Computational fluid dynamics is a common tool in cardiovascular science and engineering to simulate, predict and study hemodynamics in arteries. However, owing to the complexity and scale of cardiovascular flow problems, the evaluation of the model could be computationally expensive, especially in those cases where a large number of evaluations are required, such as uncertainty quantification and design optimisation. In such scenarios, the model may have to be repeatedly evaluated due to the changes or distinctions of simulation domains. In this work, a data-driven surrogate model is proposed for the efficient prediction of blood flow simulations on similar but distinct domains. The proposed surrogate model leverages surface registration to parameterise those similar but distinct shapes and formulate corresponding hemodynamics information into geometry-informed snapshots by the diffeomorphism constructed between the reference domain and target domain. A non-intrusive reduced-order model for geometrical parameters is subsequently constructed using proper orthogonal decomposition, and a radial basis function interpolator is trained for predicting the reduced coefficients of the reduced-order model based on reduced coefficients of geometrical parameters of the shape. Two examples of blood flowing through a stenosis and a bifurcation are presented and analysed. The proposed surrogate model demonstrates its accuracy and efficiency in hemodynamics prediction and shows its potential application toward real-time simulation or uncertainty quantification for complex patient-specific scenarios.
翻译:在心血管科学和工程学中,计算流体动态是模拟、预测和研究动脉中热动动力学的一个常见工具,但是,由于心血管流动问题的复杂性和规模,模型的评价可能计算得非常昂贵,特别是在需要大量评价的情况下,例如不确定性量化和设计优化化,在这种情形下,由于模拟域的变化或区别,可能需要反复评价模型。在这项工作中,提议了一个数据驱动的代孕模型,以有效预测类似但不同的领域的血液流动模拟。拟议的代孕模型利用表面登记,将这些相似但不同的形状进行参数化,并将相应的肝动力学信息纳入参考域和目标域和目标域之间构造的对地貌变化知情的截图中。随后,可能由于模拟域的变化或差异而需要反复评价该模型。在这个模型中,提出了一个数据驱动的替代模型模型模型模型模型模型模型,以便有效预测类似但不同的域的血液流动模型模拟。拟议的表面登记利用了这些相似但不同的形状不同的形状的参数进行参数参数比较,并将相应的肝动力学信息纳入参考领域和对结果的精确性分析。