论文标题
用于参数最佳血流控制的数据驱动的减少订单方法：应用于冠状动脉旁路移植
A data-driven Reduced Order Method for parametric optimal blood flow control: application to coronary bypass graft
论文作者
论文摘要
我们考虑在患者特异性的冠状动脉搭桥移植物中的最佳流动控制问题，目的是将血流速度与给定测量值匹配，因为雷诺数在生理范围内变化。血流以稳定的不可压缩的Navier-Stokes方程为模型。几何形状由狭窄的左前降动脉组成，其中用右胸动脉进行单个旁路。控制变量是出口边界处正常应力的未知值，需要正确设置出口边界条件。对于参数最佳流量控制问题的数值解，我们开发了一种数据驱动的减少顺序方法，该方法将正确的正交分解（POD）与神经网络相结合。我们提出了数值结果，表明我们的数据驱动方法会导致[59]中提出的更古典的Pod-Galerkin策略，同时具有可比的准确性。
We consider an optimal flow control problem in a patient-specific coronary artery bypass graft with the aim of matching the blood flow velocity with given measurements as the Reynolds number varies in a physiological range. Blood flow is modelled with the steady incompressible Navier-Stokes equations. The geometry consists in a stenosed left anterior descending artery where a single bypass is performed with the right internal thoracic artery. The control variable is the unknown value of the normal stress at the outlet boundary, which is need for a correct set-up of the outlet boundary condition. For the numerical solution of the parametric optimal flow control problem, we develop a data-driven reduced order method that combines proper orthogonal decomposition (POD) with neural networks. We present numerical results showing that our data-driven approach leads to a substantial speed-up with respect to a more classical POD-Galerkin strategy proposed in [59], while having comparable accuracy.
