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Analysis of Inferior Vena Cava Filter using STAR CCM+’s
Lagrangian Particle Tracking and DEM-CFD Modelling
Approach

Guide...
Introduction to CFD
• Computational Fluid Dynamics (CFD) is the analysis of systems involving fluid
flow, heat transfer an...
Introduction to CFD (contd..)
• The computer revolution and the availability of commercial CFD codes in last
couple of dec...
CFD in Health Care
• Health care is a broad sector which covers Biology, Pharmacy, and medicine.
• CFD in health care can ...
Present Study
• Present study aims to
1. Understand the flow of blood and blood clots in human body.
2. Predict the perfor...
Background
• Formation of clots in deep
veins is called as Deep Vein
Thrombosis (DVT).
• The condition in which these
clot...
Background (contd..)
• Treatment of Pulmonary Embolism
• Anticoagulation therapy
• Surgery
• Mechanical filters

© www.eho...
Background (contd..)
*Simon Nitinol Filter*
•

Simon Nitinol Filter is used to filter
blood clots from blood.

• Material ...
Previous Work on Mechanical Filters
• The only numerical study on mechanical filters was carried out by Stewart et
al., (2...
Modelling Blood Flow and Clots
• Blood can be modelled using “Eulerian approach”.
• Eulerian modelling approach is a way o...
Modelling Blood Flow and Clots
• Lagrangian Partical Tracking (LPT) can be considered as the simplest
modelling approach t...
DEM-CFD Modelling Approach
• DEM models particles at the individual particle
level.
• CFD models the flow at the computati...
Mathematical Modelling of DEM-CFD
CFD
• Continuity equation

DEM
• Linear momentum equation.
𝑚𝑖 ∙ 𝑎𝑖 = 𝑚𝑖 ∙ 𝑔 +

𝑘
𝑗=1

𝑓𝑐...
Validation of STAR CCM+’s Capabilities
• Since now the modelling approach is decided. ( DEM-CFD and the
incorporation of n...
Validation of STAR CCM+’s DEM-CFD Solver
• Validation is carried out by comparing the pressure drop across a pneumatic con...
Validation of non-Newtonian Flow using STAR CCM+’s
Solver
• Blood is a non-Newtonian fluid (shear thinning).
• The non-New...
Validation of non-Newtonian Flow using STAR CCM+’s
Solver (contd..)

v/U

0.5
0.4
0.3
0.2
0.1
0
-0.1
-0.2
-0.3
-0.4
-0.5
-...
Simon Nitinol Filter: Geometry & Mesh

Geometry
•

The CAD model of Simon Nitinol filter was provided by
Sandy Stewart.

•...
Newtonian Vs. non-Newtonian Blood Flow with Filter
1

0.1
0.08

0.6
Force (dynes)

Mass flow rate (kg/s)

0.8

0.06
0.04
0...
Filter Efficiency using Lagrangian Approach
• As a first step to quantify the capture efficiency of Simon Nitinol Filter, ...
Parcels per cell

8mm (probability of
inclusion 0.85)

2mm (probability
of inclusion 1)

1

14.94

4.94

3

14.94

4.94

4...
Effect of Inlet BC on Filter Efficiency

•

In the first case constant mass flow rate boundary
condition is applied at inl...
Newtonian Vs. non-Newtonian Blood Flow & Filter
Efficiency
• In both the case constant mass flow rate boundary
condition i...
Filter Efficiency using DEM-CFD Modelling
• DEM-CFD modelling for 2mm clots with
blood as a Newtonian fluids is carried ou...
Conclusions
• Newtonian and non-Newtonian assumption has a strong effect on
drag force on the filter.
• For a flow through...
Future Work
• Predict a clot-capture efficiency curve using DEM-CFD modelling
approach.
• Study the flow behavior with non...
Salient Features of STAR CCM+
• CD-adapco’s STAR CCM+ is the only commercial code which can
model such complex physics.
• ...
References
1. Blann, A. (2009), Deep Vein Thrombosis and Pulmonary Embolism: A Guide for Practitioners.
MandK Update Ltd.
...
Work presented at DMD conference

"Application of DEM-CFD Approach to Analyze Inferior Vena Cava Filters using
STAR CCM+“
...
30
Details of parameters used in DEM-CFD
Validation case.
Sr no

