Descripción del proyecto
Nuevos modelos para predecir con precisión la transición del flujo laminar al flujo turbulento
La modelización de flujos turbulentos empleando la dinámica de fluidos computacional ha experimentado un rápido progreso en los últimos decenios y ha dado lugar a cambios de calado en los procesos de diseño de aeronaves, automóviles y buques. Con todo, se necesitan nuevos modelos para mejorar la predicción de la transición del flujo laminar al flujo turbulento y lograr así un mejor control del flujo de fluidos. En este contexto, el proyecto financiado con fondos europeos HIFI-TURB empleará simulaciones de grandes torbellinos de alta fidelidad y simulaciones numéricas directas para predecir flujos complejos. Los investigadores podrán identificar correlaciones relevantes entre magnitudes turbulentas empleando nuevos algoritmos de inteligencia artificial y aprendizaje automático. La mejora de los modelos de flujos de fluidos complejos brindará la posibilidad de reducir todavía más el consumo de energía, las emisiones y el ruido de aeronaves, automóviles y buques.
Objetivo
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.
Ámbito científico
- 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
Palabras clave
Programa(s)
Convocatoria de propuestas
Consulte otros proyectos de esta convocatoriaConvocatoria de subcontratación
H2020-MG-2018-TwoStages
Régimen de financiación
RIA - Research and Innovation actionCoordinador
1170 Bruxelles / Brussel
Bélgica