Annals of Biomedical Engineering

, Volume 46, Issue 9, pp 1309–1324 | Cite as

Computational Fluid Dynamics Modeling of the Human Pulmonary Arteries with Experimental Validation

  • Alifer D. Bordones
  • Matthew Leroux
  • Vitaly O. Kheyfets
  • Yu-An Wu
  • Chia-Yuan Chen
  • Ender A. Finol


Pulmonary hypertension (PH) is a chronic progressive disease characterized by elevated pulmonary arterial pressure, caused by an increase in pulmonary arterial impedance. Computational fluid dynamics (CFD) can be used to identify metrics representative of the stage of PH disease. However, experimental validation of CFD models is often not pursued due to the geometric complexity of the model or uncertainties in the reproduction of the required flow conditions. The goal of this work is to validate experimentally a CFD model of a pulmonary artery phantom using a particle image velocimetry (PIV) technique. Rapid prototyping was used for the construction of the patient-specific pulmonary geometry, derived from chest computed tomography angiography images. CFD simulations were performed with the pulmonary model with a Reynolds number matching those of the experiments. Flow rates, the velocity field, and shear stress distributions obtained with the CFD simulations were compared to their counterparts from the PIV flow visualization experiments. Computationally predicted flow rates were within 1% of the experimental measurements for three of the four branches of the CFD model. The mean velocities in four transversal planes of study were within 5.9 to 13.1% of the experimental mean velocities. Shear stresses were qualitatively similar between the two methods with some discrepancies in the regions of high velocity gradients. The fluid flow differences between the CFD model and the PIV phantom are attributed to experimental inaccuracies and the relative compliance of the phantom. This comparative analysis yielded valuable information on the accuracy of CFD predicted hemodynamics in pulmonary circulation models.


Particle image velocimetry Computational fluid dynamics Blood flow Shear stress Pulmonary hypertension 



The authors have no conflicts of interest to disclose and would like to acknowledge research funding from American Heart Association award 14GRNT19020017 and National Institutes of Health award R01HL121293. The content is solely the responsibility of the authors and does not necessarily represent the official views of the American Heart Association or the National Institutes of Health. The use of ANSYS Fluent is gratefully acknowledged through an educational licensing agreement with Ansys Inc.


