Skip to main content
EnglishEN
European Commission logo print header

HIGH-FIDELITY LES/DNS DATA FOR INNOVATIVE TURBULENCE MODELS

Project description

DEENESFRITPL

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

Keywords

Programme(s)

Topic(s)

Call for proposal

H2020-MG-2018-2019-2020

See other projects for this call

Sub call

H2020-MG-2018-TwoStages

Funding Scheme

RIA - Research and Innovation action

Coordinator

CADENCE DESIGN SYSTEMS BELGIUM
Net EU contribution
€ 510 000,00
Address
Chaussee de la hulpe 187-189
1170 Brussels / bruxelles
Belgium

See on map
Region
Région de Bruxelles-Capitale/Brussels Hoofdstedelijk Gewest Région de Bruxelles-Capitale/ Brussels Hoofdstedelijk Gewest Arr. de Bruxelles-Capitale/Arr. Brussel-Hoofdstad
Activity type
Private for-profit entities (excluding Higher or Secondary Education Establishments)
Links
Other funding
€ 0,00

Participants (15)

Share this page

Download

EC signature date 6 May 2019
Last update: 17 July 2023

My Booklet

Permalink: https://cordis.europa.eu/project/id/814837

European Union, 2023