Numerical Flow Simulation in FDA's "Critical Path" benchmark blood pump


Master's Thesis, 2019

57 Pages, Grade: 1.3


Excerpt

Contents

Abstract

Acknowledgement

List of Figures

List of Tables

1 Introduction
1.1 Ventricular Assist Device
1.2 History of VADs
1.3 Operating Conditions for VAD
1.4 Role of Computational Fluid Dynamics

2 Literature Review
2.1 Blood
2.1.1 Red Blood Cells
2.2 Blood Rheology
2.3 Blood damage
2.4 Numerical Hemolysis Prediction

3 Description of Turbulence Modeling
3.1 RANS/URANS
3.1.1 Shear Stress Transport model

4 Experimental Study

5 Pre-Processing
5.1 Structure of FDA blood pump
5.2 Mesh Information
5.2.1 Mesh Overview
5.2.2 Mesh Generation

6 Computation

7 Results and Discussion
7.1 Convergence
7.2 Pressure Head
7.3 Flow Field
7.4 Wall Shear Stress and Hemolysis Comparison

Conclusion

Bibliography

Appendix

A.1 Velocity contour at different cut plane sections of Blood Pump

A.2 Pressure contour at different cut plane sections of Blood Pump

A.3 Eddy Viscosity contour at different cut plane sections of Blood Pump

A.4 Wall Shear Stress at different domains

Abstract

The role of Computational Fluid Dynamics (CFD) in the biomedical field has increased a lot in recent years. The problems related to biocompatibility in medical and biological industries capture the attention of CFD engineers. One such biocompatible device is a Ventricular Assist Device (VAD). The development phase of VADs associates CFD to make it more robust. VADs generally incorporated with blood pumps, so it is essential to examine the dynamics of the blood flow in the pump and also to analyze the dam-ages concerned with the blood cells. The U.S. Food and Drug Administration (FDA) and Center of Devices and Radiological Health (CDRH) have sponsored certain CFD “round-robins” to validate the efficiency of computer simulations in biomedical applica-tions. The ‘Computational round robin #2’ concerns with a ”Critical Path” benchmark Blood Pump.

The thesis work focuses on the analysis of the flow fields in the FDA’s ”Critical Path” benchmark blood pump. The CAD model of the pump is an opensource material, so the thesis works started with the mesh generation. A block-structured hexahedral mesh has been created using ANSYS ICEM CFD 15.0. An unsteady (URANS) incompressible blood flow simulation has been performed using k- ω SST turbulence model, with one of the operating conditions prescribed by the experimental literature. The simulations were performed using ANSYS CFX 19.1 solver. The validated results for the flow field shows promising results in the blade passage region and the diffuser regions but pressure head obtained has some discrepancies which were analyzed and justified. In addition, a study on hemolysis prediction using Power Law was performed, and the blood damage values of benchmark blood pump were calculated for the stress-based model in the Eu-lerian approach.

Keywords

Computational Fluid Dynamics, Ventricular Assist Device, Blood pump, block-structured hexahedral mesh, URANS, k- ω SST, hemolysis, Power Law, stress based model, Eulerian approach.

Acknowledgement

It is my privilege to express my sincere regards to Professor Frank Hendrik Wurm and supervisor Lucas Konnigk M.Sc, for offering me this thesis work and I would also like to thank my supervisor Lucas Konnigk for his patient guidance, enthusiastic encourage-ment and useful critiques for this thesis work. I would also like to thank Ben Torner M.Sc, for his advice and assistance in keeping my progress on schedule. My sincere thanks also extends to Professor G¨unther Steffen for his help, while performing the Grid generation process.

I would also like to extend my thanks to the staffs of the department and the University for their help in offering me the resources for doing my thesis effectively.

Finally, I wish to thank my parents for their support and encouragement throughout my study.

List of Figures

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List of Tables

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1 Introduction

In recent years, advanced heart failure has been increased a lot more, especially in the United States and Europe. A survey stated that at least 26 million people worldwide suf-fer from heart failure 1. The heart transplantation remains to be the standard therapy for advanced heart failure, but the problem lies with the available donors, who were out-numbered.

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Figure 1.1 shows the rate of heart transplantation over the last few decades

The limitations in the heart transplantation lead to the use of mechanical devices which serve as an alternative. One such mechanical device is the Ventricular Assist Device (VAD). VAD serves as both long term and short term solution for heart failure patients. According to the survey from 2, the total number of long term VAD implan-tation in the United States and Europe per year were over 1700 and 430 respectively (Statistics before the year 2010).

