Three-dimensional cardiovascular fluid dynamics simulations typically require computation of several cardiac cycles before they reach a periodic solution, rendering them computationally expensive. Furthermore, there is currently no standardized method to determine whether a simulation has yet reached that periodic state. In this work, we propose use of the asymptotic error measure to quantify the difference between simulation results and their ideal periodic state using lumped-parameter modeling. We further show that initial conditions are crucial in reducing computational time and develop an automated framework to generate appropriate initial conditions from a one-dimensional model of blood flow. We demonstrate the performance of our initialization method using six patient-specific models from the Vascular Model Repository. In our examples, our initialization protocol achieves periodic convergence within one or two cardiac cycles, leading to a significant reduction in computational cost compared to standard methods. All computational tools used in this work are implemented in the open-source software platform SimVascular. Automatically generated initial conditions have the potential to significantly reduce computation time in cardiovascular fluid dynamics simulations.

中文翻译：

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关于心血管流体动力学模拟的周期性

三维心血管流体动力学仿真通常需要在达到周期解之前先计算几个心动周期，从而使它们的计算成本很高。此外，当前还没有确定模拟是否已经达到该周期性状态的标准化方法。在这项工作中，我们建议使用渐近误差度量来使用集总参数建模来量化仿真结果与理想周期状态之间的差异。我们进一步表明，初始条件对于减少计算时间至关重要，并开发了一种自动化的框架以从一维血流模型生成适当的初始条件。我们使用血管模型存储库中的六个患者特定模型演示了初始化方法的性能。在我们的示例中 我们的初始化协议可在一两个心动周期内实现周期性收敛，与标准方法相比，可显着降低计算成本。这项工作中使用的所有计算工具都在开源软件平台SimVascular中实现。自动生成的初始条件有可能显着减少心血管流体动力学模拟中的计算时间。