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Article

Study on Propellers Distribution and Flow Field in the Oxidation Ditch Based on Two-Phase CFD Model

1
College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China
2
College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China
3
School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
4
Technological Institute of Merida, Technological Avenue, Merida 97118, Mexico
*
Author to whom correspondence should be addressed.
Water 2019, 11(12), 2506; https://doi.org/10.3390/w11122506
Submission received: 15 October 2019 / Revised: 17 November 2019 / Accepted: 25 November 2019 / Published: 27 November 2019

Abstract

:
The oxidation ditch (OD) plays an important role in wastewater treatment plants. With increasing demand and production costs, the energy consumption and sludge deposition occurring in the OD must be diminished to enhance its development. In this paper, a two-phase computational fluid dynamics (CFD) model of water and activated sludge examined the flow field characteristics of an OD, consisting of two side-by-side propellers. The system was studied under five configurations, where the spacing between the propellers was set equal to −0.2, −0.1, 0, 0.1, 0.2 times the length of the OD. The viscosity and settling rate of activated sludge was imported in the numerical simulation through a user defined function (UDF). The optimal scheme of the propeller’s power consumption, velocity distribution, and sludge concentration distribution was obtained. The result shows that sludge concentrations are linked with dead zone velocity but not necessarily with low velocities. Experiments confirmed the validity of the velocity flow field simulated by the two-phase CFD model. Overall, these findings form the basis for the propellers distribution optimization and allow a deeper insight into the flow field of OD systems.

1. Introduction

To deal with the rapid increase in wastewater, lots of treatment plants have been installed to clean and reuse the water in industrial processes [1]. Currently, about 32.1% of the Chinese wastewater treatment plants employ the use of the oxidation ditch (OD) [2]. The OD, also known as the continuous loop reactor, is a cyclic aeration system used to remove the organic matter from residual flows. Key aspects of ODs include their simplicity, low sludge production, and confidence in the market, including operation and maintenance processes [3]. Therefore, they are one of the most widely used methods for activated sludge biological treatment [4]. Despite the existing research recognizes the critical role played by OD in the treatment plant’s production effectiveness and economics, old and inefficient technology still prevails in many OD systems; thus, studies suggest their modernization will enhance the flow field and reduce costs and environmental impact [5,6,7,8].
Since the primary concern of an OD relies on the hydrodynamic characteristics of the operating flow, its predictions through engineering tools, such as computational fluid dynamics (CFD), become important and are a concern in the plant design and economics [9,10,11,12]. Traditionally, studies of OD systems have treated the water and suspended solids as a single phase to determine the flow patterns and OD optimal configurations. Luo et al. [13] simulated the flow field in a small OD by the Reynolds-Averaged Navier-Stokes (RANS) equation. The flow pattern was analyzed in detail, and both trials and simulations were found to be similar. Zhang et al. [3] experimentally validated a CFD model to optimize the energy consumption and flow field. The least power density and requirement of the flow pattern were obtained and validated against physical tests. Yang et al. [14] used a moving wall method to show the suitability of a more complex, 3D single-phase CFD method, whilst Wu et al. [15] conducted a numerical simulation based on a standard k ε turbulence model to optimize the installation of submerged propellers in OD. According to results, the sludge deposit due to low velocity was avoided in the optimal arrangement of the propellers. In particular, both the influence of position and energy consumed by single and multiple impellers is a continuing concern within OD systems.
In the investigation conducted by Hartley [16], the author described the design and implementation of a process for settling the sludge deposits. By using a single-phase model, Gancarski [17] studied the settling of sludge on the basis of tracking the particles. However, the concept of sludge concentration of suspended solids was not an important aspect in the simulations. Likewise, by treating the sludge as a single-phase scalar, Brannock [18] investigated the wastewater behavior in the vessel. Importantly, for the above studies, the setting of the minimum velocity [19,20,21,22,23] in the single-phase model and the neglect of the other phases have been subject to debate within the research community. The single-phase approach, although being practical and computationally effective, it suffers from simulating high suspended-solid concentrations in ODs.
Therefore, it is essential to include the liquid–solid phase to investigate the sludge settling and flow field in the simulation. To date, only a few multi-phase works have investigated the influence of propeller positions on the hydrodynamic characteristics of the OD. In the studies of Yang et al. [1], a two-phase CFD model was put forward to optimize the flow field and dissolved oxygen concentration in the OD. The model was able to predict the oxygen mass transfer features and flow patterns equipped with submerged impellers, although it was limited to treat water and suspended solids as a single phase leading to the disparity between results and trials. To gain a fuller understanding, Xie et al. [2] proposed a modified two-phase CFD model to investigate the distribution of suspended solids, thus showing that the model can help optimization of design and operation of the OD. Fan et al. [24] focused on the hydrodynamics of the turbulent two-phase (liquid-solid) flow in an OD by simulating the flow field with surface aerators. The results of liquid velocity and volume variation formed the basis for the optimization of OD. In the study of Climent et al. [4], a two-phase CFD model was investigated to improve the hydraulic characteristics in the OD, which provides a further understanding of the interaction between liquid behavior and phase motion. However, there is a limited understanding of the mechanism of the flow field characteristics influenced by the propeller’s arrangement and sludge concentration.
In view of the above, the aim of this paper is to study, through a two-phase CFD model, the characteristics of flow and sludge distribution of a full-scale OD. In the model, the viscosity and settling rate of the activated sludge is defined in terms of user defined function (UDF), and the speed of the propellers is selected based on the energy requirement of the OD. An optimal scheme is then determined on the basis of flow field characteristics by changing the position of the propellers. Finally, this arrangement is further analyzed and compared with the experimental data.

