Data-Driven Integral Boundary-Layer Modeling for Airfoil Performance Prediction in Laminar Regime

Abstract

Many simulation tools for airfoil analysis and design are based on an integral approximation of the boundary layer. This approximate formulation cannot resolve the full dynamics of boundary-layer flows and hence requires additional models to account for unresolved effects. This paper introduces a new, data-driven, probabilistic model of these unresolved effects for the incompressible and laminar regime. To construct this model, methods from supervised learning have been applied to a dataset containing over 1550 airfoils. The result is a model that 1) is based on a large dataset of realistic airfoil configurations, and 2) quantifies the model inadequacy associated with the use of an approximate boundary-layer formulation. A stochastic version of the airfoil design tool XFOIL has also been created by replacing its original boundary-layer model with the probabilistic model developed here. This stochastic version of XFOIL has been applied to compute the drag polars of two airfoils at low Reynolds numbers and the results are compared with experimental data.