REAL-LIFE CONFIGURATION STIRRED TANKS: A COMPUTATIONAL INVESTIGATION USING CFD

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1 Proceedings of the 13 th International Conference on Environmental Science and Technology Athens, Greece, 5-7 September 2013 REAL-LIFE CONFIGURATION STIRRED TANKS: A COMPUTATIONAL INVESTIGATION USING CFD A. A. ALLEYNE 1, S. XANTHOS 1,2, H. TANG 1, K. RAMALINGAM 1 1. Environmental Division, Department of Civil Engineering, Grove School of Engineering, City College of New York. 2. NIREAS International Water Research Center, University of Cyprus, P.O. Box 20537, 1678 Nicosia, Cyprus aalleyn02@ccny.cuny.edu or spunjah83@gmail.com ABSTRACT This study outlines the development of a modeling approach for mixing in stirred tanks found in wastewater treatment plants with emphasis on the Hyperboloid mixing technology introduced by the INVENT HyperClassic mixer. The methodology adopted was to use Computational Fluid Dynamics (CFD) in small, medium and large scale mixing units to identify the best suitable technique while comparing model results data available in the literature. The modeling of the moving zones was carried out using the multiple reference frame (MRF) model in full scale with the steady state approximation which is well documented for providing very reasonable time-average flow predictions in many applications. ANSYS was used as the base platform for the 3D CFD calculations whose flow predictions were compared to published simulation results and experimental data by Delgon et al. (2006), Wu et al. (1989), Delafosse et al. (2008), Escudié et al. (2003) and Roussinova et al. (2003) respectively. The simulation results from this study for the k-ε turbulence model compare reasonably well with the published data (both CFD models and experimental data primarily obtained by the LDV (Laser Doppler Velocimetry) technique) for the small, medium and large case studies. Moreover the results obtained indicate that the mean velocity time-averaged flow predictions are not significantly influenced by either the grid resolution or discretization scheme provided reasonably fine grids are used. The preliminary MRF simulations on the pilot study at the Wards Island WWTP, shows visually good results when compared to the results from INVENT. The mixing plane model, where flow field data from adjacent zones are spatially averaged at the interface will also be performed along with the sliding mesh model approach which provides unsteady flow field information. Additional parameters such as velocity gradient, G, and dissipation rate will be used to compare field data obtained by the City College of New York (CCNY) for validation of the model output of the HyperClassic mixer. This will in-turn allow for optimizing the mixer configuration at the wastewater treatment plant. Keywords: Computational Fluid Dynamics; Mixing units; INVENT mixer; moving reference frame 1. INTRODUCTION Wastewater treatment plants mainly employ mixing processes for suspending and dispersing organic or inorganic solids. The energy used by a single mixer becomes significant in a wastewater treatment plant with 24/7 operation. Hence, mixing technologies that can optimize energy use become relevant and essential to lowering power consumption and costs.

