Projektbeschreibung
Neue Modelle sagen Übergang von laminar zu turbulent genau voraus
Die Modellierung turbulenter Strömungen mittels rechnergestützter Strömungsdynamik ist in den letzten Jahrzehnten rasant fortgeschritten und hat maßgebliche Veränderungen in den Entwurfs- und Konstruktionsprozessen von Flugzeugen, Autos und Schiffen angestoßen. Nun werden neue Modelle benötigt, um die Vorhersage des Übergangs einer laminaren Strömung in die Turbulenz zu präzisieren, damit Fluidströmungen besser gesteuert werden können. Vor diesem Hintergrund wird das EU-finanzierte Projekt HIFI-TURB äußerst zuverlässige Grobstruktursimulationen und direkte numerische Simulationen anwenden, um komplexe Strömungen vorherzusagen. Das Forscherteam wird dank neuer Algorithmen auf Basis künstlicher Intelligenz und maschinellen Lernens wichtige Korrelationen zwischen turbulenten Größen ermitteln können. Verbesserte Modelle für komplexe Fluidströmungen bieten viele Möglichkeiten, den Energieverbrauch, die Emissionen und den Lärm von Flugzeugen, Schiffen und Autos weiter zu reduzieren.
Ziel
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.
Wissenschaftliches Gebiet
- 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
Schlüsselbegriffe
Programm/Programme
Aufforderung zur Vorschlagseinreichung
Andere Projekte für diesen Aufruf anzeigenUnterauftrag
H2020-MG-2018-TwoStages
Finanzierungsplan
RIA - Research and Innovation actionKoordinator
1170 Bruxelles / Brussel
Belgien