Descrizione del progetto
Nuovi modelli per prevedere con precisione la transizione dal regime laminare al regime turbolento
La modellizzazione di scorrimenti turbolenti utilizzando la fluidodinamica computazionale è progredita rapidamente negli ultimi decenni e ha dato origine a cambiamenti significativi nei processi di progettazione di aeromobili, automobili e navi. Sono necessari nuovi modelli per migliorare la previsione della transizione dello scorrimento da laminare a turbolento per un migliore controllo dello scorrimento del fluido. In questo contesto, il progetto HIFI-TURB, finanziato dall’UE, utilizzerà simulazioni di grandi vortici ad alta fedeltà e simulazioni numeriche dirette per prevedere flussi complessi. Nuovi algoritmi di intelligenza artificiale e apprendimento automatico consentiranno ai ricercatori di individuare importanti correlazioni tra quantità turbolente. I modelli migliorati per scorrimenti di fluidi complessi permetteranno di ridurre ulteriormente il consumo energetico, le emissioni e il rumore di aeromobili, navi e automobili.
Obiettivo
The most significant challenge in applied fluid dynamics (covering aerospace, energy and propulsion, automotive, maritime industries, chemical process industries) is posed by a lack of understanding of turbulence-dependent features and laminar-to-turbulent transition. As a consequence, the design and analysis of industrial equipment cannot be relied upon to be accurate in challenging flow conditions. Improving the capabilities of models for complex fluid flows, offers the potential of reducing energy consumption of aircraft, cars, and ships, with consequent reduction in emissions and noise of combustion-based engines The inevitable result is a major impact on economical and environmental factors as well as on economy, industrial leadership in the highly competitive global position. Hence, the ability to understand, model and predict turbulence and transition phenomena is the key requirement in the design of efficient and environmentally acceptable fluids-based energy transfer systems. Against this background, the present proposal sets out a highly ambitious and innovative program of work designed to address some influential deficiencies in advanced statistical models of turbulence. The program rests on the following pillars of excellence: • The exploitation of high-fidelity LES/DNS data for a range of -reference flows that contain key flow features of major interest • The application of novel artificial intelligence and machine-learning algorithms to identify significant correlations between representative turbulent quantities • The guidance of the research towards improved models by four world-renown industrial and academic experts in turbulence. The consortium is formed by major industrial aeronautical companies and software editor, an SME acting as coordinator, well-known research centra and academic groups, including ERCOFTAC, acting as a source of turbulence expertise and as a repository for the generated data, to be made openly available.
Campo scientifico
- natural sciencescomputer and information sciencessoftware
- engineering and technologymechanical engineeringvehicle engineeringaerospace engineeringaircraft
- natural sciencesphysical sciencesclassical mechanicsfluid mechanicsfluid dynamics
- natural sciencesmathematicsapplied mathematicsstatistics and probability
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Parole chiave
Programma(i)
Invito a presentare proposte
Vedi altri progetti per questo bandoBando secondario
H2020-MG-2018-TwoStages
Meccanismo di finanziamento
RIA - Research and Innovation actionCoordinatore
1170 Bruxelles / Brussel
Belgio