Unraveling multi-parameter coupling dynamics and building a machine learning-based predictive model for viscous dissipation rate in pin-type stirred mills

The rod and pin stirred mill is a key device for micron-sized powder production, yet the quantitative understanding of its grinding mechanism under multi-parameter coupling remains insufficient. This study develops a coupled flow field model based on computational fluid dynamic to investigate how agitator diameter, shaft diameter, and rotational speed influence viscous dissipation. Results reveal a positive correlation between these parameters and viscous dissipation rate, following a power-law relationship. Specifically, the agitator diameter shows a two-stage linear effect, while the shaft diameter exhibits Gaussian-type nonlinear growth. Numerical simulation combined with machine learning enables sensitivity analysis, indicating that rotational speed has the most significant impact, followed by shaft diameter and agitator diameter. The Gradient Boosting model demonstrates the highest prediction accuracy. These findings provide a quantitative basis for the engineering design of high-performance stirred mills.