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Figure 1.1: Number of Heart transplantation reported over the years 2.

1.1 Ventricular Assist Device

Ventricular Assist Device (VAD), also called a mechanical circulatory support device, that helps to pump blood and to maintain the heart’s function. Generally, VADs consist of a pump, which will be implanted inside the body. The inflow tube for the pump will be attached to the heart’s pumping chamber, and the outflow tube will be connected to the arterial system of the body. The control system for the pump and the power source were placed outside of the patient’s body.

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Figure 1.2 shows the typical arrangement of VAD (with axial flow blood pump) to the patient’s body 3.

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Figure 1.2: Implanted LVAD Arrangement in a human body 3.

VADs include Left Ventricular Assist Devices LVADs, Right Ventricular Assist Devices RVADs, and Biventricular Assist Devices BIVADs. Depending upon the medical con-dition of patients and the failure part of the heart, appropriate VADs will be chosen LVADs are the most commonly used VAD system for patients. The VADs hydraulic performance should assist the physiological pumping system of the heart and also should provide blood’s hemocompatibility. One of the main aspects of a blood pump is hemo-compatibility, because of the risks in high blood damage 5.

1.2 History of VADs

The history of VADs was based on their advancements. The first generation VADs were volume displacement devices. These device pump blood through pneumatically (or) electromagnetically actuated pusher plates, which replicates the pulsing action of the heart; hence these pumps are known as Pulsatile pumps. These pumps were made up with multiple moving parts which result in more tissue and blood contacting surfaces. These pumps lead to complication in thrombus formation, blood trauma and high risk of infection. The pulsatile pumps were larger and came up with a lot of limitations, to improve these limitations and to overcome the disadvantages, the second generation VADs enters into the market. These are mechanical support devices or continuous flow pumps, also known as non-pulsatile pumps. These are small efficient and durable. Furthermore, the importance of efficiency, durability, and design parameters leads the way for the third generation VADs. The third generation VADs is an improvisation of the second-generation VAD. These are magnetically levitated pumps. These allow the rotor of the pump to rotate without friction or wear. This third generation VADs shows very promising results for long term support 6.

1.3 Operating Conditions for VAD

On the basis of the blood’s flow path, the blood pumps categorized as centrifugal pumps and axial pumps. Performance plays a significant role in the case of blood pumps. Pressure head, flow rate, driving power input and pump efficiency are some of the critical factors which affect the performance 7. IN VAD systems the flowing fluid will be blood, blood shows a complex rheological behavior; hence the viscosity and density of blood should be adequately maintained, temperature plays a vital role in affecting the rheological behavior of blood, care must be taken, while designing the pump to maintain the temperature almost constant throughout the flow. The typical operating conditions for a blood pump are, a flow rate of 4-5 Lpm with a head pressure of 100 mmHg which was prescribed by M. Behbahani et al. 8. However, the operating conditions mostly depend upon the patient’s cardiovascular system. From a comparative study made by Fraser et al. 9, the working conditions of both centrifugal and axial pump (which are in the present market with a successful history of patients) observed. A typical centrifugal blood pump has an impeller radius 20 to 30 mm, flow rates of 3 to 7 Lpm, a pressure rise of 90 to 350 mmHg and an operating speed of 2000 to 7000 rpm. In axial flow blood pump has an impeller radius of 2 to 10 mm, a flow rate of 1.5 to 6 Lpm, a pressure rise of 50 to 140 mmHg and an operating speed of 6000 to 45000 rpm 9.

1.4 Role of Computational Fluid Dynamics

Computational fluid dynamics (CFD) has the ability to produce a qualitative (some-times quantitative) prediction of fluid flow, heat transfer and associated phenomena using computer-based simulation 10. CFD has a wide range of industrial and non-industrial application areas. In the field of biomedical, Computational Fluid Dynamics (CFD) become one of the powerful tools in design and optimization especially in the case of VAD blood pumps. Design improvements can be made by analyzing the perfor-mance parameters, flow fields, etc. The application of CFD in blood pumps provides information about the shear forces and stagnation zones etc. In recent years, CFD was used in the prediction of blood damage due to the prolonged shear stress in blood pumps

Damage models were made and incorporated in the governing equation of CFD, which increases the credibility of CFD in biomedicals applications. In this thesis work, CFD is used to analyze the hemodynamics of blood flow in a rotary blood pump.

