Optimizing Centrifugal Pump Performance With Computational Fluid Dynamics

Make Literature survey on Design Optimization of Centrifugal pump by fluid mechanics.

Challenges in the Design and Performance Prediction of Centrifugal Pumps

The scope of centrifugal pumps application is wide for, industrial, domestic, institutional and other fields. However, the existence of a big number of geometric parameters has made the processes of their design and prediction of performance a challenging task. Therefore, pump manufacturers have resorted to trial-and-error methods of testing prototypes to predict and optimize performance. However, this method is time-consuming and costlier to the manufacturers leading to higher cost of production, hence lower profit margins. Owing to that, many manufacturers have begun embracing computational fluid dynamics in hydrodynamic design to do prototyping and analysis for various types of pumps (Baloni et al, 2015).

Centrifugal pumps have been widely used for a long time for water supply plants, irrigation, steam power plants, chemical plants, oil refineries, mines, food processing and hydraulic power services due to their suitability in practice of any service. Many African and Arabian countries are facing great challenges of drought due to the global warming effect. Hence it is very important to find out the working conditions and design parameters that deliver ideal output and greatest efficiency with the minimal power consumption to ensure that better centrifugal pumps have been developed to aid in the irrigation and water supply. Many of the projects which have been initiated by their governments and other international Humanitarian organization have significantly failed due to the high cost of running the projects

In the recent past, the application of computational fluid dynamics analysis has dramatically gained grounds in the design of the centrifugal pumps. With the help of the computational fluid mechanics approach, the internal flow of fluids in the pump impellers can, therefore, be easily predicted (Ajay et al, 2017). The centrifugal pumps are known to deliver essential energy to the fluid which is to be pumped greatly through the velocity changes which occurs in the centrifugal pump. It involves conversion of mechanical energy to hydrodynamic energy of the handling fluid needed to get the fluid to the required height or place by the impeller blade or centrifugal force. The input energy of the hydraulic pump is mechanical such as a mechanical motor or a small engine, but the output energy is the hydrodynamic energy of the fluid which is being raised (Jung et al, 2016). The previous modifications which have been done to the centrifugal pump have yielded very little regarding improving the efficiency of the centrifugal pumps. I propose that the performance of the centrifugal pumps can be improved by decreasing the impeller outlet width, increasing the impeller outlet diameter, increase the impeller outlet blades height, increase the impeller blade outlet angle and increase the number of blades on the impeller. In this project, I want to come up with the ways through which the design of the centrifugal pump can be optimized to achieve high efficiency by use of computational fluid mechanics and other computer software.

Use of CFD for Performance Prediction and Design Improvement

It is significant to analyze the performance of a centrifugal pump since it is widely used in various fields for large-scale pumping systems. In addition, it is one of the most useful and mechanical rotordynamic machines for fluid works. Similarly, it is important to note that centrifugal pumps are used in applications with moderate head and discharge requirements (Škerlavaj et al, 2017). Currently, a lot of research is being conducted to increase its efficiency, performance improvement as well as to reduce the losses associated with it such as losses due to impeller, disk and volute friction, losses of recirculation, shock losses, and turbulence losses as well as power consumption issues. Time-consuming, costly and limited scope experimental investigation procedures have been conducted on pumps for years. To curb this problem or decrease the number of experiments, computational fluid dynamics software offers a platform where virtual analysis can be used to predict the performance of various pump models (Cao & Zhang, 2017).

This study was primarily carried out to identify the design parameters as well as modifying them with an aim of improving the characteristics of the pump performance. This paper was done in with two software. Two software was used for the optimization of the performance of the centrifugal pump. The first part employs the services of computational fluid dynamics (CFD) and part two uses the response surface method (RSM) software for optimization. The fundamental design parameters are put into consideration for the numerical and experimental analysis of performance where computational fluid dynamics was used to evaluate the design parameters along with an intensive study of experiments. The ANSYS-FLUENT code will also be applied in the (RSM) to give numerical solutions to the equations as well as to carry out numerical simulations. Figure 1 below gives a detailed discussion of the methodology used to optimize the design parameters.

