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RObust simulation Systems exploiting AI based turbulence models and hight-fidelity algorithmS

Project description

Smart tools to accelerate green aircraft designs

Aircraft design requires groundbreaking advancements to achieve climate-neutral aviation while staying competitive. The EU-funded ROSAS project will work towards this using smart tools to evaluate new ideas. Researchers will combine AI, machine learning and computational fluid dynamics simulations to streamline the design process and reduce reliance on expensive and time-consuming physical testing. They will simulate key aerodynamic challenges encountered in industrial applications. This data will drive the development of AI-powered tools and improved turbulence models. ROSAS will bring together 14 partners across Europe, including research organisations, universities and aeronautical companies. All partners will contribute innovation, infrastructure, and expertise in design and aerodynamics, enabling effective exploration of new aircraft and engine concepts for industrial use.

Objective

To meet global EU objectives related to aeronautical industry competitiveness and climate-neutrality, highly advanced design technologies 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.

Fields of science (EuroSciVoc)

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Keywords

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Programme(s)

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Topic(s)

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Funding Scheme

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HORIZON-RIA - HORIZON Research and Innovation Actions

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Call for proposal

Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.

(opens in new window) HORIZON-CL5-2023-D5-01

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Coordinator

OFFICE NATIONAL D'ETUDES ET DE RECHERCHES AEROSPATIALES
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 769 930,00
Address
CHEMIN DE LA HUNIERE
91120 Palaiseau
France

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Region
Ile-de-France Ile-de-France Essonne
Activity type
Research Organisations
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 769 930,00

Participants (12)

Partners (1)

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