EU Horizon2020 Project - High-Fidelity LES/DNS Data for Innovative Turbulence Models

The most significant challenge in applied fluid dynamics today is posed by a lack of understanding of turbulence-dependent features. 

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 phenomena is key in the design of efficient and environmentally acceptable fluids-based energy transfer systems. Against this background, the HIFI-TURB project sets out a highly ambitious and innovative program, designed to address some of the most influential deficiencies in advanced statistical models of turbulence.

The HiFi-TURB project 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 world-renown industrial and academic experts in turbulence.

The consortium is formed by major industrial aeronautical companies and software editors, 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.


Case study: Studying the nature of turbulence with high-fidelity simulation and machine learning 

 

 

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Project information:

Grant agreement ID 814837

Funded under H2020-EU.3.4.

Overall budget € 3,853,460


 

HIFI-TURB Work Program

Work Package 1: Management            

  • Task 1: General Coordination / project steering              
  • Task 2: Knowledge exchange (inter-communication / web site) 
  • Task 3: Dissemination / exploitation

Work Package 2: Further improvement of HOM towards reduced CPU cost and curved grid generation

  • Task 1: Reducing CPU cost for high-fidelity LES/DNS
  • Task 2: Towards industrial curved-grid generation techniques

Work Package 3:Generation of high-fidelity LES/DNS data sets for identified physical phenomena

  • Task 1: Selection of basic and industrial relevant test cases with identified physical properties
  • Task 2: Generating new h - High-fidelity LES/DNS data sets
  • Task 3: Evaluation, reliability and quality of LES/DNS data

Work Package 4: Feature detection and advanced analysis of LES/DNS data

  • Task 1: Analysis of basic turbulence- averaged data via data driving methodologies
  • Task 2: Analysis of time turbulence data, via AI and deep learning methodologies, connected to HRLM and WMLES

Work Package 5: Turbulence modelling assessments and improvements – monitored by WP5 Task Group

  • Task 1: Develop, improve and assess EARSM turbulence models
  • Task 2: Develop, improve and assess DRSM turbulence models
  • Task 3: Develop, improve and assess wall models for WMLES and Hybrid RANS-LES

Work Package 6: Validation of new turbulence models applied to representative and industrial relevant test cases

  • Task 1: Validation on external flow configurations (High-Lift and Drag Pred.  WS cases); baseline to new
  • Task 2: Validation on internal flow configurations for fixed (diffusor) and rotating. cases; baseline to new
  • Task 3: Assessment & recommendations

Work Package 7: Management of the LES/DNS databases for open accessibility (ERCOFTAC)

  • Task 1: Definition of database criteria and implementation rules
  • Task 2: Creation and management of the LES/DNS database
  • Task 3: Integrating results of WP6 in the ERCOFTAC Wiki Knowledge Base

 

HiFi-TURB Consortium Members

  • NUMECA (coordinator)
  • Dassault Aviation
  • SAFRAN S.A.
  • Imperial College London
  • ANSYS Germany
  • Cineca Consorzio Interuniversitario
  • Barcelona Supercomputing Center – CENTRO National de Supercomputacion
  • Centre de Recherche en Aéronautique ASBL - CENAERO
  • Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique - CERFACS
  • Office National d’Etudes et de Recherches Aerospatiales - ONERA
  • Deutsches Zentrum für Luft und Raumfahrt - DLR
  • Università degli Studi di Bergamo
  • Universite Catholique de Louvain
  • European Research Community on Flow Turbulence and Combustion – ERCOFTAC
  • Central Aerohydrodynamic Institute -TsAgi