New generation of machine-learning based Models for Aerodynamics Computations – NEWMAC

In this project, we study new machine-learning methodologies to improve statistical turbulence models for aeronautical applications. In aerodynamics, currently used turbulence models in industry are based on strong approximations and are tuned on very simple configurations. Such models exhibit strong weaknesses as soon as more complex industrial flows are considered. Machine-learning techniques may leverage the wealth of numerical high-fidelity and (incomplete) experimental data that is currently available to improve such models. ONERA / DLR / SAFRAN TECH / ROLLS-ROYCE intend to explore and improve the so-called field-inversion (FI)-machine learning (ML) methodology introduced recently by Duraisamy. The goal is to explore more sophisticated turbulence models, new AI-based techniques for the regularisation of the FI step in the case of incomplete reference data, new systematic tools for the selection of input flow features, and new learning strategies. These disruptive methodologies will be incorporated within numerical platforms that allow CFD codes to take advantage of data-driven techniques. The capabilities of such tools will finally be evaluated and assessed on industrially relevant configurations, such as an aircraft in high-lift configuration and a compressor cascade.