1. Introduction
High-speed emergency drainage pumps have been widely applied in rapid responses to solve urgent water-logging problems because they are of light weight and have powerful water transmission capacity [
1]. Similar to ordinary centrifugal pumps, the main design methods of drainage pumps are the affinity law and the velocity coefficient method, but with the in-depth study of the traditional basic theory and design methods, some new design methods have been created, such as the increasing flow rate design method, clogging flow channel design method, non-clogging pump design method, two-phase flow design method, and so on [
2]. Some pumps are difficult to experiment on because of their large size and higher rotating speed, the traditional affinity law is still a dominant method that is widely used in the process of designing pump products. This method can shorten the product development cycle; however, the applicability of the affinity law for high-speed, axial-flow emergency drainage pumps is challenged. Under the conditions where the pumps runs at small flow rates and where high lift or cavitation occurs at large flow rates, the similarity of the internal flow in high-speed, axial-flow emergency drainage pumps seems to be quite different from that of ordinary centrifugal pumps, making their internal flow characteristics worth studying.
Shi verified the similarity of the scaling model of a typical submersible well pump with a scaling factor of 0.66, and it was found that the designed pump and the model pump basically showed consistent variation tendency for their head, efficiency, and shaft power under 0.4–1.6-times rated flow conditions, and the highest efficiency points were both achieved under 1.2-times rated flow rate conditions, which means the predicted performance met similar conversion rules [
3]. Si studied the applicability of the similarity law under the condition of gas–liquid two-phase flow in centrifugal pumps, and the comparisons between the experimental and simulated results showed that the greater the inlet gas volume fractions was, the worse the applicability to the similarity law [
4]. Naoki studied the air–water two-phase flow performance in a conventional centrifugal pump and found the similarity law of the impeller diameter was experimentally confirmed, even in two-phase flow condition; influences of blade height on air–water two-phase flow performances indicated little difference from the similarity law [
5]. Zhu studied the hydraulic characteristics between prototype and model pumps in pumping stations, because the local hydraulic loss was the main part in the inlet and outlet channels of the pumping station, but during the efficiency correction, the local loss had not been separated; in fact, it did not need to be converted and had nothing to do with the scale effect [
6]. Chen analyzed the approximation of hydraulic simulation, mechanical constraints, and geometric similarity between the model and the prototype for pumps and pump sets. The hydraulic efficiency expression was deduced in terms of its original definition, where pump and pump-set efficiency conversion formulas were derived, which concerned hydraulic friction loss only or all hydraulic losses, including shape resistance and shock vortex resistance [
7].
Zhen studied the rotating stall characteristics of an axial-flow pump. The results showed that there were two reversed-flow field areas in the leading edge close to the shroud and in the trailing edge close to the hub of the blade suction surface under rotating stall conditions, and the dominant frequency under the designed conditions in the impeller inlet and the middle of the guide vane was blade passing frequency. However, the dominant frequency under the designed conditions in the impeller outlet was guide passing frequency, because of rotor–stator interaction [
8]. Li studied the flows in an axial-flow pump at the design flow rate for five different tip gap sizes by using large eddy simulation. The results indicated that the head, shaft power, and efficiency of the pump decreased as the tip gap size increased, and the velocity of the tip leakage flow in the tip gap increased gradually along the radial direction; when the tip gap size was larger than 1.0‰ of the impeller diameter, the dominant tip leakage vortex extended to the pressure side of the neighbor blade, and the tip separation vortex and several secondary vortices were also found [
9]. Zhang simulated and analyzed the formations of a three-dimensional tip leakage vortex cavitation cloud and the periodic collapse of vortex-induced suction side perpendicular cavitation vortices. The improved turbulence model and the homogeneous cavitation model were validated by comparing the simulation results with an experiment by measuring unsteady cavitation shedding flow around a hydrofoil. The unsteady cavitation cloud occurred near the blade trailing edge and the shapes of the sheet cavitation and normal cavitation fluctuated [
10]. Zhang also studied the tip leakage vortex in an axial flow pump under small flow rate conditions. Owing to the large pressure difference between the pressure side and suction side at the tip, the axial velocity of flow in the gap was negative, and the absolute value of the axial velocity increased gradually from the pressure side to the suction side. High-speed photography experimental results showed that as the cavitation number decreased gradually, the leakage vortex cavitation was initiated in the tip clearance under small flow rate conditions [
11]. Wang studied the internal flow field in a pump installation, where an unstable pattern behind the pump and significant numbers of vortices occurred in the outlet passage, the pressure distribution was irregular in the middle of the blade suction side, and a high-pressure zone existed on the blade pressure side near the leading edge and on the blade suction side near the trailing edge [
12]. Zhou studied the stability of the reverse-power generation of a pumping station; the frequency of the pressure pulsation was influenced by the frequency of the runner and was concentrated at low frequencies. The flow pressure fluctuation in the middle and edge of the inlet of the runner was obvious, and the maximum pressure pulsation occurred in the middle of the runner outlet. The amplitude of the pressure pulsation was nearly 3 times that of the outlet edge of the runner, and it was nearly 2 times the amplitudes of the middle and edge of the inlet of the runner [
13].