2. Materials and Methods

2.1. Parameters of Experiments

A conventional OD of the sewage treatment plant was used in the investigation, as shown in Figure 1. The length (L) of the OD is 48.6 m, the width (W) is 12.3 m, and the depth (h) is 6.5 m. The height of the liquid level is 6 m above the bottom of the ditch, and that of the propeller axle is 1.8 m. The designed flow of the OD is 4792 m3/h, and the thickness of the middle curved diversion wall is 0.3 m. The radius of the annular curved diversion wall is 3 m. Two side-by-side, identical propellers were spaced apart from one to another, along the channel’s length, as illustrated in Figure 2. The impeller diameter (D) was 2.5 m. An Anaerobic/Anoxic/Oxic (A/A/O) process with multi- inlet and multi-mode operation was used in the OD, and it can be adjusted to a conventional A/A/O process, A + A/A/O process, inverted A/A/O process, and denitrification A/A/O process, according to water quality.
In order to describe the distribution of velocity in the flow field, 9 measuring points were spaced in each of the 12 sections encompassing the ditch, as indicated in Figure 3. The flow-velocity measuring instrument adopted an LJ20A-type convenient tachymeter with OA-type fiber-optic rotor sensor, as shown in Figure 4 [3,25]. The diameter of the tachymeter is 15 mm, and the measuring range is 0.01–4.00 m/s, whilst the measuring error is less than 1.5%. The tachymeters were installed side-by-side within a row on the measuring bracket, as shown in Figure 5. These were equipped with both a standard positioning and a lengthened rod. Different flow field points were obtained by changing the position of the measuring bracket. Following a new positioning of the tachymeter, it was necessary to wait for the flow velocity to be stable at a certain value. Once the data was recorded for 20 sets, the bracket was moved to another plane for further measurement.

2.2. Numerical Simulation

2.2.1. Governing Equations

The flows in the OD are by nature highly turbulent, and therefore, the most common, less intensive resource tool to model them is through the solution of the RANS equations [3,26,27,28]. These equations apply the Reynolds decomposition in the velocity and pressure [29], as follows:
Continuity :   u i ¯ x i = 0 ,
Momentum :   ρ u i ¯ t + ρ ( u i ¯ u j ¯ ) x j = ρ f ¯ i + x j ( p ¯ δ i j + 2 μ S ¯ i j ρ u i u j ¯ ) ,
where u i is velocity, x i is the spatial co-ordinate, p is the pressure, ρ is the fluid density, μ . is the dynamic viscosity, f i ¯ the time mean external force tensor, and S i j ¯ = 0.5 ( u i ¯ / x j   + u j ¯ / x i ) is the mean rate of the strain tensor. The Dirac function is δ i j = 1 for i = j and zero otherwise. To closure the RANS equations, the Reynolds stresses u i u j ¯ requires the use of a turbulence model. The k ε turbulence model [3] is adopted to account for the swirl and secondary flow phenomenon in the curve of the OD.
The mixture model adopts a two-phase flow representation. The first item is the water-liquid coming from the material library, and the second one is the activated sludge, in this paper. The density of clean water is 998.2 kg/m3, and the viscosity is 0.001003 Pa·s. The activated sludge has a density of 1050 kg/m3, and its viscosity is influenced by the temperature and concentration. The liquid temperature used in this paper was 25 °C, which is 298.15 K. According to [30,31], the sludge viscosity is most affected by the sludge concentration, while the effect of temperature is comparatively smaller. In the condition of sludge concentration less than 60 g/L and liquid temperature ranging from 12 °C to 32 °C, the equation of sludge viscosity could be obtained by the index model. The expression is:
μ s v = 0.63 exp ( 120 T + 0.078 M L S S ) ,
where μ s v is sludge viscosity, 10−3 Pa·s, T is absolute temperature, K, MLSS represents the mixed liquor suspended solids, g/L.
The sedimentation rate of sludge in clean water is obtained according to the double-index sludge sedimentation model [2]. The given procedure initially consists of measuring the sedimentation rate of sludge under different concentrations. Subsequently, these are fitted into formulations to obtain the following coefficients:
V s = 0.004 × ( e 0.46 x e 1.86 x ) ,
where Vs is sludge sedimentation rate, m/s; x indicates sludge concentration, g/L.
Equations (3) and (4) were applied by UDF and introduced into ANSYS Fluent [3]. The viscosity and settling rate of the activated sludge were defined, where the particle diameter of sludge was 0.4 mm. Prior to the two-phase flow calculation, the result of the single-phase flow was firstly introduced into the ANSYS Fluent as the initial condition of the full flow field.

