Journal of Hydraulic Structures

Journal of Hydraulic Structures

Machine Learning and Statistical Analysis of Measured Velocities by ADV of Free Surface Vortices at Intakes

Document Type : Research Paper

Authors
Hamid Ahani 1
Saeed Reza Khodashenas 2
Hamed Sarkardeh 3
1 Department of Water Engineering, Agriculture Faculty, Ferdowsi University of Mashhad, Mashhad, Iran
2 Water science and Engineering Department, Agriculture Faculty , Ferdowsi University of Mashhad, Mashhad, Iran
3 Department of Civil Engineering, Hakim Sabzevari University, Sabzevar, Iran.
10.22055/jhs.2026.50962.1367
Abstract
Free-surface vortices in hydraulic intakes can lead to air entrainment, vibrations, and energy loss. This study aimed to optimize an Acoustic Doppler Velocimeter (ADV) for measuring tangential velocities in vortices. The used statistical methods include calculating the median, mode, and arithmetic mean without outlier removal; and removing outliers using truncated mean, Z-Score, box plot, histogram, and a two-standard deviation range. The employed machine learning models were Isolation Forest, One-Class SVM (Support Vector Machines), and DBSCAN (Density-Based Spatial Clustering of Applications with Noise). Experiments were conducted at Froude numbers of 0.85, 1.00, and 1.27. The results showed that central tendency methods like median, Z-score, and mean provided smooth profiles consistent with the Rankine model, while mode, One-Class SVM, and DBSCAN showed significant fluctuations. Isolation Forest performed well under all conditions. As the Froude number increased, fluctuations intensified in the weaker methods. These findings enhance the accuracy of ADV in noisy environments and aid in the better design of hydraulic intakes.
Keywords: Free-surface vortex, ADV device, Statistical analysis, Tangential velocity, Inlet intake.
Keywords
Free-surface vortex
ADV device
Statistical analysis
Tangential velocity
Inlet intake
Subjects
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