使用RapidMiner工具和机器学习算法进行离心泵空化分析与预测
Centrifugal pump analysis and prediction in cavitation using RapidMiner tool and machine learning algorithms.
摘要(中文译文)
离心泵（CP）作为一种可靠且高效的泵，如今广泛应用于众多机械和工业领域。但空化是一个影响泵性能的严重问题，其原因在于吸入端压力下降导致液体汽化。本研究旨在利用基于计算流体动力学（CFD）和机器学习（ML）方法的空化测量技术（CMT）对离心泵中的空化进行预测和分析。该CMT基于有效净正吸入压头（NPSHa）与必需净正吸入压头（NPSHr）之比，该比值由在700至2900 RPM各种转速下的CFD仿真结果计算得出。该临界比值是机器学习方法用于空化起始分类的主要输入特征。通过CFD仿真模拟了泵的正常运行和非正常运行工况，并在RapidMiner平台上采用不同的机器学习算法对结果进行了分析。实验结果表明，Fine Tree算法在预测空化状态方面准确率达到96.1%，Naive Bayes算法达到95.5%。此外，在CFD分析过程中，在叶轮的水流经过部位观察到最大蒸汽体积分数为0.9477。所提出的框架表明，CFD和ML方法能够准确预测离心泵中的空化并评估离心泵性能。
Abstract
Being a reliable and efficient pump, Centrifugal Pumps (CP) are widely used today in many mechanical and industrial applications. But cavitation is a serious problem that impacts pump performance, as it involves the vaporisation of the liquid at the suction end as a result of the pressure drop. The study is aimed at predicting and analysing cavitation in CP using a Cavitation Measuring Technique (CMT) based on Computational Fluid Dynamics (CFD) and Machine Learning (ML) approach. The CMT is based on the Net Positive Suction Head Available (NPSHa) and Net Positive Suction Head Required (NPSHr) ratio calculated from the results of the CFD simulation at various rotational speeds of 700 to 2900 RPM. The critical ratio is the main input feature for a machine learning approach to cavitation onset classification. CFD simulations were performed to simulate both functional and non-functional pump conditions and results were analyzed on RapidMiner platform with different machine learning algorithms. Experimental results show that the Fine Tree algorithm gave an accuracy of 96.1% and Naive Bayes 95.5% in predicting the cavitation status. In addition, the maximum vapour volume fraction of 0.9477 was observed at the water-moving part of the impeller during CFD analysis. The proposed framework illustrates the ability of CFD and ML approaches to accurately predict cavitation in centrifugal pumps and to assess centrifugal pump performance.
如何引用
AMA
Saiprasad Potharaju, Satish Dekka, Miguel Villagómez-Galindo, Anandkumar S Malipatil, Dasari Siva Krishna. Centrifugal pump analysis and prediction in cavitation using RapidMiner tool and machine learning algorithms.. Scientific reports. 2026; doi:10.1038/s41598-026-60315-1.
APA
Saiprasad Potharaju, Satish Dekka, Miguel Villagómez-Galindo, Anandkumar S Malipatil, Dasari Siva Krishna (2026). Centrifugal pump analysis and prediction in cavitation using RapidMiner tool and machine learning algorithms.. Scientific reports. https://doi.org/10.1038/s41598-026-60315-1
GB/T 7714
Saiprasad Potharaju, Satish Dekka, Miguel Villagómez-Galindo, Anandkumar S Malipatil, Dasari Siva Krishna. Centrifugal pump analysis and prediction in cavitation using RapidMiner tool and machine learning algorithms.[J]. Scientific reports, 2026 doi:10.1038/s41598-026-60315-1.
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