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REDAFLOW

financed

Closed

REconstruction based DAta assisted frameworks for turbulent reacting FLOWs.

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 sta... see more

31/08/2024

INSA Rouen

295K€

Project Budget: 295K€

Project leader

INSTITUT NATIONAL DES SCIENCES APPLIQUEES DE... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

PARTICIPATION DEPralty Sin fecha límite de participación.

Financing granted El organismo H2020 notifico la concesión del proyecto The day 2024-08-31

MSCA-IF-2020: Individual Fellowships Objective:The goal of the Individual Fellowships is to enhance the creative and innovative potential...

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Cerrada does 5 years

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characteristics of the participant

Este proyecto no cuenta con búsquedas de partenariado abiertas en este momento.

Private Information

No hay información privada compartida para este proyecto. Habla con el coordinador.

1 Participantes

INSA Rouen

295.06K€ | Leader

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Project duration: 41 months Date Start: 2021-03-22
End date: 2024-08-31

Project leader

INSTITUT NATIONAL DES SCIENCES APPLIQUEES DE... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Project Budget 295K€

participation deadline Sin fecha límite de participación.

Project description 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.

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