UN-BIASED (UNcertainty quantification and modelling Bias Inhibition by means of an Agnostic Synergistic Exploitation of multi-fidelity Data) is a research project funded by the European Union within the Horizon Europe Programme. It aims to develop innovative techniques for modelling complex aerodynamic systems, including their aeroacoustic footprint.
A prominent application concerns the investigation of vertical take-off and landing aircraft characterized by unconventional multi-rotor configurations. Striking examples are futuristic aero-taxi currently developed by many start-ups spread worldwide or emergency aerial rescue services to isolated and metropolitan areas.
Currently, the process leading to the establishment of conceptual models for studying such machines largely relies upon the experience gained in investigating helicopters or, more generally, conventional aircraft.
As such, and given the considerable innovation level concerning multi-rotor machines, the modelling process of their aerodynamics and aeroacoustic is strongly biased by preconceived or misleading assumptions due to a lack of specific experience.
The methodology developed thanks to the UN-BIASED project will allow the implementation of a multi-fidelity approach for the analysis and investigation of complex aerodynamic systems.
In particular, the goal is to integrate the modeler’s experience with objective data, from both experimental and numerical studies, in a rigorous manner using machine learning algorithms.
As a result, this novel method will highlight possible inconsistencies between the conceptual models and the observed reality, opening the path for identifying and correcting any modelling bias.
The ultimate objective is optimizing the performance of concept aircraft, including mitigating the noise generated by the propellers, therefore providing new design tools for hastening their certification and commercialization.
The project is coordinated by Prof. Giulio Gori (Principal Investigator), Prof. Luigi Vigevano, and Prof. Alex Zanotti, from the Department of Aerospace Science and Technology.