Research data supporting: "Learning rheological parameters of non-Newtonian fluids from velocimetry data"

This dataset contains 3D flow-MRI data of steady axisymmetric laminar jet flow of a shear-thinning fluid. The research data supports Kontogiannis, A., Hodgkinson, R., Reynolds, S., Manchester, E. L. "Learning rheological parameters of non-Newtonian fluids from velocimetry data", available at https://doi.org/10.48550/arXiv.2408.02604, 2024. The flow MRI experiment is described in section 3 of that paper. The 3D velocity data of the axisymmetric confined laminar jet of a shear-thinning fluid have been obtained from flow-MRI data on a mid-plane (2D) slice. We first straighten and centre the flow-MRI 2D image, and, since the flow is axisymmetric, we mirror-average the image to further increase the signal-to-noise ratio and enforce (exact) mirror-symmetry. The 2D (slice) axisymmetric flow-MRI data (piecewise constant approximation - pixels) are axisymmetrically L^2-projected on the 3D space of trilinear finite elements (piecewise linear approximation). The files (u_x,u_y,u_z).npy contain the 3D velocity field components along the x,y, and z direction, respectively. The mask.npy provides a mask for the region of interest, which is the sudden expansion part of the Food and Drug Administration (FDA) nozzle. The code "RUNME . py" is a Python file that converts these npy files to centimetres-grammes-seconds (cgs) dimensions and plots slice number 'y_slice_idx' of the data to screen.