Blood pump and splitter blade
The optimized design of the splitter blades is aimed at an axial blood pump with splitter blades. The main structure of the blood pump was previously designed through theory, CFD, and experiments. Figure 7 is the three-dimensional structure of the blood pump. The main structure includes a front diffuser, impeller, and back diffuser. A permanent magnet is installed inside the impeller, and an external magnetic driving device generates an alternating magnetic field to drive the impeller. The high rotation speed of the impeller can generate the fluid energy required for blood circulation, but the complicated flow that follows will bring the risk of hemolysis, so the structural design of the impeller is the most critical issue affecting the application.
Splitter blades are auxiliary blades on the impeller, which are located between every two main blades. The shape and thickness of the splitter blades are consistent with the main blade, but its length is shorter than the main blade. The splitter blades are equivalent to increasing the total length of the blades and subdividing the larger flow channels between the main blades. Therefore, the splitter blades can improve the hydraulic performance of the pump and make the flow between the main blades more stable. However, the blood pump is a device applied in the blood environment. The design of the blood pump needs to consider both hydraulic performance and blood damage, and the structure design has more stringent requirements. In this study, the structural optimization design of the splitter blade takes into account three core parameters, which are the number of blades, axial length, and circumferential offset.
Parameter 1 is the number of split blades. A larger number of blades can have a stronger effect on the fluid, but an excessive amount will make the flow channel too narrow, which may cause blockage of the fluid and blood damage. Parameter 2 is the axial length ratio of splitter blades, which is the ratio of the axial length of the splitter blade to the axial length of the main blade. Longer splitter blades can have a sufficient effect on the fluid, but it is necessary to control the arrangement of the splitter blades in a position with enough flow channel space. Parameter 3 is the circumferential offset of the splitter blade, which is the offset angle of the splitter blade to the back of the main blade. Splitter blades are generally offset towards the back of the main blade. This is to improve the unevenness of the circumferential velocity in the flow channel.
Neural network method
The optimal design of blood pump splitter blades has the characteristics of multi-parameters, multi-objectives, and non-linearity. The traditional design method has the disadvantages of a long cycle and inaccuracy. BP neural network is a multilayer network trained according to the error backpropagation algorithm. It is a machine learning method that simulates biological nerves. The sample data is used to train the
model to modify the network weights and thresholds, so that the error function decreases in the direction of the negative gradient, thereby improving the reliability of the output results. In application, this method is suitable for dealing with multiple fuzzy factors and non-linear relationships, which meets the optimization design requirements of the splitter blades. In this study, the mapping relationship between parameters and performance was established by this method to achieve performance prediction under corresponding parameters. Figure 8 shows the structure of the BP network established in this study.
The input layer has 3 neural units, which correspond to the number of blades, axial length ratio, and circumferential offset, respectively. There are two neural units in the output layer, which correspond to the pressure head and the hemolytic prediction index, respectively. The research determined the analysis range of the input parameters through theoretical analysis and simulation calculations. The relationship between the input and output of the neural network model is as follows:
In this equation, f1 and f2 are the output, which is the pressure head and the hemolytic prediction index, respectively. x1 is the number of blades, and its analysis range is 0 to 4. x2 is the axial length ratio, which refers to the ratio of the axial length of the splitter blades to the axial length of the main blade. The analysis range of x2 is 0.3 to 0.7. x3 is the degree of circumferential offset. It refers to the angle at which the splitter blades are offset to the back of the main blade. Its analysis range is -15 ° to 30 °. x3 is the circumferential offset, which is the degree to which the splitter blades are offset toward the back of the main blade. The analysis range of x3 is -15 ° to 30 °.
CFD simulation was used to predict the performance of the 3D model with different parameters. The simulation uses ANSYS CFX 17.0 software (ANSYS, Inc., Canonsburg, PA, USA). Because the internal flow channel of the blood pump is distorted, the grid uses wall prism layer grids and unstructured grids. An independence analysis was performed on the grids, and the number of grids was determined to be about 2.5 million. The model is divided into three areas: front diffuser, impeller, and back diffuser. The impeller area is the rotation area, and the Frozen Rotor interface is used to achieve the connection between the regions.