2 Literature Review

2.1 Blood

Blood is the river of life for most of the living creatures. The main functions of blood are to deliver oxygen and nutrients between the lungs and the cells of the tissues and also to carry back the waste products 12. The blood circulation also maintains body tem-perature. Blood has a lot of importance in the practice of medicine. Blood contributes about 7 to 8 percent of weight in an average human body. By centrifugation, blood separated into four components which were stated in increasing order of their specific gravity 13.

Plasma, the liquid part of the blood, It contributes 55 percent of whole blood. It is a mixture of sugar and water. It acts as a carrier of proteins and other nutrients, salts, hormones, respiratory gases, and waste products of cell metabolism 12.

White Blood Cells (WBC), also known as leukocytes are the protector of the body from infections caused by bacteria and viruses. These contribute about 1 percent of whole blood 12.

Platelets, also known as Thrombocytes, these represented as small fragments of cells.

The function of these is in aiding the blood clots (Coagulation) 12.

Red Blood Cells (RBC) also known as Erythrocytes, will be discussed briefly in the upcoming section.

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Figure 2.1: Components of Blood after centrifugation 13.

2.1.1 Red Blood Cells

RBCs are small, ”disc-shaped biconcave” cells that measure 7 - 8 µ m in diameter and has a thickness of 2.8 µ m [13, 14, 15, 16]. The shape of RBCs is almost perfect symmetry. The biconcave shape provides RBCs with more surface area than other spherical cells of the same diameter 14. From the work of Pondex 1948, the biconcave shape of the RBC is the optimum shape for oxygen transfer. An average life span of RBC is 100 to 120 days. The RBCs has a higher specific gravity (1.10) comparing to the other blood components 14. Almost 33% of a red blood cell contributes to concentrated hemoglobin with a relative viscosity of 6cp; this hemoglobin plays an essential role in the deformation of RBC 15. One of the important features of the RBCs is Rouleaux formation. When blood doesn’t flow (stagnant), the RBCs stack together, which is known as Rouleaux formation, increases the viscosity of the blood and so the blood acts as an elastic solid. When the blood starts to flow the rouleaux formation of RBC starts to break up, forming individual red blood cells. The individual RBC will line up along the flow direction to make the flow easier, which decreases the viscosity of blood 14.

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Figure 2.2: Dimensions of a Red Blood Cell (left), Rouleaux formation of Red Blood Cells (right) 16.

2.2 Blood Rheology

Rheology is the science, ”which deals with the deformation and flow of matter” 17. Bloods rheological behavior mainly depends on its composition and flow conditions. The properties of RBCs and plasma are the critical factors, which determine the me-chanical and rheological properties of blood 18. In general, blood is considered to be a non-Newtonian fluid. According to significant research works [14, 15], it has been found that, plasma is a Newtonian fluid with viscosity 1.2 cP. The viscosity of the plasma depends upon the concentration of proteins and ions in it 15. The non-Newtonian be-havior of blood is observed from the behavior of RBCs, as discussed early, the formation and disintegration of rouleaux in RBCs significantly impact on the viscosity of whole blood. One of the essential parameters in blood rheology is Hematocrit, defined as the volume concentration of RBC, for an average human the hematocrit ranges from 42 to 45 % [14, 15, 18]. In most of the computational flow simulations, which were carried out with blood as a fluid, blood is considered as a Newtonian fluid. This assumption made from the blood’s behavior at a shear rate greater than 100 s−1. Figure 2.3 14 shows that, after a shear rate of 100 s−1, the viscosity of blood does not changes, which makes the blood as a Newtonian fluid. The figure also indicates, the change in hematocrit value will also lead to an increase or decrease in viscosity.

In general, the shear rate of blood at the walls of the large and small arteries are 100 to 2000 sec−1 and in large and small veins are 20 to 200 sec− 1. Only at the center of the veins, the shear rate tends to be zero 14, this shows the bloods rheological behavior varies even within the human circulatory system. In all together it has been observed that, the viscosity of blood decreases as the shear rate increases and vice versa. In case of computational simulation of blood flow the Newtonian properties were chosen because of this blood behavior to shear and also in order to reduces the complexity of the problem and even computation time and memory.

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Figure 2.3: Relation between viscosity and shear rate for different hematocrit values (Chein et al. 1966) 14

2.3 Blood damage

On considering the VAD systems, the main concern is on blood damage which occurs in the blood pumps because of the flow behavior. Usually, blood damage includes hemoly-sis, thrombosis, platelet activation, alteration of the coagulation cascade, reduced func- tionality of the white blood cells, etc. 19. The primary concern for a VAD system will be on hemolysis and thrombosis. The damage to the premature red blood cells which results in the release of hemoglobin into the blood plasma leads to hemolysis. The cause for hemolysis is mainly due to the high shear stress induced in the RBCs. This hemolysis will lead to biochemical alterations in blood, a shortened life span of RBCs 19. In VAD history, most of the patients suffer from partial blood damage which makes a pathway for severe disorders 20.

