Prediction of Wing Pressure Distribution Using an Autoencoder-Based Surrogate Model
In the present work, an alternative methodology was developed for the rapid prediction of pressure distributions over wings of low-speed aircraft. A hybrid neural network architecture, named “MARTHA” (Model for Airloads Reconstruction using a Trained Hybrid Architecture), was presented, which is composed of a Multilayer Perceptron and the decoder of an Autoencoder. Three compact representation models—Principal Component Analysis (PCA), Autoencoder (AE), and Variational Autoencoder (VAE)—were systematically evaluated to determine the optimal dimensionality reduction architecture; the AE was selected based on its superior reconstruction accuracy and training stability. The main feature of MARTHA is that it provides predictions of the differential pressure coefficient field in the form of monochrome images, where the pixel intensity directly represents the normalized pressure value. One of the main objectives of developing MARTHA was to create a rapid surrogate model that can approximate vortex lattice method (VLM) simulations in preliminary design and optimization tasks, particularly when thousands of wing configurations need to be evaluated. The key feature of the proposed model is its ability to predict the pressure distribution for trapezoidal wings of various geometries 101–104 times faster than numerical models, while maintaining accuracy (R2 = 0.9998). The data obtained are presented in a convenient format for their further use in CAE systems of strength analysis. To assess the practical utility of the proposed model, implementation cases were carried out using the finite element software ANSYS 18.2 for three wing configurations not present in the training dataset. The pressure fields predicted by MARTHA were mapped onto the wing meshes, and linear static structural analyses were performed. The obtained Von Mises stress distributions showed good agreement with the corresponding distributions obtained using numerical models.