的血液流动模式.
- 为了扩展升降功能的方法,用于非均的出口压力边界条件.
- 整合一个神经网络来近似出流压力,创建一个混合模型.
主要方法:
- 适当的直角分解 (POD) 用于基础函数计算.
- 加勒金投影用于减少系数计算.
- 扩展升降功能的方法用于出口压力条件.
- 神经网络集成用于依赖时间的外流压力近似值.
- 纳维埃-斯托克斯离散的有限体积方法.
- 两个元素的风模型用于外流压力估计.
主要成果:
- 开发的混合ROM准确地接近了全订单模型 (FOM).
- 该方法成功地处理了非均的出口压力边界条件.
- 与FOM相比,计算成本得到了显著的降低.
- 在理想化血管和患者特定的大动脉上进行验证.
结论:
- 混合ROM为心血管流动模拟提供了一个高效和准确的工具.
- 对出口边界条件的新处理提高了模拟可靠性.
- 这种方法将基于方程的建模与数据驱动的技术融合在一起,以提高性能.
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