Reduced-Order Modeling of Non-Newtonian Fluid Mixing in a Twin-Blade Planetary Mixer Using Data-Driven Singular Value Decomposition

Twin-blade planetary mixers are widely employed in the mixing of particle-laden non-Newtonian fluids. Their unique blade configuration makes accurate blade load distribution determination crucial for structural integrity and mixing efficiency. However, computational fluid dynamics (CFD) simulations are often prohibitively expensive, limiting their practical application. To address this, this study develops a reduced-order model (ROM) from CFD data to rapidly predict the blade load distribution of a 1 L twin-blade planetary mixer at key operational points. Flow field analysis shows blade pressure extremes arise from blade-to-blade and blade-to-wall interactions, with magnitudes determined by rotational and gyrational speeds; local shear extremes mainly stem from blade–wall interactions. Validation demonstrates the ROM achieves over 93% prediction accuracy in key regions covering over 30% of the dataset, cutting computational time from days (full CFD) to seconds. This model enables fast, accurate blade load prediction across varying speeds, providing a practical tool for blade design and real-time monitoring.