Parameters

Value

1

Length of conveyer

1000 mm

2

Diamet...
Details of parameters for non-Newtonian
Validation case
Sr

Parameter

Case I

no
1

Case

Case III

II
Renolds number (Re...
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Analysis of Inferior Vena Cava Filter using STAR CCM+’s Lagrangian Particle Tracking and DEM-CFD Modelling Approach

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Analysis of Inferior Vena Cava Filter using STAR CCM+’s Lagrangian Particle Tracking and DEM-CFD Modelling Approach

  1. 1. Analysis of Inferior Vena Cava Filter using STAR CCM+’s Lagrangian Particle Tracking and DEM-CFD Modelling Approach Guide Prof Jeevan Jaidi Mechanical Engineering Dept. BITS-Pilani Hyderabad Campus, India. By Deshpande Ruturaj Ramesh 2011H148042H, M.E Thermal Science, BITS-Pilani Hyderabad Campus. Co-guide Dr Sridhar Hari Manager Energy sector, CD-adapco Bangalore, India. 1
  2. 2. Introduction to CFD • Computational Fluid Dynamics (CFD) is the analysis of systems involving fluid flow, heat transfer and associated phenomena by the use of computer based calculations. • Military-related needs in 1950’s and 1960’s initiated the development of CFD. • Until 1980’s CFD was a specialized tool catering military needs. 2
  3. 3. Introduction to CFD (contd..) • The computer revolution and the availability of commercial CFD codes in last couple of decades has changed the field of CFD entirely. • CFD today is a tool for engineers to carry out design, analysis, and optimization of various systems. • CFD is being successfully used in industries such as aerospace, automotive, chemical, electronics, pneumatic and hydraulic industries. • Researchers are now encouraged to use CFD in unconventional fields like environmental science and health care. 3
  4. 4. CFD in Health Care • Health care is a broad sector which covers Biology, Pharmacy, and medicine. • CFD in health care can be used to o Understand a phenomenon. o Guide new product development. o Improve manufacturing process. o Predict device or drug performance. 4
  5. 5. Present Study • Present study aims to 1. Understand the flow of blood and blood clots in human body. 2. Predict the performance of a mechanical filter using CFD. 3. Study the effectiveness of filters with respect to different clot sizes. © www.stoptheclot.org 5
  6. 6. Background • Formation of clots in deep veins is called as Deep Vein Thrombosis (DVT). • The condition in which these clots block the blood flow in lungs is called as pulmonary embolism (PE). • PE is the third most common cause of death due to cardiovascular disease. © www.thehealthmagic.com 6
  7. 7. Background (contd..) • Treatment of Pulmonary Embolism • Anticoagulation therapy • Surgery • Mechanical filters © www.ehow.com © Wikipedia. 7
  8. 8. Background (contd..) *Simon Nitinol Filter* • Simon Nitinol Filter is used to filter blood clots from blood. • Material used is nickel-titanium alloy (Nitinol) and has thermal memory properties . • 7cm in length and 2 cm wide. • Simon Nitinol filter is placed in the Inferior Vena Cava. 8
  9. 9. Previous Work on Mechanical Filters • The only numerical study on mechanical filters was carried out by Stewart et al., (2008). • They were successful in reproducing the flow patterns observed when a single blood clot was injected. • They also concluded that the inclusion or exclusion of Vena cava branching hardly and any effect on flow patterns. 9
  10. 10. Modelling Blood Flow and Clots • Blood can be modelled using “Eulerian approach”. • Eulerian modelling approach is a way of looking at fluid motion that focuses on specific locations in the space through which the fluid flows as time passes. • Blood clots can be modelled using “Lagrangian approach”. • Lagrangian approach is a way of looking at particle motion where the observer follows an individual particle as it moves through space and time. 10
