用自学习强化学习优化航空发动机自由曲率管路，适应动态设计环境。
Self-Learning-Based Optimization for Free-form Pipe Routing in Aeroengine with Dynamic Design Environment
- 基于PPO算法构建自学习管路优化框架，融合连续空间规则建模
- 在静态与动态环境中均实现更短管路、更低复杂度和更高效率
- 可快速适应设计变更，避免重新搜索，适合工业级管路设计
管路布线是航空发动机设计中高度复杂、耗时且属于NP难的问题。尽管已有大量研究针对定曲率管路优化，但日益增长的自由曲率管路需求带来了新挑战。动态设计环境与模糊布局规则进一步影响优化性能与效率。为此，本文提出一种基于自学习的自由曲率管路优化方法（SLPR），其基于近端策略优化（PPO）算法，并集成统一规则建模框架以实现连续空间中的高效障碍物检测与模糊规则建模。此外，构建势能表以实现布局倾向与干涉的快速查询。SLPR中的智能体通过与环境交互不断优化管路路径并积累设计知识。当设计环境变化时，仅需微调网络参数即可快速适应。对比测试表明，SLPR通过三次非均匀B样条（NURBS）曲线确保管路平滑，避免了定曲率方法中的冗余段。在静态与动态环境中，其在管路长度缩减、规则遵循度、路径复杂度及计算效率方面均优于三种代表性基线。动态环境测试还显示，SLPR无需从头搜索，甚至优于重新训练模型。结果凸显其在满足现代航空发动机轻量化、高精度与可持续性要求方面的实际价值。
原文摘要 · Abstract (English)
Pipe routing is a highly complex, time-consuming, and no-deterministic polynomial-time hard (NP-hard) problem in aeroengine design. Despite extensive research efforts in optimizing constant-curvature pipe routing, the growing demand for free-form pipes poses new challenges. Dynamic design environments and fuzzy layout rules further impact the optimization performance and efficiency. To tackle these challenges, this study proposes a self-learning-based method (SLPR) for optimizing free-form pipe routing in aeroengines. The SLPR is based on the proximal policy optimization (PPO) algorithm and integrates a unified rule modeling framework for efficient obstacle detection and fuzzy rule modeling in continuous space. Additionally, a potential energy table is constructed to enable rapid queries of layout tendencies and interference. The agent within SLPR iteratively refines pipe routing and accumulates the design knowledge through interaction with the environment. Once the design environment shifts, the agent can swiftly adapt by fine-tuning network parameters. Comparative tests reveal that SLPR ensures smooth pipe routing through cubic non-uniform B-spline (NURBS) curves, avoiding redundant pipe segments found in constant-curvature pipe routing. Results in both static and dynamic design environments demonstrate that SLPR outperforms three representative baselines in terms of the pipe length reduction, the adherence to layout rules, the path complexity, and the computational efficiency. Furthermore, tests in dynamic environments indicate that SLPR eliminates labor-intensive searches from scratch and even yields superior solutions compared to the retrained model. These results highlight the practical value of SLPR for real-world pipe routing, meeting lightweight, precision, and sustainability requirements of the modern aeroengine design.
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