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arXiv · 2602.21696

Dual-Regime Hybrid Aerodynamic Modeling of Winged Blimps With Neural Mixing

Xiaorui Wang·Hongwu Wang·Yue Fan·Hao Cheng·Feitian Zhang
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Abstract

Winged blimps operate across distinct aerodynamic regimes that cannot be adequately captured by a single model. At high speeds and small angles of attack, their dynamics exhibit strong coupling between lift and attitude, resembling fixed-wing aircraft behavior. At low speeds or large angles of attack, viscous effects and flow separation dominate, leading to drag-driven and damping-dominated dynamics. Accurately representing transitions between these regimes remains a fundamental challenge. This paper presents a hybrid aerodynamic modeling framework that integrates a fixed-wing Aerodynamic Coupling Model (ACM) and a Generalized Drag Model (GDM) using a learned neural network mixer with explicit physics-based regularization. The mixer enables smooth transitions between regimes while retaining explicit, physics-based aerodynamic representation. Model parameters are identified through a structured three-phase pipeline tailored for hybrid aerodynamic modeling. The proposed approach is validated on the RGBlimp platform through a large-scale experimental campaign comprising 1,320 real-world flight trajectories across 330 thruster and moving mass configurations, spanning a wide range of speeds and angles of attack. Experimental results demonstrate that the proposed hybrid model consistently outperforms single-model and predefined-mixer baselines, establishing a practical and robust aerodynamic modeling solution for winged blimps.

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Xiaorui Wang, Hongwu Wang, Yue Fan, Hao Cheng, Feitian Zhang. 2026-02-25. Dual-Regime Hybrid Aerodynamic Modeling of Winged Blimps With Neural Mixing. https://arxiv.org/abs/2602.21696

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