In the simulation, blood is considered as an incompressible single-phase Newtonian fluid, which is a common and reliable simplification in blood pump calculations. The simulation uses the SST k-ω model, which has good adaptability to complex turbulent flows. The boundary conditions of the inlet and outlet are flow inlet and pressure outlet, respectively. The Simplec algorithm was used to solve the pressure-velocity coupling equation, and the convergence accuracy reached 10-5. In the results, the pressure head was calculated from the pressure difference between the inlet and outlet, and the hydraulic performance of the model was evaluated with this head.
The shear stress in the blood pump flow field will cause hemolytic damage. In the design of the splitter blades, it is necessary to ensure that no additional hemolysis will occur. The blood test of the prototype has the problems of a long cycle and high cost. And there are many factors that are difficult to control, which will affect the accuracy of the blood experiment, so the CFD simulation is generally used to calculate the hemolysis index in the design stage .
The calculation of the hemolysis index is based on the classic hemolysis index model proposed by Giersiepen . Five hundred random streamlines were extracted from the simulation results, and the hemolytic index was calculated by the shear stress loading of the streamlines, and their average value was used as the model result.
In this equation, D is the blood damage index, which is an estimate of the proportion of free hemoglobin. τ is the shear stress (Pa), and t is the exposure time (s) of the shear stress. Hb is the total hemoglobin concentration, dHb is the released hemoglobin concentration.
This study used hydraulic and micro PIV experiments. The purpose of the hydraulic experiment is to obtain the hydraulic performance of the blood pump prototype. The performance indicators include flow rate and pressure head. Moreover, the accuracy of the CFD simulation results is verified by the results of hydraulic experiments. The hydraulic experimental device includes pump prototype, external magnetic drive system, pressure gauge, ultrasonic flow meter, and thermostatic water bath. The experiment uses a 38% volume glycerol solution, which is close to the fluid properties of blood under normal temperature conditions.
The rotor (impeller and diffuser) of the blood pump prototype is made of titanium alloy. The impeller is equipped with N48 Ru-Fe-B permanent magnets. The alternating magnetic force generated by the magnetic drive system makes the impeller rotate, and the maximum speed can reach about 12000 rpm. There are two types of impellers for hydraulic experiments: prototype impellers without splitter blades and impellers with optimized parameter splitter blades.
In addition, micro PIV experiments were used to measure and analyze the flow inside the pump. PIV is a non-contact experimental technique for flow velocity measurement. This method calculates the instantaneous velocity by obtaining the displacement of the tracer particles in a short time. When the followability of the tracking particles is good enough, the particle velocity can be regarded as the flow field velocity[20,21]. The PIV system used in this experiment was produced by TSI. The camera is Zyla 5.5, which can capture up to 100 instantaneous particle images in 1 second.
To meet the needs of PIV experiments, the pump casing is made of highly transparent plexiglass material. Besides, a square water tank is arranged on the outside of the pump to reduce the influence of the refractive index. The water tank uses the same high-permeability plexiglass material as the pump casing. In the experiment, both the water tank and the pump are filled with the same glycerin solution. Since the organic glass material and the glycerin solution have a close refractive index, the influence of the refractive index on the result can be reduced. The tracer particles were rhodamine fluorescent particles with a diameter and density of 7.0 μm and 1.1 kg/m3, respectively. The reflection on the metal surface is filtered by a 560 nm camera filter. Figure 9 (a) is the PIV experiment system. Figure 9 (b) is the particle image and the method of particle velocity calculation. In the calculation, identify the same particle in two photos with Δt interval, and obtain the velocity by the particle's movement position. The Δt of this PIV experiment is set to 100 microseconds. Because Δt is sufficiently small, the result can be regarded as a transient velocity.