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.
复制标题
通过稳健适当正交分解实现降阶模型，以捕获个性化的主动脉血流动力学。
DOI：
10.1016/j.jbiomech.2023.111759
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发表时间：
2023
影响因子：
2.4
通讯作者：
Chatpattanasiri C
中科院分区：
文献类型：
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作者：
Chatpattanasiri C
数据驱动的降阶建模在解决与计算和实验血液动力学模型相关的挑战方面显示出了希望。在这项工作中，我们专注于使用降阶模型（ROM）重建速度场在患者特定的夹层主动脉，其目的是比较从鲁棒正交分解（RPOD）获得的ROM从传统的正交分解（POD）。POD和RPOD适用于在体外，血流动力学数据采集的粒子图像测速和比较的分解流来自计算流体动力学（CFD）的数据相同的几何形状和流动条件。在这项工作中，PIV和CFD结果作为临床血液动力学数据（如MR）的替代品，有助于证明ROMS在真实的临床场景中的潜在用途。使用不同数量的POD模式重建血流，并将整个心动周期获得的血流特征与原始全阶模型（FOM）进行比较。RPOD的第一步鲁棒主成分分析（RPCA）已被发现可以提高PIV数据的质量，允许POD仅以与没有测量噪声的数值数据类似的两种模式捕获流的大部分动能。重建误差沿心动周期沿着不同，舒张期血流需要更多模式以进行准确重建。一般情况下，发现模式1-10足以代表流场。结果表明，描述主动脉夹层流动的相干结构由前几个POD模式描述，这表明在低维空间中表示主动脉流动的宏观行为是可能的;从而显著地简化了问题，并允许更有效的计算流量模拟或基于机器学习的流量预测，这可以为将这些模型转化为诊所
Data driven, reduced order modelling has shown promise in tackling the challenges associated with computational and experimental haemodynamic models. In this work, we focus on the use of Reduced Order Models (ROMs) to reconstruct velocity fields in a patient-specific dissected aorta, with the objective being to compare the ROMs obtained from Robust Proper Orthogonal Decomposition (RPOD) to those obtained from the traditional Proper Orthogonal Decomposition (POD). POD and RPOD are applied toin vitro, haemodynamic 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. In this work, PIV and CFD results act as surrogates for clinical haemodynamic data e.g. MR, helping to demonstrate the potential use of ROMS in real clinical scenarios. 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).Robust Principal Component Analysis (RPCA), the first step of RPOD, has been found to enhance the quality of PIV data, allowing POD 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 1–10 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 or machine learning based flow predictions that can pave the way for translation of such models to the clinic.