2.2.2. Geometry and Mesh Grid

The three-dimensional model of the OD was established by using the software of Unigraphics NX (UG). The blade has a radius of 1.25 m, a root thickness of 0.04 m, and a tip thickness of 0.036 m. The hub radius is 0.1 m. A rotating region with a radius of 1.4 m and a height of 0.89 m was created for each impeller. According to the concept of energy balance, the fluid in this region obtains and transfers energy to the surrounding liquids. The whole area of the OD was divided into the OD flow channel and the submerged propeller region.
Tetrahedral meshes were selected for the control of flow direction, distribution, and the orthogonality of the boundary layer direction. The small parts, such as the blade edge, were locally encrypted, and the number of the overall meshes was set as 2.5 million, 3.9 million, 4.3 million, and 5.1 million. Table 1 illustrates the variation of the evaluation index caused by the mesh density.
As clearly seen in the table, as the number of grids increases, the power consumed by the submerged propeller gradually decreases. When the mesh number reaches 4.3 million, the trend of power consumption reduction is no longer obvious, but using a larger number of elements translates into more computer resources. Therefore, a medium mesh with 4.3 million elements is used, and the mesh of the OD is as shown in Figure 6.

2.2.3. Boundary Conditions

The flow rate boundary condition was assigned at the inlet with the value of 1331.11 kg/s, while the zero pressure was set at the outlet. The symmetry boundary condition was adopted at the water surface. The no-slip boundary conditions were applied in the bottom surface, side wall, bracket, and curved wall. The wall surface was cement, and the surface roughness was set as 0.5 mm based on the test condition and our previous work of simulation [3]. The finite volume method was applied to discretize the governing equations. The second-order central difference scheme was adopted in the diffusion term, and the second-order upwind style was used in the convection term. The SIMPLE algorithm was used in the coupling process of speed and pressure. For the unsteady simulation, the blades rotated one degree per time step.

3. Results and Discussion

3.1. Determination of the Power Consumption

The power consumption of the submerged propeller was obtained after determining the liquid medium and type of the OD, in accordance to the methodology provided by the LJM company in Denmark [1,32]. This approach includes the energy balance of the oxidation ditch, and it is divided into three parts:
(1) Requirement for pushing 1-kg liquid,
H f = ( Σ ξ + λ U w L F ) U 2 2 = 0.43   J / kg ,
where Σ ξ is the overall local resistance factor; for the 90° circular angle, ξ = 1 , and for the 90° right angle, ξ = 1.53 . Since six circular-angled corners exist, thus Σ ξ = 6 . F denotes the discharge area; λ = 0.03, the frictional resistant coefficient; L, the length of the OD; Uw, the wetted perimeter, and U, the flow velocity.
(2) Losses,
W r = H f Q p = 4.64   kW ,
where Q p is the discharge through the water section of the OD.
(3) And net power,
W R = W r η = 7.73   kW ,
where η is the efficiency of the propeller; 60% here.
Since two propellers are employed in the study, the minimum power is WR/2 = 3.86 kW, thus a rated power of WR = 4 kW is set to ensure a correct operation.

