Efficient Double-Tee Junction Mixing Assessment by Machine Learning

Luka Grbčić,Lado Kranjčević,Ivana Lučin,Siniša Družeta

doi:10.3390/w12010238

Abstract

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.

Highlights

The mixing of fluids in water distribution networks is a complex phenomenon that has been extensively subjected to research as it is relevant to several specific areas of application such as water distribution quality and safety [1,2,3], pollution source detection systems, and optimal pollution sensor placement in a water distribution network [7,8,9,10].The elements that form water distribution networks are pipes and junctions
Of the 75 obtained simulation results, 70% were used for model training, and 30% were used for the purpose of model accuracy testing for both Artificial Neural Network (ANN) and Support Vector Regression (SVR) since this train-to-test ratio provides that the training dataset should include all possible patterns used for defining the problem and should extend to the edge of the modeling domain
The computational fluid dynamics (CFD) obtained results were taken to be exact and ANN and SVR model results were compared with them to examine their accuracy

Summary

Introduction

The mixing of fluids in water distribution networks is a complex phenomenon that has been extensively subjected to research as it is relevant to several specific areas of application such as water distribution quality and safety [1,2,3], pollution source detection systems (both large [4,5] and small networks [6]), and optimal pollution sensor placement in a water distribution network [7,8,9,10].The elements that form water distribution networks are pipes and junctions. When modeling mixing in a complex system, a correct mixing model must be applied to accurately describe the contaminant transport through the network due to the fact that a wrong solution could present a hazard to a great number of network users. Mixing in a pipe network is modeled as either complete mixing or bulk mixing. Complete mixing can be described as an even split of contamination at a network junction. Complete mixing models such as the one developed in the hydraulic analysis software EPANET are implemented by calculating the flow-weighted concentrations at the inlet pipes of a junction and assuming an even split in the outlet pipes. The complete mixing model can be assumed correct only if there is a single outlet at a junction and if the distance between two junctions is great enough

Methods

Results

Conclusion