Rapid Analysis of Disinfection Efficiency Through Computational Fluid Dynamics

Zhang, Jie; Tejada-Martínez, Andrés; Zhang, Qiong

doi:10.5942/jawwa.2016.108.0005

Cited by 7 publications

(6 citation statements)

References 43 publications

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“…With Sc t = 1, [33] obtained after several simulations the best agreement with the experimental data and stressed that the turbulent Schmidt number has a significant impact on the numerical simulations of tracer transport. Kim et al [31] and Zhang et al [34][35][36] compared the results from numerical simulations based on the RANS with those obtained using LES. They found that in a tank where the flow field is far from ideal plug flow conditions, Sc t = 0.3 was needed to match with the LES data in the concentration RTD (Retention Time Distribution) curve, whereas for Sc t = 1, the numerical results overestimated the LES data.…”

Section: Water Systemsmentioning

confidence: 99%

“…Arnold et al [22] Flow and tracer transport in open channels Exp − Sc t = 0.5-0.9 Djordjevic [23] Flow and tracer transport in open channels Exp/Num − Sc t = 1 Lin and Shiono [24] Flow and tracer transport in open channels Exp/Num − Sc t = 0.72 Simões and Wang [25] Flow and tracer transport in open channels Exp/Num − Sc t = 0.5 (horizontal), 1 (vertical) Gualtieri [30] Flow and tracer transport in a contact tank Exp/Num − Sc t = 1 Rauen et al [33] Flow and tracer transport in a contact tank Exp/Num − Sc t = 1 Kim et al [31] Flow and tracer transport in a contact tank Exp/Num − Sc t = 0.3 Zhang et al [34][35][36] Flow and tracer transport in a contact tank Exp/Num − Sc t = 0.7 Angeloudis et al [26][27][28][29] Flow and tracer transport in a contact tank Exp/Num − Sc t = 0.7 Martínez-Solano et al [32] Flow and tracer transport in a water tank Exp/Num − Sc t = 0.7 Oliver et al [37] Inclined negatively buoyant discharges Exp/Num − Sc t = 0.6 Graf and Cellino [41] Sediment-laden open channel flows Exp − Sc t > 1 (no bedforms), Sc t < 1 (bedforms) Hsu and Liu [42] Sediment-laden open channel flows Exp/Num − Sc t = 0.7 Amoudry et al [39] Sediment-laden open channel flows Exp/Num − Sc t = 0.7 (bed), Sc t = 0.52 (surface)…”

Section: Referencementioning

confidence: 99%

“…A number of studies about the turbulent Schmidt number have dealt with the simulation of flow and tracer transport in open channels [22][23][24][25], while others have addressed those issues in contact or water tanks [26][27][28][29][30][31][32][33][34][35][36], inclined negatively-buoyant discharges [37], sediment-laden open channel flows [38][39][40][41][42][43][44][45][46][47][48], density stratified turbulence [49,50] and T-junction mixing experiments [51] ( Table 2). The terms "Exp" and "Num" mean the application of experimental and numerical methods, respectively, in each study.…”

Section: Water Systemsmentioning

confidence: 99%

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On the Values for the Turbulent Schmidt Number in Environmental Flows

Gualtieri

Angeloudis

Bombardelli

et al. 2017

Fluids

166

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Computational Fluid Dynamics (CFD) has consolidated as a tool to provide understanding and quantitative information regarding many complex environmental flows. The accuracy and reliability of CFD modelling results oftentimes come under scrutiny because of issues in the implementation of and input data for those simulations. Regarding the input data, if an approach based on the Reynolds-Averaged Navier-Stokes (RANS) equations is applied, the turbulent scalar fluxes are generally estimated by assuming the standard gradient diffusion hypothesis (SGDH), which requires the definition of the turbulent Schmidt number, Sc t (the ratio of momentum diffusivity to mass diffusivity in the turbulent flow). However, no universally-accepted values of this parameter have been established or, more importantly, methodologies for its computation have been provided. This paper firstly presents a review of previous studies about Sc t in environmental flows, involving both water and air systems. Secondly, three case studies are presented where the key role of a correct parameterization of the turbulent Schmidt number is pointed out. These include: (1) transverse mixing in a shallow water flow; (2) tracer transport in a contact tank; and (3) sediment transport in suspension. An overall picture on the use of the Schmidt number in CFD emerges from the paper.

