在机械和热负荷下的复合层结构中关键的参数分析:有限元和机器学习方法
Omar Shabbir Ahmed1,2, Jaffar Syed Mohamed Ali2, Abdul Aabid1
1Department of Engineering Management, College of Engineering, Prince Sultan University, P.O. Box 66833, Riyadh 11586, Saudi Arabia.
Materials (Basel, Switzerland)
|September 14, 2024
概括
这项研究优化了复合结构,用于曲强度的孔. 机器学习准确地预测了机械和热负荷下的弹性最佳参数.
科学领域:
- 复合材料科学 复合材料科学
- 结构工程 结构工程
- 计算力学 计算力学 计算力学
背景情况:
- 薄壁复合结构在工程应用中至关重要.
- 了解它们在各种负载下曲的行为对于结构完整性至关重要.
- 孔隙和材料变化显著影响结构稳定性.
研究的目的:
- 为了研究带有洞的薄壁复合结构的屈曲强度.
- 优化材料和结构参数,以提高弹性.
- 为关键曲折负载开发准确的预测模型.
主要方法:
- 用于结构设计和模拟的有限元分析 (FEA).
- 参数研究不同孔形状,层次序列和材料特性.
- 机器学习 (ML) 算法 (线性回归,随机森林,梯度提升) 用于参数优化和预测.
主要成果:
- FEA模拟显示了结构和材料特性如何影响关键曲折负载.
- 在机械和热条件下,ML模型准确地预测了最佳的临界曲负荷.
- 确定了参数的最佳组合,以提高结构稳定性.
结论:
- 与ML相结合的FEA提供了一种有效的方法来研究复合结构稳定性.
- 这项研究提供了关于设计具有洞穴的弹性薄壁复合结构的见解.
- 优化的参数提高了结构在联合机械和热应力下的性能.
相关概念视频
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