On the other hand, thrombosis is the clot formation; it is the functionality of blood in the time of bleeding. In the case of blood pump accumulation of substances were found in the walls 19. In general, the flow stagnation will lead to thrombosis, and high-velocity flow with high shear leads to hemolysis. From the operating conditions of blood pumps, it is evident that the possibilities of thrombosis were considerably less comparing with hemolysis.

As discussed early hemolysis is due to the rupture of red cells, the damage in red cells depends upon the duration, and the intensity of the shear stress acted 18. When shear stress acts on the RBCs, tumbling occurs which gradually reduces when the shear stress is around 0.2 Pa. The deformation of RBCs occur slowly from biconcave to an ellipsoidal shape, and the RBCs will align themselves in the direction of flow. As the shear stress gets increases (greater than 1 Pa) Schmid-Schoenbein and Wells 1969, found that the membrane of the RBCs undergoes tank-treading. The cell membrane of individual RBCs tends to rotate around the encapsulated fluid resulting in tank-treading; this results in the deformation of individual cells [15, 21]. From the work of Leverett et al. 22 it was observed that shear stress greater than 150 Pa would lead to potential damage in RBC. In experimental techniques, hemolysis based empirical results were obtained from a simple Couette Viscometer [23, 24, 25]. To predict the hemolysis in blood flow lot of numerical models had been proposed and also validated for their accuracy by different researchers.

2.4 Numerical Hemolysis Prediction

Giersiepen and Wurzinger, 1990 made the base foundation for the numerical hemolysis prediction. This model is the widely used hemolysis prediction model, commonly known as Power Law Model. This model develops a power law function for the damage fraction, in other words, it is a simple relation between the magnitude of shear stress, exposure time and hemolysis 23.

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This model has a shear stress covering range up to 255 Pa and exposure time up to 700 ms, the constants used by Giersiepen et al. were (C, α, β) (3.63 10−7, 2.416, 0.785) respectively. From the blood damage correlation of Giersiepen and Wurzinger many methods have been evolved. Arora et al. 21 found that in the real flow this model has a very poor prediction. Results obtained from this model has a variation of 1 to 2 in magnitude from the measured values. Some researchers claim that the values of the constants are the reason for this poor prediction. From Zhang et al. 25, and Heuser and Optiz 24, the constants in Giersiepen and Wurzinger model, were obtained from a Couette type device, which uses mechanical seal so there may be an over-prediction of blood damage. These researchers introduced certain alterations made in the experimen-tal setup and various new sets of constants were published. From Heuser and Optiz, the constants proposed were (C, α, β) (1.8 10−8, 1.991, 0.765) respectively with a covering range for shear stress 30 to 600 Pa and exposure time 0.0034 to 0.69 s 24. From Zhang et al, the constants proposed were (C, α, β) (1.228 10−7, 1.9918, 0.6606) respectively with a covering range for shear stress 30 to 320 Pa and exposure time 0.003 to 1.5 s 25.

The American Society for Testing and Materials ASTM F1841-97 proposes standard practice for the prediction and evaluation of hemolysis in a ventricular assist blood pumps. According to the standard, three relations for the hemolysis were suggested, and they are, Normalized Index of Hemolysis (NIH), Normalized milligram Index of Hemolysis (mmgNIH), Modified Index of Hemolysis (MIH) [26, 27].

Garon and Farinas 27 made a significant work on the blood damage prediction and in MIH, NIH with the help of Giersiepen and Wurzinger power law model 23. On observ-ing the power law, Garon and Farinas proposed that the exposure time is nonlinear, to avoid this problem, a damage function introduced and a time derivative of the function has been taken which results in the source term or general transport equation for blood damage, commonly known as σ 27.

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To take all the areas contributing to the hemolysis a volumetric integration of the source term was made which results in the blood damage D through which the MIH and NIH can be calculated.

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Where D is the linear damage index, and Hb is the blood hemoglobin concentration. The Garon and Farinas approach gives a global index of hemolysis, so the source of damages cannot be identified.

