This is data for: "Modelling the filtration efficiency of a woven fabric: The role of multiple lengthscales", on arXiv Files are (this is also in README file): 1) FinalFused.tif : stack of slices taken with confocal at Bristol by Ioatzin Rios de Anda. This is the imaging data of the fabric used 2) processDataTo3D_PAPER.py : Python code to analyse 1) to produce mask of fibre voxels needed for LB simulation, by Jake Wilkins 3) LBregionstack.tiff : image stack for region in LB simulations 4) masknx330ny280nz462_t10.txt : mask in right format to be read in to Palabos LB code to specify which voxels are fibre and so need bounce-back 5) Ioatzin3D.cpp : C++ code for Palabos LB. NB need Palabos LB code: https://palabos.unige.ch/, should go in directory "~/palabos-v2.2.0/examples/Ioatzin/3D ". Needs 4) 6) make_pkl.py : converts output of LB code into Python pickled format for .py codes below. 7) IoatzinDarcy_pkl.py : takes pickled output of LB code and computes Darcy k etc 8) traj2_pkledge.py : computes trajectories of particles and so filtration efficiency, needs pickled output of LBC code and 9) 9) lattice_params.yaml : parameter values for 7) and 8) 10) eff_filter_edges.txt : filtration efficiencies computed by 8) WITH inertia 11) eff_filter0Stokes.txt : filtration efficiencies computed by 8) WITHOUT inertia 12) plot_filtration.py : plots 10) and 11) 13) Final_render.mp4 : rotating animation showing region simulated by LB code, by Jake Wilkins 14) alpha_ofz.txt : alpha - fraction of fibres voxels as function of z 15) plot_justalpha.py : plots 14) 16) vtk01.vti : flow field velocity field in vti format - as used by Paraview 17) vel3D.pkl : flow field velocity field in Python's pkl format 18) slice_heatmap.py : produces heatmap of velocities in xy slice through the flow field 19) plot_sigma_streamlines.py : plots Sigma (curvature lengthscale) from 20), 21), 22), 23) 20) stream4.txt: streamline for flow field 21) stream5.txt: streamline for flow field 22) stream6.txt: streamline for flow field 23) stream7.txt: streamline for flow field 24) plot_Stokes.py : plots Stokes number as function of particle diameter 25) 0traj20.0_47.xyz : trajectory in format that Paraview can read 26) intraj20.0_47.xyz : another trajectory 27) streamlines_pkl.py : calculates streamlines, eg 20), 21), 22) and 23) 28) this README file Abstract of that work: During the COVID-19 pandemic, many millions have worn masks made of woven fabric, to reduce the risk of transmission of COVID-19. Masks are essentially air filters worn on the face, that should filter out as many of the dangerous particles as possible. Here the dangerous particles are the droplets containing virus that are exhaled by an infected person. Woven fabric is unlike the material used in standard air filters. Woven fabric consists of fibres twisted together into yarns that are then woven into fabric. There are therefore two lengthscales: the diameters of: (i) the fibre and (ii) the yarn. Standard air filters have only (i). To understand how woven fabrics filter, we have used confocal microscopy to take three dimensional images of woven fabric. We then used the image to perform Lattice Boltzmann simulations of the air flow through fabric. With this flow field we calculated the filtration efficiency for particles around a micrometre in diameter. We find that for particles in this size range, filtration efficiency is low ($\sim 10\%$) but increases with increasing particle size. These efficiencies are comparable to measurements made for fabrics. The low efficiency is due to most of the air flow being channeled through relatively large (tens of micrometres across) inter-yarn pores. So we conclude that our sampled fabric is expected to filter poorly due to the hierarchical structure of woven fabrics.