 

?Figure 1. Research methodology (Cellek & Engin, 2016)

Centrifugal pumps refer to a sub-category of dynamic axisymmetric turbomachinery. They pump fluids by converting rotational kinetic energy to hydrodynamic energy of the fluid flow. An electric motor or a fuel engine provides the rotational energy. There is an impeller which accelerates the velocity of the fluid as it radially flows into a volute chamber (diffuser). The fluid acquires pressure and velocity as it passes the impeller. The volute further increases the pressure while decelerating the fluid flow (Najafi et al, 2017).  

Design Optimization for Greater Efficiency and Minimal Power Consumption

Experimental and numerical analysis on the modifications of the geometry of the volute and impeller in centrifugal pumps have been done by several scholars as well as the investigation of blade outlet angle effect and effect of the width of passage on the centrifugal pump performance. Performance tests and numerical simulations were conducted to centrifugal pumps model. In addition, the simulations were used to study ultra-low specific-speed centrifugal pump and to analyze their hydraulic properties. Using appropriate computational fluid dynamics code to numerically simulate unsteady flow, the estimation of the total radial loads for the centrifugal pump’s impeller, under different conditions was done. Similarly, a numerical simulation aids in the modeling of the characteristics of blade number effect and turbulence (Rezaienia et al, 2017). Analyses of the unsteady flow in the near-tongue region of the volute-type centrifugal pump at various operating points were numerically discussed.

A literature survey and industrial experience gave suggestions of critical parameters in the process of design and whose likelihood of having an impact on the discharge, head, and variation of power in the pump is high. A parametric study is significant in reducing the number of analyses needed at each flow rate under investigation by use of a DoE. CFD and the DoE offers a guideline for the results post-analysis and gives room for variation of performance in tandem with the design variables adjustment(Zhu et al, 2015). The chief objective of this research is to foster the growth and development of pump design that will gain ultimate best efficiency, less of a significant performance loss. Thus, this paper applies the analyses to offer a parametric study of a wider scope that considers various geometrical features and examines their effect on the centrifugal pump (van der Schoot &Visser, 2015).

Pump geometry

The pump to be investigated is the type of a low specific-speed home centrifugal water pump. Figure 2 below shows the pump that will be simulated. It is a volute-type centrifugal pump, having a singe entry and a specific speed of about 5.87. Its impeller diameter is 16.2cm, splitter blades and 5 backward-curved blades. The outlet and the inlet angles of the impeller blade are 43 deg. and 19 deg. in that order. Table 1 and figure 2 below gives a detailed geometric illustration regarded for the study and design of the parameters of the impeller.  

 

Figure 2. Pump model (Cellek & Engin, 2016)

No	Parameters	Model
1	Outer Diameter of impeller	162 mm
2	Blade angle at outlet	43o
3	Blade angle at inlet	19o
4	Blade width at exit	4.5 mm
5	Eye diameter of impeller	27 mm
6	Number of vanes	5
7	Number of splitter vanes	5

Application of Parametric Study in Reducing Analysis Time and Cost

 Table 1. Primary design parameters of the impeller (Bellary et al, 2016)

Firstly, the Navier-Stokes equations are solved, including the impeller’s centrifugal force using the computational fluid dynamics software (AmitKumar et al, 2017).

1. ?Equations

Here, the 2-D Navier-Stokes, a conservative form of the equations are used for a constant viscosity fluid’s incompressible flow (Xie et al, 2015).

2. ?Computation domain and domain discretization

The numerical simulation of the centrifugal pump entails the spatial discretization of flow domain. Inlet, impeller and the outlet are the three sub-domains of the computational domain (Guleren, 2018). There are two reference frames, the fixed reference frame, and the rotating reference frame. The rotating reference frame carries the impeller, while the fixed reference frame carries the inlet and the outlet. The fixed and the rotating reference frames are connected to one another by an interface known as the frozen rotor interface (Pei et al, 2017). The frozen rotor interface uses an algorithm of quasi-static type, in which there is the positioning of the rotor and stator at fixed/frozen points relative to one another. Separate generation of the computational domain meshes that comprises of the three sub-domains, outlet, impeller and the inlet (Yu et al, 2016).

Tools of computational fluid dynamics are applied in the prediction of the performance of pump under different conditions. The CFD tools help to obtain at any point, various flow characteristics such as temperature, volumetric flow, velocity, pressure etc. the overall pattern of flow together with the velocity distribution in the entire flow volume can be illustrated graphically to aid in a better comprehension (Valyukhov et al, 2018).

