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

2026-02-25

AI Summary

This work addresses the challenge of modeling the markedly distinct aerodynamic characteristics of winged airships under high-speed, low-angle-of-attack and low-speed, high-angle-of-attack conditions, which cannot be adequately captured by a single unified model. To this end, the authors propose a hybrid aerodynamic modeling framework that integrates a fixed-wing aerodynamic coupling model (ACM) with a generalized drag model (GDM). A physically regularized, learnable neural network mixer is introduced to enable smooth transitions between the two models. This approach achieves, for the first time, a unified, continuous, and interpretable representation of the dual-mode aerodynamics of winged airships. Validation on 1,320 real flight trajectories from the RGBlimp platform demonstrates that the proposed model consistently outperforms both individual models and baseline strategies with predefined blending rules across diverse configurations and operating conditions.

Technology Category

• Machine Learning: Mixture of Experts (MoE)
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

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.

Problem

• winged blimps
• aerodynamic regimes
• model transition
• lift-attitude coupling
• flow separation

Innovation

• hybrid aerodynamic modeling
• neural mixing
• physics-based regularization
• winged blimps
• aerodynamic regime transition