Neural prediction model for transition onset of a boundary layer in presence of two-dimensional surface defects - Calcul Intensif, Simulation, Optimisation Access content directly
Journal Articles FLOW: Applications of Fluid Mechanics (En ligne) Year : 2023

Neural prediction model for transition onset of a boundary layer in presence of two-dimensional surface defects

Abstract

Predicting the laminar to turbulent transition is an important aspect of computational fluid dynamics because of its impact on skin friction. Traditional transition prediction methods such as local stability theory or the parabolized stability equation method do not allow for the consideration of strongly non-parallel boundary layer flows, as in the presence of surface defects (bumps, steps, gaps, etc.). A neural network approach, based on an extensive database of two-dimensional incompressible boundary layer stability studies in the presence of gap-like surface defects, is used. These studies consist of linearized Navier–Stokes calculations and provide information on the effect of surface irregularity geometry and aerodynamic conditions on the transition to turbulence. The physical and geometrical parameters characterizing the defect and the flow are then provided to a neural network whose outputs inform about the effect of a given gap on the transition through the ${\rm \Delta} N$ method (where N represents the amplification of the boundary layer instabilities).
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hal-04165542 , version 1 (19-07-2023)

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Adrien Rouviere, Lucas Pascal, Fabien Méry, Ehouarn Simon, Serge Gratton. Neural prediction model for transition onset of a boundary layer in presence of two-dimensional surface defects. FLOW: Applications of Fluid Mechanics (En ligne), 2023, 3, pp.E20. ⟨10.1017/flo.2023.17⟩. ⟨hal-04165542⟩
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