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

Approach

GuideProf Jeevan JaidiMechanical Engineering Dept.BITS-Pilani Hyderabad Campus,India.

Co-guideDr Sridhar HariManager Energy sector,CD-adapco Bangalore,India.

By Deshpande Ruturaj Ramesh

2011H148042H,M.E Thermal Science,

BITS-Pilani Hyderabad Campus.

1

Introduction to CFD

• Computational Fluid Dynamics (CFD) is the analysis of systems involving fluidflow, heat transfer and associated phenomena by the use of computer basedcalculations.

• 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

Introduction to CFD (contd..)

• The computer revolution and the availability of commercial CFD codes in lastcouple of decades has changed the field of CFD entirely.

• CFD today is a tool for engineers to carry out design, analysis, andoptimization 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 likeenvironmental science and health care.

3

CFD in Health Care

• Health care is a broad sector which covers Biology, Pharmacy, and medicine.

• CFD in health care can be used to

oUnderstand a phenomenon.

oGuide new product development.

o Improve manufacturing process.

oPredict device or drug performance.

4

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

Background

• Formation of clots in deepveins is called as Deep VeinThrombosis (DVT).

• The condition in which theseclots block the blood flow inlungs is called as pulmonaryembolism (PE).

• PE is the third most commoncause of death due tocardiovascular disease.

© www.thehealthmagic.com

6

Background (contd..)

• Treatment of Pulmonary Embolism

• Anticoagulation therapy

• Surgery

• Mechanical filters

© Wikipedia.© www.ehow.com

7

Background (contd..)

• Simon Nitinol Filter is used to filterblood clots from blood.

• Material used is nickel-titanium alloy(Nitinol) and has thermal memoryproperties .

• 7cm in length and 2 cm wide.

• Simon Nitinol filter is placed in theInferior Vena Cava.

*Simon Nitinol Filter*

8

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

Modelling Blood Flow and Clots

• Blood can be modelled using “Eulerian approach”.

• Eulerian modelling approach is a way of looking at fluid motion that focuseson 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 observerfollows an individual particle as it moves through space and time.

10

Modelling Blood Flow and Clots

• Lagrangian Partical Tracking (LPT) can be considered as the simplestmodelling approach to model discrete particles in continuous medium.

• The complex nature of flow can be captured by incorporating two-waycoupling between particles and fluid.

• Eulerian-Lagrangian modelling approach can be made more realistic byconsidering the interactions between the particles.

11

DEM-CFD Modelling Approach

• DEM models particles at the individual particlelevel.

• CFD models the flow at the computational celllevel.

• At each time step, DEM gives the position andvelocity of individual particles.

• CFD then use this data to determine the fluid flowfield which in turn yields the fluid drag forcesacting on individual particles.

• Incorporation of the resulting forces into DEM thenprovides information about the motion ofindividual particles for the next time step.

DEM-CFD coupling 12

Mathematical Modelling of DEM-CFD

DEM

• Linear momentum equation.𝑚𝑖 ∙ 𝑎𝑖 = 𝑚𝑖 ∙ 𝑔 + 𝑗=1

𝑘 𝑓𝑐𝑗 + 𝑉𝑖 ∙ 𝛻𝑃 + 𝑓𝑑 𝑖 ≠ 𝑗

• Angular momentum equation.

𝐼𝑖 ∙𝑑𝜔𝑖

𝑑𝑡= 𝑗=1

𝑘 𝜏𝑖𝑗

CFD• Continuity equation 𝜕𝜀𝜌𝑓

𝜕𝑡+ 𝛻 ∙ 𝜀𝜌𝑓𝑈 = 0

• Momentum equation𝜕𝜀𝜌𝑓𝑈

𝜕𝑡+ 𝛻 ∙ 𝜀𝜌𝑓𝑈𝑈 = −𝛻𝑃 + 𝛻 ∙ 𝜏 + 𝑆 + 𝜀𝜌𝑓𝑔

• Source Term 𝑆 = 𝑗𝑛𝑤𝑗𝑓𝑑𝑗

• Drag Force 𝑓𝑑 =

1

2𝐶𝐷 𝜌𝑓 𝐴𝑝 𝑣𝑓 − 𝑣𝑖 (𝑣𝑓 − 𝑣𝑖)

13

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

Validation of STAR CCM+’s DEM-CFD Solver

0

1

2

3

4

5

6

5 10 15 20 25 30 35

Pre

ssu

re D

rop

(m

bar

/m)

Inlet velocity of air (m/s)

mass flow rate 0.3455 kg/s Case I

mass flow rate 0.3455 kg/s Case II

mass flow rate 0.3455 kg/sExperimentalmass flow rate 0.2063 kg/s Case I

mass flow rate 0.2063 kg/s Case II

mass flow rate 0.2063 kg/sExperimentalmass flow rate 0.0697 kg/s Case I

mass flow rate 0.0697 kg/s Case II

mass flow rate 0.0697 kg/sExperimental

• Good match between the experimental and numerical results is obtained for case II.

• Study validates the STAR CCM+’s DEM-CFD modelling approach.

• Study underlines the importance of initial condition of particles.

• 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 initialcondition of particles ( case I vp =0.5 vf and case II vp =0.3 vf ).

Details of parameters used in the study

15

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 theapparent viscosity in the governing equations.

• Carreau-Yasuda model is used to calculate apparent viscosity of shear thinning fluids.

𝜇 𝛾 = 𝜇∞ +𝜇0−𝜇∞

1+(𝜆 𝛾)𝑎 1−𝑛 𝑎

a is the parameter which controls shear-thinning nature of fluid.

