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1. Tomographic X-ray particle tracking velocimetry: Proof-of-concept in a creeping flow

   https://doi.org/10.1007/s00348-021-03362-w  

   Mäkiharju, Simo A. ; Dewanckele, Jan ; Boone, Marijn ; Wagner, Christian ; Griesser, Andreas ( December 2021 , Experiments in Fluids)

   We investigate the feasibility of in-laboratory tomographic X-ray particle tracking velocimetry (TXPTV) and consider creeping flows with nearly density matched flow tracers. Specifically, in these proof-of-concept experiments we examined a Poiseuille flow, flow through porous media and a multiphase flow with a Taylor bubble. For a full 360$$^\circ$$${}^{\circ }$computed tomography (CT) scan we show that the specially selected 60 micron tracer particles could be imaged in less than 3 seconds with a signal-to-noise ratio between the tracers and the fluid of 2.5, sufficient to achieve proper volumetric segmentation at each time step. In the pipe flow, continuous Lagrangian particle trajectories were obtained, after which all the standard techniques used for PTV or PIV (taken at visible wave lengths) could also be employed for TXPTV data. And, with TXPTV we can examine flows inaccessible with visible wave lengths due to opaque media or numerous refractive interfaces. In the case of opaque porous media we were able to observe material accumulation and pore clogging, and for flow with Taylor bubble we can trace the particles and hence obtain velocities in the liquid film between the wall and bubble, with thickness of liquid film itself also simultaneously obtained from the volumetric reconstruction after segmentation. While improvements in scan speed are anticipated due to continuing improvements in CT system components, we show that for the flows examined even the presently available CT systems could yield quantitative flow data with the primary limitation being the quality of available flow tracers.

    

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2. Why is Denali (6,194 m) so big? Caught inside the tectonic wake of a migrating restraining bend

   https://doi.org/10.1111/ter.12571  

   Benowitz, Jeff A. ; Cooke, Michele L. ; Terhune, Patrick J. ; Herreid, Sam ; Bemis, Sean P. ; Toeneboehn, Kevin ; Waldien, Trevor S. ; O'Sullivan, Paul B. ( November 2021 , Terra Nova)

   Topography along strike‐slip fault restraining bends is theoretically self‐limited by erosion, block translation and the expected abandonment of fault bends. However, Denali (6,194 m) and Foraker (5,304 m) are located within a restraining bend of the dextral Denali Fault system. We reveal the role of bend evolution in mountain building with physical experiments scaled to simulate the Alaska Mount McKinley restraining bend (MMRB). Despite the natural complexity of the MMRB, first‐order patterns (of strike‐slip separation rates, uplift and lateral bend migration) from the geologic data align with patterns from scaled experiments. Thermochronology, seismicity, and slip rate data show that the persistence of a single Denali Fault strand through the ~6 Ma MMRB is facilitated by simultaneous advection of crust through the bend and migration of the eastern vertex of the bend.

    

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3. Observing flow of He II with unsupervised machine learning

   https://doi.org/10.1038/s41598-022-21906-w  

   Wen, X. ; McDonald, L. ; Pierce, J. ; Guo, W. ; Fitzsimmons, M. R. ( November 2022 , Scientific Reports)

   Time dependent observations of point-to-point correlations of the velocity vector field (structure functions) are necessary to model and understand fluid flow around complex objects. Using thermal gradients, we observed fluid flow by recording fluorescence of$${\text{He}}_{2}^{*}$$${\text{He}}_2^{\ast }$excimers produced by neutron capture throughout a ~ cm³volume. Because the photon emitted by an excited excimer is unlikely to be recorded by the camera, the techniques of particle tracking (PTV) and particle imaging (PIV) velocimetry cannot be applied to extract information from the fluorescence of individual excimers. Therefore, we applied an unsupervised machine learning algorithm to identify light from ensembles of excimers (clusters) and then tracked the centroids of the clusters using a particle displacement determination algorithm developed for PTV.

    

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4. Experimentally validated x-ray image simulations of 50 μm x-ray PIV tracer particles

   https://doi.org/10.1088/1361-6501/ac4c0d  

   Parker, Jason T ; Mäkiharju, Simo A ( February 2022 , Measurement Science and Technology)

   Abstract We evaluate Beer–Lambert (BL) ray-tracing and Monte Carlo N-Particle (MCNP) photon tracking simulations for prediction and comparison of x-ray imaging system performance. These simulation tools can aid the methodical design of laboratory-scale x-ray particle image velocimetry (XPIV) experiments and tracer particles by predicting image quality. Particle image signal-to-noise ratio (SNR) is used as the metric of system performance. Simulated and experiment data of hollow, silver-coated, glass sphere tracer particles (AGSF-33) are compared. As predicted by the simulations, the AGSF-33 particles are visible with a SNR greater than unity in 100 ms exposure time images, demonstrating their potential as x-ray PIV or particle tracking velocimetry (XPTV) tracers. The BL approach predicts the image contrast, is computationally inexpensive, and enables the exploration of a vast parameter space for system design. MCNP simulations, on the other hand, predict experiment images slightly more accurately, but are more than an order of magnitude more computationally expensive than BL simulations. For most practical XPIV system design applications, the higher computational expense of MCNP is likely not justified by the modest accuracy improvement compared to BL. 

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5. Measuring porous media velocity fields and grain bed architecture with a quantitative PLIF-based technique

   https://doi.org/10.1088/1361-6501/acfb2b  

   Hilliard, Brandon ; Budwig, Ralph ; Skifton, Richard S. ; Durgesh, Vibhav ; Reeder, William J. ; Bhattarai, Bishal ; Martin, Benjamin T. ; Xing, Tao ; Tonina, Daniele ( September 2023 , Measurement Science and Technology)

   Porous media flows are common in both natural and anthropogenic systems. Mapping these flows in a laboratory setting is challenging and often requires non-intrusive measurement techniques, such as particle image velocimetry (PIV) coupled with refractive index matching (RIM). RIM-coupled PIV allows the mapping of velocity fields around transparent solids by analyzing the movement of neutrally buoyant micron-sized seeding particles. The use of this technique in a porous medium can be problematic because seeding particles adhere to grains, which causes the grain bed to lose transparency and can obstruct pore flows. Another non-intrusive optical technique, planar laser-induced fluorescence (PLIF), can be paired with RIM and does not have this limitation because fluorescent dye is used instead of particles, but it has been chiefly used for qualitative flow visualization. Here, we propose a quantitative PLIF-based methodology to map both porous media flow fields and porous media architecture. Velocity fields are obtained by tracking the advection-dominated movement of the fluorescent dye plume front within a porous medium. We also propose an automatic tracking algorithm that quantifies 2D velocity components as the plume moves through space in both an Eulerian and a Lagrangian framework. We apply this algorithm to three data sets: a synthetic data set and two laboratory experiments. Performance of this algorithm is reported by the mean (bias error,B) and standard deviation (random error,SD) of the residuals between its results and the reference data. For the synthetic data, the algorithm produces maximum errors ofB & SD= 32% & 23% in the Eulerian framework, respectively, andB & SD= −0.04% & 3.9% in the Lagrangian framework. The small-scale laboratory experimental data requires the Eulerian framework and produce errors ofB & SD= −0.5% & 33%. The Lagrangian framework is used on the large-scale laboratory experimental data and produces errors ofB & SD= 5% & 44%. Mapping the porous media architecture shows negligible error for reconstructing calibration grains of known dimensions.

    

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