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以黑翼为灵感的压缩机的初始设计优化可解释的强化学习
Mingming Zhang1, Zhuang Miao1, Xi Nan2
1School of Mathematics Statistics and Mechanics, Beijing University of Technology, Beijing 100124, China.
Biomimetics (Basel, Switzerland)
|August 27, 2025
概括
这项研究引入了一种结合深度强化学习和决策树蒸用于压缩机设计的新方法. 它增强了优化能力和模型可解释性,从而提高了效率和设计透明度.
科学领域:
- 机械工程
- 航空航天工程
- 计算科学
背景情况:
- 人工智能 (AI) 方法,包括强化学习 (RL),为优化压缩机设计提供了潜力.
- 人工智能驱动的压缩机设计的关键挑战包括有限的设计变量和不足的模型可解释性.
- 现有的方法往往难以平衡优化性能与透明,可解释的设计流程.
研究的目的:
- 为初始压缩机设计提出技术方法,将深度强化学习和决策树蒸集成.
- 在压缩机设计中提高AI模型的优化能力和可解释性.
- 将数据驱动的智能优化转化为实际应用的明确工程经验.
主要方法:
- 使用深度决定性政策梯度 (DDPG) 算法构建了压缩机初始设计方案的预选平台.
- 通过共同设计25个关键变量来扩大优化空间,包括进气流角,反应和负载系数.
- 用Shapley添加式解释 (SHAP) 分析和黑翼 (BKA) 算法启发的决策树进行模型解释和规则提取.
主要成果:
- 六级轴向压缩机的初始设计成功完成,实现了84.65%的轴向效率和10.75%的冲压率.
- SHAP分析揭示了关键设计参数对压缩机性能的影响,提高了模型的解释性.
- 由BKA启发的决策树提取了可解释的设计规则,具有明确的物理含义,指导了最初的设计过程.
结论:
- 提出的方法显著提高了压缩机设计过程的透明度,同时保持了高性能.
- 提取的设计规则有效地指导了最初的压缩机设计,为智能设计提供了优化和可解释性的新范式.
- 这种方法为推进压缩机等复杂系统的AI驱动工程设计提供了有价值的框架.
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