Learning dense gas-solids flows with physics-encoded neural network model

Computational fluid dynamics (CFD) simulations are widely employed for investigating dense gas-solid flows. However, conducting numerical simulations covering varying geometries and operating conditions is prohibitively expensive. In recent years, neural network-based methods have shown immense potential for accelerating flow field simulation. Still, forecasting the spatiotemporal evolution of gas-solid flow fields remains an open challenge for surrogate models. This study presents a physics-encoded neural network model to predict gas-solid dynamics in bubbling fluidized beds with different geometry sizes. With a novel module to estimate the particle migration distribution, the model overcomes the limitation present in pure data-driven approaches and intrinsically ensures the conservation of solid mass in the system. Additionally, it not only utilizes grid-scale information but also learns particle-scale details, thereby enhancing the forecasting performance. Through comprehensive evaluations, the physics-encoded model demonstrates significant improvements in accuracy of predicting instantaneous distributions, time-averaged and fluctuating fields, as well as bubble characteristics, in comparison to traditional data-driven models. Furthermore, our approach exhibits robust generalization capabilities, enabling it to handle previously unseen conditions with varied particle number. In contrast, data-driven models tend to memorize flow patterns seen during training, resulting in drastic deviations. In summary, the proposed method offers for a thousand-fold speedup and provides reasonable predictions for gas-solid systems with varying geometrical dimensions.