Finite Element Modeling of NiTiNol in Transcatheter Aortic Valve Implantation: Assessing Material Influence on Simulation Reliability

Verification, Validation, and Uncertainty Quantification of NiTiNol Behavior in TAVI Computational Modeling

Master Thesis (2025)
Author(s)

L. Ferilli (TU Delft - Mechanical Engineering)

Contributor(s)

Mohammad J. Mirzaali Mazandarani – Mentor (TU Delft - Biomaterials & Tissue Biomechanics)

Behrooz Fereidoonnezhad – Mentor (TU Delft - Medical Instruments & Bio-Inspired Technology)

Nils Götzen – Mentor (4RealSim)

Ali C. Akyildiz – Graduation committee member (TU Delft - Medical Instruments & Bio-Inspired Technology)

Faculty
Mechanical Engineering
More Info
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Publication Year
2025
Language
English
Graduation Date
07-04-2025
Awarding Institution
Delft University of Technology
Programme
['Biomedical Engineering | Neuromusculoskeletal Biomechanics']
Faculty
Mechanical Engineering
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Abstract

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.

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