Reconstruction of numerical inlet boundary conditions using machine learning: Application to the swirling flow inside a conical diffuser
A new approach to determine proper mean and fluctuating inlet boundary conditions is
proposed. It is based on data driven techniques, ie, machine learning approach, and its goal
is to use any known information about the downstream flow to reconstruct the unknown or
incomplete inlet boundary conditions for a numerical simulation. The European Research
Community On Flow, Turbulence And Combustion (ERCOFTAC) test case of the swirling
flow inside a conical diffuser is investigated. Despite its relatively simple geometry, it …
proposed. It is based on data driven techniques, ie, machine learning approach, and its goal
is to use any known information about the downstream flow to reconstruct the unknown or
incomplete inlet boundary conditions for a numerical simulation. The European Research
Community On Flow, Turbulence And Combustion (ERCOFTAC) test case of the swirling
flow inside a conical diffuser is investigated. Despite its relatively simple geometry, it …
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