Machine Learning-Based Methodology for Predicting 2D Propeller–Airfoil–Flap Interactions
TL;DR
A surrogate modeling framework is developed that predicts the section-level aerodynamic response of a propeller–airfoil–flap configuration across a multi-dimensional space of propeller positioning, flap geometry, and operational conditions, enabling rapid, optimization-ready exploration of propeller–airfoil–flap configurations.
Abstract
Aero-propulsive interactions in flapped configurations are a critical consideration for Short Take-Off and Landing (stol) aircraft, where extreme operational requirements demand robust, optimization-ready methodologies during preliminary design. This study develops a surrogate modeling framework that predicts the section-level aerodynamic response of a propeller–airfoil–flap configuration across a multi-dimensional space of propeller positioning, flap geometry, and operational conditions. A paired powered and unpowered design of experiments isolates the propulsive contribution to lift, drag, and pitching moment, while a virtual-disk propeller model parameterized by volumetric thrust decouples the prediction from any specific blade design. The framework couples two-dimensional steady Reynolds-averaged Navier–Stokes (rans) dataset generation with a Deep Neural Network (dnn) surrogate, which achieves coefficient of determination values above 0.96 for all three coefficients and reduces evaluation cost by several orders of magnitude relative to direct cfd, a benefit that is decisive in optimization. Single- and multi-objective optimization identify Pareto-optimal configurations, and independent cfd verification confirms prediction accuracies within 5 to 10% across the operational envelope. The resulting surrogate enables rapid, optimization-ready exploration of propeller–airfoil–flap configurations, providing actionable trade-off information for the preliminary design of stol aircraft.