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This work intends to study the effect of aortic annulus eccentricity and leaflet rigidity on the performance, thrombogenic risk and calcification risk in bioprosthetic aortic valve replacements (BAVRs). To address these questions, a two‐way immersed fluid–structure interaction (FSI) computational model was implemented in a high‐performance computing (HPC) multi‐physics simulation software, and validated against a well‐known FSI benchmark. The aortic valve bioprosthesis model is qualitatively contrasted against experimental data, showing good agreement in closed and open states. Regarding the performance of BAVRs, the model predicts that increasing eccentricities yield lower geometric orifice areas (GOAs) and higher normalized transvalvular pressure gradients (TPGs) for healthy cardiac outputs during systole, agreeing with in vitro experiments. Regions with peak values of residence time are observed to grow with eccentricity in the sinus of Valsalva, indicating an elevated risk of thrombus formation for eccentric configurations. In addition, the computational model is used to analyze the effect of varying leaflet rigidity on both performance, thrombogenic and calcification risks with applications to tissue‐engineered prostheses. For more rigid leaflets it predicts an increase in systolic and diastolic TPGs, and decrease in systolic GOA, which translates to decreased valve performance. The peak shear rate and residence time regions increase with leaflet rigidity, but their volume‐averaged values were not significantly affected. Peak solid stresses are also analyzed, and observed to increase with rigidity, elevating risk of valve calcification and structural failure. To the authors' knowledge this is the first computational FSI model to study the effect of eccentricity or leaflet rigidity on thrombogenic biomarkers, providing a novel tool to aid device manufacturers and clinical practitioners. [ABSTRACT FROM AUTHOR]

Coronary obstruction is a complication that may affect patients receiving Transcatheter Aortic Valve Replacement (TAVR), with catastrophic consequences and long-term negative effects. To enable healthy coronary perfusion, it is fundamental to appropriately position the device with respect to the coronary ostia. Nonetheless, most TAVR delivery systems do not control commissural alignment to do so. Moreover, no in silico study has directly assessed the effect of commissural alignment on coronary perfusion. This work aims to evaluate the effect of TAVR commissural alignment on coronary perfusion and device performance. A two-way computational fluid-structure interaction model is used to predict coronary perfusion at different commissural alignments. Moreover, in each scenario, hemodynamic biomarkers are evaluated to assess device performance. Commissural misalignment is shown to reduce the total coronary perfusion by − 3.2 % and the flow rate to a single coronary branch by − 6.8 %. It is also observed to impair valvular function by reducing the systolic geometric orifice area by − 2.5 % and increasing the systolic transvalvular pressure gradients by + 5.3 % and the diastolic leaflet stresses by + 16.0 %. The present TAVR patient model indicates that coronary perfusion, hemodynamic and structural performance are minimized when the prosthesis commissures are fully misaligned with the native ones. These results support the importance of enabling axial control in new TAVR delivery catheter systems and defining recommended values of commissural alignment in upcoming clinical treatment guidelines. • A fluid-structure interaction model of TAVRs was used to analyze the effect of commissural alignment on coronary perfusion. • Commissural misalignment reduced total coronary perfusion by -3.2% and up to -6.8% in a single coronary outlet. • No significant effects of commissural alignment on thrombogenic risk were observed. • Misalignment reduced the systolic orifice area by -2.5% and increased transvalvular pressure gradients by +5.3%. • Results support the importance of enabling axial control in novel TAVR catheter delivery systems. [ABSTRACT FROM AUTHOR]

Validating numerical models against experimental models of nasal spray deposition is challenging since many aspects must be considered. That being said, it is a critical step in the product development process of nasal spray devices. This work presents the validation process of a nasal deposition model, which demonstrates a high degree of consistency of the numerical model with experimental data when the nasal cavity is segmented into two regions but not into three. Furthermore, by modelling the flow as stationary, the computational cost is drastically reduced while maintaining quality of particle deposition results. Thanks to this reduction, a sensitivity analysis of the numerical model could be performed, consisting of 96 simulations. The objective was to quantify the impact of four inputs: the spray half cone angle, mean spray exit velocity, breakup length from the nozzle exit and the diameter of the nozzle spray device, on the three quantities of interest: the percentage of the accumulated number of particles deposited on the anterior, middle and posterior sections of the nasal cavity. The results of the sensitivity analysis demonstrated that the deposition on anterior and middle sections are sensitive to injection angle and breakup length, and the deposition on posterior section is only, but highly, sensitive to the injection velocity. [Display omitted] [ABSTRACT FROM AUTHOR]

Validating numerical models against experimental models of nasal spray deposition is challenging since many aspects must be considered. That being said, it is a critical step in the product development process of nasal spray devices. This work presents the validation process of a nasal deposition model, which demonstrates a high degree of consistency of the numerical model with experimental data when the nasal cavity is segmented into two regions but not into three. Furthermore, by modelling the flow as stationary, the computational cost is drastically reduced while maintaining quality of particle deposition results. Thanks to this reduction, a sensitivity analysis of the numerical model could be performed, consisting of 96 simulations. The objective was to quantify the impact of four inputs: the spray half cone angle, mean spray exit velocity, breakup length from the nozzle exit and the diameter of the nozzle spray device, on the three quantities of interest: the percentage of the accumulated number of particles deposited on the anterior, middle and posterior sections of the nasal cavity. The results of the sensitivity analysis demonstrated that the deposition on anterior and middle sections are sensitive to injection angle and breakup length, and the deposition on posterior section is only, but highly, sensitive to the injection velocity. [Display omitted] [ABSTRACT FROM AUTHOR]

Targeted nasal drug delivery can provide improved efficacy for drug formulations to be delivered at high efficacy rates. Some parameters that influence drug delivery have a dependency on the patient's technique of administration and the spray device itself. When the different parameters, each having a specific range of values are combined, the combinatory permutations for studying its effects on particle deposition become large. In this study, we combine six input spray parameters (the spray half-cone angle, the mean spray exit velocity, the breakup length from the nozzle exit, the diameter of the nozzle spray device, the particle size, and the sagittal angle of the spray) with a range of values to produce 384 combinations of spray characteristics. This was repeated for three inhalation flow rates of 20, 40, and 60 L/min. To reduce the computational costs of a full transient Large Eddy Simulation flow field, we create a time-averaged frozen field and perform the time integration of particle trajectories through the flow field to determine the particle deposition in four anatomical regions of the nasal cavity (anterior, middle, olfactory and posterior) for each of the 384 spray field. A sensitivity analysis determined the significance of each input variable on the deposition. It was found the particle size distribution significantly affected deposition in the olfactory and posterior regions, while the spray device insertion angle was significant for deposition in the anterior and middle regions. Five machine learning models were evaluated based on 384 cases and it was found that despite the small sample dataset the simulation data was sufficient to provide accurate machine-learning predictions. [Display omitted] [ABSTRACT FROM AUTHOR]