Evaluation of solute mixing in water distribution pipe junctions

Abstract

Growing public concern about potential contaminant transport in water distribution systems has increased the use of models to assess risk and detect sources of contamination. The movement and distribution of contaminants depend largely on mixing at pipe junctions, where different flow rates and contaminant concentrations can exist. This article presents experimental observations of solute mixing in various pipe junction configurations. Analytical models are derived for each configuration, and results are compared with experimental data. A key finding—that impinging fluid streams within a junction often do not mix completely—is contrary to the most common assumption of complete mixing in pipe junctions. This study finds that if concentrations of two incoming fluid streams differ, they tend to bifurcate and reflect off one another, affecting subsequent solute distribution and mixing of fluid streams. The authors introduce a new bulk‐advective mixing (BAM) model that has been shown to accurately represent this behavior and lead to more accurate water quality assessments.

Experimental and Numerical Investigation of Mixing Phenomena in Double-Tee Junctions

“…Bulk mixing model greatly depends on the flow momentum in each inlet pipe as it is calculated and used to determine how the inlet concentrations will split into the outlet pipes i.e., the pipe flow with higher momentum will be dominant in spreading the concentration in the outlet pipe with the same direction, while the pipe with the lower momentum flow will only spread to its neighboring outlet pipe. In addition, an experimental cross junction mixing model was derived and implemented into EPANET as an extension called EPANET-BAM [12]. In the EPANET-BAM model, a mixing parameter s between 0 (bulk mixing) and 1 (complete mixing) can be chosen but this empirical parameter needs to be predetermined by the user.…”

“…Experimental results for the S configuration-relationship between branch outlet to main inlet pipe conductivity ratio R c and inlet flow ratio R f compared with previous studies[8,9] of cross junction mixing results. Dashed lines represent the value of mixing parameter s used in EPANET-BAM[12].…”

Validation of a Computational Fluid Dynamics Model for a Novel Residence Time Distribution Analysis in Mixing at Cross-Junctions

“…where: i, link with flow leaving node k; I k , set of links with flow into k; L j , length of link; Q j , flow (volume/time) in link j; Q k,ext , external source flow entering the network at node k; and, C k , ext , concentration of the external source flow entering at node k. Several authors [25][26][27][28][29][30][31][32][33][34][35][36] have numerically and experimentally demonstrated that the mixing in these unions is far from being "complete and instantaneous". Most of these researches were based on physical experiments and simulations through Computational Fluid Dynamics (CFD) using tracers for the concentration distribution analysis.…”

“…Most of these researches were based on physical experiments and simulations through Computational Fluid Dynamics (CFD) using tracers for the concentration distribution analysis. Some researchers [26][27][28][31][32][33][34][35][36] implement experimental scale models of a cross-junction and tee junctions with different pipe diameters, which are driven by elevated tanks where tracer is injected into one of them. Once the mix of the flows was made, mixing coefficients were formulated based on their experimental measurements.…”

“…The most commonly used tracers were salts, such as NaCl. Other authors considered colored dyes and high speed photography too [34,35]. Conducting a study directly with chlorine as a solute turns out to be somewhat complicated, although Mompremier [33] used in-line chlorine loggers/dosers to analyze the mixing at different conditions.…”