This study investigates the impact of shape-memory alloy (NiTiNol) parameter variability on the predictive reliability of finite element (FE) models for transcatheter aortic valve implantation (TAVI). A fully verified and validated FE model was developed following ASME V&V 40 guidelines, integrating experimental tests, numerical verification, and uncertainty quantification to isolate material effects from numerical artifacts. A Monte Carlo parameter fitting approach was employed using three independent tensile datasets to calibrate the NiTiNol model, resulting in a quantified uncertainty range of ±5.34%. Simulation results revealed that only Austenite Young’s modulus significantly influenced the mechanical response under physiological loading, while other transformation parameters had negligible effects. In contrast, literature-derived parameters introduced up to 30% deviation, highlighting the inadequacy of non-case-specific data. This work emphasizes the critical need for validated material parameters to ensure simulation credibility, particularly in clinical and regulatory contexts where simulation outcomes increasingly inform patient-specific treatment and device design.

Congenital heart disease (CHD) affects almost 1% of newborns. Right ventricular outflow tract (RVOT) CHD affects 20% of newborns and includes anomalies such as tetralogy of Fallot (TOF) with or without pulmonary atresia, transposition of the great vessels, and truncus arteriosus. All these anomalies require RVOT reconstruction. Prosthetic heart valves are needed to improve the quality of life of patients suffering from CHD. One such prosthetic device is the pulmonary valved Conduit developed by XeltisTM. This study aimed to investigate the difference in the mechanical response of the XeltisTM pulmonary valved Conduit sizes 16, 18, and 20 (XPV16, XPV18, and XPV20) using mechanical experiments and a predictive finite element model.
Experiments of two load cases, Leaflet opening behavior (LC1) and parallel compression (LC2) have been done where measurements were taken for input parameters used for uncertainty quantification (UQ) in the FE model. Furthermore, the reaction force and displacement were measured to calculate the force values and stiffness values of the device during each experiment. The experiments were replicated with a developed FE model. From the results of the FE model, a metamodel (MM) was developed and a Monte Carlo simulation was performed to retrieve a distribution of the force values and stiffness values obtained in the simulation. Furthermore, UQ was performed and the sensitivity of the input parameters on the force values and stiffness values were quantified. Finally, with an area metric the accuracy of computational finite element (FE) models in simulating the mechanical response observed in the experiments of the XeltisTM pulmonary valved Conduit size 16, 18, and 20 mm was quantified.
For the Leaflet opening behavior, the reaction force increases when the device size increases while the stiffness doesn’t change with device size. As for the accuracy of the predictive FE model, the predictive FE model can simulate the mechanical response of the stiffness with an accuracy of at least 70.7% for the reaction force and at least 50.3% for the stiffness.
For the parallel plate compression, the reaction force and the stiffness increase when the XPV size decreases from size 18 to size 16. Furthermore, the difference between the XPV18 and the XPV20 is smaller for both the reaction force and the stiffness. As for the accuracy of the predictive FE model, the predictive FE model can simulate the mechanical response of the stiffness with an accuracy of at least 37.3% for the reaction force and at least 38.1% for the stiffness.
As for the important variables influencing the mechanical response, the reaction force and the stiffness are influenced mostly by the fiber stiffness of the component that is subjected to the load. Furthermore, the reaction force and stiffness are also influenced by the direction of the fibers. If the fibers are in the same direction as the load, the reaction force and stiffness increase. Although specifically for the Leaflet the amount of material has more influence on the reaction force and stiffness for larger XPV sizes with the Leaflet opening behavior load case. This indicates that for larger XPV sizes the material properties of the Leaflet have a smaller influence and the Leaflet geometry has a higher influence on the stiffness of the Leaflet opening.
Improvements are possible for a better agreement between the simulation and the experiment. These include performing more experiments with different samples from different production batches, and better estimation of input parameters with a high sensitivity to the output parameters.
This study provides a step in the direction of predictive computational device modeling that will help shorten the development time of new pulmonary heart valve devices. As the devices in this study are designed for pediatrics, this will help improve the quality of life of pediatrics suffering from congenital heart disease.