2 A recent introduction of a new innovative mixer in the wastewater field is the INVENT HyperClassic evolution 6 hyperboloid mixer. This mixer was developed and designed especially for applications in the area of water and wastewater treatment. This hyperboloid mixer rotates close to the bottom of the tank/channel and its eight (8) motion fins produce a bottom flow which is directed radially outwards [1]. A vortex-shaped main flow is created throughout the whole tank, which ensures excellent homogenization [1]. Hyperboloid mixers have been developed to lower the consumption of energy and also the operational costs [2]. These mixers are distinguished from other mixers by their form and close clearance installation and are efficient in suspending precipitating sludge particles with the strong bottom flow they produce [2]. Computational Fluid Dynamics (CFD) modeling based on the Reynolds averaged Navier- Stokes approach is a useful tool for determining detailed information of complex mixing regimes. CFD simulations of turbulent flows provide a wealth of information (e.g. velocity vectors, turbulent kinetic energy, energy dissipation rate, etc.) at different positions [3]. Detailed analysis of the large amount of data generated by CFD simulations can reveal flow behavior, circulation patterns, vortex structures and, at a smaller scale, turbulence intensity, dissipation rates, Reynolds stresses, etc. [3]. A CFD model for mixing in tanks and channels requires amongst others, an appropriate grid resolution, discretization scheme, turbulence model and impeller rotating model [4]. All of these characteristics are large numerical considerations which can have a significant influence on both the associated computational expense and the accuracy of the simulation. Barring computational limitations, CFD models will always maintain a compromise between reasonable accuracy and reasonable computational expense [4]. CFD models in the engineering field often reduce computational expense by using the steady-state MRF impeller rotation model and the engineering standard k-ε turbulence model [4]. For the steady-state simulation of stirred tanks, the MRF model has been found to give similar results in a faster time to the more refined transient Sliding Mesh impeller rotation model [5]. Conversely, the standard k-ε turbulence model has been found to poorly predict turbulence in mixing tanks, but can accurately model turbulent fluid flow provided very fine grids/meshes coupled with higher-order discretization schemes are used [4]. To successfully model a full scale INVENT hyperboloid mixer, sufficient experimental or actual data would have to be available for validation. Since there is no known published data for this impeller at full scale size, an alternative path was chosen to validate the model output for this type of mixer. The methodology and approach was used on small, medium and large scaled mixing, in a sequential progression, using the MRF impeller rotation model and the k-ε turbulence model. The model results were compared to published simulation and experimental data of Delgon et al [4] and Wu et al [6], Delafosse et al [7] and Escudié et al [8], and Roussinova et al [9] respectively. Once a satisfactory validation was obtained, the same approach was used to model the Invent mixer and results obtained. The goal is that once we have sufficient simulations for the Invent mixer, confirmation would be carried out with some field data that is available from a similar installation at one of the waste water treatment plants in New York City. 2. CFD METHODOLOGY The 3 dimensional CFD ANSYS package (13.1 ) was used as a base platform for the beginning and was subsequently upgraded to ANSYS 14.0 package for the remainder of the study. The ANSYS package was used for all the complex geometry building, meshing and simulation. ANSYS FLUENT 14.0 is integrated in the package for simulations. The standard k-ε turbulence model and the MRF impeller model were used for all three size configurations, namely small, medium and large and at each step confirmation and validation

3 were obtained before progressing to the next larger size. For the MRF model, steady-state calculations are performed with a rotating reference frame in the impeller region and a stationary reference frame in the outer region. In this way, the effects of the blade rotation are accounted for by virtue of the frame of reference, allowing for explicit modeling of the impeller geometry [4]. In order to use the MRF technique, two fluid zones were defined and created: an inner rotating cylindrical volume centered on the impeller and an outer stationary zone, containing the rest of the tank and the baffles. Unlike the Delgon et al study [4] the entire mixing tank geometry was used instead of only half the geometry with cyclic boundary conditions chosen. Experiments have shown that the region where flow is strongly influenced by the periodic passage of the blades extends to a radius of D/2 away from the impeller tip and 1.5 blade heights above and below the impeller disc [4]. Therefore, the inner volume, for all 3 mixer configurations, where the flow equations were computed in a rotating reference frame was defined using the above mentioned boundary conditions. This region was termed the impeller rotating region. Fully structured, non-uniform grids with hexahedral body-fitted control volumes were used in this study for all three configurations. The inner volume/impeller rotating region is expected to have large flow gradients; therefore the number of control volumes was increased in this region to resolve these. The mesh/grid was also refined near the impeller blades with a mesh sizing feature in all three configurations so as to acquire more information from the simulation around these important regions. A mesh sensitivity analysis was conducted on 3 meshes of different resolutions (diff. no. of control volumes) for the small mixing tank configuration using first order upwind differencing. The results from this analysis determined the mesh resolutions of the other 2 mixing tank configurations. The number of control volumes/elements of each mesh used in this study for all of the mixing tank configurations and the compared data from the various published papers can be found in table 2. Table 2. Number of Control Volumes for Meshes/Grids in all mixing tank configurations Configuration Mesh/Grid # of control volumes 1:Small CCNY Grid 1 ~960, 000 CCNY Grid 2 ~2, 400, 000 CCNY Grid 3 ~8, 200, 000 Grid 4 Delgon et al ~1, 900, 000 2: Medium CCNY ~2, 400, 000 Delafosse et al ~250, 000 ~750, 000 3: Large CCNY ~7, 420, 000 Roussinova et al ~500,000 For all three mixing tank configurations the impeller blades, discs (discs only applicable to the Rushton impeller) and baffles are treated as zero thickness walls with no slip boundary conditions and the shaft and hub (hub only applicable to the 45 PBT impeller) of the impellers is treated as a solid zone. Symmetry boundary conditions were set for the top of the tank; this way the top acts as a slip wall with zero shear. The impeller rotational speeds were 200 rpm, 150 rpm and 200 rpm for the small, medium and large sized mixing configurations respectively. To investigate the effect of higher order discretization schemes, simulations were carried out in first order upwind, second order upwind and QUICK discretization schemes. The discretized equations were solved iteratively using the pressure-based segregated SIMPLE algorithm scheme and the solution was considered converged when the total residuals for the continuity equation, dropped to below The results of the medium and large scale configurations are not shown in this paper.

