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 laminarto--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 thatcontain key flow features of major interest". The application of novel artificial intelligence and machine-learning algorithms toidentify significant correlations between representative turbulent quantities. The guidance of the research towards improvedmodels by four world-renown industrial and academic experts in turbulence. The consortium is formed by major industrialaeronautical companies and software editor, an SME acting as coordinator, well-known research centra and academicgroups, including ERCOFTAC, acting as a source of turbulence expertise and as a repository for the generated data, to be made openly available.


This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 814837.