Development of a robust design methodology for mixed-flow fans

As the European Union intensifies its focus on energy efficiency under Regulation (EU) No 327/2011, the demand for high-performance turbomachinery has never been greater. Current projections suggest that fan energy consumption could reach 384 TWh by 2030, highlighting a critical need for advanced design solutions to mitigate CO2 emissions. While axial and radial fans are well-documented, mixed-flow fans, which bridge the gap by providing high flow rates with significant pressure ratios, remain underrepresented in technical literature and the commercial market. This thesis addresses this gap by proposing a robust design methodology for mixed-flow fans, specifically focusing on the meridional contour. Unlike blade parameter optimization, which has been extensively investigated over the years, the meridional shape lacks standardized design frameworks for this specific type of turbomachinery. Using a specific duty point as the design target, a machine configuration that satisfies performance requirements is identified. This work lays the groundwork by employing fundamental principles of turbomachinery design and established engineering best practices to introduce a parametrized geometry approach for the meridional channel. This approach facilitates a comprehensive sensitivity analysis. The parametrization is executed via a MATLAB code that defines the hub and shroud contours; these are subsequently implemented in CFTurbo for solid geometry generation and exported to Ansys Meshing. The performance of the proposed geometry is validated using single-channel Computational Fluid Dynamics (CFD) RANS simulations via Ansys CFX solver. A comparative analysis of flow patterns across different configurations was conducted to identify the fan exhibiting the highest performance at the design point within the explored design space. Furthermore, the fan performance curves were evaluated. The results provide not only a high-efficiency fan design but also a scalable methodology that serves as a foundation for future optimization and industrial implementation.