Neural prediction model for transition onset of a boundary-layer in presence of 2D surface defects
Résumé
Predicting the laminar to turbulent transition is an important aspect of computational fluid dynamics because of its impact on skin friction. The traditional methods of transition prediction do not make it possible to consider configurations where the boundary layer develops in the presence of surface defects (bumps, steps, gaps, etc.). A neural network approach is used in this paper, based on an extensive database of boundary layer stability studies in the presence of gap-like surface defects. These studies consists on Linearized Navier-Stokes (LNS) calculations and provide information on the effect of surface irregularity geometry and aerodynamic conditions on the transition to turbulence. The physical and geometrical parameters characteristic of 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 ΔN method.
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