The hemolysis prediction subdivided into Stress based (Power law), and Strain-based models and the computational methods involved for this hemolysis prediction model cat-egorized as Eulerian and Lagrangian approaches. In the stress-based model, hemolysis assumed to be in a direct ratio to the local shear stress and strain based model, hemolysis associated with the deformation of RBCs and other strain in cells 28. On considering the approaches, in the Eulerian approach, a volumetric integration of damage index is made over the complete computational domain while in Lagrangian approach the inte-gration is made along the flow pathlines [27, 28].

The Eulerian approach identifies potential blood cell damage using a single damage pa-rameter on the whole fluid domain 28. The computational effort used for this approach is very less comparing to Lagrangian, but the drawback of this approach is, it does not consider the exposure time. The Lagrangian approach finds blood damage by tracking particles along path lines through the pump and finding shear stress and exposure time for each particle. This approach can also calculate the shear history of the RBCs 28. The effective prediction of blood damage through this model lies in the number of par-ticles used for tracking. As the number of particles increases, the extensive amount of computational resources needed 28. From Taskin and Fraser 28, it has been shown that the accuracy of the power law based hemolysis model lies on the power law constants and the numerical approaches. Works of Linda and Pauli 20 focus on the strain based blood damage estimation; they proposed that the strain based models predict the dam-age and physical properties of RBC more accurate comparing to the stress-based models.

The hemolysis models mostly based on the power law equation, it is one of the significant drawbacks of numerical hemolysis prediction because the power law equation encoun-ters on the macroscopic properties of the blood 21. Also, the equations derived based on strong assumptions which do not confront the full physiology of blood. One such assumption RBCs are considered to be one hundred percent healthy at the beginning of the hemolysis process 21. However, investigations were to be made in the field of numerical hemolysis prediction to improvise the development phase of involvement of CFD in VADs.

In this thesis work the blood damage prediction was calculated with the prescription from Garon and Farinas using Power law by applying the constants proposed by Giersiepen and Wurzinger, Heuser and Optiz and Zhang et al. for stress-based model in Eulerian approach. Even though the proposed Eulerian approach does not account for exposure time, it will be helpful for comparison and optimization in engineering task.

3 Description of Turbulence Modeling

Boussinesq and Reynolds made the very first concepts in the study of turbulence. Even a simple flow in computational fluid dynamics is always challenging task to compute. The complexity of the problem grows bigger when accounting the turbulence in the flow; this is because of the unpredictable behavior and irregularity of the flow in both space and time 29. The statistical averaging of the random variation in fluid properties and velocity results in accountable and turbulence related transport mechanism, this char-acteristic leads for Turbulence modeling 30. There are three important computational approaches in turbulence modeling, they are [10, 29, 30, 32], Direct Numerical Simulations (DNS) Large Eddy Simulation (LES) Reynolds Averaged Navier-Strokes (RANS) model. In this thesis work, the Reynolds Averaged Navier-Strokes (RANS) model used in the unsteady version known as URANS. While performing a RANS/URANS simulation, turbulence models play an essential role. The phenomena such as boundary layer separa-tion or shock boundary layer interaction depend strongly on the choice of the turbulence model used 31.

3.1 RANS/URANS

According to Reynolds, each fluctuating quantity can be represented as the sum of its averaged value and its fluctuation. By averaging the original Navier-Stokes equation, Reynolds Averaged Naiver Stokes equation can be obtained. RANS equation can be given by 32, Abbildung in dieser Leseprobe nicht enthalten This thesis work utilises k- ω Shear Stress Transport (SST) turbulence model for the simulation.

3.1.1 Shear Stress Transport model

The k- ω Shear Stress Transport (SST) model introduced by Menter in 1994 34 is a combination of both k- ω model of Wilcox and high Reynolds number k- ε model 34. All the positive features of both the models considered in the SST model. The turbulent eddy viscosity function is one of the distinct features of the SST turbulence model. The two equation model in the conservative form can be given as [34, 36],

Abbildung in dieser Leseprobe nicht enthalten

The SST model utilizes the advantages of the k- ω model such as employment in logarith-mic region, no damping function, high numerical stability, superior in adverse pressure flow and compressible flow and advantages of k- ε model such as employment in wake region of boundary layer and free shear layer, sensitivity towards free stream 10. The primary purpose of this SST model is to improve the accuracy of prediction of flow with induced boundary layer separation and strong adverse pressure gradients. It also has certain disadvantages such as the distance to the nearest wall, which have to be known explicitly 35.

