Partners

A European Team dedicated to develop AI-augmented aerodynamic simulation tools to accelerate aircraft design and reduce costly physical testing.

The ROSAS consortium brings together 14 partners from 8 European countries and the United Kingdom, combining academic excellence, industrial expertise, and innovation support.

Meet the ROSAS consortium on this page.

4 leading research organisations with outstanding expertise in research in computational methods, modelling, simulation and design optimization in the aerospace sector

ONERA

CFD activities of the Aerodynamics, Aeroelasticity and Acoustics department focus on the development of accurate, robust, efficient numerical methods, as well as reliable, software packages that enable industry players to predict and optimize the performance of airplanes, helicopters, turbomachinery, launch vehicles. The team involved ROSAS has high skills on high-order numerical methods, advanced turbulence models and modern code development.

Role in ROSAS: ONERA coordinates ROSAS and leads WP1, WP2 for the production of HiFi data from training test cases, with participation in scale-resolved simulations (LES/DNS). In addition, ONERA leads and participates in several tasks developing new AI-based HPC algorithms and surrogate models, AI/ML-based data-driven turbulence models for aerodynamic simulations, as well as validating new methods and models for application challenges.

DLR

Institute AS, C²A²S²E department is a known expert in the development of highly accurate, robust and reliable physical models and numerical simulation methods, reduced order and surrogate models as well as multi-disciplinary applications and optimization methods for aerospace research and industries applications. DLR is also involved in several national self-funded AI-related projects, such as projects DIGIFly and ADaMant, as well as the ONERA-DLR AI Center.

Role in ROSAS: DLR leads WP4 and brings to ROSAS relevant knowledge covering turbulence modelling, differential Reynolds stress modelling, hybrid application of ML and numerical simulation methods, and definition, simulation and evaluation of basic and advanced aerodynamics testcases.

CENAERO

Is an applied research centre providing high-fidelity numerical simulation methods and tools to invent and design more competitive products.
CENAERO aims at bridging the gap between academia and industry by developing and industrializing new numerical technologies from TRL 2 to TRL 6.
CENAERO has developed a widely recognized expertise in ML based optimization applied to turbomachinery design, adaptive high order CFD methods targeted at scale-resolving simulations for aerodynamic design and ML/data-driven turbulence modelling.

Role in ROSAS: CENAERO will provide expertise in computational methods, turbulence modelling, simulation, and design optimization in the aerospace sector. CENAERO will be involved in 3 technical WPs.

CERFACS

Is a research organization with multidisciplinary expertise in numerical modelling and simulation for a wide range of challenges, including aerodynamics, combustion and propulsion, and new fuels including hydrogen. It is an active contributor to the European research on HiFi simulation, as well as hybrid simulation that incorporates AI into physical solvers.

Role in ROSAS: It will apply these skills to ROSAS in several WPs by producing HiFi simulation databases of relevant configurations, explore new audacious numerical schemes based on AI-driven local adaptation, and on using AI models directly inside HiFi solvers to yield better estimates of subgrid-scale quantities.

3 major aeronautical industries ensuring the industrial relevance of targeted digital tools will be actively involved in the project and ensure the coordination of work on each test case

Dassault Aviation

Is an aerospace company designing and building military aircraft, business jets and space systems. The modelling, method and tools for aerodynamics team owns expertise in CFD software development including numerical methods and models. DAV also owns expertise in design and optimization process through continuous development of in-house meshing, simulation, and analysis tools.

Role in ROSAS: In ROSAS, DAV will lead WP5.

Rolls Royce

is a global business providing integrated (jet propulsion) power system for use on land, at sea and in the air. RR also support a global network of 31 University Technology Centers. Over two-thirds of RR’s R&D expenditure is dedicated to improving the environmental performance of its products.

Role in ROSAS: In WP2, RR will gather challenging test cases and data relevant to the compressor strutted S-duct training case. In WP3&4, RR will improve the high-resolution meshes and develop new AI-based turbulence methods to enhance simulation accuracy. In WP5, these HiFi methods will be applied to the combustor / HP turbine interaction industrial application challenge.