  11. 11. Modelling Blood Flow and Clots • Lagrangian Partical Tracking (LPT) can be considered as the simplest modelling approach to model discrete particles in continuous medium. • The complex nature of flow can be captured by incorporating two-way coupling between particles and fluid. • Eulerian-Lagrangian modelling approach can be made more realistic by considering the interactions between the particles. 11
  12. 12. DEM-CFD Modelling Approach • DEM models particles at the individual particle level. • CFD models the flow at the computational cell level. • At each time step, DEM gives the position and velocity of individual particles. • CFD then use this data to determine the fluid flow field which in turn yields the fluid drag forces acting on individual particles. • Incorporation of the resulting forces into DEM then provides information about the motion of individual particles for the next time step. DEM-CFD coupling 12
  13. 13. Mathematical Modelling of DEM-CFD CFD • Continuity equation DEM • Linear momentum equation. 𝑚𝑖 ∙ 𝑎𝑖 = 𝑚𝑖 ∙ 𝑔 + 𝑘 𝑗=1 𝑓𝑐𝑗 + 𝑉𝑖 ∙ 𝛻𝑃 + 𝑓 𝑑 𝜕𝜀𝜌 𝑓 𝑖≠ 𝑗 𝜕𝑡 • Angular momentum equation. 𝐼𝑖 ∙ 𝑑𝜔 𝑖 𝑑𝑡 = 𝑘 𝑗=1 + 𝛻 ∙ 𝜀𝜌 𝑓 𝑈 = 0 • Momentum equation 𝜕𝜀𝜌 𝑓 𝑈 𝜏 𝑖𝑗 𝜕𝑡 • Source Term 𝑆= 𝑛 𝑗 + 𝛻 ∙ 𝜀𝜌 𝑓 𝑈𝑈 = −𝛻𝑃 + 𝛻 ∙ 𝜏 + 𝑆 + 𝜀𝜌 𝑓 𝑔 𝑤 𝑗 𝑓 𝑑𝑗 • Drag Force 𝑓𝑑 = 1 2 𝐶 𝐷 𝜌 𝑓 𝐴 𝑝 𝑣 𝑓 − 𝑣 𝑖 (𝑣 𝑓 − 𝑣 𝑖 ) 13
  14. 14. Validation of STAR CCM+’s Capabilities • Since now the modelling approach is decided. ( DEM-CFD and the incorporation of non-Newtonian nature of blood) • Two validation exercises are carried out. 1. Pressure drop in pneumatic conveyer. 2. Lid driven cavity with non-Newtonian fluid. 14
  15. 15. Validation of STAR CCM+’s DEM-CFD Solver • Validation is carried out by comparing the pressure drop across a pneumatic conveyer. • Initial velocity of particles is unknown so a study is carried out to understand the effect of initial condition of particles ( case I vp =0.5 vf and case II vp =0.3 vf ). mass flow rate 0.3455 kg/s Case I 5 Pressure Drop (mbar/m) 6 mass flow rate 0.3455 kg/s Case II 4 mass flow rate 0.3455 kg/s Experimental mass flow rate 0.2063 kg/s Case I 3 mass flow rate 0.2063 kg/s Case II 2 mass flow rate 0.2063 kg/s Experimental mass flow rate 0.0697 kg/s Case I 1 0 5 10 15 20 25 Inlet velocity of air (m/s) 30 35 mass flow rate 0.0697 kg/s Case II mass flow rate 0.0697 kg/s Experimental • Good match between the experimental and numerical results is obtained for case II. • Study validates the STAR CCM+’s DEM-CFD modelling approach. Details of parameters used in the study • Study underlines the importance of initial condition of particles. 15
  16. 16. Validation of non-Newtonian Flow using STAR CCM+’s Solver • Blood is a non-Newtonian fluid (shear thinning). • The non-Newtonian nature of blood can be captured by incorporating the apparent viscosity in the governing equations. • Carreau-Yasuda model is used to calculate apparent viscosity of shear thinning fluids. 𝜇 𝛾 = 𝜇∞ + 𝜇0 −𝜇∞ 𝑎 1−𝑛 𝑎 1+(𝜆 𝛾) a is the parameter which controls shear-thinning nature of fluid. 16
  17. 17. Validation of non-Newtonian Flow using STAR CCM+’s Solver (contd..) v/U 0.5 0.4 0.3 0.2 0.1 0 -0.1 -0.2 -0.3 -0.4 -0.5 -0.6 0 0.1 0.2 0.3 0.4 Re 400 Reference Data Re nearly equal to zero Reference Data Re 400 STAR CCM+ 0.5 x/H 0.6 0.7 0.8 0.9 1 Re 1000 Reference Data Re 1000 STAR CCM+ Re nearly equal to zero STAR CCM+ • The basic features of Lid driven cavity such as the primary and secondary vortex are observed. • Results obtained from STAR CCM+ showed good agreement with the results available in literature. Details of parameters used in the study 17
  18. 18. Simon Nitinol Filter: Geometry & Mesh Geometry • The CAD model of Simon Nitinol filter was provided by Sandy Stewart. • Inferior vena cava, is a 2 cm diameter by 25 cm long cylinder created using STAR CCM+ • Boolean operation is performed to obtain final geometry Mesh • Total Number of cells, 2902529. • Total Number of Interior Faces, 8682299. • maximum cell size 0.15 mm. 18
