@article{CORCIONE2025, 
author = {Salvatore CORCIONE and Agostino DE MARCO and Vincenzo CUSATI},
title = {A data-driven methodology to predict ice-induced aerodynamic degradation applied to aircraft tailplane design},
year = {2025},
journal = {Chinese Journal of Aeronautics},
volume = {38},
number = {8},
keywords = {Data-driven aerodynamics, Forward swept tailplane, Gaussian process regression, Ice accretion prediction, Machine learning for icing analysis},
url = {https://www.sciopen.com/article/10.1016/j.cja.2025.103476},
doi = {10.1016/j.cja.2025.103476},
abstract = {This study presents a data-driven approach to predict tailplane aerodynamics in icing conditions, supporting the ice-tolerant design of aircraft horizontal stabilizers. The core of this work is a low-cost predictive model for analyzing icing effects on swept tailplanes. The method relies on a multi-fidelity data gathering campaign, enabling seamless integration into multi-disciplinary aircraft design workflows. A dataset of iced airfoil shapes was generated using 2D inviscid methods across various flight conditions. High-fidelity CFD simulations were conducted on both clean and iced geometries, forming a multidimensional aerodynamic database. This 2D database feeds a nonlinear vortex lattice method to estimate 3D aerodynamic characteristics, following a ‘quasi-3D’ approach. The resulting reduced-order model delivers fast aerodynamic performance estimates of iced tailplanes. To demonstrate its effectiveness, optimal ice-tolerant tailplane designs were selected from a range of feasible shapes based on a reference transport aircraft. The analysis validates the model’s reliability, accuracy, and limitations concerning 3D ice shapes and aerodynamic characteristics. Most notably, the model offers near-zero computational cost compared to high-fidelity simulations, making it a valuable tool for efficient aircraft design.}
}