一种新的单变Legendre多项式方法用于概率性故障负载预测在复合式开孔层层中
Mingxuan Li1, Ben Yuan1, Fugui Li1
1School of Mechanical and Power Engineering, Nanjing Tech University, Nanjing, 211816, China.
Scientific reports
|December 30, 2025
概括
一个新的单变Legendre多项式方法准确地预测了复合开孔层材的概率性故障负载. 这种新的方法,ULAM,性能优于现有的模型,并提供高精度与最小的错误.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 计算力学 计算力学 计算力学
背景情况:
- 预测复合开孔层材的概率性故障负载对于结构完整性至关重要.
- 现有的方法可能在概率预测中缺乏准确性或效率.
- 与多项式近似相结合的缩小维度技术显示出有希望的结果.
研究的目的:
- 引入一种新型的单变Legendre多项式方法 (ULAM) 用于在复合开孔层材中进行概率性故障负载预测.
- 通过实验数据验证ULAM的准确性和有效性,并将其与已建立的模型进行比较.
- 调查多项式顺序对ULAM预测准确性的影响.
主要方法:
- 开发一个与维度缩小集成的单变量列德多项式方法.
- 使用复合式开孔层层样本对拟议方法进行实验验证.
- 对ULAM与响应表面方法 (RSM) 和Kriging模型进行比较分析.
主要成果:
- ULAM的预测准确度很高,平均预测误差低于3%,变化系数低于15%.
- 对于同等数量的样本点,ULAM在预测准确度方面超过了RSM和Kriging模型.
- 该研究确定了ULAM的最佳多项式顺序,避免了不足和过度配合的问题.
结论:
- 提出的单变列列德多项式方法 (ULAM) 是一种高效且准确的工具,用于在复合开孔层材中进行概率性故障负载预测.
- 与RSM和Kriging相比,ULAM提供了更高的性能,提供了更可靠的预测框架.
- 仔细选择多项式顺序对于最大限度地提高ULAM的预测能力和避免性能下降至关重要.
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