Gas flow in soil plays a crucial role in terrestrial ecosystems, and numerical simulation of their movement needs to know their effective diffusion coefficients. How pore structure influences the effective diffusion coefficient has been studied intensively for dry porous media, but much remains unknown for unsaturated soils. Here, we employed the X-ray tomography technique at the pore scale to directly obtain the soil structures, the geometry of their pores and the water distribution under different water saturation levels were calculated using a morphological model. The results show that pore structures including porosity, interface area of gas–solid–water and pore diameter are closely related to water saturation. The increase of mean pore diameter with gas saturation can be fitted into a power law. We also investigated the impact of pore geometry and water saturation on the effective diffusion coefficients, which is independent of the molecular mass of gas after normalization. As the normalized effective Knudsen diffusion coefficient increases with average pore diameter following a power law, with the scaling factor related to pore geometry and the exponent is a constant, we explained and proved that the Knudsen diffusion coefficient increases with gas saturation, also following a power law.

Field measurements and numerical simulations demonstrate the existence of an upper saline plume in tidally dominated beaches. The effect of tides on the saltwater-freshwater mixing occurring at both the upper saline plume and lower salt wedge is well understood. However, it is poorly understood whether the tidal driven force acts equally on the mixing behaviours of above two regions and what factors control the mixing fluctuation features. In this study, variable-density, saturated-unsaturated, transient groundwater flow and solute transport numerical models are proposed and performed for saltwater-freshwater mixing subject to tidal forcing on a sloping beach. A range of tidal amplitude, fresh groundwater flux, hydraulic conductivity, beach slope and dispersivity anisotropy are simulated. Based on the time sequential salinity data, the gross mixing features are quantified by computing the spatial moments in three different aspects, namely, the centre point, length and width, and the volume (or area in a two-dimensional case). Simulated salinity distribution varies significantly at saltwater-freshwater interfaces. Mixing characteristics of the upper saline plume greatly differ from those in the salt wedge for both the transient and quasi-steady state. The mixing of the upper saline plume largely inherits the fluctuation characteristics of the sea tide in both the transverse and longitudinal directions when the quasi-steady state is reached. On the other hand, the mixing in the salt wedge is relatively steady and shows little fluctuation. The normalized mixing width and length, mixing volume and the fluctuation amplitude of the mass centre in the upper saline plume are, in general, one-magnitude-order larger than those in the salt wedge region. In the longitudinal direction, tidal amplitude, fresh groundwater flux, hydraulic conductivity and beach slope are significant control factors of fluctuation amplitude. In the transverse direction, tidal amplitude and beach slope are the main control parameters. Very small dispersivity anisotropy (e.g., αL/αT<5) could greatly suppress mixing fluctuation in the longitudinal direction. This work underlines the close connection between the sea tides and the upper saline plume in the aspect of mixing, thereby enhancing understanding of the interplay between tidal oscillations and mixing mechanisms in tidally dominated sloping beach systems. © 2018

Reducing the reliance on intrusive flow probes is a critical task in active flow control based on deep reinforcement learning (DRL). Although a scarcity of flow data captured by probes adversely impacts the control proficiency of the DRL agent, leading to suboptimal flow modulation, minimizing the use of redundant probes significantly reduces the overall implementation costs, making the control strategy more economically viable. In this paper, we propose an active flow control method based on physics-informed DRL. This method integrates a physics-informed neural network into the DRL framework, harnessing the inherent physical characteristics of the flow field using strategically placed probes. We analyze the impact of probe placement, probe quantity, and DRL agent sampling strategies on the fidelity of flow predictions and the efficacy of flow control. Using the wake control of a two-dimensional cylinder flow with a Reynolds number of 100 as a case study, we position a specific number of flow probes within the flow field to gather pertinent information. When benchmarked against traditional DRL techniques, the results are unequivocal: in terms of training efficiency, physics-informed DRL reduces the training cycle by up to 30 rounds. Furthermore, by decreasing the number of flow probes in the flow field from 164 to just 4, the physics-based DRL achieves superior drag reduction through more precise control. Notably, compared to traditional DRL control, the drag reduction effect is enhanced by a significant 6%.

The drag force and flow-induced noise of underwater vehicles significantly affect their hydrodynamic and stealth performance. This paper investigates the impact of helical grooves on the drag force and flow-induced noise of underwater vehicles through numerical simulations of the flow around cylinders with two types of helical grooves under various subcritical Reynolds numbers. The simulation scheme employs the large-eddy simulation framework combined with the Lighthill acoustic analogy method. The results show that the helical-groove structure can achieve reductions of up to 30% in drag and 5 dB in noise. These helical grooves have a significant effect in terms of suppressing the formation of a Karman vortex street downstream of the cylinder. Under subcritical Reynolds numbers, the drag-reduction effect of the helically grooved cylinder decreases as the number of helical grooves increases, while the noise-reduction effect increases with increasing number of helical grooves.