Towards Reduced Order Models via Robust Proper Orthogonal Decomposition to capture personalised aortic haemodynamics
Towards Reduced Order Models via Robust Proper Orthogonal Decomposition to capture personalised aortic haemodynamics
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DOI：
10.1101/2023.01.21.524933
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发表时间：
2023-01
影响因子：
2.4
通讯作者：
Chotirawee Chatpattanasiri;G. Franzetti;M. Bonfanti;V. Díaz-Zuccarini;S. Balabani
中科院分区：
文献类型：
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作者：
Chotirawee Chatpattanasiri;G. Franzetti;M. Bonfanti;V. Díaz-Zuccarini;S. Balabani
数据驱动的降阶模型在解决与计算和实验血流动力学模型相关的挑战方面显示出了希望。在这项工作中，我们探索使用降阶模型(ROMs)来捕捉患者特定的主动脉夹层中的主要流动特征。我们对粒子图像测速仪获得的体外血流动力学数据进行了适当的正交分解(POD)和稳健主成分分析(RPCA)，并在相同的几何形状和流动条件下将分解后的流动与计算流体动力学(CFD)数据得到的流动进行了比较。使用不同数目的POD模式重建流动，并将得到的整个心脏周期的流动特征与原始的全阶模型(FOM)进行比较。RPCA已被发现可以提高PIV数据的质量，并仅以两种模式捕获流动的大部分动能，这两种模式类似于没有测量噪声的数值数据。重建误差随着心脏周期的不同而不同，舒张期血流需要更多的模式才能进行准确的重建。一般而言，模式Φ1-10被发现足以表示流场。结果表明，这种主动脉夹层流动的相干结构由最初的几个POD模式描述，这表明在低维空间中描述主动脉血流的大尺度行为是可能的；从而显著简化了问题，并允许更高效的计算效率的流动模拟，为将此类模型移植到临床铺平道路。
Data driven, reduced order modelling has shown promise in tackling the challenges associated with computational and experimental hemodynamic models. In this work, we explore the use of Reduced Order Models (ROMs) to capture the main flow features in a patient-specific dissected aorta. We apply Proper Orthogonal Decomposition (POD) and Robust Principle Component Analysis (RPCA) on in vitro, hemodynamic data acquired by Particle Image Velocimetry and compare the decomposed flows to those derived from Computational Fluid Dynamics (CFD) data for the same geometry and flow conditions. The flow is reconstructed using different numbers of POD modes and the flow features obtained throughout the cardiac cycle are compared to the original Full Order Models (FOMs). RPCA has been found to enhance the quality of PIV data and to capture most of the kinetic energy of the flow in just two modes similar to the numerical data that are free from measurement noise. The reconstruction errors differ along the cardiac cycle with diastolic flows requiring more modes for accurate reconstruction. In general, modes Φ1-10 are found sufficient to represent the flow field. The results demonstrate that the coherent structures that characterise this aortic dissection flow are described by the first few POD modes suggesting that it is possible to represent the macroscale behaviour of aortic flow in a low-dimensional space; thus significantly simplifying the problem, and allowing for more computationally efficient flow simulations that can pave the way for translation of such models to the clinic.