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Open AccessArticle

Efficient Double-Tee Junction Mixing Assessment by Machine Learning

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Department of Fluid Mechanics and Computational Engineering, Faculty of Engineering, University of Rijeka, 51000 Rijeka, Croatia
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Center for Advanced Computing and Modeling, University of Rijeka, 51000 Rijeka, Croatia
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Author to whom correspondence should be addressed.
Water 2020, 12(1), 238; https://doi.org/10.3390/w12010238
Received: 8 November 2019 / Revised: 28 December 2019 / Accepted: 11 January 2020 / Published: 15 January 2020
(This article belongs to the Special Issue Machine Learning Applied to Hydraulic and Hydrological Modelling)
A new approach in modeling of mixing phenomena in double-Tee pipe junctions based on machine learning is presented in this paper. Machine learning represents a paradigm shift that can be efficiently used to calculate needed mixing parameters. Usually, these parameters are obtained either by experiment or by computational fluid dynamics (CFD) numerical modeling. A machine learning approach is used together with a CFD model. The CFD model was calibrated with experimental data from a previous study and it served as a generator of input data for the machine learning metamodels—Artificial Neural Network (ANN) and Support Vector Regression (SVR). Metamodel input variables are defined as inlet pipe flow ratio, outlet pipe flow ratio, and the distance between the pipe junctions, with the output parameter being the branch pipe outlet to main inlet pipe mixing ratio. A comparison of ANN and SVR models showed that ANN outperforms SVR in accuracy for a given problem. Consequently, ANN proved to be a viable way to model mixing phenomena in double-Tee junctions also because its mixing prediction time is extremely efficient (compared to CFD time). Because of its high computational efficiency, the machine learning metamodel can be directly incorporated into pipe network numerical models in future studies. View Full-Text
Keywords: mixing phenomena; double-Tee junctions; machine learning; artificial neural networks; support vector regression; CFD model mixing phenomena; double-Tee junctions; machine learning; artificial neural networks; support vector regression; CFD model
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Grbčić, L.; Kranjčević, L.; Družeta, S.; Lučin, I. Efficient Double-Tee Junction Mixing Assessment by Machine Learning. Water 2020, 12, 238.

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