SAFRAN

Safran Tech is the corporate research center of the Safran Group. Low TRL and future preparation for the group is its core activity. In this project, SAFRAN is interested in development of 1/ the turbulence model based on fundamental physical test cases 2/ Turbulence model implementation and associated numerical robustness 3/ Validation on representative industrial test cases.

Role in ROSAS: SAFRAN will participate in activities for turbulence model development (definition of adapted test cases into WP2) and turbulence models implementation and validation (WP5) on representative turbomachinery cases.

6 leading university groups and a Super-Computing Centre providing the project with all the needed skills and expertise

BSC

The LS/CFD team is focused on developing numerical tools, turbulence models, multi-physics algorithms, data driven methodologies and large-scale CFD simulations. The team is collaborating, among others, with ITPAero and Aernnova, besides academic collaborations with KTH, CTR Stanford, MIT and Queen’s University.

Role in ROSAS: In this project, BSC will generate DNS/LES data bases, develop new EARSM models by MEP, and novel WMLES strategies using WMLES.

KTH

has an outstanding experience and record of large-scale scientific fluid-physics computations and flow-physics research. Of importance for the proposal are experiences of both direct-numerical and large-eddy simulations (DNS and LES) of generic turbulence cases as well as turbulence modelling. An emerging expertise in machine learning, reflected in a recently-awarded ERC Consolidator Grant, is relevant for ROSAS. Moreover, the long experience in turbulence research and modelling is of outmost importance for the AI driven turbulence modelling, RANS as well as wall-modelling for LES.

Role in ROSAS: KTH will contribute to the development of wall-modelling for LES based on deep reinforcement learning as well as further developments of EARSM’s. Moreover, KTH will contribute to validation and verification activities.

UCL

Pr. Remacle is Full Professor at UCL since 2002. He is a world leader scientist in mesh generation and one of the two authors of Gmsh, the world’s most used open-source mesh generation software. He received two ERC grants. UCL will bring to the project its knowledge in general unstructured high mesh generation and high order/curvilinear meshing. ROSAS can also count on world-renowned UCL experts in AI such as Pr. Glineur.

UNIBG

The Fluid Mechanics research group at UNIBG is expert in the development of robust and efficient high-order, possibly adaptive, Discontinuous Galerkin (DG) methods to be applied to the solution of different flow models, ranging from DNS to RANS.

Role in ROSAS: Using the same numerical framework, UNIBG can perform both the DNS of the basic test cases within the ROSAS training suite and participate in verifying and validating the new RANS-based AI-enhanced turbulence models proposed by Partners.

UPM

UPM has extensive experience in industrial solver development, particularly in accurate and efficient methods for solving the Navier-Stokes equations. This includes high-order DG schemes, implicit solvers, h/p adaptation, and under-resolved turbulence modeling, all available in the open-source code HORSES3D, optimized for HPC and GPU platforms. UPM has also developed ML/AI tools for model decomposition, data-driven analysis, and error estimation, widely adopted in industry and available in the open-source library pyLOM.

Role in ROSAS: UPM will lead AI-driven mesh adaptation (WP3) and contribute to data generation (WP2) and the development of AI-enhanced turbulence models (WP4).

UNIPI

The Fluid Dynamics research group at UNIPI has a consolidated experience in the experimental analysis and HiFi simulation and modelling of turbulent and highly-separated flows. It also focuses on the combined use of experiments, stochastic sensitivity analysis and data assimilation to develop strategies and models to reduce the costs of LES, while maintaining the result reliability.

Role in ROSAS: UNIPI will carry out experiments to provide the consortium with detailed velocity and pressure data for validation of numerical results and models improvement. Finally, UNIPI will be involved in LES simulation and in the development of novel strategies for WM-LES of separated flows.

1 SME will reinforce the consortium with key expertise in project management, dissemination, and exploitation

ERDYN

Specialised in innovation and European project set-up and implementation.

Role in ROSAS: With a long track record in EU project management and communication, ERDYN will support ROSAS in project management (WP1) and communication and dissemination activities (WP6).