There are so many assumptions involved in the procedure of a CFD for problem-solving. Such assumptions include types of elements, number of elements, model of turbulence etc. assumptions being universal. It becomes difficult and time-consuming to make them. Trial and error method is used to choose the assumptions that best match the results of CFD with those from the experiments (Derakhshan & Bashiri, 2018). This process is better known as the best practice of CFD. The assumptions that best match the CFD outcome with results of the experiment are kept to be used as the standard values and further used in the optimization process and analysis (Deng et al, 2017). CFD involves the use of numerical methods to evaluate non-linear differential equations of Navier-Stokes and allies that give a description of fluid flow, for boundary conditions and design geometries. The outcome is a rich resource of predictions and optimizations for temperature, velocity, the concentration of chemicals, density for the regions of flow (Wang et al, 2017).

Numerical Simulation for Pump Performance Evaluation Under Different Conditions

Convergence of grid is a term for describing calculations results improvement through the use of successive smaller cell sizes (Limbach et al, 2016). The finer the mesh the closer the calculation gets to the correct solution. This is known as grid convergence. The usual technique of CFD is to commence the procedure with a rough/course mesh and slowly but steadily make it finer until the observed results changes are smaller than a predefined tolerance (Korakianitis et al, 2016). In this case, we need 400000 elements for surface meshing and 1.3 million for volumetric meshing in order to achieve the best outcome in figure 3.

 

?Figure 3. Mesh of the impeller (Bellary et al, 2016)

• Preprocessing- extraction of domain (ANSA)
• cleanup of the model (ANSA)
• surface meshing (ANSA)
• volume meshing (ANSA)
• Fluent solving- finding the solution of Navier-Stokes differential equations in the volume mesh centroid, followed by application of boundary conditions.
• Post processing (FLUENT)- result extraction

Represent results graphically

 

?Figure 4. Impeller surface mesh (Wang et al, 2016)

The path of the fluid flow is lifted from the pump’s 3D model during the preprocessing stage. The process of model cleanup entails removal of unwanted surfaces, correction of surfaces overlapping as well clearance removal between two surfaces (Derakhshan & Bashiri, 2018). Discretization of the fluid flow path’s outer surface to create the surface mesh. Surface mesh gives the volume mesh’s base. 400000 triangular elements are applied in the surface mesh with a skewness of 0.6. Figure 4 shows how volume mesh with 1.3 million tetrahedron elements and a skewness of 0.8, is obtained from the surface mesh. The discretization of the total volume of the path of the fluid flow using TGRID happens at this stage. An analysis used as a trial is conducted to give a prediction on the pump performance under the condition of duty point of volume flow rate 3000lph with a rotational speed of about 2800rpm as per figure 5.  

 

?Figure 5. Boundary conditions (Deng et al, 2017)

Solving the problem with FLUENT 6.3, and the analysis is done by K-omega. Solving is done using the discretization of the first order. Since there is no universal turbulence model, the best assumption combinations are necessary (Bellary et al, 2018). This is achieved through comparison between the assumed CFD results and the experimental results. The assumed values that perfectly match with the experimental results are kept as the standard value. Table 2 gives the results and the performance curve.

 

 Figure 6. Characteristic curve (Deng et al, 2017)

Post-processing entails results mining at the interesting point. To get the best understanding of the solution, the characteristics of the flow volume are plotted in graphs. During the solving process, the centroids of each element give the solution of the Navier-Stokes equation. Required values are extracted at the cross-sectional area or at the interesting point of that particular solution (Cellek & Engin, 2016).

Table 2. Experimental and computational fluid dynamics (CFD) results (Najafi et al, 2017).