  1. 1.
    Ambrosi, D., A. Quarteroni, and G. Rozza. Modeling of Physiological Flows. Milano: Springer, 2013.Google Scholar
  2. 2.
    Augst, A., D. Barratt, A. Hughes, F. Glor, S. M. Thom, and X. Yu. Accuracy and reproducibility of CFD predicted wall shear stress using 3D ultrasound images. J. Biomech. Eng. 125(2):218–222, 2003.CrossRefGoogle Scholar
  3. 3.
    Bouillot, P., O. Brina, R. Ouared, H. Yilmaz, K. Lovblad, M. Farhat, et al. Computational fluid dynamics with stents: quantitative comparison with particle image velocimetry for three commercial off the shelf intracranial stents. J Neurointerv. Surg. 8(3):309–315, 2016.CrossRefGoogle Scholar
  4. 4.
    Boutsianis, E., S. Gupta, K. Boomsma, and D. Poulikakos. Boundary conditions by Schwarz-Christoffel mapping in anatomically accurate hemodynamics. Ann. Biomed. Eng. 36(12):2068–2084, 2008.CrossRefGoogle Scholar
  5. 5.
    Chen, C. Y., R. Anton, M. Hung, P. Menon, E. A. Finol, and K. Pekkan. Effects of intraluminal thrombus on patient-specific abdominal aortic aneurysm hemodynamics via stereoscopic particle image velocity and computational fluid dynamics modeling. J. Biomech. Eng. 136(3):0310011, 2013.Google Scholar
  6. 6.
    Ford, M., H. Nikolov, J. Milner, S. Lownie, E. Demont, W. Kalata, F. Loth, D. Holdsworth, and D. Steinman. PIV-measured versus CFD-predicted flow dynamics in anatomically realistic cerebral aneurysm models. J Biomech. Eng. 130(2):021015, 2008.CrossRefGoogle Scholar
  7. 7.
    Galiè, N., M. Humbert, J. L. Vachiery, S. Gibbs, I. Lang, A. Torbicki, et al. 2015 ESC/ERS Guidelines for the diagnosis and treatment of pulmonary hypertension: the joint task force for the diagnosis and treatment of pulmonary hypertension of the European Society of Cardiology (ESC) and the European Respiratory Society (ERS). Eur. Heart J. 37:67–119, 2016.CrossRefGoogle Scholar
  8. 8.
    Galiè, N., A. Torbicki, R. Barst, P. Dartevelle, S. Haworth, T. Higenbottan, et al. Guidelines on diagnosis and treatment of pulmonary arterial hypertension: the task force on diagnosis and treatment of pulmonary arterial hypertension of the European Society of Cardiology. Eur. Heart J. 25:2243–2278, 2004.CrossRefGoogle Scholar
  9. 9.
    Gay, S., J. Olazagasti, J. Higginbotham, A. Gupta, A. Wurm, and J. Nguyen. Pulmonary Vasculature. Charlottesville: University of Virginia Health Sciences Center, Department of Radiology, 2013.Google Scholar
  10. 10.
    Hoi, Y., S. Woodward, M. Kim, D. Taulbee, and H. Meng. Validation of CFD simulations of cerebral aneurysms with implication of geometric variations. J. Biomech. Eng. 128(6):844–851, 2006.CrossRefGoogle Scholar
  11. 11.
    Humphrey, J. Mechanisms of arterial remodeling in hypertension: coupled roles of wall shear and intramural stress. Hypertension. 52(2):195–200, 2008.CrossRefGoogle Scholar
  12. 12.
    Hunter, K., J. Albietz, P. Lee, S. Lanning, S. Lammers, S. Hofmeister, P. Kao, H. Qi, K. Stenmark, and R. Shandas. In vivo measurement of proximal pulmonary artery elastic modulus in the neonatal calf model of pulmonary hypertension: development and ex vivo validation. J. Appl. Physiol. 108(4):968–975, 2010.CrossRefGoogle Scholar
  13. 13.
    Hunter, K., J. Feinstein, D. Ivy, and R. Shandas. Computational simulation of the pulmonary arteries and its role in the study of pediatric pulmonary hypertension. Prog. Pediatr. Cardiol. 30(1–2):63–69, 2010.CrossRefGoogle Scholar
  14. 14.
    Katz, I., E. Shaughnessy, and B. Cress. A technical problem in the calculation of laminar flow near irregular surfaces described by sampled geometric data. J. Biomech. 28(4):461–464, 1995.CrossRefGoogle Scholar
  15. 15.
    Khadir, M. M., A. Chaturvedi, M. S. Nguyen, J. C. Wandtke, S. Hobbs, and A. Chaturvedi. Looking beyond the thrombus: essentials of pulmonary artery imaging on CT. Insights Imaging 5(4):493–506, 2014.CrossRefGoogle Scholar
  16. 16.
    Kheyfets, V. O., W. O’Dell, T. Smith, J. J. Reilly, and E. A. Finol. Considerations for numerical modeling of the pulmonary circulation—a review with a focus on pulmonary hypertension. J. Biomech. Eng. 135(6):61011–61015, 2013.CrossRefGoogle Scholar
  17. 17.
    Kheyfets, V., L. Rios, T. Smith, T. Schroeder, J. Mueller, S. Murali, D. Lasorda, A. Zikos, J. Spotti, J. Reilly, and E. Finol. Patient-specific computational modeling of blood flow in the pulmonary circulation. Comput. Methods Programs Biomed. 120(2):88–101, 2015.CrossRefGoogle Scholar
  18. 18.