  19. 19. Newtonian Vs. non-Newtonian Blood Flow with Filter 1 0.1 0.08 0.6 Force (dynes) Mass flow rate (kg/s) 0.8 0.06 0.04 0.02 0.4 0.2 0 -0.2 -0.4 -0.6 0 0 0.2 0.4 Time (s) 0.6 0 0.4 0.5 0.6 0.7 0.8 Flow Time (s) Blood as a Newtonian Fluid Blood as a non-Newtonian Fluid 0.8 Inlet boundary condition 0.1 0.2 0.3 Drag force on filter • • Blood Flow in Inferior vena cava is a cyclic function of time to account for this a time variant inlet boundary condition is used. It is found that the drag force in Newtonian case is higher than in the non-Newtonian case. • This encourages to carry out further studies to find out the effect of Newtonian and non-Newtonian assumptions 19 on the flow behavior of blood and blood clots.
  20. 20. Filter Efficiency using Lagrangian Approach • As a first step to quantify the capture efficiency of Simon Nitinol Filter, LPT approach is used. • Studies are carried out at peak inlet mass flow rate (0.08805 kg/s). • It is assumed that there is one way coupling between blood and blood clots. • “Incident mass flux” assumption: It is assumed that all the clots incident on filter are captured. 20
  21. 21. Parcels per cell 8mm (probability of inclusion 0.85) 2mm (probability of inclusion 1) 1 14.94 4.94 3 14.94 4.94 4 14.94 4.94 5 14.94 4.95 STAR CCM+’s allows to perform some statistical studies like • Injection of multiple parcels from a single injection point ( improves accuracy of results). • Random inclusion of a point as a Injector ( For example if probability of inclusion is set to 0.85 implies that STAR CCM+ selects any 85 points out of 100 as injector points ). Filter efficiency (%) Statistical studies on Filter Efficiency 40 35 30 25 20 15 10 5 0 0.5 0.6 0.7 0.8 Probability of Inclusion 8mm clots 6mm clots 0.9 1 2mm clots Conclusion • Increasing the parcels per cell doesn’t have any effect on efficiency. (1 parcel per cell is sufficient) • Since the filter has a complex shape maximum number of available points must be used as injectors. 21
  22. 22. Effect of Inlet BC on Filter Efficiency • In the first case constant mass flow rate boundary condition is applied at inlet. • In the second case corresponding constant velocity boundary condition is applied at inlet. • In both the cases particles at injection point are assumed to have a velocity corresponding to the fluid velocity at that point. 30 Filter Efficiency (%) • A study is carried out to find the effect of inlet boundary condition on filter capture efficiency. 25 20 15 10 5 0 0 2 4 6 8 Clot diameter (mm) 10 12 constant massflow rate condition constant inlet velocity condition Conclusion • Mass flow rate boundary condition should be applied at the inlet. 22
  23. 23. Newtonian Vs. non-Newtonian Blood Flow & Filter Efficiency • In both the case constant mass flow rate boundary condition is applied at inlet. • The particles at injection point are assumed to have a velocity corresponding to the fluid velocity at that point. 30 Filter Efficiency (%) • A study is carried out to find the effect of Newtonian and non-Newtonian assumption on filter capture efficiency. 25 20 15 10 5 0 0 2 4 6 8 Clot diameter (mm) Blood as a Newtonian fluid 10 12 Blood as a non-Newtonian fluid Conclusion • non-Newtonian nature of blood should be considered in a system involving blood and blood clots. 23
  24. 24. Filter Efficiency using DEM-CFD Modelling • DEM-CFD modelling for 2mm clots with blood as a Newtonian fluids is carried out. • Constant velocity condition is applied at the Inlet. • 250 clots are injected out of which 19 clots are captured by Simon Nitinol Filter. • The clot capture efficiency is found out to be 7.6 % . Case Newtonian mass flow rate case (LPT) Newtonian velocity inlet case (LPT) Non-Newtonian Newtonian DEMmass flow rate case CFD modelling (LPT) case Efficiency 3.60 % 2.98 % 3.15 % 7.60 % 24