3.2. Distribution of the Propellers

The inlet sludge concentration was set as 3.5 g/L, and the rotating speed of the propeller was 81 r/min. The distribution of the propellers was controlled by changing the longitudinal distance (∆L) between the two propellers, assuming (∆L < 10 m for a proper ditch operation [3,15]. Five schemes were studied: −0.2L (a), −0.1L (b), 0 (c), 0.1L (d), 0.2L (e), as shown in Figure 7.

3.2.1. Velocity Distribution near the Bottom

Since the sludge deposition is most likely to occur when the flow velocity near the bottom of the OD is too low [33]. A horizontal section of 0.3 m from the bottom of the OD was intercepted, and the velocity contour is shown in Figure 8. Clearly, the flow passing the propeller augmented in velocity, forming a high-speed area in the downstream region. Comparing the velocity distribution of the five schemes, it shows that the scheme (b) has the largest high-speed area with a velocity over 0.7 m/s. A low-speed area is basically located below the inlet and around the curved wall, with velocities less than 0.1 m/s. The given trend is more apparent in schemes (c) and (d) rather than in (a) and (b).
In order to quantify and analyze the pros and cons of each scheme, the dead zone rate of different schemes was calculated using a histogram chart of velocities less than 0.1, 0.15, and 0.2 m/s, as shown in Figure 9. In the present report, the term dead zone rate refers to regions with velocities falling below the criterion for significance in the flow field characteristics.
From the data in Figure 9, schemes (a) and (b) contain lower dead zones rates than the remaining cases since propellers were closer to the inlet flow. The distances of the propeller A from the inlet ranged from 11.94 to 14.37 m in schemes (a) and (b). In addition, the propellers farther away from the curved wall produced better performance than other schemes, due to lesser losses produced by flow hitting the wall. A large velocity reduction near the intermediate curved wall is formed in all schemes but (a) and (b) as a result of the centrifugal force in the turn.

3.2.2. Sludge Concentration Distribution near the Bottom

Because the sludge concentration of the ditch is defined in terms of the phase volume fraction of the sludge, contour results near the bottom were obtained, as shown in Figure 10. As observed, the areas of sludge deposition in schemes (a) and (b) are small and mainly located at the two ends of the intermediate curved wall. Remarkably, schemes (c), (d), and (e) develop as well sludge deposition beneath the inlet and behind section of propeller B. Comparing Figure 8 with Figure 9 shows that most of the sludge deposition occurs at the velocity dead zone; hence, this may be used as a parameter to measure the sludge deposition [34].

3.2.3. Sludge Distribution in the Oxidation Ditch

Plane sections at intervals of 0.3 m were set along the water depth direction to calculate the overall sludge concentration, as shown in Figure 11. In the five schemes, the sludge distribution in the oxidation ditch increases gradually from top to bottom, as a consequence of the given settling rate of sludge and the pushing effect of the propellers. In general, the larger the propeller’s push, the larger the uniform sludge distribution along the water depth direction. To quantify the uniformity of sludge distribution, the depth sections influenced by the propellers (0.9–5.1 m) were normalized with the extreme value of sludge concentration variation. For schemes (b), (d), and (e), the extreme value of the sludge concentration per section is relatively small, and the sludge distribution is uniform. Scheme (a) provides the largest extreme value of the sludge concentration, and the rate of change of sludge concentration depths is more pronounced above 2 m. Meanwhile, the sludge distribution is not uniform, thus reducing the treatment effect of the activated sludge on the biochemical and chemical oxygen demand in the sewage. The evidence from this analysis suggests scheme (b) has a more uniform flow velocity and sludge distribution near the bottom of the OD than the other cases, but the dead zone rate and overall sludge concentration of the passage is smaller.

