加快复合结构的多目标优化,使用多忠实替代模型和课程学习
Bartosz Miller1, Leonard Ziemiański1
1Faculty of Civil and Environmental Engineering and Architecture, Rzeszów University of Technology, Al. Powstancow Warszawy 12, 35-959 Rzeszow, Poland.
Materials (Basel, Switzerland)
|April 24, 2025
概括
本研究介绍了一种使用课程学习 (CL) 和多忠实代用模型优化复合结构的高效方法. 这种方法可以降低计算成本,同时保持工程设计的准确性.
科学领域:
- 工程 工程师 工程师 工程师
- 计算科学 计算科学
- 材料科学 材料科学 材料科学
背景情况:
- 优化多层复合结构需要平衡机械性能,成本和计算需求.
- 现有的方法经常面临复杂设计的计算效率的挑战.
研究的目的:
- 开发一种创新的方法来提高工程设计中的计算效率.
- 整合课程学习 (CL) 与多忠实替代模型进行结构优化.
主要方法:
- 采用多忠度策略,使用高忠度有限元模型和低忠度模型来有效生成数据.
- 应用课程学习 (CL) 用于代地改进替代模型以提高准确性.
- 采用基因算法 (GA) 来优化结构参数并最大限度地降低计算成本.
主要成果:
- 在保持预测准确性的同时,显著减少了计算负担.
- 通过使用绩效指标评估帕雷托前线质量来验证方法.
- 在现实世界的结构设计问题中展示了实际的应用性.
结论:
- 集成CL,多忠度建模和GA,为复合结构优化提供了一个高效的框架.
- 这种可扩展的方法解决了对复杂工程问题的高计算效率的需求.
- 该研究通过创新的计算策略来推进工程设计的优化技术.
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