基于数据的预测相当于沙粒粗度的数据驱动
Haoran Ma1, Yuhao Li2, Xin Yang3
1Department of Ocean Engineering, Texas A&M University, College Station, TX, 77843, USA. mhr930608@tamu.edu.
Scientific reports
|November 5, 2023
概括
这项研究引入了一种新的粒子集群优化反向传播 (PSO-BP) 方法,用于预测等效的沙粒粗度. 与现有方法相比,PSO-BP模型在流体动力学计算中表现出更高的准确性.
科学领域:
- 流体动力学 流体动力学
- 计算流体动力学 (CFD) 是一种计算流体动力学.
- 表面科学是一门学科.
背景情况:
- 表面粗程度显著影响近壁流体的速度和阻力.
- 相当的沙粒粗度 (k_s) 对于流体动力学建模至关重要.
- 现有的k_s预测公式缺乏普遍性,并且通常仅限于特定的粗度类型.
研究的目的:
- 开发一种更准确,更普遍的方法来预测等效的沙粒粗度 (k_s).
- 与传统方法相比,评估粒子群优化反向传播 (PSO-BP) 模型的性能.
- 为计算流体动力学 (CFD) 和工程应用提供改进的解决方案.
主要方法:
- 利用粒子群优化反向传播 (PSO-BP) 模型来预测k_s.
- 使用DNS,LES和各种粗度的实验数据的表面参数训练模型.
- 将PSO-BP性能与现有的粗度相关公式和标准反向传播 (BP) 模型进行比较.
主要成果:
- 该PSO-BP方法实现了卓越的性能,平均绝对误差 (MAE) 为0.0390,平均平方误差 (MSE) 为0.0026,平均绝对百分比误差 (MAPE) 为28.12%.
- 在预测准确度方面,PSO-BP的表现优于传统的BP模型和现有的粗度校正公式.
- 一个优化的多项式函数也被提出作为一个透明的预测模型.
结论:
- 该PSO-BP方法提供了一个更精确,更有效的方法来估计等效的沙粒粗度.
- 这种新的方法提高了CFD模拟和工程计算的准确性.
- 这些发现有望为涉及表面粗度的流体动力学问题提供更有效的解决方案.
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