Increasing the Degrees Of Freedom (DOFs) of Kinetic Façade Systems (KFS) potentially enhances environmental adaptability but presents challenges in mechanical feasibility and optimization complexity due to high-dimensional design spaces. This paper investigates the mechanism design and optimization strategies for multi-DOF KFS, and assesses the performance trade-offs associated with increased motion and control freedom. An Infinite Rotation Freedom (IRF) prototype is proposed and experimentally validated, and a two-stage surrogate-based optimization framework is developed for multi-DOF façade systems by integrating machine learning-based surrogate models with optimization algorithms for both static feature selection and kinetic motion control. Comparative performance analyses demonstrated that the IRF system significantly improves daylight distribution and thermal regulation compared to conventional louvers, with multi-DOF motion enhancing daylight distribution and increased control freedom enabling more precise glare mitigation. These findings highlight the feasibility and environmental advantages of multi-DOF KFS. Future research should address movement continuity issues to improve operational efficiency.

中文翻译：

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使用机器学习代理模型对无限旋转自由立面系统进行两阶段优化

增加动态幕墙系统 （KFS） 的自由度 （DOF） 可能会增强环境适应性，但由于高维设计空间，在机械可行性和优化复杂性方面提出了挑战。本文研究了多自由度 KFS 的机构设计和优化策略，并评估了与增加运动和控制自由度相关的性能权衡。提出了无限旋转自由度 （IRF） 原型并进行了实验验证，通过将基于机器学习的代理模型与静态特征选择和动力学运动控制的优化算法集成，为多自由度立面系统开发了一个基于代理的两阶段优化框架。比较性能分析表明，与传统百叶窗相比，IRF 系统显著改善了日光分布和热调节，多自由度运动增强了日光分布并增加了控制自由度，从而能够更精确地减轻眩光。这些发现突出了多自由度 KFS 的可行性和环境优势。未来的研究应解决移动连续性问题，以提高运营效率。