  25. 25. Conclusions • Newtonian and non-Newtonian assumption has a strong effect on drag force on the filter. • For a flow through inferior vena cava, mass flow rate BC should be applied at the inlet. • Non-Newtonian nature of the blood should be considered in the simulations. • Interactions between clots, clot-wall and clot-filter should be 25 modelled to have more realistic results.
  26. 26. Future Work • Predict a clot-capture efficiency curve using DEM-CFD modelling approach. • Study the flow behavior with non-spherical and multi sized clots. • Carry out a detailed study by including fluid-structure interaction between filter and blood. DEM-CFD modeling approach Nonnewtonion + Pulasting flow Elastic non uniform vena cava + FSI between filter and flow Future Work 26
  27. 27. Salient Features of STAR CCM+ • CD-adapco’s STAR CCM+ is the only commercial code which can model such complex physics. • Single integrated environment of STAR CCM+ makes the whole analysis easier. 27
  28. 28. References 1. Blann, A. (2009), Deep Vein Thrombosis and Pulmonary Embolism: A Guide for Practitioners. MandK Update Ltd. 2. Shamekhi, A and Aliabadi, A. (2009), Non-Newtonian Lid-driven Cavity Flow Simulation by Mesh Free Method, ICCES: International Conference on Computational & Experimental Engineering and Sciences, vol. 11(3), pp 67-72. 3. Stewart, S. F., Robinson, R. A., Nelson, R. A., and Malinauskas, R. A. (2008), Effects of thrombosed vena cava filters on blood flow: flow visualization and numerical modeling. Annals of biomedical engineering, vol. 36(11), pp 1764-1781. 28
  29. 29. Work presented at DMD conference "Application of DEM-CFD Approach to Analyze Inferior Vena Cava Filters using STAR CCM+“ According to a 25-year population-based study, formation of blood clots in deep veins is found in 48 per 100,000 Americans per year. This disease is usually treated by anticoagulation therapy. In some patients (nearly 60,000 per year) anticoagulation is ineffective, necessitating the placement of inferior vena cava (IVC) mechanical filters to trap blood clots. Design and analysis is critical to ensure successful placement and working of such IVC filters. We have embarked on an in-depth analysis of inferior vena cava filter using the state-of-the-art Computational Fluid Dynamics (CFD) tool STAR-CCM+ in which we intend to model blood as a non-Newtonian fluid and blood clots under the framework of the discrete elements method (DEM), which is the first of its kind in the current scenario. As a first step in this direction, studies were undertaken to validate the non-Newtonian and DEM implementations in the STAR-CCM+ code against published data in literature, on the respective topics, and a summary of our findings is presented herein. . Authors: Ruturaj Deshpande*, Sridhar Hari+, Christopher Penny+, Kristian Debus+; Dr. Jeevan Jaidi* http://www.dmd.umn.edu/sessions_2013/virtual_prototyping.html © Desktop Engineering (IVC-Filter image CD-adapco) 29
  30. 30. 30
  31. 31. Details of parameters used in DEM-CFD Validation case. Sr no Parameters Value 1 Length of conveyer 1000 mm 2 Diameter of conveyer 52.6 mm 3 Density of fluid 1.184 kg/m3 4 Viscosity of fluid 1.855×10-5 Pa-s 5 Particle diameter 2.345 mm 6 Particle Density 1050 kg/m3 7 Particle mass flow rates 0.0697, 3-D , Transient, K-ε turbulent. 0.2063, and 0.3455 kg/s 8 Gravity (-Y direction) -9.81 m/s2 Sr no Parameters (particle-particle and particle-wall) Value 1 Coefficient of friction (static/kinetic) 0.3 2 Coefficient of rolling friction 0 3 coefficient of restitutions (normal/tangential) 0.8 Polyhedral mesh 31 Back
  32. 32. Details of parameters for non-Newtonian Validation case Sr Parameter Case I no 1 Case Case III II Renolds number (Re) 1000 400 0.1 (nearly zero) 2 Density (𝜌) 1 (kg/m3) 3 Lx=Ly=L 1 (m) 4 Zero shear viscosity (𝜇0 ) 5 Pa-s 5 Infinite-shear 1 Pa-s viscosity(𝜇∞ ) 6 Relaxation time constant 1 (s) (𝜆) 7 power constant (n) 0.5 8 parameter to controlling 2 Laminar, 2D, non-Newtonian fluid. shear-thinning (a) 32 Back

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