Prediction of flow-fields by combining high-fidelity CFD data and Machine Learning algorithms

High-fidelity optimisation studies are a useful asset in the design of critical components for large gas turbines. These studies require the computation of numerous computationally expensive CFD simulations and result in predominantly optimisation graphs of design objectives (e.g. Pareto-front figure). The quantity of generated data is...

Multi-fidelity Co-Kriging Optimization using Hybrid Injected RANS and LES

The computation of complex turbulent flows design optimization processes is currently limited by the lack of accuracy of Reynolds-Averaged Navier-Stokes (RANS) in massively separated flows and the infeasible cost of multiple Large-Eddy Simulation (LES) evaluations. A novel method is presented, injecting data from LES or other high-fidelity...