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 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.