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REconstruction-based DAta-assisted frameworks for turbulent reacting FLOWs.

Project description

A novel fluid dynamics modelling framework geared towards emerging advanced architectures

All sorts of natural and engineered systems exhibit turbulent reactive flows (with chemical reactions occurring), from arterial blood flow to oil and gas in pipelines, air over aeroplane wings, working fluids in pumps and turbines and the reactants in chemical reactors. Using computational fluid dynamics software for modelling these processes has become standard procedure in related fields. Computer hardware has evolved extremely quickly enabling significant increases in the speed and computational load that can be accommodated. With the support of the Marie Skłodowska-Curie Actions programme, the REDAFLOW project is developing a flexible and generalised modelling framework for turbulent and reactive flows tailored to harness emerging high-performance computing architectures.

Objective

REDAFLOW aims to develop a generalised, computationally efficient and scalable modelling framework for simulating turbulent and reacting flows, aimed at the latest and emerging high-performance computing architectures. Current state of the art classic modelling approaches developed from simplifying assumptions (in-compressible, self-similar, non-reacting) limit the generality and application domain of computational fluid dynamic simulations which is becoming the workhorse in industry for virtual prototyping. At the same time, a large number of flow-dependent and reaction-dependent model parameters limit the predictive ability and robustness of numerical simulations. The novelty of the proposed framework is twofold: reconstruction/deconvolution will be employed for modelling in a generalised and parameter-free framework unresolved terms in the governing equations while machine-learning will be employed to model the chemical kinetics including detailed-chemistry effects. The necessary filtering and interpolation schemes as well as all the deconvolution algorithms and chemistry neural network libraries will be developed in-house in stand-alone libraries, and optimised for use with state of the art parallelisation libraries. The proposed framework is expected to reduce the computational time required for tabulation-based reacting flow simulations, improve the simulation predictions, and allow a wider range of practical flows to be simulated under a generalised framework, irrespective of the flow or reaction regime. The tools and libraries developed are expected to attract the interest of a range of industries (chemical, automotive, aerospace, software, consulting) where simulation is the main tool for developing improved processes and designs for a wide range of engineering devices.

Coordinator

INSTITUT NATIONAL DES SCIENCES APPLIQUEES DE ROUEN
Net EU contribution
€ 295 061,76
Address
Avenue de l'Université
76 801 SAINT ETIENNE DU ROUVRAY
France

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Region
Normandie Haute-Normandie Seine-Maritime
Activity type
Higher or Secondary Education Establishments
Links
Total cost
€ 295 061,76