Analysis of Inferior Vena Cava Filter using STAR CCM+’s Lagrangian Particle Tracking and DEM-CFD Modelling Approach
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
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
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.
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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.
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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
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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.
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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.
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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.
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Filter Efficiency (%)
• A study is carried out to find the effect of inlet
boundary condition on filter capture efficiency.
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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.
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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.
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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 %
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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
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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
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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.
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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.
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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
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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)
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