16

Validation of non-Newtonian Flow using STAR CCM+’s Solver (contd..)

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

v/U

x/HRe 400 Reference Data Re 1000 Reference Data

Re nearly equal to zero Reference Data Re 1000 STAR CCM+

Re 400 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

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

Newtonian Vs. non-Newtonian Blood Flow with Filter

0

0.02

0.04

0.06

0.08

0.1

0 0.2 0.4 0.6 0.8

Mas

s fl

ow

rat

e (k

g/s

)

Time (s)

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Forc

e (d

ynes

)

Flow Time (s)Blood as a Newtonian Fluid Blood as a non-Newtonian Fluid

Inlet boundary condition

• Blood Flow in Inferior vena cava is a cyclicfunction of time to account for this a timevariant inlet boundary condition is used.

Drag force on filter• 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 on the flow behavior of blood and blood clots. 19

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

Statistical studies on Filter Efficiency

0

5

10

15

20

25

30

35

40

0.5 0.6 0.7 0.8 0.9 1

Fil

ter

effi

cien

cy (

%)

Probability of Inclusion

8mm clots 6mm clots 2mm clots

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 studieslike• Injection of multiple parcels from a single injection

point ( improves accuracy of results).

• Random inclusion of a point as a Injector ( Forexample if probability of inclusion is set to 0.85implies that STAR CCM+ selects any 85 points out of100 as injector points ).

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

Effect of Inlet BC on Filter Efficiency

0

5

10

15

20

25

30

0 2 4 6 8 10 12

Fil

ter

Eff

icie

ncy

(%

)

Clot diameter (mm)

constant massflow rate condition

constant inlet velocity condition

• A study is carried out to find the effect of inletboundary condition on filter capture efficiency.

• In the first case constant mass flow rate boundarycondition is applied at inlet.

• In the second case corresponding constant velocityboundary condition is applied at inlet.

• In both the cases particles at injection point areassumed to have a velocity corresponding to thefluid velocity at that point.

Conclusion• Mass flow rate boundary condition

should be applied at the inlet.22

Newtonian Vs. non-Newtonian Blood Flow & Filter Efficiency

0

5

10

15

20

25

30

0 2 4 6 8 10 12

Fil

ter

Eff

icie

ncy

(%

)

Clot diameter (mm)

Blood as a Newtonian fluid Blood as a non-Newtonian fluid

• A study is carried out to find the effect of Newtonian andnon-Newtonian assumption on filter capture efficiency.

• In both the case constant mass flow rate boundarycondition is applied at inlet.

• The particles at injection point are assumed to have avelocity corresponding to the fluid velocity at that point.

Conclusion• non-Newtonian nature of blood should

be considered in a system involvingblood and blood clots.

23

Filter Efficiency using DEM-CFD Modelling

Case Newtonian mass flow rate case (LPT)

Newtonian velocity inlet case (LPT)

Non-Newtonian mass flow rate case (LPT)

Newtonian DEM-CFD modellingcase

Efficiency 3.60 % 2.98 % 3.15 % 7.60 %

• DEM-CFD modelling for 2mm clots withblood as a Newtonian fluids is carried out.

• Constant velocity condition is applied at theInlet.

• 250 clots are injected out of which 19 clotsare captured by Simon Nitinol Filter.

• The clot capture efficiency is found out to be7.6 % .

24

Conclusions

• Newtonian and non-Newtonian assumption has a strong effect ondrag force on the filter.

• For a flow through inferior vena cava, mass flow rate BC should beapplied at the inlet.

• Non-Newtonian nature of the blood should be considered in thesimulations.

• Interactions between clots, clot-wall and clot-filter should bemodelled to have more realistic results. 25

Future Work

Future Work

Elastic non uniform

vena cava +

FSI between filter and

flow

DEM-CFDmodeling approach

Non-newtonion

+

Pulasting

flow

• Predict a clot-capture efficiency curve using DEM-CFD modellingapproach.

• Study the flow behavior with non-spherical and multi sized clots.

• Carry out a detailed study by including fluid-structure interactionbetween filter and blood.

26

Salient Features of STAR CCM+

• CD-adapco’s STAR CCM+ is the only commercial code which canmodel such complex physics.

• Single integrated environment of STAR CCM+ makes the wholeanalysis easier.

27

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

"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 per100,000 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 andworking of such IVC filters. We have embarked on an in-depth analysis of inferior vena cava filter using thestate-of-the-art Computational Fluid Dynamics (CFD) tool STAR-CCM+ in which we intend to model blood asa non-Newtonian fluid and blood clots under the framework of the discrete elements method (DEM), whichis the first of its kind in the current scenario. As a first step in this direction, studies were undertaken tovalidate the non-Newtonian and DEM implementations in the STAR-CCM+ code against published data inliterature, 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*

Work presented at DMD conference

http://www.dmd.umn.edu/sessions_2013/virtual_prototyping.html © Desktop Engineering (IVC-Filter image CD-adapco)

30

Details of parameters used in DEM-CFD Validation case.

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) 0.8Polyhedral mesh

Back

3-D , Transient, K-ε turbulent.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, 0.2063, and

0.3455 kg/s

8 Gravity (-Y direction) -9.81 m/s2

31

Details of parameters for non-Newtonian Validation case

Sr

no

Parameter Case I Case

II

Case III

1 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

viscosity(𝜇∞)

1 Pa-s

6 Relaxation time constant

(𝜆)

1 (s)

7 power constant (n) 0.5

8 parameter to controlling

shear-thinning (a)

2

Back32

Laminar, 2D, non-Newtonian fluid.