4 Experimental Study

The lack of credibility in CFD and also to improve the validation, the inter-laboratory study had been performed in FDA benchmark blood pump by Hariharan et al. 37. The experimental setup was as follows; the blood pump was modeled using clear acrylic. Particle Image Velocimetry (PIV) was used for the experiment, to take measurements from different sections of the pump. The test performed in three different laboratories (mentioned in Table 4.1) to verify the consistency of the results obtained. A standard protocol had been developed, to ensure the flow conditions were comparable and also to minimize the systematic errors during PIV image acquisition and processing. Different cross section planes were chosen within the pump to extract the velocity fields. Figure 4.2. shows the cross sections used in PIV measurements. A blood analog fluid (used to analyze the flow fields, blood will be used for the hemolysis prediction) with Newtonian behavior used in the experimental study, the fluid composed of 50% sodium iodide, 17% glycerin and 33% water. The results were dimensionalized to match blood viscosity of 3.5 cP and density 1035 kgm−3. The flow quantities of the PIV experiment were made to match the CFD conditions by scaling the non-dimensional parameters such as Reynolds number and flow coefficient, and these parameters were also maintained the same across the three different laboratories. The flow fields measured for six different operating conditions, with a flow rate ranging between 2.5 - 7 Lpm for pump speeds of 2500 and 3500 rpm. Figure 4.1 shows the experimental setup. The viscosity of the PIV fluid was measured at a shear rate of 100 to 250 s−1. One of the operating condition from the experiment has been validated using CFD in this thesis.

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Table 4.1: Properties of blood analog fluid used in PIV experiment 37.

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Figure 4.1: Schematic diagram of the experimental setup for FDA blood pump 38.

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Figure 4.2: Cross sections used for the flow fields measurements 38.

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5 Pre-Processing

5.1 Structure of FDA blood pump

The Computer Aided Design (CAD) file of the blood pump was provided by FDA 38. The pump is a simplified centrifugal blood pump shown in Figure 5.1. The centrifugal pump geometry of benchmark blood pump designed on considering, design parameters from industrial centrifugal blood pumps 37. The geometry data provided consist of two Initial Graphics Exchange Specification (iges) files, the housing and the rotor which was positioned inside the housing.

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Figure 5.1: Housing and Rotor arrangement in FDA blood pump with flow direction indication 38.

The pump is designed to have a simplified geometrical feature with a broad operating range — a 2D CAD drawing of the pump illustrated in Figure 5.2. The rotor geometry consists of four equally spaced straight blades with a 90◦angle and has a dimension of 18.5 * 3 * 3 mm. In general, the blade curvature has a significant impact on performance. In the FDA blood pump geometry, the edges of the blades are blunt with fillets of radius ranging from 0.17 mm at the top edge of the blade to 0.6 mm at the long bottom edge of the blade. The blades were attached to a circular disk of 5 mm thickness and have a radius of 26mm. A hub of diameter of 8 mm and a height of 14 mm is located at the center of the disk. The rotor geometry was placed inside the housing geometry. The housing geometry consists of a long curved inflow channel and a diffuser outlet chan-nel. The volute of this blood pump has a concentric arrangement with the rotor, which implies the radial clearance is constant. The radial and axial clearance of this pump are 4 mm and 1 mm respectively. The inflow channel has a diameter of 12 mm and a length of 285 mm. The outlet throat has a diameter of 4.39 mm which is connected to a diffuser of length 21.54 mm and has a diameter expansion of 20◦leading to main outlet channel with 12 mm diameter. Certain highlighted geometrical features of this pump were shown in Figure 5.3, the fillet which connects the throat and volute is one of the important features in this pump, has a radius of 0.04 mm. Special attention was taken in the selected regions while performing discretization and computational simulations.

Figure 5.2: 2D CAD drawing of the FDA blood pump 38.

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Figure 5.3: Important features of blood Pump in rotor and housing geometry 38.

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Details

Title
Numerical Flow Simulation in FDA's "Critical Path" benchmark blood pump
College
University of Rostock
Course
Computational Science and Engineering
Grade
1.3
Author
Year
2019
Pages
57
Catalog Number
V915240
ISBN (eBook)
9783346238061
ISBN (Book)
9783346238078
Language
English
Tags
CFD, Ansys ICEM CFD, Ansys CFX, Blood pump, VAD, block-structuredhexahedral mesh, Computational round robin #2, hemolysis, Power Law
Quote paper
Krishnaraj Narayanaswamy (Author), 2019, Numerical Flow Simulation in FDA's "Critical Path" benchmark blood pump, Munich, GRIN Verlag, https://www.grin.com/document/915240

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