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Section: Water Systemsmentioning

confidence: 99%

Section: Referencementioning

confidence: 99%

Section: Water Systemsmentioning

confidence: 99%

See 1 more Smart Citation

On the Values for the Turbulent Schmidt Number in Environmental Flows

Gualtieri

Angeloudis

Bombardelli

et al. 2017

Fluids

166

View full text Add to dashboard Cite

show abstract

“…Zhang et al. performed simulations of Ozone contactor () and baffled contact tanks () using the Reynolds‐averaged Navier‐Stokes (RANS) equations. However, both these studies were for scaled models <11 m in the longest dimension.…”

Section: Introductionmentioning

confidence: 99%

Full simulation of disinfection stage in a water recycling plant using low‐cost, hybrid 3‐dimensional computational fluid dynamics

Issakhanian

Sáez²,

Helmns

et al. 2019

Water Environment Research

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Water purification is a crucial process in the operation of a municipality. Ensuring that water treatment plants are meeting regulatory requirements is a vital, but complicated and costly process. Because water treatment plant influent and effluent rates are demand driven, and vary both diurnally and seasonally, controlling flow rates for the disinfection stage can be challenging both operationally and economically. Thus, performing large-scale field experiments to verify water quality regulatory criteria such as modal transport time of conservative tracers in a chlorine disinfection contact tank under extreme operating conditions can range from difficult and costly to impossible. In this paper, a computational fluid dynamics (CFD) approach is used to verify the compliance of a water reclamation plant disinfection stage with respect to modal time. CFD allows a large parameter space to be tested without the need to build large physical models or taking functioning systems in a treatment plant offline. This can save facilities large amounts of time and money when designing, optimizing, and developing plants or checking compliance. This paper introduces a hybrid approach of computational analysis of a water reclamation plant's chlorine contact tank in Southern California. The method uses a hybrid approach which combines three-dimensional CFD with hydraulic grade line analysis of the open water surface. Verification cases were compared to experimental measurements at a functioning, full-scale plant with modal contact time differences below 15%. The method was then used to predict residence time distributions (RTDs) for cases which could not be artificially induced at the plant, but represented peak flow conditions that could be expected. © 2018 Water Environment Federation • Practitioner points• The hybrid 3-dimensional CFD method allows low cost simulation of the disinfection stage. • By using head loss calculations to properly define the water level at each section, a steady-state single phase flow simulation can be run in 3D without the need to scale down geometries. • This allows for more accurate transport results and parameter studies. Figure 8. Predicted flow-through curve for hypothetical extreme flow case.

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“…Computational fluid dynamics (CFD) presents the opposite end of the spectrum, producing flexible models with a large number of input parameters. Improvements in CFD modeling have enabled prediction of residence time indexes (e.g., t 10 / τ ) within approximately 10% (Zhang et al , Templeton et al ). While CFD has utility in estimating performance before reactors are constructed or modified, tracer tests provide greater accuracy for existing reactors at lower cost using more transparent methods.…”

mentioning

confidence: 99%

Modeling Water Treatment Reactor Hydraulics Using Reactor Networks

2018

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Reactor hydraulics are integral to water treatment processes such as disinfection and chemical contaminant oxidation. This work uses reactor networksconceptual reactors arranged in parallel and series combinations-to simplify the accurate modeling of residence time in water treatment reactors. Reactor networks were selected for 14 clearwells, ozone contactors, clarifiers, and filters using tracer data sets from literature. For seven of 14 full-scale reactors, two parallel tanks-in-series reactors best balanced accuracy and simplicity. Reactor networks accurately represented tracer data using between two and eight fitting parameters, while segregated flow analysis required 32-164 inputs (two per tracer data point). The modeling work revealed that tracer data sets may have overestimated baffle factors by >10% in 10 of the 14 full-scale reactors, potentially as a result of inaccurate measurement of flow rate or volume. Applications of reactor networks include more accurate approaches to disinfection regulation and modeling the degradation of emerging contaminants.

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Rapid Analysis of Disinfection Efficiency Through Computational Fluid Dynamics

Cited by 7 publications

References 43 publications

On the Values for the Turbulent Schmidt Number in Environmental Flows

On the Values for the Turbulent Schmidt Number in Environmental Flows

Full simulation of disinfection stage in a water recycling plant using low‐cost, hybrid 3‐dimensional computational fluid dynamics

Modeling Water Treatment Reactor Hydraulics Using Reactor Networks

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