4 3. MODEL CALIBRATION AND VALIDATION 3.1. Mixing Tank Configurations The systems considered in this study consist of flat bottom cylindrical tanks with 4 equally spaced baffles agitated by a standard 6 blade Rushton impeller for the small and medium cases and a 45 pitched blade turbine (PBT) impeller for the large case. The diameters of these tanks are 0.15m, 0.45m and 1.22m for the small, medium and large tanks respectively. Figure 1 shows the tank configurations, with figure 1a showing the Rushton impeller and figure 1b showing the 45 PBT impeller. The Rushton impeller configurations were used because of the extensive amount of available experimental data and the 45 PBT impeller configuration was used because it was one of the few relatively large scale configurations for which published data was available. Water at 20 C was used as the test liquid in all three model configurations. In the case of the small sized configuration, results were compared to the MRF model data from Delgon et al [4] and the experimental data from Wu and Patterson [6]. In the paper from Delgon et al [4], their aim was to show that the MRF model could accurately model turbulent fluid flow provided very fine grids/meshes coupled with higher-order discretization schemes are used. Their model was found to give adequate results for the steady state simulation but the k-ε turbulence model either over or under-predicted turbulence when compared to the experimental data for Wu and Patterson [6]. Wu and Patterson published an extensive phase-averaged experimental data set obtained from measuring turbulent flow parameters with a Laser-Doppler Velocimeter (LDV). Figure 1. Tank configurations; (a) Rushton impeller, (b) 45 PBT impeller Small scale mixing tank configuration Power Number Prediction Table 3 shows the modeled power numbers for the different mesh resolutions and discretization schemes obtained in this paper. The modeled power numbers are very close to a wide range of experimental and modeled power numbers in literature. Falk et al [10] showed that power numbers for the Rushton impeller for Reynolds numbers greater than 10 4 are between In the sliding mesh study of the Rushton turbine in a similar baffled stirred tank from Lane et al [11], a power number of 4.5 was calculated. Brucato et al [12] used power number value of 5 to calculate boundary conditions for his Impeller Boundary Condition (IBC) method for modeling the Rushton impeller in a baffled stirred tank. This value