3.3. Analysis of Flow Field Characteristics

3.3.1. Analysis of Sludge Concentration and Average Flow Velocity

To examine the links between the scheme (b) and the flow behavior, the variation of the average velocity and the sludge concentration were calculated using the 12 plane sections. As shown in Figure 1, the locations of the sections in the ditch are as follows: section 1, below the inlet of water flow; sections 2–4, running upstream of propeller A; sections 5 and 11, at the curve of wall; sections 7–9, at the second straight; section 10, at the outlet; section 12, at the second curved wall.
The results, as presented in Figure 12, show a scatter trend of the average velocity and sludge concentration at each section. The most surprising aspect of the data was the dissociation of high velocity areas with high sludge concentrations. These differences can be explained in part by the proximity of the section with interferences in the flow. For example, the sections (1, 7–9) close to the propeller have a high velocity, a scatter flow pattern, and low sludge concentration. However, section 1 is located below the inlet of water flow, and its flow pattern is disordered. The increased sludge concentration at the bottom of the inlet side accords with findings of Section 3.2. The curved wall (section 6) augmented these two parameters, as a result of the flow direction. At the propeller’s wake, the average velocity and sludge concentration decreased in sections 2–4. Through the strong guidance of the curved wall, the flow increased in speed, and thus, the sludge concentration was low (sections 5 and 11). The outlet changed the flow direction, resulting in the dead zone flow (section 9); thus, the sludge concentration was high. At the back of the outlet (section 10), since water and sludge flow out from the system, the average flow velocity and the sludge concentration were small. Because the incoming flow impedes the movement of water at the second curved wall, the average velocity was small in section 12.

3.3.2. Velocity Distribution in Each Section

To develop a deeper understanding of relationships between velocity and locations, the characteristics of the flow field in scheme (b) were analyzed, both experimentally and numerically. This is illustrated in Figure 13 by using six sections (1–6).
As shown in Figure 13, the experiments have a good consistency with the simulation data. In Figure 12a, propeller A affects little the inlet flow; thus, the flow velocity is stable. However, due to the influence of vertical inlet flow, a vortex is generated near the wall, resulting in a lower velocity near the bottom of measuring points, which is the dark blue area below the inlet in Figure 8, and therefore, the deposition of sludge occurs. Because the pushing effect of the propeller is concentrated in front of the wheel, which is the lower middle of the section, the flow at this point attains the highest speed, up to 1 m/s. The bottom of both sides is not affected by the flow significantly, so the flow velocity is relatively low.
To better understand the propeller’s influence on the flow, predictions of velocity vectors are made for sections 3 and 5 (Figure 14). It could be seen that the flow velocity in the lower part is the highest, and the spiral flow formed strikes the curved wall, but then it collides with the upper water flow, resulting in a decreased velocity near the middle-curved wall. Since section 4 is located at the inlet of the first and second curve and is far away from the propeller, the flow pattern is seen as more stable. The flow’s velocity decreases and then increases from top to bottom of the channel.
As can be seen from the first curve’s velocity pattern (section 5, Figure 14b), the water, resulting from inertial and centrifugal forces, strikes the wall, and thus, a vortex occurs. The high-speed flow driven by the propeller goes upwards after hitting the diversion wall at the bottom, so the flow velocity in the plane increases from the top to the bottom in Row I. The measuring points in Row II are close to the curved wall, so they are strongly influenced by the forced diversion; therefore, the flow velocity at these points fluctuates. The velocity at measuring points Row III is relatively lower than Row II. Under the strong influence of the curved wall in section 6, the water flow is accelerated by the propeller and then diffuses to the upper part. The velocity distribution is lower in the low part and higher in the top part.

4. Conclusions

A numerical simulation of water-activated sludge using a two-phase CFD model was used in this paper. The viscosity and settling rate of activated sludge was imported into the FLUENT software through UDF. The influence of the propellers position was analyzed and calculated, and the flow field characteristics of the most appropriate arrangement were compared with the experimental results. The following points can be inferred from results:
(1) In order to meet the energy requirements in the OD, the power of the propeller was selected as 4 kW using the LJM formulation.
(2) When the structure of the oxidation ditch remained unchanged, changing the installation position of the propellers could effectively improve the velocity distribution of OD and reduce sludge deposition. In comparison to the other arrangements, the scheme (b) (∆L = −1L) provided a more uniform velocity and sludge distribution at the bottom of the oxidation ditch and a lower dead zone rate. Furthermore, the extreme value of sludge concentration in the whole flow passageway was smaller, so this distribution was the most reasonable propeller configuration.
(3) For each section, a higher velocity did not always result in higher sludge concentration. However, the area with a high dead zone rate usually has a high sludge concentration. As a result, it is reasonable to use a velocity dead zone as a parameter to measure the sludge deposition.
(4) The section of flow velocity obtained by numerical simulation and experiment measurements was similar. Overall, the numerical simulation is able to predict velocities of the measured points in the flow field reasonably well.

Author Contributions

Conceptualization, Y.Z. (Yuquan Zhang), Y.X. and C.L.; investigation, C.L.; writing—original draft preparation, Y.Z. (Yuquan Zhang), H.L., and E.F.-R.; writing—review, Q.T., Y.Z. (Yuan Zheng) and H.L.; writing—editing, Y.Z. (Yuquan Zhang) and Y.X.

