Description
To meet global EU objectives related to aeronautical industry competitiveness and climate-neutrality, highly advanced designtechnologies are needed to allow fast and reliable evaluations of innovative configurations. ROSAS aims at exploiting Artificial Intelligence (AI)/Machine Learning (ML), coupled with recent advances in Computational Fluid Dynamics (CFD) technology and the underlying turbulence modelling to reduce expensive and time-consuming physical testing and drastically accelerate the whole design optimization process. Partners will build a methodology based on defining test cases, targeting the key flow problems encountered in industrial applications and reproducing them precisely in a controlled environment via Hi-Fi simulations or experiments.
The results will be gathered in a database which will serve the development and testing of novel data-driven methodologies (AI-ML) and mesh generation algorithms. Improved turbulence models, including multi-fidelity surrogate models with advanced verification and validation process, will be created with modifications based on new theoretical considerations and AI-ML-based modifications,resulting in hybridization of specific turbulence models. In addition, Application Challenge test cases will be defined in relation to Clean Aviation applications to match closely industrial configurations of interest, to assess and demonstrate the new methodologies developed in the project.ROSAS brings together 14 partners from 8 EU countries and 1 from UK. The consortium comprises 4 leading research organizations, 5 leading university groups, a Super-Computing Centre, and 3 major aeronautical industries. All partners will bring their innovativeness, infrastructures, and long experience in design and aerodynamics modelling, ensuring industrial exploitation of results by exploring new aircraft and engine concepts more effectively. 1 SME will bring experience in project management, communication, and exploitation.