基于人工智能的穆迪图技术在管道流动摩擦系数预测中的应用

J Pub Date : 2023-10-07 DOI:10.3390/j6040036
Ritusnata Mishra, Chandra Shekhar Prasad Ojha
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引用次数: 0

摘要

摩擦系数是表征管道和明渠流动阻力的一个广泛使用的参数。最近,机器学习和人工智能(AI)的应用在水资源工程中得到了一些应用。鉴于此,本研究考虑了在穆迪图上应用人工智能技术来预测过渡区和湍流区管道流动中的摩擦系数。各种人工智能方法,如随机森林(RF)、随机树(RT)、支持向量机(SVM)、M5树(M5)、M5Rules和REPTree模型,被用于预测摩擦因子。在进行统计分析(均方根误差(RMSE)、平均绝对误差(MAE)、平方相关系数(R2)和纳什-萨特克里夫效率(NSE))时,发现与其他人工智能工具相比,随机森林模型的预测是最可靠的。本研究的主要目的是强调人工智能(AI)技术在试图有效捕获湍流某些区域摩擦曲线的特征和模式时的局限性。为了进一步证实这种行为,传统的代数方程被用作测试当前人工智能工具工作情况的基准。研究发现,在人工智能模型无法捕捉摩擦因子的性质和变化的区域,使用代数方程估计的摩擦因子比随机森林模型更准确,相对误差≤±1%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of AI-Based Techniques on Moody’s Diagram for Predicting Friction Factor in Pipe Flow
The friction factor is a widely used parameter in characterizing flow resistance in pipes and open channels. Recently, the application of machine learning and artificial intelligence (AI) has found several applications in water resource engineering. With this in view, the application of artificial intelligence techniques on Moody’s diagram for predicting the friction factor in pipe flow for both transition and turbulent flow regions has been considered in the present study. Various AI methods, like Random Forest (RF), Random Tree (RT), Support Vector Machine (SVM), M5 tree (M5), M5Rules, and REPTree models, are applied to predict the friction factor. While performing the statistical analysis (root-mean-square error (RMSE), mean absolute error (MAE), squared correlation coefficient (R2), and Nash–Sutcliffe efficiency (NSE)), it was revealed that the predictions made by the Random Forest model were the most reliable when compared to other AI tools. The main objective of this study was to highlight the limitations of artificial intelligence (AI) techniques when attempting to effectively capture the characteristics and patterns of the friction curve in certain regions of turbulent flow. To further substantiate this behavior, the conventional algebraic equation was used as a benchmark to test how well the current AI tools work. The friction factor estimates using the algebraic equation were found to be even more accurate than the Random Forest model, within a relative error of ≤±1%, in those regions where the AI models failed to capture the nature and variation in the friction factor.
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