In a transcatheter aortic valve implantation (TAVI) procedure, the native aortic valve is replaced by an artificial valve prosthesis. One of the objectives of SIMCor is to develop a simulation workflow to estimate TAVI treatment effects on large virtual patient cohorts. As part of this simulation workflow, this deliverable presents a fast-to-evaluate TAVI deployment model that provides quantitative insights into the positioning of the implanted device and its effect on post-implantation flow characteristics. The fast-to-evaluate model derives its computational performance from two key simulation technologies, viz.: (i) a multi-patch non-uniform rational b-splines (NURBS)-based anatomical model, and (ii) a time-dependent discrete contact mechanics model. The NURBS-based geometry description provides a parameterisation of the aorta and the aortic leaflets, from which a set of potential contact points is obtained. The time-dependent mechanical contact model, which solves for the balance of forces, determines the contact points between the TAVI device and the aorta at each time step. The performance of the developed fast-to-evaluate model is assessed through a series of numerical experiments. A comparison with a high-fidelity finite element simulation (part of D8.6 - Report on 3D finite element simulation (PHI, M24)) is presented in this report, demonstrating the ability of the fast- to-evaluate model to predict the global characteristics of the TAVI procedure. Furthermore, a sensitivity analysis of the most important model parameters is considered to demonstrate the robustness of the fast-to-evaluate model. Furthermore, the developed model is used to study the impact of leaflet calcification on the TAVI procedure. Paravalvular leakage areas are compared between a case with and a case without leaflet calcification. The results demonstrate the capability of the fast-to-evaluate model to distinguish between such cases. SIMCor (In-Silico testing and validation of Cardiovascular IMplantable devices) has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 101017578.