Aerodynamics-constrained generative design of exterior form for a high-speed maglev experimental vehicle

Abstract Under high-speed operating conditions, the form–performance matching between aerodynamic performance and formal aesthetics becomes a core challenge in the design of high-speed maglev experimental vehicles, while conventional experience-driven approaches struggle to achieve coordinated optimization. Taking a single-unit high-speed maglev experimental vehicle as the research object, this paper proposes a generative intelligent exterior-form design method integrating aerodynamics with Stable Diffusion (SD). Parametric modeling of two-dimensional primary profiles and numerical simulations of three-dimensional topological forms are first conducted to extract the mapping relationships between morphological features and aerodynamic performance under representative operating conditions, thereby determining optimal parameter combinations in both two- and three-dimensional domains. The correlation mechanisms between profile geometry and the perceptual semantics of lightweight, elongation, and sharpness are then clarified, and a quantitative aesthetic evaluation framework is established. Based on the optimal aerodynamic contour, a performance-constrained generative design workflow is constructed through ControlNet-guided conditional generation and customized LoRA model training, enabling the coupled generation of styling features, aerodynamic performance, and formal aesthetics, followed by multi-scheme exploration and secondary numerical evaluation. Engineering validation indicates that the proposed scheme achieves a drag coefficient of 0.042, with pitching-moment fluctuations controlled within ±175 N, and full-scale tests at 300 km/h on the China Railway Rolling Stock Corporation Limited (CRRC) Zhuzhou test line confirm that all key performance indicators meet the required standards. By integrating simulation-based analysis, generative learning, and aesthetic evaluation, this study establishes a form–performance collaborative design framework, providing a technically feasible and theoretically grounded pathway for the innovative design of next-generation high-speed maglev experimental vehicles.