AIChE Journal最新文献

Flocculation–thickening is widely used in mineral processing and various chemical engineering fields. The flocculation in the thickener feedwell plays a key role in the tailings slurry thickening process. Hydrodynamic conditions directly affect particle flocculation kinetics and subsequent settling rates, thus determining the overall performance of the thickener. This study employs a multiscale modeling approach to investigate how feed solid concentration affects flow characteristics and flocculation–settling performance in a pilot-scale deep cone thickener, in which Computational Fluid Dynamics-Population Balance Model (CFD-PBM) and a Two-Fluid Model with Kinetic Theory of Granular Flow (TFM-KTGF) were applied to simulate flocculation and settling behavior, respectively. Results show that medium solid concentration promotes particle aggregation via optimal turbulence dissipation. Increasing concentration reduces both the initial settling rate ratio of flocs and the settling differential between particle sizes. These findings enhance the understanding of flocculation–thickening mechanisms and support process optimization in solid–liquid separation fields.

The erosion of bed materials and coal ash on water-cooled walls presents a persistent technical challenge in circulating fluidized bed boiler systems, manifesting as increased frequency of unplanned shutdowns and elevated maintenance requirements. Traditional metal anti-wear devices, due to inherent structural limitations, struggle to achieve optimal coordination between velocity and pressure gradient fields, making them prone to erosion and limiting their overall wear resistance. This study proposes bionic anti-wear devices inspired by squid fin and shark dorsal fin. The experimental and simulation results show that bionic devices can optimize the coordination between the velocity and pressure gradient fields. Compared to the traditional right-angle triangular device, the shark dorsal fin-inspired device reduces the windward surface area by 3.25%, maximum pressure coefficient by 50%–60%, and the erosion rate by 93.55%. This study provides an innovative approach for developing next-generation anti-wear devices with enhanced wear resistance.

The standard Dynamic Mode Decomposition (DMD), when used in complex fluid flow modeling, often suffers from situations like noisy data and translational motion, leading to high errors and non-physical results. Meanwhile, purely physics-based numerical methods offer high accuracy but are computationally intensive. To bridge this gap, this paper proposes a Physics-Constrained Dynamic Mode Decomposition (PCDMD) framework, which integrates governing physical laws into the DMD to constrain predicted results by using Kalman correction. This hybrid approach retains the speed of DMD while improving accuracy by ensuring that predictions obey the underlying physics. We systematically evaluated the PCDMD on flow problems with increasing complexity, including lid-driven cavity flow, flow around a cylinder with concentration transport, and a rising bubble system. In each case, PCDMD significantly improves both the predictive accuracy and physical consistency. By balancing between the data-driven modeling and physical correction, the PCDMD remains robust under imperfect data and physical equations.

Flow pattern prediction in multiphase systems is essential for characterizing hydrodynamic properties and optimizing mass/heat transfer efficiency. Herein, we propose a generative artificial intelligence (GenAI) flow pattern prediction framework for rapidly processing and analyzing large–scale flow pattern image data, with the first application to predictive modeling of gas–liquid Taylor flow in microchannels. The forecasting results of this GenAI–based prediction framework do not consist of discrete flow pattern classification labels but rather intuitive, spatially resolved high-fidelity visualization results comparable to experimental observations under steady–state operating conditions (e.g., high–resolution flow pattern images captured by high–speed cameras). Notably, the proposed prediction framework overcomes the limitations of conventional methods that only provide category information of flow patterns. More importantly, the model evaluation results demonstrate that this framework can effectively model the correlation between operating conditions and corresponding flow characteristics within microchannels, thereby validating the great potential of this GenAI technology for multiphase flow research.