Exploration of axial fan design space with data-driven approach

Since the 1960s, turbomachinery design has mainly been based on similarity theory and empirical correlations derived from experimental data and manufacturing experience. Over the years, this knowledge was consolidated and summarized by parameters such as specific speed and diameters that represent the flow features on the meridional plane, hiding however the direct correlations between all the actual design parameters (e.g., blade number or hub-to-tip ratio). Today a series of statistical tools developed for big data analysis sheds new light on correlations among turbomachinery design and performance parameters. In the following article we explore a dataset of over 10,000 axial fans by means of principal component analysis and projection to latent structures. The aim is to find correlations between design and performance features and comment on the capabilities of this approach to give new insights on the design space of axial fans.