    Kobs, R., N. Muvarak, J. Eickhoff, and N. Chesler. Linked mechanical and biological aspects of remodeling in mouse pulmonary arteries with hypoxia-induced hypertension. Am. J. Physiol. Heart. Circ. Physiol. 288(3):H1209–H1217, 2005.CrossRefGoogle Scholar
  19. 19.
    Ku, D. N., D. P. Giddens, C. K. Zarins, and S. Glagov. Pulsatile flow and atherosclerosis in the human carotid bifurcation. Positive correlation between plaque location and low oscillating shear stress. Arterioscler. Thromb. Vasc. Biol. 5(3):293–302, 1985.Google Scholar
  20. 20.
    Lammers, S., P. Kao, H. Qi, K. Hunter, C. Lanning, J. Albietz, S. Hofmeister, R. Mecham, K. Stenmark, and R. Shandas. Changes in the structure-function relationship of elastin and its impact on the proximal arterial mechanics of hypertensive calves. Am. J. Physiol. Heart. Circ. Physiol. 295(4):H1451–H1459, 2008.CrossRefGoogle Scholar
  21. 21.
    Leverett, L. B., J. D. Hellums, C. P. Alfrey, and E. C. Lynch. Red blood cell damage by shear stress. Biophys. J. 12(3):257, 1972.CrossRefGoogle Scholar
  22. 22.
    Ma, B., V. Ruwet, P. Corieri, R. Theunissen, M. Riethmuller, and C. Darquenne. CFD simulation and experimental validation of fluid flow and particle transport in a model of alveolated airways. J. Aerosol Sci. 40(5):403–414, 2009.CrossRefGoogle Scholar
  23. 23.
    Marshall, I., S. Zhao, P. Papathanasopoulou, P. Hoskins, and X. Xu. MRI and CFD studies of pulsatile flow in healthy and stenosed carotid bifurcation models. J. Biomech. 37:679–687, 2004.CrossRefGoogle Scholar
  24. 24.
    Mehta, Y., and D. Arora. Newer methods of cardiac output monitoring. World J Cardiol. 6(9):1022, 2014.CrossRefGoogle Scholar
  25. 25.
    Narrow, T., M. Yoda, and S. Abdel-Khalik. A simple model for the refractive index of sodium iodide aqueous solutions. Exp Fluids. 28:282–283, 2000.CrossRefGoogle Scholar
  26. 26.
    Ooi, C., Z. Wang, D. Tabima, J. Eickhoff, and N. Chesler. The role of collagen in extralobar pulmonary artery stiffening in response to hypoxia-induced pulmonary hypertension. Am. J. Physiol. Heart. Circ. Physiol. 299(6):H1823–H1831, 2010.CrossRefGoogle Scholar
  27. 27.
    Parasuraman, S., S. Walker, B. L. Loudon, N. D. Gollop, A. M. Wilson, C. Lowery, and M. P. Frenneaux. Assessment of pulmonary artery pressure by echocardiography—a comprehensive review. IJC Heart Vasc. 12:45–51, 2016.CrossRefGoogle Scholar
  28. 28.
    Poelma, C., P. Vennemann, R. Lindken, and J. Westerweel. In vivo blood flow and wall shear stress measurements in the vitelline network. Exp. Fluids. 45(4):703–713, 2008.CrossRefGoogle Scholar
  29. 29.
    Ponzini, R., M. Lemma, U. Morbiducci, F. M. Montevecchi, and A. Redaelli. Doppler derived quantitative flow estimate in coronary artery bypass graft: a computational multiscale model for the evaluation of the current clinical procedure. Med. Eng. Phys. 30(7):809–816, 2008.CrossRefGoogle Scholar
  30. 30.
    Prakash, S., and C. Ethier. Requirements for mesh resolution in 3D computational hemodynamics. J. Biomech. Eng. 123(2):134–144, 2001.CrossRefGoogle Scholar
  31. 31.
    Proença, M., F. Braun, J. Solà, A. Adler, M. Lemay, J. P. Thiran, and S. F. Rimoldi. Non-invasive monitoring of pulmonary artery pressure from timing information by EIT: experimental evaluation during induced hypoxia. Physiol Meas 37(6):713, 2016.CrossRefGoogle Scholar
  32. 32.
    Raschi, M., F. Mut, G. Byrne, C. Putman, S. Tateshima, F. Viñuela, K. Tanishita, and J. Cebral. CFD and PIV analysis of hemodynamics in a growing intracranial aneurysm. Int. J. Numer. Method Biomed. Eng. 28(2):214–228, 2012.CrossRefGoogle Scholar
  33. 33.
    Schäfer, M., V. O. Kheyfets, J. D. Schroeder, J. Dunning, R. Shandas, J. K. Buckner, et al. Main pulmonary arterial wall shear stress correlates with invasive hemodynamics and stiffness in pulmonary hypertension. Pulm. Circ. 6(1):37–45, 2016.CrossRefGoogle Scholar
  34. 34.
    Schäfer, M., C. Myers, R. Brown, M. Frid, W. Tan, K. Hunter, and K. Stenmark. Pulmonary arterial stiffness: toward a new paradigm in pulmonary hypertension pathophysiology and assessment. Curr. Hypertens. Rep. 18(1):4, 2016.CrossRefGoogle Scholar
  35. 35.
    Schreier, D., T. Hacker, G. Song, and N. Chesler. The role of collagen synthesis in ventricular and vascular adaptation to hypoxic pulmonary hypertension. J. Biomech. Eng. 135(2):0210181–0210187, 2013.CrossRefGoogle Scholar
  36. 36.
    Sotelo, J. A., J. Urbina, I. Valverde, C. Tejos, P. Irarrazaval, D. E. Hurtado, and S. Uribe. 3D quantification of hemodynamics parameters of pulmonary artery and aorta using finite-element interpolations in 4D flow MR data. J. Cardiovasc. Magn. Reson. 17(1):1, 2015.CrossRefGoogle Scholar
