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Background and objective: Wall shear stress (WSS) has been known to play a critical role in the development of several complications following coronary artery stenting, including in-stent restenosis and thrombosis. Computational fluid dynamics is often used to quantify the post-stenting WSS, which may potentially be used as a predictive metric. However, large-scale studies for WSS-based risk stratification often neglect the footprint of the stent due to reconstruction challenges. The primary objective of this study is to statistically evaluate the impact of the stent footprints (Xience and Resolute stents) on the computed endothelial WSS and quantitatively identify the relationship between these local hemodynamic alterations and the global properties of the vessel, such as curvature, on WSS. The ultimate goal is to evaluate whether and when it is worth including the footprint of the stent in an in-silico study to compute the WSS reliably. Methods: A previously developed semi-automated reconstruction approach for patient-specific coronaries was employed as a part of the SHEAR-STENT trial. A subset of patients was analyzed (N=30), and CFD simulations were performed with and without the stent to evaluate the impact of the stent footprint on WSS. Due to the computationally expensive nature of transient analyses, a sub-cohort of ten patients were used to assess the reliability of WSS obtained from steady computations as a surrogate for the time-averaged results. Global and local vessel curvature data were extracted for all cases and evaluated against stent-induced alterations in the WSS. The differences between the Xience and Resolute stent platforms were also examined to quantify each stent's unique WSS footprint. Results: Results from the surrogate analysis indicate that steady WSS serves as an excellent approximation of the time-averaged computations. The presence of either stent footprint causes a statistically significant decrease in the space-averaged WSS, and a significant increase in the endothelial regions exposed to very low WSS as well (<0.5 Pa). Negative correlations were observed between vessel curvature and WSS differences, indicating that macroscopic vessel characteristics play a more prominent role in determining endothelial WSS at higher curvature values. In our pool of cases, comparison of Xience and Resolute stents revealed that the Resolute platform seems to lead to lower space-averaged WSS and an increase in areas of very low WSS. Conclusion: These results outline (1) the necessity of including the stent footprint for accurate in-silico WSS analysis; (2) the global features of stented arteries serving as the dominant determinant of WSS past a certain curvature threshold; and (3) the Xience stent resulting in a milder presence of hemodynamically unfavorable WSS regions compared to the Resolute stent. Keywords: Computational fluid dynamics; Drug-eluting stents; In-silico clinical trials; Percutaneous coronary intervention; Wall shear stress. 

Reduced-order models (ROMs) have achieved a lot of success in reducing the computational cost of traditional numerical methods across many disciplines. In fluid dynamics, ROMs have been successful in providing efficient and relatively accurate solutions for the numerical simulation of laminar flows. For convection-dominated (e.g., turbulent) flows, however, standard ROMs generally yield inaccurate results, usually affected by spurious oscillations. Thus, ROMs are usually equipped with numerical stabilization or closure models in order to account for the effect of the discarded modes. The literature on ROM closures and stabilizations is large and growing fast. In this paper, instead of reviewing all the ROM closures and stabilizations, we took a more modest step and focused on one particular type of ROM closure and stabilization that is inspired by large eddy simulation (LES), a classical strategy in computational fluid dynamics (CFD). These ROMs, which we call LES-ROMs, are extremely easy to implement, very efficient, and accurate. Indeed, LES-ROMs are modular and generally require minimal modifications to standard (“legacy”) ROM formulations. Furthermore, the computational overhead of these modifications is minimal. Finally, carefully tuned LES-ROMs can accurately capture the average physical quantities of interest in challenging convection-dominated flows in science and engineering applications. LES-ROMs are constructed by leveraging spatial filtering, which is the same principle used to build classical LES models. This ensures a modeling consistency between LES-ROMs and the approaches that generated the data used to train them. It also “bridges” two distinct research fields (LES and ROMs) that have been disconnected until now. This paper is a review of LES-ROMs, with a particular focus on the LES concepts and models that enable the construction of LES-inspired ROMs and the bridging of LES and reduced-order modeling. This paper starts with a description of a versatile LES strategy called evolve–filter–relax (EFR) that has been successfully used as a full-order method for both incompressible and compressible convection-dominated flows. We present evidence of this success. We then show how the EFR strategy, and spatial filtering in general, can be leveraged to construct LES-ROMs (e.g., EFR-ROM). Several applications of LES-ROMs to the numerical simulation of incompressible and compressible convection-dominated flows are presented. Finally, we draw conclusions and outline several research directions and open questions in LES-ROM development. While we do not claim this review to be comprehensive, we certainly hope it serves as a brief and friendly introduction to this exciting research area, which we believe has a lot of potential in the practical numerical simulation of convection-dominated flows in science, engineering, and medicine. 

Abstract Numerical simulations for computational hemodynamics in clinical settings require a combination of many ingredients, mathematical models, solvers and patient-specific data. The sensitivity of the solutions to these factors may be critical, particularly when we have a partial or noisy knowledge of data. Uncertainty quantification is crucial to assess the reliability of the results. We present here an extensive sensitivity analysis in aortic flow simulations, to quantify the dependence of clinically relevant quantities to the patient-specific geometry and the inflow boundary conditions. Geometry and inflow conditions are generally believed to have a major impact on numerical simulations. We resort to a global sensitivity analysis, (i.e., not restricted to a linearization around a working point), based on polynomial chaos expansion (PCE) and the associated Sobol' indices. We regard the geometry and the inflow conditions as the realization of a parametric stochastic process. To construct a physically consistent stochastic process for the geometry, we use a set of longitudinal-in-time images of a patient with an abdominal aortic aneurysm (AAA) to parametrize geometrical variations. Aortic flow is highly disturbed during systole. This leads to high computational costs, even amplified in a sensitivity analysis -when many simulations are needed. To mitigate this, we consider here a large Eddy simulation (LES) model. Our model depends in particular on a user-defined parameter called filter radius. We borrowed the tools of the global sensitivity analysis to assess the sensitivity of the solution to this parameter too. The targeted quantities of interest (QoI) include: the total kinetic energy (TKE), the time-average wall shear stress (TAWSS), and the oscillatory shear index (OSI). The results show that these indexes are mostly sensitive to the geometry. Also, we find that the sensitivity may be different during different instants of the heartbeat and in different regions of the domain of interest. This analysis helps to assess the reliability of in silico tools for clinical applications. 

Kidney dialysis is the most widespread treatment method for end-stage renal disease, a debilitating health condition common in industrialized societies. While ubiquitous, kidney dialysis suffers from an inability to remove larger toxins, resulting in a gradual buildup of these toxins in dialysis patients, ultimately leading to further health complications. To improve dialysis, hollow fibers incorporating a cell-monolayer with cultured kidney cells have been proposed; however, the design of such a fiber is nontrivial. In particular, the effects of fluid wall-shear stress have an important influence on the ability of the cell layer to transport toxins. In the present work, we introduce a model for cell-transport aided dialysis, incorporating the effects of the shear stress. We analyze the model mathematically and establish its well-posedness. We then present a series of numerical results, which suggest that a hollow-fiber design with a wavy profile may increase the efficiency of the dialysis treatment. We investigate numerically the shape of the wavy channel to maximize the toxin clearance. These results demonstrate the potential for the use of computational models in the study and advancement of renal therapies.