Data-driven model order reduction for aerodynamics

The accurate assessment of the aerodynamic interaction in potentially non-conventional scenarios is a key step to developing next-generation aircraft capable to respond to the need for sustainable and resilient aviation. Multi-disciplinary Design Optimization processes, and parametric and sensitivity aerodynamics studies, require the repeated evaluation of aerodynamic loads and performance for a range of operating conditions and geometric parameters. The goal is to find a suitable trade-off between computational cost and accuracy, a data-driven approach based on Principal Components Analysis and Manifold Learning will be presented with application to the numerical prediction of the aerodynamic environment surrounding wings and airframes in support of the analysis and design of next-generation aircraft.

Short bio
Dr. Fossati is a Full Professor at the Department of Mechanical and Aerospace Engineering of the University of Strathclyde. At Strathclyde, he is a member of the Aerospace Centre of Excellence (ACE) and he is the director of the Laboratory for Future Air-Space Transportation Technologies (FASTT-Lab).

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