Craven Brent A, Aycock Kenneth I, Manning Keefe B
Division of Applied Mechanics, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, United States Food and Drug Administration, Silver Spring, MD, USA.
Department of Biomedical Engineering, Pennsylvania State University, University Park, PA, USA.
Cardiovasc Eng Technol. 2018 Dec;9(4):654-673. doi: 10.1007/s13239-018-00392-0. Epub 2018 Nov 16.
The embolus trapping performance of inferior vena cava (IVC) filters critically depends on how emboli flow through the IVC and, thereby, on the underlying hemodynamics. Most previous studies of IVC hemodynamics have used computational fluid dynamics (CFD), but few have validated their results by comparing with quantitative experimental measurements of the flow field and none have validated in an anatomical model of the IVC that includes the primary morphological features that influence the hemodynamics (iliac veins, infrarenal curvature, and non-circular vessel cross-section). In this study, we perform verification and validation of CFD simulations in a patient-averaged anatomical model of the IVC.
Because we are most interested in the fluid dynamics that influence embolus transport and IVC filter embolus trapping, we focus our analyses on the velocity distribution and the amount of swirl and mixing in the infrarenal IVC. A rigorous mesh refinement study is first conducted at the highest flow rate condition to verify the computed solutions. To validate the CFD predictions of the flow patterns, we then compare with particle image velocimetry (PIV) data acquired in the same model in two planes (coronal and sagittal) within the infrarenal IVC at two flow rates corresponding to rest and exercise conditions.
Using unstructured hexahedral meshes ranging in size from 800,000 to 102.5 million computational cells, we demonstrate that a coarse mesh may be used to resolve the gross flow patterns and velocity distribution in the IVC. A finer mesh is, however, required to obtain asymptotic mesh convergence of swirl and mixing in the IVC, as quantified by the local normalized helicity, LNH, and the volume-averaged helicity intensity, [Formula: see text]. Based on the results of the mesh refinement study, we use a moderately fine mesh containing approximately 26 million cells for comparison with experimental data. The validation study demonstrates excellent qualitative agreement between CFD predictions and PIV measurements of the velocity field at both conditions. Quantitatively, we show that the global relative comparison error, E, between CFD and PIV ranges from 3 to 11%. By performing sensitivity studies, we demonstrate that the quantitative discrepancy is attributable to a combination of uncertainty in the inlet flow rates and uncertainty associated with precisely aligning the PIV data with the CFD geometry.
Overall, the study demonstrates mesh-convergent CFD simulations that predict IVC flow patterns that agree reasonably well with PIV data, even at exercise conditions where the flow in the IVC is extremely complex.
下腔静脉（IVC）滤器的栓子捕获性能关键取决于栓子如何流经IVC，进而取决于潜在的血流动力学。此前大多数关于IVC血流动力学的研究都使用了计算流体动力学（CFD），但很少有研究通过与流场的定量实验测量结果进行比较来验证其结果，且没有研究在包含影响血流动力学的主要形态特征（髂静脉、肾下曲率和非圆形血管横截面）的IVC解剖模型中进行验证。在本研究中，我们在患者平均的IVC解剖模型中对CFD模拟进行了验证。
由于我们最感兴趣的是影响栓子运输和IVC滤器栓子捕获的流体动力学，我们将分析重点放在肾下IVC的速度分布以及涡旋和混合量上。首先在最高流速条件下进行了严格的网格细化研究，以验证计算解。为了验证CFD对流动模式的预测，我们随后将其与在同一模型中肾下IVC内两个平面（冠状面和矢状面）在对应静息和运动条件的两种流速下获取的粒子图像测速（PIV）数据进行比较。
使用从800,000到1.025亿个计算单元不等的非结构化六面体网格，我们证明可以使用粗网格来解析IVC中的总体流动模式和速度分布。然而，需要更精细的网格来获得IVC中涡旋和混合的渐近网格收敛，这通过局部归一化螺旋度（LNH）和体积平均螺旋度强度（[公式：见正文]）来量化。基于网格细化研究的结果，我们使用包含约2600万个单元的适度精细网格与实验数据进行比较。验证研究表明，在两种条件下，CFD预测与PIV对速度场的测量之间在定性上具有极好的一致性。在定量方面，我们表明CFD和PIV之间的全局相对比较误差E在3%至11%之间。通过进行敏感性研究，我们证明定量差异归因于入口流速的不确定性以及将PIV数据与CFD几何形状精确对齐相关的不确定性的综合影响。
总体而言，该研究展示了网格收敛的CFD模拟，其预测的IVC流动模式与PIV数据相当吻合，即使在IVC内血流极其复杂的运动条件下也是如此。