Funding

The research was supported by the following funding programs: National Natural Science Foundation of China (51809083); Natural Science Foundation of Jiangsu Province (BK20180504); Fundamental Research Funds for the Central Universities (2019B15114, 2018B48614); China Postdoctoral Science Foundation (2019M651678) and Water Conservancy Science & Technology Project of Jiangsu Province (2018026).

Conflicts of Interest

The authors declare no conflict of interest.

Nomenclature

ODoxidation ditchCFDcomputational fluid dynamics
UDFuser-defined function x i spatial co-ordinate
ppressure ρ fluid density
μ dynamic viscosity f i ¯ time mean external force tensor
S i j ¯ mean rate of strain tensor μ s v sludge viscosity
Tabsolute temperatureMLSSmixed liquor suspended solids
Vssludge sedimentation ratexsludge concentration
H f requirement for pushing 1-kg liquid Σ ξ the overall local resistance factor
Fdischarge areaLlength of the OD
Uwwetted perimeterUflow velocity
W r power losses for pushing 1-kg liquid Q p discharge through the water section of the OD
W R net power for pushing 1-kg liquid η efficiency of the propeller

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Figure 1. Dimensional drawing of the oxidation ditch (OD).
Figure 1. Dimensional drawing of the oxidation ditch (OD).
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Figure 2. Submerged propellers.
Figure 2. Submerged propellers.
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Figure 3. Measuring points in each section.
Figure 3. Measuring points in each section.
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Figure 4. LJ20A-type convenient tachymeter.
Figure 4. LJ20A-type convenient tachymeter.
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Figure 5. The tachymeters installed on the bracket.
Figure 5. The tachymeters installed on the bracket.
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Figure 6. Mesh of the oxidation ditch and rotor.
Figure 6. Mesh of the oxidation ditch and rotor.
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Figure 7. Layout diagram of propeller when ∆L = −0.2L~0.2L.
Figure 7. Layout diagram of propeller when ∆L = −0.2L~0.2L.
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Figure 8. Velocity contour near the bottom of the OD.
Figure 8. Velocity contour near the bottom of the OD.
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Figure 9. Dead zone rate of different schemes.
Figure 9. Dead zone rate of different schemes.
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Figure 10. Sludge concentration contour near the bottom of the oxidation ditch.
Figure 10. Sludge concentration contour near the bottom of the oxidation ditch.
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Figure 11. Sludge distribution by different schemes.
Figure 11. Sludge distribution by different schemes.
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Figure 12. Average velocity and the sludge concentration at 12 plane sections.
Figure 12. Average velocity and the sludge concentration at 12 plane sections.
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Figure 13. Distribution of flow velocity in each section, simulations (lines) against experiments (markers).
Figure 13. Distribution of flow velocity in each section, simulations (lines) against experiments (markers).
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Figure 14. Velocity vectors of different sections.
Figure 14. Velocity vectors of different sections.
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Table 1. Variation of the evaluation index.
Table 1. Variation of the evaluation index.
Mesh DensityNumber of Elements (million)Consumed PowerConsumed Power (kW)
Coarse2.55.2%3.717
Medium-coarse3.91.4%3.866
Medium4.30.3%3.910
Fine5.13.921

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MDPI and ACS Style

Zhang, Y.; Li, C.; Xu, Y.; Tang, Q.; Zheng, Y.; Liu, H.; Fernandez-Rodriguez, E. Study on Propellers Distribution and Flow Field in the Oxidation Ditch Based on Two-Phase CFD Model. Water 2019, 11, 2506. https://doi.org/10.3390/w11122506

AMA Style

Zhang Y, Li C, Xu Y, Tang Q, Zheng Y, Liu H, Fernandez-Rodriguez E. Study on Propellers Distribution and Flow Field in the Oxidation Ditch Based on Two-Phase CFD Model. Water. 2019; 11(12):2506. https://doi.org/10.3390/w11122506

Chicago/Turabian Style

Zhang, Yuquan, Chengyi Li, Yanhe Xu, Qinghong Tang, Yuan Zheng, Huiwen Liu, and E. Fernandez-Rodriguez. 2019. "Study on Propellers Distribution and Flow Field in the Oxidation Ditch Based on Two-Phase CFD Model" Water 11, no. 12: 2506. https://doi.org/10.3390/w11122506

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