This paper introduces a modular and scalable design optimization framework for the glider wing design process that enables faster early-phase design while ensuring aerodynamic stability. The pipeline starts with the generation of initial wing geometries and then proceeds to optimize the wing using several algorithms. Aerodynamic performance is assessed using a Vortex Lattice Method (VLM) applied to a carefully selected dataset of wing configurations. These results are employed to develop surrogate neural network models, which can predict lift and drag rapidly and accurately. A timing analysis shows that the surrogate model provides a speedup of approximately 785 times compared to the combined VLM and stability analysis, enabling efficient large-scale optimization. The stability evaluation is implemented by setting the control surfaces and components to fixed positions in order to have realistic flight dynamics. The approach unifies and compares several optimization techniques, including Particle Swarm Optimization (PSO), Genetic Algorithms (GA), gradient-based MultiStart methods, Bayesian optimization, and Lipschitz optimization. Each method ensures constraint management via adaptive strategies and penalty functions, where the targets for lift and design feasibility are enforced. The progression of aerodynamic characteristics and geometries over the optimization iterations will be investigated in order to clarify each algorithm's convergence characteristics and performance efficiency. Our results show improvement in aerodynamic qualities and robust stability properties, offering a mechanism for wing design at speed and precision. In the interest of reproducibility and community development, the complete implementation is publicly available on GitHub.

翻译：本文提出了一种模块化、可扩展的滑翔机翼型设计优化框架，可在确保气动稳定性的同时加速初始设计阶段。该流程首先生成初始翼型几何构型，随后通过多种算法对翼型进行优化。气动性能采用涡格法(VLM)对精心挑选的翼型配置数据集进行评估，并利用这些结果开发神经代理网络模型，该模型能够快速准确地预测升力与阻力。时序分析表明，与联合VLM和稳定性分析相比，代理模型可实现约785倍的加速，从而支持高效的大规模优化。稳定性评估通过将控制面与部件固定于特定位置实现，以模拟真实的飞行动力学。本方法统一并比较了多种优化技术，包括粒子群优化(PSO)、遗传算法(GA)、基于梯度的多起点方法、贝叶斯优化以及Lipschitz优化。每种方法均通过自适应策略和惩罚函数进行约束管理，同时对升力目标和设计可行性实施约束。通过分析优化迭代过程中气动特性与几何构型的演变规律，阐明各算法的收敛特性与性能效率。结果表明，本方法在提升气动品质与鲁棒稳定性的同时，为翼型设计提供了兼具速度与精度的实现机制。为促进成果可复现与社区发展，完整实现代码已在GitHub上公开。