Search

PediaFlow is a miniature mixed-flow ventricular assist device for neonates and toddlers. PediaFlow has a fully magnetically levitated rotor which improves biocompatibility, but the increased length of the rotor creates a long annular passage where fluid energy is lost. Therefore, a set of helical stator blades was proposed immediately after the impeller stage to remove the swirling flow and recover the dynamic head as static pressure. Automated computational fluid dynamics (CFD) shape optimization of the stator blades was performed to maximize pressure recovery at the operating point of 1.5 LPM and 16,000 RPM. Additionally, the effect on hemolysis and thrombogenicity was assessed using numerical modeling. The optimization algorithm favored fewer blades of greater length over a larger number of short blades. The ratio of wrap angle to axial length emerged as a key constraint to ensure the viability of a design. The best design had 2 blades and generated 73 mmHg of pressure recovery in an isolated stage. When re-introduced to the CFD simulation of the complete flow path, the added stator stage increased the pump head by 46% and improved the pump efficiency from 21.9% to 25.7% at the selected operating point. Automated CFD shape optimization combined with in silico evaluation of hemocompatibility can be an effective tool for exploring design choices and informing early development process.

International audience; Mathematical models of thrombosis are currently used to study clinical scenarios of pathological thrombus formation. As these models become more complex to predict thrombus formation dynamics high computational cost must be alleviated and inherent uncertainties must be assessed. Evaluating model uncertainties allows to increase the confidence in model predictions and identify avenues of improvement for both thrombosis modeling and anti-platelet therapies. In this work, an uncertainty quantification analysis of a multi-constituent thrombosis model is performed considering a common assay for platelet function (PFA-100®). The analysis is facilitated thanks to time-evolving polynomial chaos expansions used as a parametric surrogate for the full thrombosis model considering two quantities of interest; namely, thrombus volume and occlusion percentage. The surrogate is thoroughly validated and provides a straightforward access to a global sensitivity analysis via computation of Sobol' coefficients. Six out of fifteen parameters linked to thrombus consitution, vWF activity, and platelet adhesion dynamics were found to be most influential in the simulation variability considering only individual effects; while parameter interactions are highlighted when considering the total Sobol' indices. The influential parameters are related to thrombus constitution, vWF activity and platelet to platelet adhesion dynamics. The surrogate model allowed to predict realistic PFA-100® closure times of 300,000 virtual cases that followed the trends observed in clinical data. The current methodology could be used including common anti-platelet therapies to identify scenarios that preserve the hematological balance.