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跨多个概念的设计优化框架
Angus Kenny1, Tapabrata Ray1, Hemant Singh2
1School of Engineering and Technology, University of New South Wales, Canberra, ACT, 2600, Australia.
Scientific reports
|April 3, 2024
概括
这项研究引入了一个新的框架,用于高效的多概念工程设计优化. 它使用机器学习智能地探索解决方案,节省计算资源,避免过早地趋同到次优设计.
科学领域:
- 工程设计 工程设计 工程设计
- 计算科学 计算科学
- 机器学习 机器学习
背景情况:
- 最初的概念设计阶段显著影响产品生命周期成本 (高达70%).
- 由于固定的概念假设和计算需求,标准优化方法不适合多概念设计阶段.
- 现有的方法难以有效地搜索具有不同类型变量的多种概念解决方案.
研究的目的:
- 开发一个有效的框架,在多个工程设计概念中寻找最佳解决方案.
- 解决在早期的多概念设计阶段应用优化方法的差距.
- 通过逐步评估有前途的概念和解决方案,尽量减少计算预算.
主要方法:
- 一个新的框架,用于概念和解决方案评估的数据驱动决策.
- 树结构帕森估计器 (TPE) 与高斯过程 (GP) 和随机森林 (RF) 回归器的集成.
- 扩展GP和RF用于多概念搜索,并突出了TPE在设计优化中的好处.
主要成果:
- 在多个概念中使用异质变量类型 (连续,二进制,离散,分类) 的有效搜索.
- 在有限的计算预算下,在各种案例研究中成功应用,包括悬臂梁,冠状动脉支架和格子结构.
- 验证框架能够降低计算成本并避免过早的融合的能力.
结论:
- 开发的框架有效地支持设计师在基于概念的设计阶段.
- 机器学习的进步,特别是TPE,为多概念设计优化提供了显著的好处.
- 这种方法填补了关键的空白,提高了早期工程设计的效率和有效性.
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