Brent A Craven1, Kenneth I Aycock2, Keefe B Manning3. 1. 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. brent.craven@fda.hhs.gov. 2. 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. 3. Department of Biomedical Engineering, Pennsylvania State University, University Park, PA, USA.
Abstract
PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.