Numerical study of droplet behavior passing through a constricted square channel

Crude oil in underground porous media can be emulsified by surfactants, which can lead to improved oil recovery. However, there have been limited investigations revealing the interactions between emulsions and grains and comparing the influence of different factors on the distribution of emulsions in porous media at the pore scale. In this study, a solution containing surfactant was injected into an oil-saturated microchip with heterogeneous pore geometries. A series of tests were conducted by varying the injection rate, surfactant concentration, oil viscosity, and displacing phase viscosity. Qualitative analysis of flow mechanisms and quantitative analysis of the distribution of emulsions were performed through pore-scale snapshots. The results demonstrate that emulsions undergo deformation, breakup, and retention in porous media. Furthermore, new micro-interactions between emulsion droplets and grains are discovered, including the impact effect, cutting effect, and the synergy of both effects. A high flow rate of the displacing phase promotes the formation of more and smaller emulsions, while a low flow rate encourages flow diversion. An increase in oil viscosity and a decrease in surfactant concentration are not conducive to the formation of emulsions. However, an increase in the displacing phase viscosity promotes the formation of emulsions and flow diversion. The experiments provide detailed insights into the flow behaviors of emulsions at pore scale and offer valuable information for surfactant flooding.

Investigation of microflow mechanisms and emulsion size distribution in porous media

Wang,

Wei,

Hou

et al. 2023

Comparative analysis and computational optimization of potential-based multiphase lattice Boltzmann models

Zhong,

Huang,

Liu

et al. 2023

The potential-based multiphase lattice Boltzmann models are widely used because they root in thermodynamics and evade the interface tracking or integrating. This paper investigates several potential-based models with the common equations of state (EOS) by the theoretical analyses and numerical computations of the thermodynamic consistency and spurious currents. Surprisingly, the Shan–Chen model presents a superior accuracy compared to the Zhang–Chen models, although they are mathematically equivalent. We find that the great improvement is attributed to the square root form of the pseudopotential model, which significantly lessens the error of numerical gradient calculation. Inspired by the improvement, a general formula φ′=n−1φ1−n∂x(φn) is introduced for calculating the gradient, and the coefficient n=0.1 yields better results than n=0.5, which is equal to the pseudopotential model. This scheme is further applied to optimize the evaluation of the chemical potential model. The improved chemical potential model displays lower numerical errors in the liquid–gas transition region and smaller spurious currents near the curved phase interface than the pseudopotential model. Additionally, the improved model is confirmed to meet the Young–Laplace law and Galilean invariance.

An accelerated lattice Boltzmann method for natural convection coupled with convolutional neural network

Wang,

Xu,

Zhang

et al. 2024

A novel approach is proposed in the present study, which combines the lattice Boltzmann method (LBM) with convolutional neural networks and is suitable for square cavity natural convection and conjugate natural convection problems, exhibiting remarkable acceleration capabilities and potentials. The density distribution function obtained from LBM at time t and the temperature distribution function at time t+Δt under different Rayleigh numbers (Ra) are, respectively, utilized as input and output datasets for training and comparison in three convolutional neural networks, aiming to select the optimal coupling model, namely, half-Res-Unet. The coupling model can accurately simulate the natural convection in a square cavity within six times the upper limit of the Ra under the training condition, which can save the central processing unit (CPU) calculation time and the iteration steps by up to 29.2% and 30.3%, respectively. The coupling model is further extended in the current study to incorporate conjugate natural convection, enabling the accurate simulation of temperature distribution under training conditions with a thermal conductivity ratio (Ka) of 25 and an upper limit of Ra increased by 20 times. The corresponding maximum relative errors for the average Nusselt numbers (Nu) are found to be 1.8% and 0.7%, respectively, providing strong evidence for the generalization capability of the coupling model. Furthermore, the coupling model demonstrates a remarkable acceleration performance, as evidenced by its ability to reduce the CPU calculation time by up to 39.6% and iteration steps in the simulation process by 36.5%. It offers valuable insights into the integration of LBM with machine learning techniques, thereby enhancing the computational efficiency of LBM.