The paper introduces FlightLLM, a prior-guided semantic approach that uses large language models to explain flight safety events. It tackles challenges such as modal inconsistency, limited classification ability, and scarce domain data by combining feature engineering, semantic discretization, a CatBoost statistical expert, contrastive few-shot learning, and structured prompts. Evaluated on 704 real‑world A320 flights, FlightLLM achieves competitive classification and produces clear, aviation‑specific explanations for hard landing events.
By Lu Xu, Xu Li, Linjiang Zheng, Fan Li, Riquan Zhang, Jiaxing Shang
The paper presents a method for detecting and classifying damage in wind turbine blades using aerodynamic pressure measurements from the Aerosense system. A convolutional neural network was trained on data from a NACA 633418 airfoil mounted on a vibrating cantilever, where damage was introduced via saw cuts. The study also incorporates physics-based insights and explainable ML techniques to interpret how damage affects dynamic response and pressure fields, enhancing transparency and robustness of the monitoring pipeline.
By Philip Franz, Max von Danwitz, Gregory Duth\'e, Alexander Popp, Eleni Chatzi
arXiv:2512. 13069v2 Announce Type: replace Abstract: Accurate aerodynamic prediction often relies on high-fidelity simulations; however, their prohibitive computational costs severely limit their applicability in data-driven modeling.
By Javier Nieto-Centenero, Esther Andr\'es, Rodrigo Castellanos
arXiv:2606. 28871v1 Announce Type: cross Abstract: Predicting the aerodynamic performance (e.
By Geoffrey Davis, Ashwin Renganathan
arXiv:2609.06660v1 Announce Type: cross
Abstract: Accurate aerodynamic prediction is critical for designing fuel-efficient and safe transportation systems such as aircraft and automobiles, yet tradit...
By Wenxuan Jin, Jianguo Yao, Haibing Guan, Xijun Li
The paper presents a dataset of roughly 10,000 RANS simulations of steady, incompressible, two‑dimensional subsonic flow around downforce‑generating NACA 4‑digit airfoils, aimed at automotive and motorsport regimes. It explores geometry‑conditioned neural surrogates—comparing neural fields, neural ODEs, MLPs, graph models, and spectral conditioning—and introduces Airfoil2Vec, a spectral geometry encoder that fuses joint contour, camber, and thickness spectra to predict continuous pressure and velocity fields. The surrogate models are evaluated on angle‑of‑attack interpolation, unseen NACA geometries, and non‑NACA airfoils, achieving accurate aerodynamic predictions with orders‑of‑magnitude speedups over traditional CFD.
By Haitz S\'aez de Oc\'ariz Borde, Flavio Savarino, Andrei Cristian Popescu, Pietro Innocenzi, Pantelis Papageorgiou, Xerxes Xian Chong