A deep-learning-based compact method for accelerating the electrowetting lattice Boltzmann simulations

作者：Zijian Zhuang, Qin Xu, H. Zeng, Yongcai Pan, Binghai Wen · 发表于：Physics of Fluids · 年份：2024 · DOI：10.1063/5.0206608 · 被引用次数：4 · 研究领域：Electrowetting and Microfluidic Technologies、Music Technology and Sound Studies、Lattice Boltzmann Simulation Studies

Research on the electrowetting of micro- and nanoscale droplets is essential for microfluidics and nanomaterials applications. A lattice-Boltzmann-electrostatics (LBES) method is an effective and accurate method for simulating this process. However, the electric potential field in each time step requires numerous iterative calculations to converge. Therefore, there is a trade-off dilemma between using high-density lattice fields to improve simulation refinement and low-density lattice fields to reduce computing costs in simulations. Fortunately, deep learning techniques can enhance the computing efficiency of electric potential fields, providing an efficient and accurate solution for electrowetting studies in fine-grained fields. In this study, a compact LBES (C-LBES), a computationally accelerated model for an electric potential field with spatiotemporal prediction capability, is developed by combining the advantages of a recurrent residual convolutional unit and a convolutional long-short-term memory unit. A loss function incorporating a geometric boundary constraint term and a self-cyclic prediction scheme are introduced according to the characteristics of the prediction task, which further improves the prediction accuracy of the model and the computing efficiency of the electric potential field. The model is validated with small datasets, and the results show that the C-LBES model with the self-cyclic prediction scheme improves the computing efficiency of the conventional...