This document summarizes a study analyzing inferior vena cava filters using STAR CCM+'s Lagrangian particle tracking and DEM-CFD modeling approach. The study aims to understand blood and blood clot flow in the human body and predict the performance of mechanical filters. Blood flow and clots are modeled using Eulerian and Lagrangian approaches. Validation tests of STAR CCM+'s DEM-CFD solver are performed. A Simon Nitinol filter geometry is modeled and its effectiveness at capturing different sized blood clots is evaluated using Lagrangian particle tracking and DEM-CFD modeling. The effects of Newtonian vs. non-Newtonian blood assumptions and inlet boundary conditions are also analyzed. Future work involving non-spherical cl

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. 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. 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. 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. 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. 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. Background (contd..)
• Treatment of Pulmonary Embolism
• Anticoagulation therapy
• Surgery
• Mechanical filters
© www.ehow.com
© Wikipedia.
7

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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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

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
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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
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