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.
Fields of science
Programme(s)
Call for proposal
H2020-MSCA-IF-2020
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Funding Scheme
MSCA-IF-EF-CAR - CAR – Career Restart panelCoordinator
76 801 Saint Etienne Du Rouvray
France