Neural-Network-Based Model for Trailing-Edge Flap Loads in Preliminary Aircraft Design

Abstract

Accurately predicting the forces and moments acting on trailing-edge devices under different flight conditions is a critical aspect in the design of the kinematics and actuation for high-lift or variable-camber applications. However, accurate modeling without elaborated computational fluid dynamics (CFD) analyses in the subsonic and transonic regimes needs a sophisticated model. Thus, the objective of this paper is to create such a model that accurately predicts the forces and moments acting on flaps during different flight conditions while remaining applicable to the preliminary aircraft design. The target values in this model are the three-dimensional (3D) forces and moments on the flap, which were obtained through 3D CFD simulations. The chosen input values required for the model include two-dimensional airfoil data, and wing geometry data for three different aircraft types: short-, medium-, and long-range, including a high-aspect-ratio configuration. Among several potential approaches, a neural network was deemed to be the most promising for predicting the target values. The neural network was used as a regression tool to accelerate the model development process in the preliminary aircraft design. Consequently, multiple studies were conducted on how the setup of the neural network, including the number of neurons, activation functions, and initialization, influences the results. The results reveal that the developed neural network accurately predicts the flap forces and moments with a mean deviation of under 2% for the vertical force [Formula: see text] and the lateral force [Formula: see text] and under 4% for the moment [Formula: see text].