Static Mixers Producible by Additive Manufacturing: Novel Rapid Automatic Optimisation and Practical Evaluation.

Area of Science:

• Materials Science and Engineering
• Computational Fluid Dynamics
• Manufacturing Technology

Background:

• Melt homogeneity in plastic extrusion is crucial for extrudate quality.
• Manual optimization of static mixers is time-consuming and limits design possibilities.
• Existing methods struggle to balance pressure loss, mixing efficiency, and thermal management.

Purpose of the Study:

• To develop an automated optimization method for static mixer design.
• To enhance thermal and material homogeneity in plastic melts.
• To reduce pressure loss while maximizing heat exchange in static mixers.

Main Methods:

• Utilized OpenFOAM, a computational fluid dynamics (CFD) software.
• Implemented immersed boundary methods and adjoint shape optimization.
• Developed a novel algorithm for additive manufacturing suitability and real-time geometry adjustment.
• Optimized an X-type static mixer with integrated oil channels.

Main Results:

• Achieved a 10% reduction in pressure loss compared to traditional designs.
• Reduced core melt temperature by 6°C, indicating improved thermal homogenization.
• Demonstrated effectiveness in both on-design and off-design operating points.
• Validated simulative results through selective laser melting and experimental trials.

Conclusions:

• The developed automated method significantly improves static mixer performance.
• This approach enables rapid, cost-effective design and manufacture of customized static mixers.
• The optimization technique offers benefits for both targeted operating conditions and broader applications.