使用机器学习在流壁喷气下对冲孔特征的建模
Jnana Ranjan Khuntia1, Kamalini Devi2, Mohd Aamir Mumtaz3
1Civil Engineering Department, Chaitanya Bharathi Institute of Technology (Autonomous), Hyderabad, 500075, India. jnanaranjan444@gmail.com.
Scientific reports
|July 6, 2024
概括
这项研究研究了来自流壁喷射的扫孔特征,开发了新的数学关系. 人工神经网络-粒子群集优化 (ANN-PSO) 模型在预测扫描参数方面表现出优异的准确性,与回归和其他方法相比.
科学领域:
- 流体力学 流体力学 流体力学
- 液压工程 液压工程是指水利工程.
- 计算建模计算建模
背景情况:
- 在液压结构周围进行扫描是一个关键问题.
- 流的墙体喷流显著地影响了扫孔的形成.
- 准确预测扫除参数对于结构完整性至关重要.
研究的目的:
- 为了研究影响流墙喷射的扫孔特征的参数.
- 为了开发新的数学关系,扫描现象.
- 为了比较各种计算模型的预测准确度.
主要方法:
- 确定了关键参数:扫地深度,扫地深度位置,沙丘高度和沙丘顶部位置.
- 在现有数据集上使用多回归分析 (线性和非线性).
- 开发并比较了人工神经网络-粒子群优化 (ANN-PSO) 和基因表达编程 (GEP) 模型.
主要成果:
- 密度对称的Froude数,围长度,尾水水平和中位沉积物大小被确定为关键影响参数.
- 与回归和GEP相比,ANN-PSO模型显示了更高的准确性 (RMSE在1.512和6.644之间)
- 统计绩效指标,不确定性分析和可靠性指数证实了ANN-PSO的预测能力.
结论:
- 该ANN-PSO模型提供了一个更准确和可靠的预测扫孔特征.
- 开发的数学关系和模型有助于理解和减轻scour效应.
- 这项研究有助于改善易受污染的液压结构的设计和安全性.
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