Speaker
Description
Understanding fluid dynamics within porous materials is fundamental to a wide range of critical applications, from the design of geo-energy systems, such as subsurface hydrogen storage, to electrochemical devices. Accurate flow modelling remains challenging due to the inherently multiscale and dynamic nature of these systems, for instance in multiphase and viscoelastic flows, resulting in high computational costs and significant physical uncertainties. To complement modelling efforts, experimental techniques provide direct access to pore-scale flow behaviour but introduce their own challenges and limitations. For example, optical Lagrangian particle tracking enables direct measurement of flow patterns near pore walls using tracer particles, but remains restricted to transparent systems.
X-ray computed tomography methods offer a non-destructive means to access the fluid dynamics inside opaque media. By acquiring X-ray projection images from many viewing angles, high-resolution time-resolved 3D reconstructions of a sample’s interior can be generated. In porous media, such time-resolved reconstructions have been widely applied to investigate evolving fluid distributions, interfaces, and displacement mechanisms at the pore scale (Berg et al., 2012; Scanziani et al., 2018). These capabilities also enable the precise tracking of tracer microparticles, which has been demonstrated more recently, using silver-coated hollow glass tracers to investigate single and multiphase flows in opaque porous media (Bultreys et al., 2022, 2024). Despite these advances, a key limitation of existing reconstruction algorithms is the assumption of negligible motion during acquisition, which is clearly invalid for these measurements and leads to motion-blur artifacts that obscure dynamic pore-scale flow phenomena. These artifacts degrade tracking capabilities for particles with velocities exceeding approximately 1 µm/s, preventing the investigation of faster flow regimes crucial to industrial processes.
To overcome this temporal resolution limitation and capture faster pore-scale dynamics, we have developed a novel iterative 4D tomographic reconstruction algorithm. By explicitly accounting for particle motion during image acquisition, the method recovers 3D particle trajectories directly from raw tomography data, enabling artifact-free reconstruction of fast-moving particles. This is achieved through iterative refinement of candidate particle trajectories, enabled by comparison of experimental measurements with projections generated through forward simulation of the X-ray acquisition process. We implemented this algorithm in a new GPU-accelerated, PyTorch-based software package named CTracks.
We demonstrate the resulting improvement in temporal resolution using both simulated and experimental flows, tracking particles in porous media at velocities up to five times higher than those accessible with classical methods. This achievement, together with future developments including more complex motion models, improved data processing workflows, and refined experimental configurations, will extend the achievable temporal resolution towards faster, unsteady 3D flows while maintaining micrometer-scale measurement capabilities. These advances will enable microparticle velocimetry to inform continuum-scale flow models through measurements of pore-scale dynamics across a broader range of flow conditions.
| References | Berg, S. et al., 2012. Real-time 3D imaging of Haines jumps in porous media flow. Proceedings of the National Academy of Sciences, 110(10), 3755-3759. https://doi.org/10.1073/pnas.1221373110; Bultreys, T. et al., 2022. X-ray tomographic micro-particle velocimetry in porous media. Physics of Fluids, 34(4), 042008. https://doi.org/10.1063/5.0088000; Bultreys, T. et al., 2024. 4D microvelocimetry reveals multiphase flow field perturbations in porous media. Proceedings of the National Academy of Sciences, 121(12), e2316723121. https://doi.org/10.1073/pnas.2316723121; Scanziani, A. et al., 2018. In situ characterization of immiscible three-phase flow at the pore scale for a water-wet carbonate rock. Advances in Water Resources, 121, p446-455. https://doi.org/10.1016/j.advwatres.2018.09.010. |
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| Country | Belgium |
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