Multi-fidelity autoencoders: RANS-LES jet flow predictions
Abstract In Fluid Mechanics, developing deep learning models for relevant engineering configurations using exclusively high-fidelity data is often impractical due to the large computational cost of the corresponding simulations. We introduce a Machine Learning procedure that combines three algorithms to construct a multi-fidelity autoencoder capable of providing real-time high-fidelity predictions with quantified uncertainties while substantially reducing the computational cost of database construction. We demonstrate the method on a problem of practical interest: the prediction of jet flow fields emanating from a family of parametric nozzle configurations. The proposed multi-fidelity dataset comprises a limited number of computationally expensive Large Eddy Simulations augmented by a larger ensemble of computationally cheap Reynolds-Averaged Navier–Stokes simulations. The trained model learned the underlying relation between nozzle geometry and jet properties from low-fidelity data, and used high-fidelity ones to construct a correction term from low to high fidelity, thus providing a high-fidelity reduced-order model in the full design space.