Effect of Triangular Vanes on Mixing Length and Transverse Mixing Coefficient in Straight Channel

Understanding of the fate of pollutants, disposed of in streams, is a matter of concern in recent years for the effective control of pollution. Transverse mixing of the pollutants in open channels is arguably more important than the longitudinal mixing and near-field mixing. Several attempts have been made to establish the relationship between the transverse mixing coefficient and bulk channel and flow parameters such as width, depth, shear velocity, friction factor, curvature and sinuosity. This paper presents adaptive neuro fuzzy inference system (ANFIS) approach to predict the transverse mixing coefficient in open channel flows. Available laboratory and field data for the transverse mixing coefficients covering wide range of channel and flow conditions are used for the development and testing of the proposed method. The proposed ANFIS approach produces satisfactory results (R2=0.945) compared to the artificial neural network (ANN) model and existing predictors for mixing coefficient.

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Transverse Mixing Coefficient on Dunes with Vegetation on a Channel Wall

World Environmental and Water Resources Congress 2013 ◽

10.1061/9780784412947.186 ◽

2013 ◽

Cited By ~ 2

Author(s):

S. H. Tabatabaei ◽

M. Heidarpour ◽

M. Ghasemi ◽

E. Z. Hoseinipour

Keyword(s):

Channel Wall ◽

Transverse Mixing ◽

Mixing Coefficient

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A genetic algorithm-based support vector machine to estimate the transverse mixing coefficient in streams

Water Quality Research Journal ◽

10.2166/wqrj.2021.003 ◽

2021 ◽

Vol 56 (3) ◽

pp. 127-142

Author(s):

Hosein Nezaratian ◽

Javad Zahiri ◽

Mohammad Fatehi Peykani ◽

AmirHamzeh Haghiabi ◽

Abbas Parsaie

Keyword(s):

Genetic Algorithm ◽

Support Vector Machine ◽

Support Vector ◽

Effective Parameters ◽

Meandering Streams ◽

Transverse Mixing ◽

Mixing Coefficient ◽

Wide Range ◽

Input Parameters ◽

Dimensional Simulation

Abstract Transverse mixing coefficient (TMC) is known as one of the most effective parameters in the two-dimensional simulation of water pollution, and increasing the accuracy of estimating this coefficient will improve the modeling process. In the present study, genetic algorithm (GA)-based support vector machine (SVM) was used to estimate TMC in streams. There are three principal parameters in SVM which need to be adjusted during the estimating procedure. GA helps SVM and optimizes these three parameters automatically in the best way. The accuracy of the SVM and GA-SVM algorithms along with previous models were discussed in TMC estimation by using a wide range of hydraulic and geometrical data from field and laboratory experiments. According to statistical analysis, the performance of the mentioned models in both straight and meandering streams was more accurate than the regression-based models. Sensitivity analysis showed that the accuracy of the GA-SVM algorithm in TMC estimation significantly correlated with the number of input parameters. Eliminating the uncorrelated parameters and reducing the number of input parameters will reduce the complexity of the problem and improve the TMC estimation by GA-SVM.

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Transverse mixing coefficient measurements in an open rectangular channel

Advances in Environmental Research ◽

10.1016/s1093-0191(00)00028-9 ◽

2000 ◽

Vol 4 (4) ◽

pp. 287-294 ◽

Cited By ~ 16

Author(s):

K.W. Chau

Keyword(s):

Rectangular Channel ◽

Transverse Mixing ◽

Mixing Coefficient

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Mixing coefficient for ice-covered and free-surface flows

Canadian Journal of Civil Engineering ◽

10.1139/l85-059 ◽

1985 ◽

Vol 12 (3) ◽

pp. 521-526 ◽

Cited By ~ 6

Author(s):

Y. L. Lau

Keyword(s):

Free Surface ◽

Open Water ◽

Free Surface Flows ◽

Flow Conditions ◽

Surface Flows ◽

Transverse Mixing ◽

Mixing Coefficient ◽

Natural Streams ◽

River Reach ◽

Selection Of

Dispersion experiments were conducted in four river reaches under both open-water and ice-covered flow conditions. The data were used to obtain the transverse mixing coefficient and to investigate which dimensionless mixing coefficient should be used for ice-covered flows. The results also demonstrate that the sinuosity of a river reach affects the value of the mixing coefficient and can thus be used to guide the selection of mixing coefficient for natural streams.

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Numerical Investigation and Geometric Parameterization of Lamination Inlet of Passive Micromixer

ASME 2010 8th International Conference on Nanochannels, Microchannels, and Minichannels: Parts A and B ◽

10.1115/fedsm-icnmm2010-30145 ◽

2010 ◽

Author(s):

Yanfeng Fan ◽

Ibrahim Hassan

Keyword(s):

Flow Characteristics ◽

Reynolds Numbers ◽

Mixing Efficiency ◽

Mixing Length ◽

Straight Channel ◽

Interface Area ◽

Mixing Performance ◽

Passive Micromixer ◽

Two Parameters ◽

Parallel Type

A lamination inlet is proposed and optimized in this paper. The perpendicular incoming fluids are applied instead of parallel type. The total mixing length is fixed at 3.2 mm and the depth of channel is fixed at 0.1 mm. The tested Reynolds number is calculated at the entrance of downstream straight channel. The tested Reynolds numbers range from 5 to 200. The perpendicular incoming type enhances the mass-convection and enlarges the interface area. Two parameters, the radius of holes (R) and the distance between two holes (D1), are selected to achieve the optimization. Numerical simulation is used to estimate the mixing performance and flow characteristics. The results show that the vortices are generated in the microchannel. The interface becomes irregular. In order to evaluate the mixing improvement, the parallel lamination is also simulated. The comparison shows that the perpendicular inlet type has better mixing efficiency than the parallel lamination type. This inlet type could be connected with certain mixing element to achieve the applications in biochemistry.

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