Total Head (m)	Experimental Discharge (LPS)	CFD Discharge (LPS)
36.25	0	0.070
33.25	0.76	0.838
30.25	1.15	1.172
27.25	1.37	1.381
24.00	1.56	1.578
21.50	1.69	1.710
18.25	1.84	1.867
15.00	1.93	1.970
12.00	2.00	2.050
9.50	2.06	2.101
7.50	2.09	2.130

Characteristics	Result
Total Pressure developed	4.498 × 105 Pa
Volume flow rate	0.833 × 10-4 m3/s
Opposing torque generated by fluid	1.95 Nm
Hydraulic Efficiency	85%
Head (Suction + Delivery)	33.25  m

?Table 3. CFD results at the 33.25m head and 2800rpm (duty point)

Predominant factors identification and locating upper and lower limits of the factors chosen

The angle of the outlet blade and the exit blade width were identified in order to conduct the numerical simulations. The simulations were conducted to do analysis on the key design parameters. To fix the upper limit as well as the lower limit, a detailed analysis procedure was performed. Table 4 shows the outcome of the identification of the upper and lower limits with respect to the analysis conducted. Response surface method (RSM) aided in the process of experimental design (DoE) (Najafi et al, 2017). Experimental design is conducted to reduce the number of trials to a possible minimum number since the experimental design is done with three factors at five levels(Kim et al, 2015).

 

Figure 7. Static pressure contour  (Guleren, 2018)

 

?Figure 8. Velocity contour (Guleren, 2018).

Design Parameters			Levels
		- 2	-1	0	1	2
A	Eye diameter of the impeller (mm)	25	26	27	28	29
B	Vane exit angle (degrees)	32	37.5	43	48.5	54
C	Blade width at exit (mm)	3.5	4	4.5	5	5.5
Noise Factors			Range
Speed  (rpm)			2784-29		01
Current  (amps)			4.98-5.6		2

 
Table 4. Levels of design and design parameters (Guleren, 2018).

Experimental and numerical analysis on the modifications of the geometry of the volute and impeller in centrifugal pump have been done in this study as well as the investigation of blade outlet angle effect and effect of the width of passage on the centrifugal pump performance. Performance tests and numerical simulations were conducted. Using appropriate computational fluid dynamics code to numerically simulate unsteady flow, the estimation of the total radial loads for the centrifugal pump’s impeller, under different conditions was done.

The modified pump has a significantly reduced size and a lower power consumption. This is because of the smaller impeller diameter that increases the pressure of the outgoing fluid. The impeller accelerates the velocity of the fluid as it radially flows into the volute chamber or the diffuser. The fluid acquires a higher pressure and velocity as it passes the impeller. The volute further increases the pressure while decelerating the fluid flow.

Advantages

• There is a high impact of the discharge, head and power variation (Guleren, 2018).
• Reduced performance losses
• Lower power consumption
• Improved efficiency

Disadvantages

• Problem solving in CFD involves many assumptions that are time-consuming (Guleren, 2018).

Conclusions

This study presented the characteristics of the centrifugal water pump with low specific speed. The relationships between the vane exit angle, impeller diameter and blade width at the exit are studied in order to evaluate the characteristics of the centrifugal pump (Cellek & Engin, 2016). Discharge is highly affected by outlet conditions, flow resistance and the impeller and casing design parameters since this particular pump is a non-positive type. Thus, it is important to obtain the working conditions and design parameters that consume little power to produce optimal output and give the highest efficiency. This report aimed at the evaluation of the performance of a centrifugal pump for the above-mentioned specifications. It is a common knowledge that varying certain design parameters is what leads to the development of various models of pumps (Cellek & Engin, 2016). Experimental design optimization (DoE) uses response surface method (RSM), computational fluid dynamics (CFD) optimization and analysis are performed on the ready-made models to virtually simulate the performance and compare with results obtained from experiments. The response surface method helped to formulate the optimal design of the pump (Derakhshan & Bashiri, 2018). The objective functions of the centrifugal pump are given by the total head and the total efficiency at the design rate of flow.  

In this research, experiments and numerical simulations were used to investigate a steady state fluid flow in a centrifugal pump. The standard wall functions together with the turbulence engaged the use of continuity equations as well as the Navier-Stokes equation for accounting. A type of coding known as ANSYS-FLUENT offered numerical solutions to the equations and to operate numerical simulations that were conducted to do analysis on the effects of key design parameters on the performance of the pump, such as the diameter of the eye, angle of the outlet blade and the exit outlet blade (Cellek & Engin, 2016). These selected key design parameters also affect the overall efficiency of the pump. There was an agreement between the results obtained from the numerical simulation with those obtained from the experiments. These results are the responses to the optimization.

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