  37. 37.
    Sun, Q., A. Groth, M. Bertram, I. Waechter, T. Bruijns, R. Hermans, et al. Experimental validation and sensitivity analysis for CFD simulations of cerebral aneurysms. Proc. IEEE Int. Symp. Biomed. Imaging 1049–1052, 2010Google Scholar
  38. 38.
    Tang, B., S. Pickard, F. Chan, P. Tsao, C. Taylor, and J. Feinstein. Wall shear stress is decreased in the pulmonary arteries of patients with pulmonary arterial hypertension: An image-based, computational fluid dynamics study. Pulm. Circ. 2(4):470–476, 2012.CrossRefGoogle Scholar
  39. 39.
    Tian, L., H. Kellihan, J. Henningsen, A. Bellofiore, O. Forouzan, A. Roldán-Alzate, et al. Pulmonary artery relative area change is inversely related to ex vivo measured arterial elastic modulus in the canine model of acute pulmonary embolization. J Biomech. 47(12):2904–2910, 2014.CrossRefGoogle Scholar
  40. 40.
    Tian, L., S. Lammers, P. Kao, J. Albietz, K. Stenmark, H. Qi, R. Shandas, and K. Hunter. Impact of residual stretch and remodeling on collagen engagement in healthy and pulmonary hypertensive calf pulmonary arteries at physiological pressures. Ann. Biomed. Eng. 40(7):1419–1433, 2012.CrossRefGoogle Scholar
  41. 41.
    Truong, U., B. Fonseca, J. Dunning, S. Burgett, C. Lanning, D. D. Ivy, R. Shandas, K. Hunter, and A. J. Barker. Wall shear stress measured by phase contrast cardiovascular magnetic resonance in children and adolescents with pulmonary arterial hypertension. J. Cardiovasc. Magn. Reson. 15(1):1, 2013.CrossRefGoogle Scholar
  42. 42.
    Tu, J., G. H. Yeoh, and C. Liu. Computational Fluid Dynamics—A Practical Approach. Burlington, MA: Elsevier Inc, 2008.Google Scholar
  43. 43.
    Van Ertbruggen, C., P. Corieri, R. Theunissen, M. Riethmuller, and C. Darquenne. Validation of CFD predictors of flow in a 3D alveolated bend with experimental data. J Biomech. 41(2):399–405, 2008.CrossRefGoogle Scholar
  44. 44.
    Wang, Z., R. Lakes, J. Eickhoff, and N. Chesler. Effects of collagen deposition on passive and active mechanical properties of large pulmonary arteries in hypoxic pulmonary hypertension. Biomech. Model. Mechanobiol. 12:1115–1125, 2013.CrossRefGoogle Scholar
  45. 45.
    Wang, Z., R. Lakes, M. Golob, J. Eickhoff, and N. Chesler. Changes in pulmonary arterial viscoelasticity in chronic pulmonary hypertension. PLoS ONE 8(11):e78569, 2013.CrossRefGoogle Scholar
  46. 46.
    Weinbaum, S., X. Zhang, Y. Han, H. Vink, and S. C. Cowin. Mechanotransduction and flow across the endothelial glycocalyx. Proc. Natl. Acad. Sci. 100(13):7988–7995, 2003.CrossRefGoogle Scholar
  47. 47.
    Wicker, R., and F. Medina. Framework for physical modeling of complex internal flow passages using rapid prototyping and water-soluble molds. In: Proceedings of the 31st International Conference on Computers and Industrial Engineering, San Francisco, CA. 23:559–564, 2013.Google Scholar
  48. 48.
    Xu, L., M. Yang, L. Ye, and Z. Dong. Computational fluid dynamics analysis and PIV validation of a bionic vortex flow pulsatile LVAD. Technol. Health Care. 23(2):S443–S451, 2015.CrossRefGoogle Scholar
  49. 49.
    Yousif, M., D. Holdsworth, and T. Poepping. Deriving a blood-mimicking fluid for particle image velocimetry in Sylgard-184 vascular models. IEEE Eng. Med. Biol. Soc. 1412–1415, 2009.Google Scholar
  50. 50.
    Yousif, M., D. Holdsworth, and T. Poepping. A blood-mimicking fluid for particle image velocimetry with silicone vascular models. Exp. Fluids. 50(3):769–774, 2011.CrossRefGoogle Scholar
  51. 51.
    Zhang, W., J. Liu, Q. Yan, J. Liu, H. Hong, and L. Mao. Computational haemodynamic analysis of left pulmonary artery angulation effects on pulmonary blood flow. Interact. CardioVasc. Thorac. Surg. 23(6), 2016.Google Scholar

Copyright information

© Biomedical Engineering Society 2018

Authors and Affiliations

  • Alifer D. Bordones
    • 1
  • Matthew Leroux
    • 1
  • Vitaly O. Kheyfets
    • 2
  • Yu-An Wu
    • 3
  • Chia-Yuan Chen
    • 3
  • Ender A. Finol
    • 1
    • 4
  1. 1.UTSA/UTHSA Joint Graduate Program in Biomedical EngineeringUniversity of Texas at San AntonioSan AntonioUSA
  2. 2.Department of Bioengineering, Anschutz Medical CampusUniversity of Colorado DenverAuroraUSA
  3. 3.Department of Mechanical EngineeringNational Chen Kung UniversityTainanTaiwan
  4. 4.Department of Mechanical EngineeringUniversity of Texas at San AntonioSan AntonioUSA

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