When the pumps work in off-design conditions, it is easy to observe stall, leakage, cavitation, and other phenomena, which lead to a certain deviation between the scaling value and the simulation value after the affinity law is used. The above scholars have mainly focused on the study of the scaling methods of hydraulic characteristics of the actual pump and the model pump. The affinity law was modified according to improving various losses, such as hydraulic loss and volume loss, and various scaling formulas have been proposed to improve the affinity law of pumps. However, there are few studies on the reasons for the differences between the scaling results and the experimental results after the affinity law is used, particularly the study of the affinity law characteristics in emergency drainage pumps at different rotating speeds. In order to study the influencing factors on the affinity law and to figure out the applicability of the similarity theory at variable operating conditions, commercial ANSYS CFX 15.0 software was adopted to simulate the whole flow field of an emergency drainage pump at different flow rates and rotating speeds. The internal flow characteristics were analyzed, and the simulation results were compared with those of experiments. The distributions of cavitation volume fraction in the impeller under different flow rates and different cavitation numbers, the variations of cavitation flow fields in the blade tip region, and the relationship between leakage loss and affinity law in the pump under different rotating speeds were all analyzed. Through analyzing the leakage flow with small flow rates and cavitation flow with large flow rates, affinity law mechanisms related to energy and cavitation characteristics will be discussed.
2. Numerical Model and Method
2.1. Flow Control Equations
The basic equations of fluid mechanics include the continuity equation, momentum equations, and energy equation. The fluid flow in pumps changes with time and space, and it is a complex, three-dimensional, and unsteady process. The water transported by the emergency drainage pump can be considered an incompressible medium. The equations adopted are as follows.
The continuity equation is the mathematical expression of the law of mass conservation:
where
ρ is the fluid density;
t is the time;
,
and
represent the velocity components in
x,
y, and
z directions.
The momentum equations, named N-S equations, are the mathematical expression of the law of momentum conservation. Although the N-S equations can describe the motion of turbulence, due to the huge difference between the time and space characteristic scales in turbulent flow fields, it is difficult to solve N-S equations in actual flows. The Reynolds time-averaged equations are generally used, and the tensor expression is as follows,
where
is the Reynolds average velocity;
is the velocity fluctuation;
is the kinematic viscosity.
The energy equation, meaning the amount of energy change in the controlling body, is equal to the sum of the heat entering the controlling body minus the heat escaping from the controlling body per unit time, and the equation is expressed as,
where
T is the temperature;
k is the heat transfer coefficient of the medium;
is the specific heat capacity;
is the heat source inside the fluid.
2.2. Turbulence Mode
According to the number of differential equations used, the commonly used turbulence models can be divided into the zero-equation model, one-equation model, two-equation model, and multiple-equation model. The two-equation turbulence model is widely used at present. Strong shear stress and a reverse pressure gradient exist in the emergency drainage pump. Therefore, the SST (Shear Stress Transport) k-ω model is selected to close the equations, with its turbulent energy k and turbulent fluctuation frequency ω equation.
Turbulent kinetic energy
k equation:
Turbulent pulsation frequency
ω equation:
The relationship between turbulent viscosity
, turbulent kinetic energy
k and turbulent pulsation frequency
ω is,
where
is the turbulence generation rate, and also the pressure generation term produced by the velocity gradient; other variables are obtained using the N-S formula. In addition, the constant terms in the model are
,
,
, and
,
.
2.3. Hydraulic Model
The emergency drainage pump studied in this paper is mainly used in urban emergency drainage projects. Its compact structure makes it convenient to be transported and used. As shown in
Figure 1, the pump mainly includes the motor, inlet section, impeller, and guide vane. The design parameters are as follows: rated flow
Qopt is 220 m
3/h, head
H is 7.5 m, rated rotating speed is 3800 r/min, power is 15 kW, the specific speed is 756, the impeller blade number is 4, the diameter of the impeller is 131.6 mm, the hub diameter of the impeller is 61.8 mm, the clearance between impeller and drum wall is 0.7 mm, the guide vane blade number is 6, the distance between inlet edge of the guide vane and outlet of the impeller is 5 mm, and the diameter at the outlet of the guide blade is 137 mm.
2.4. Mesh Division
Commercial software Icem-cfd(The Integrated Computer Engineering and Manufacturing code for Computational Fluid Dynamics) 15.0 was used to divide the flow field into structured meshes, which are shown in
Figure 2. In order to control the mesh distribution in the boundary layer near the wall, the O-type topology is selected. The mesh in the blade boundary layer and its adjacent zone is controlled by the O-grid, and the mesh in the impeller is encrypted. A ten-layer grid is placed in the tip clearance to better capture the flow. As shown in
Table 1, in order to obtain the appropriate number of meshes, five grid sizes with different precision of division were compared, and the simulated head and efficiency values were chosen as reference indexes, as shown in
Figure 3. When the mesh number reaches over 2.75 million, the simulated results of efficiency (
) and head (
H) do not fluctuate obviously with the changing of numbers of meshes, and the total simulation mesh number is 2,756,481.
2.5. Simulation Methods and Boundary Conditions
The SST k-ω turbulent model and CFX 15.0 software were adopted in this paper. A steady numerical simulation was adopted. For the boundary conditions, the inlet was set as the total pressure, while the outlet was set as mass flow rate. The solid wall was set as a non-slip and adiabatic wall surface. The roughness of the main flow passage components, such as the impeller and guide vane, was set at 0.025 mm. The standard wall function was adopted for the near wall surface. The water in the impeller was set as the rotation area, while the rest of the water body was set as the static area, and the outer wall surface of the impeller was set as the counter-rotating wall. The coupling interfaces between the rotating areas and the static areas were set in transient frozen rotor mode, and the coupling interfaces between the static regions were not set in any mode. The convergence accuracy was set at 10−5 with the higher-order solution format.
The default ZGB(Zwart-Gerber-Belamri) cavitation model was chosen to simulate the cavitation flow, total pressure was selected as the inlet boundary condition, the volume fraction of the liquid phase was set to 1, the volume fraction of the gas phase was set to 0, and mass flow rate was selected as the outer boundary condition. The medium for computational fluid was 25 °C water. The critical cavitation pressure was set to 3574 pa according to the temperature of the saturated steam pressure, the average diameter of cavity was set to 10−6 mm, and the convergence residual was set to 10−5.