Session: 10-01 Fan Aerodynamic Design

Paper Number: 153714

Effects of Ribbed Blades on the Aerodynamic Performance of a Controlled Vortex Design Tube-Axial Fan 

Leading edge bumps and serrations as well as trailing edge indentations proved to be effective means to improve low-speed axial fan performance in terms of stall margin and/or noise emissions mitigation. In fact, these means are applied in several fan designs available in the market. In spite of the beneficial effects just recalled, literature studies documented a decrease of the fan aerodynamic performance at the design point as drawback associated with the modification of blade shape by incorporation of these local geometrical features. However, due to the primary importance of noise control some aerodynamic performance derating is well tolerated and not much effort has been yet devoted to achieving the full understanding of how local geometrical features of the blade surface affect the fan performance at design operation.

This paper aims at quantifying the level of confinement imposed to the meridional flow within the rotor passages by blade surface incorporation of chordwise-aligned bumps.

To this end, the difference between the global aerodynamic performance predicted by validated CFD for three fans at design point operation is investigated and discussed in the light of the local flow features. In particular, the baseline fan is a 0.5 hub-to-tip ratio design with aerodynamic loading distribution that induces a roughly constant swirl component downstream of the rotor, and whose blades were designed relying on the “Kahane-Wallis” method. The second fan incorporates in the baseline design a spanwise uniform distribution of rectilinear bumps that extend from the leading to the trailing edge in the whole blade surface. The third fan incorporates the same bumps as the second fan and is designed for the same requirements as the baseline fan but its design process neglected the radial flow migration imposed to controlled vortex blade designs by the radial equilibrium.

Presenting Author: Lorenzo Tieghi University of Trento

Presenting Author Biography: Lorenzo Tieghi studied mechanical engineering with a specialization in industrial design at
Sapienza University of Rome. In 2019, he obtained a PhD in Energy and Environment, focusing his
thesis on the application of machine learning and data-driven methods to turbomachinery flows. He
spent three years as a postdoctoral researcher, working primarily on turbomachinery design,
optimization, validation, and the development of machine learning applications for fluid mechanics.
During this period, he also spent several months as a visiting scholar at Lancaster University and
Friedrich-Alexander-Universität Erlangen-Nürnberg. After one year as an assistant researcher in Sapienza in 2023, he is currently a tenure track researcher at the University of Trento.
He is currently the lecturer for fluid machinery courses.

Authors:

Lorenzo Tieghi University of Trento
Piero Danieli University of Padova
Lorenzo Battisti University of Trento
Massimo Masi University of Padova