The effect of wettability on the infiltration behavior in the liquid composite molding process has not been fully studied, and the available evidence appears to be conflicting. Based on the three-dimensional microcomputed tomography images of porous media, a series of immiscible displacement simulations under a wide range of wettability conditions was established by the phase field method. Interestingly, we found that increasing the affinity of the porous matrix for the invading fluid can increase the displacement efficiency and reduce the void content until the critical wetting transition is reached, beyond which the displacement efficiency decreases sharply. The nonmonotonic behavior of the wettability effect can be explained by the competition among complex and intriguing pore-scale displacement events, mainly involving the Haines jump, cooperative pore filling, and corner flow. These novel findings provide a theoretical basis for extracting the optimal wettability range, thus minimizing the void content formed during the liquid infiltration process.

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Hydraulic tortuosity is one of the key parameters used to characterize the fluid transport properties of porous media. One of the existing debates on hydraulic tortuosity is whether it is an intrinsic property or a correction factor to match the experimental data with a particular model. In this study, a series of immiscible displacement simulations with different capillary numbers and contact angles were established by using the phase field method based on three-dimensional micro-computed tomography images of porous media. Then, the vector-based tortuosity method based on the flow velocity field is used to predict the dynamic evolution of hydraulic tortuosity of unsaturated porous media. Interestingly, the transient hydraulic tortuosity at different flow displacement patterns shows different dynamic evolutions where the quasi-steady-state hydraulic tortuosity is related to both the fluid saturation and the characteristics of the trapped voids in the porous media. These phenomena can be explained by the complex and interesting pore-scale displacement events, including viscous self-correcting smoothing, noncooperative Haines jumps, capillary self-correcting smoothing, and corner flow.

Online microparticle detection is of utmost importance for industrial production. This paper proposes a signal processing and feature identification strategy to achieve particle size statistics for online measurement in a three-phase stirred tank reactor based on the electrical sensing zone (ESZ) method. Signal denoising and de-interference are achieved using the wavelet soft threshold method combined with mathematical morphological filtering. Pulse selection is implemented using pulse width limiting conditions. The key features that distinguish the pulse waveforms are defined based on the differences in the motion characteristics of the different types of particles through the aperture. Finally, the unsupervised classification algorithm balanced iterative reducing and clustering using hierarchies clustering is employed to distinguish the pulsed features between hard particles and bubbles. The results show that the particle size distribution identified by this strategy agrees with offline measurements indicating the effectiveness of the scheme. The effects of electromagnetic noise and the interference of small bubbles that approximate the particle size in solution in the online, in-situ measurement task are solved. This study scheme has a guiding and facilitating role in applying the ESZ principle to the industrial online measurement environment.

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