5 was used because Brucato et al [12] reported that a range of values of (Rushton et al, 1950) for power numbers were reported in the literature for similar tanks stirred by Rushton impellers at comparable Reynolds numbers. Table 3. Modeled Power Numbers for different Mesh resolutions and Discretization Schemes. Model Np CCNY Grid 1, 1 st Order Upwind 5.19 CCNY Grid 2, 1 st Order Upwind 5.03 CCNY Grid 3, 1 st Order Upwind 4.91 CCNY Grid 3, 2 nd Order Upwind 5.02 CCNY Grid 3, QUICK 5.02 Grid 4 Delgon et al, 1 st Order Upwind Mean Velocity Prediction The prediction of mean velocity components is vital for the accuracy of a CFD model. The mean velocity data was obtained by averaging values at 24 locations (every 15 degrees) with a length of 3cm centered at the middle of the blades at a distance of cm (r/t=0.185) away from the center of the tank. 100 points were collected in the 3cm length. Figure 2 shows the profiles of the normalized mean radial and tangential velocities near the impeller for different meshes/grids obtained in this study compared with experimental data from Wu et al [6] taken from the plots in the Delgon et al [4] study. This figure illustrated the mesh sensitivity analysis done. The results from all three grids are comparable with each other and with the experimental data from Wu and Patterson [6]. The results show that with increase in control volumes the results get better, but in this study they are just marginally better with the slight differences being around the region of the center of the blade, where the highest velocity is expected to be. In the Delgon et al [4] study, they also have results that, just like this study, tend to deviate from the experimental data slightly in the lower half of the impeller (z/h<0.33).the experimental data is slightly skewed to the upper side of the impeller while as seen in this study and the study of Delgon et al [4] the solution is symmetric. Delgon et al [4] states that these discrepancies can be attributed to the fact that the data actually consist of 10 data points through which a smooth curve has been interpolated, although the experimental data is shown as a continuous profile for illustrative purposes. Delgon et al [4] suggest that the comparisons between CFD model predictions and experimental data in both this and subsequent sections should be interpreted with the nature of the experimental data set in mind.

6 Figure 2. Normalized mean radial and tangential velocities near the impeller for different grids (mesh sensitivity analysis) Figure 3. Axial profiles of normalized mean radial and tangential velocities near the impeller for different discretization scheme Figure 3 shows the profiles of the normalized mean radial and tangential velocities near the impeller of the finest grid, grid 3, for different discretization schemes; 1 st order upwind, 2 nd order upwind and QUICK schemes. All three grids at different discretization schemes are comparable with each other and with the experimental data from Wu and Patterson [6]. Compared to the Delgon et al [4] data, this study s data matches closer with the experimental data. In the case with the mean radial velocity plot, the 1 st order upwind, 2 nd order upwind and QUICK data is practically identical whereas with the mean tangential velocity plot the 2 nd order upwind and QUICK data is slightly better than the 1 st order upwind data. From these results one may conclude that mean velocity predictions of a fluid are not significantly

7 influenced by the grid resolution or discretization scheme provided reasonably fine grids are used. 4. SIMULATIONS OF INVENT FLOWS 4.1 Background In an attempt to identify a more cost effective strategy for mixing the Primary Settling Tanks (PST) influent channel, NYC Environmental Protection, (NYCEP), initiated a pilot study by installing four INVENT HyperClassic mixers within a 91 feet (27.74 m) section of the PST influent channel at the Wards Island WWTP. The mixers have a diameter of 6.6 feet (2 m), operate at a speed up to 26 rpm, (revolutions per minute) and are rated at 3hp per mixer. The mixer rotates 7.9 in (0.2 m) from the bottom of the channel producing a bottom flow which is directed radially outwards. 4.2 Configuration of INVENT mixer Figure 6 Shows the schematic geometry of the INVENT HyperClassic evolution 6 hyperboloid mixer; figure 6a was the schematic taken from the INVENT website and figure 6b is the geometry done in this study using the ANSYS 14 Design Modeler Preprocessor. Some of the small characteristics of the mixer were left out in this study for ease of mesh generation; for example, holes, bolts and thickness of the motion fins. Figure 6. (Left) Invent Mixer Schematic; (a) Graphic taken from invent website [1], (b) Geometry drawn in ANSYS 14 Design Modeler Preprocessor. Figure 7. (Right) 12ft x 12ft domain setup for INVENT Mixer Preliminary Simulation 4.3. INVENT Simulation Setups and Results Preliminary Simulation Before trying to model the entire setup of the influent channel at Wards Island WWTP, we decided to run a simple simulation of the invent mixer in a 12ft x 12ft box at 26 rpm, to see if we could achieve similar flow patterns (stream lines) to the ones reported by INVENT. The 12ft X 12ft size was chosen because the width if the influent channel being studied is 12ft. Figure 7 shows the setup. The MRF technique was used for this simulation as with the previous simulations. Two fluid zones were also defined and created as seen in figure 7: an inner rotating cylindrical volume centered on the impeller and an outer stationary zone, containing the rest of the tank.

