“A mi gran compañero Miguel A mis padres y mejores amigos A mi abuela, hermano y Candela A mi segunda familia durante estos tres años: Panos, Ángela, Aza, Iván, Ailin, Carmen, Burbuleta, Inja y Marzia A Coco, Paco y Triana” Publications and presentations from this thesis Publications in scientific journals Anderson DD, Thomas TP, Marin AC, Elkins JM, Lack WD, Lacroix D. “Computational techniques for the assessment of fracture repair”. Injury, 45, pp.S23-S31, 2014. A. Campos Marin and D. Lacroix, “The inter-sample structural variability of regular tissue-engineered scaffolds significantly affects the micromechanical local cell environment,” Interface Focus, vol. 5, pp. 20140097–20140097, 2015. Book chapters A. Campos Marin and D. Lacroix. Towards a new approach to analyse quality control and morphometric variability in a scaffold. Book ‘Multiscale Mechanobiology in Tissue Engineering’ published by Springer. Under review.
site. It includes scaffold geometry verification to ensure that the fabrication process delivers the expected scaffold design. 42 Figure 32 PCL scaffold design specifications. 43 Figure 33 The of µCT-images (left) was segmented to obtain the scaffold geometry (right) within the raw data. 44 Figure 34 a) 3D CAD model of the scaffold. b) Side view. 45 Figure 35 Physical boundaries of the fluid volume mesh. 46 Figure 36 Wire representation of the fluid mesh to observe the adaptation of the element size towards the scaffold. 47 Figure 37 The local velocity (a) and WSS (b) values extracted in CFD-Post and then arranged and plotted by layers with a Matlab code. 49 Figure 38 Velocity profiles extracted from all scaffold levels. 50 Figure 39 Structural comparison between CAD (a) and a µCT-reconstructed scaffold (b). Cross-sections from planes A (i) and B (ii) show the internal spatial arrangement of fibres. The two scaffolds are compared at all layers so the structural limitations of the fabrication process can be identified. 52 Figure 310 Internal micro-structure shown at the cross-sections specified in Figure 39 of the sample with different fibre distribution from the other samples. 53 Figure 311 Scaffold irregularities due to the punching process in a scaffold. 53 Figure 312 Velocity profile on a scaffold Fluid velocity data is extracted from line 1 which crosses a series of scaffold pores. 54 Figure 313 Effect of the element size within the scaffold region on the local fluid velocities. The data is extracted from line 1 shown in Figure 312. 55 Figure 314 Effect of the element size on the average WSS at the scaffold surface. 55 Figure 315 Effect of the element size outside the scaffold on the fluid velocities in the scaffold pores. Data extracted from line 1. 57 Figure 316 Line 2 from where the velocity values were extracted. 57 reconstructed
µCT-images reconstruction The µCT images data were reconstructed with Simpleware. A median filter was applied to the raw images to reduce the noise level and then the images were segmented to extract the scaffold region. The mask created in the segmentation process was smoothed using the recursive Gaussian filter and a final binarisation filter was applied to the mask in order to eliminate the connection between the background images and the segmented mask. Then, a surface triangular mesh was generated to represent the µCT-based scaffold geometry. The surface mesh was exported in STL format. This reconstruction process is explained with more details in section 3.2.1.2. Fluid volume mesh generation The STL mesh of the trimmed scaffold was imported into ICEM Ansys and located inside a CAD-based rectangular channel following the specifications of the experimental microfluidic chamber (see Figure 43). The fluid domain was meshed with tetrahedral elements using the robust octree algorithm (see section 3.2.1.5). Mesh sensitivity analysis was carried out by calculating the impact of the maximum length of the edge of the triangular faces of the tetrahedral elements inside the scaffold region and the maximum length for the elements outside it on velocity values (see results in section 4.3.1.2.1). As a result, around 4 million elements represented the fluid domain with maximum element length of 20 µm inside the scaffold and maximum length of 0.1 mm outside it. Despite of the fact that the elements outside the scaffold can be up to five times bigger than the ones inside it, the meshing algorithm applied provides a smooth transition from the elements far from the scaffold to the elements close to it. Figure Geometrical boundary conditions of the CFD model.
Figure Boundary conditions of the CFD model. In the dynamic seeding cells are injected at the inlet whereas in the static seeding they are injected from the green surface on top of the chamber where the scaffold is located. The fluid velocity is imposed at the inlet and outlet boundaries with a UDF to alternate the direction of the flow in the dynamic seeding. Regarding the cell-scaffold interactions, cells were trapped in the scaffold wall boundary as soon as they contacted the scaffold wall. The same condition was applied in the chamber walls with the exception of the inlet and outlet boundaries where the cells were subjected to reflection. The coordinates of the trapped cells in the scaffold surface and chamber walls were registered in text files during simulations for further post-processing. Numerical calculations For the static seeding, the fluid flow was previously resolved and then the cells were advanced every second in the steady flow until they were trapped either in the scaffold or at the bottom of the chamber. In the dynamic seeding, transient simulations were applied to inject cells and calculate cell path along the fluid domain. At every time step, the continuous phase was calculated with 100 iterations which was sufficient for the residuals to stabilise. Then, the cells were advanced in the resolved fluid flow at the end of the time step. The time step during the injection time was set to every 15, 1.25 and 0.35 seconds for the 12, 120 and 600 µl/min flow rate cases respectively. Once the injections were completed, the time step was set to 2,500, 250 and 50 seconds corresponding to the time to change the direction of the flow. Two hours were simulated as that was the seeding time employed in the laboratory experiments. These models were resolved in Fluent Ansys 15.0. The continuum phase was solved using the pressure-based coupled solver to calculate the continuous phase where the continuity and momentum equations are solved simultaneously. The second