Project description
New models to accurately predict laminar to turbulent transition
Modelling turbulent flows using computational fluid dynamics has progressed rapidly over the last decades and given rise to significant changes in the design processes of aircraft, cars and ships. New models are needed to enhance prediction of the laminar flow transition to turbulence for better control of the fluid flow. Against this backdrop, the EU-funded HIFI-TURB project will use high-fidelity large-eddy simulations and direct numerical simulations to predict complex flows. New artificial intelligence and machine learning algorithms will allow researchers to identify important correlations between turbulent quantities. Improved models for complex fluid flows will offer the potential of further reducing energy consumption, emissions and noise of aircraft, ships and cars.
Objective
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.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
- 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
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Programme(s)
Call for proposal
(opens in new window) H2020-MG-2018-2019-2020
See other projects for this callSub call
H2020-MG-2018-TwoStages
Funding Scheme
RIA - Research and Innovation actionCoordinator
1170 Bruxelles / Brussel
Belgium