Static Mixers for High-Viscosity Systems: From Classical Helices to Machine-Learning-Optimized Geometries

Static mixers are widely used in chemical, polymer, and process industries to handle high-viscosity media, such as polymer melts, yield-stress resins, and particle-laden suspensions, where conventional mixing is inefficient. By inducing chaotic advection through fixed internal geometries, they offer compact, low-maintenance solutions compatible with continuous production. This review charts the progression from classical helical ribbons to advanced, application-specific elements optimized by using computational fluid dynamics (CFD) and machine learning (ML). Design strategies for viscoelastic and yield-stress fluids are critically assessed, with attention to balancing mixing intensity against hydraulic losses. Remaining challenges include the scale-up of complex ML-derived designs, mitigation of viscoelastic instabilities, integration of multifunctional features, and further gains in energy efficiency. Advances in these areas will enhance the role of static mixers in sustainable, high-performance industrial processes.