8 Flow Patterns Figure 8a shows the flow schematic that the INVENT mixer produces. This figure provided by the INVENT company [1], shows the initial streamlines and the micro-vortices produced at the bottom of the tank produced from the motion of the close bottom configured INVENT mixer. Figure 8b is Section A-A is shown in figure 9 representing the flow patterns obtained by the simulation. As seen in figure 8b, our simulation accurately represents the flow scheme reported by INVENT [1]. (a) (b) Figure 8. (a) Schematic representation of the INVENT mixer with indication of streamlines [1]. (b) Simulation results of the INVENT mixer with indication of streamlines and microvortices Full Scale Simulation (Influent Channel at Wards Island WWTP) Below in figure 9 shows the full scale simulation setup of the pilot study at Wards island WWTP with boundary conditions. Figure 9. Simulation setup of the Influent Channel at Wards Island WWTP with INVENT mixers, showing the boundary conditions. Figure 10a is a simulation from INVENT [1], of the mixers in an activated sludge tank with average flow of approximately 8-10 MGD. We can see that this study s simulation results in figure 10b are comparable to these results from INVENT understanding that the flow in the influent channel is about 6 times more than that in an activated sludge tank. We can clearly see the differences where the flow significantly affects the virtual separating walls. Even with not having any actual measured data to validate our model with, these preliminary simulations, visually, are enough to let us know we are on the right track. Future work will be done to come up with the improvement of the model and our own process of validation. This

9 will intern help us to optimize the mixing process in not only this influent channel but any process that includes the INVENT HyperClassic mixer. (a) (b) Figure 10: (a) Simulation of INVENT mixers in an activated sludge tank; flow~ 8-10 MGD [1]. (b) Simulation results of the INVENT mixer with indication of streamlines and micro-vortices 5. CONCLUSIONS One of the primary goals of this paper was to establish a methodology that can be used for full-scale mixing simulations of the Invent hyperboloid mixer. Hence, the effectiveness of a CFD model using the MRF impeller rotation model and the k-ε turbulence model was examined at three different scales, namely, the small, medium and large. Satisfactory results obtained by comparing the power number and mean velocity components at all three stages reinforced the approach methodology used and was hence used for modeling the Invent hyperboloid mixer in a wastewater application. The CFD model predictions for the configurations are compared to the published simulation and experimental data of Delgon et al [4] and Wu et al [6], Delafosse et al [7] and Escudié et al [8], and Roussinova et al [9]. The modeled power numbers from this study are very close to a wide range of experimental and modeled power numbers in literature. The results from this study in terms of mean velocity predictions are comparable with the CFD model and experimental data for all three mixing tank configurations mentioned. The results from this study can lead one to conclude that mean velocity predictions of a fluid are not significantly influenced by the grid resolution or discretization scheme provided reasonably fine grids are used. To further enhance the quality of the simulations and obtain more definitive information in critical areas of tanks or channels, transient models such as the Sliding Mesh and Large Eddy simulation (LES) models can be used provided the computational expense is not prohibitive. The preliminary MRF simulations on the pilot study at the Wards Island WWTP, shows visually good results when compared to the results from INVENT. This is an ongoing effort and the results shown for the Invent mixers are preliminary as more work is being carried out to fine tune and optimize this effort. Future work for the modeling of the INVENT hyperboloid mixer, will first include employing the transient models to get a better understanding of the mixing regime. Additional parameters such as the velocity gradient, G can be obtained from the CFD model output and can be compared with onsite experimental values collected by the Environmental Engineering team at CCNY. CCNY s Environmental Engineering lab has done extensive work with this velocity gradient parameter in an earlier associated study.

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