Computational Fluid Dynamics Simulation of Air Flow in the Human Symmetrical Six-Generation Bifurcation Bronchial Tree Model

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 269)


The present study aims to reveal the velocity and pressure distributions of air flow in the human symmetrical six-generation bifurcation bronchial tree model, to determine the influences of homothetic ratio, the angle of branching, and the branching plane rotation angle on the air flow. The 3D bronchial tree model is built up using SolidWorks based on optimal parameters from the references at first. And then it is transferred into ANSYS, one computational fluid dynamics software, to simulate the air flow and get the velocity and pressure distributions. Similarly, three different models are established by varying homothetic ratio, the angle of branching, and the branching plane rotation angle. Their impacts on the air flow are clarified through comparing the results. It is found the velocity and pressure distributions are symmetical genrally, but they start uneven from the fourth generation bronchus. The velocities in the inner and inferior bronchi are smaller than in the outer and superior ones. The pressure drop will decrease with the homothetic ratio, but it also increases the tree volume. The variations of the branching angle and the branching plane rotation angle will increase the pressure drop. The simulation results can reveal the air flow patterns in the models, and work as the benchmark for the future ventilation assessment of realistic human lung.


Bronchial tree Computational fluid dynamics Respiratory 



This work was financially supported by the National Science Foundation Council (No. 51006021), the Fundamental Research Funds for the Central Universities (N110419001), and the Scientific Research Fund of Liaoning Provincial Education Department (L2012080).


  1. 1.
    Naidich DP, Webb WR, Harkin TJ et al (2005) Imaging of the airways: functional and radiological correlations. Lippincott Williams & Wilkins/Wolters Kluwer Health Inc., PhiladelphiaGoogle Scholar
  2. 2.
    Mauroy B, Filoche M, Weibel ER et al (2004) An optimal bronchial tree may be dangerous. Nature 427(6975):633–636CrossRefGoogle Scholar
  3. 3.
    Horsfield K, Cumming G (1967) Angles of branching and diameters of branches in the human bronchial tree. Bull Math Biol 29(2):245–259Google Scholar
  4. 4.
    Tawhai MH, Hoffman EA, Lin CL (2009) The lung physiome: merging imaging-based measures with predictive computational models of structure and function. Wiley Interdiscip Rev Syst Biol Med 1(1):61–72CrossRefGoogle Scholar
  5. 5.
    Liu Y, So RMC, Zhang CH (2002) Modeling the bifurcating flow in a human lung airway. J Biomech 35:465–473CrossRefGoogle Scholar
  6. 6.
    Weibel ER (1997) In: Crystal RG, West JB, Weibel ER, Barnes PJ (eds) The lung: scientific foundations, vol. 1, 2nd edn. Lippincott-Raven, Philadelphia, pp 1061–1071Google Scholar
  7. 7.
    Montaudon M, Desbarats P, Berger P et al (2007) Assessment of bronchial wall thickness and lumen diameter in human adults using multi-detector computed tomography: comparison with theoretical models. J Anat 211(5):579–588CrossRefGoogle Scholar
  8. 8.
    Tawhai MH, Hunter P, Tschirren J et al (2004) CT-based geometry analysis and finite element models of the human and ovine bronchial tree. J Appl Physiol 97(6):2310–2321CrossRefGoogle Scholar
  9. 9.
    Lin CL, Tawhai MH, McLennan G et al (2007) Characteristics of the turbulent laryngeal jet and its effect on airflow in the human intra-thoracic airways. Respir Physiol Neurobiol 157(2):295–309CrossRefGoogle Scholar
  10. 10.
    Vial L, Chet D, Fodil R et al (2005) Airflow modeling of steady inspiration in two realistic proximal airway trees reconstructed from human thoracic tomodensitometric images. Comput Methods Biomech Biomed Eng 8(4):267–277CrossRefGoogle Scholar
  11. 11.
    Gemci T, Ponyavin V, Chen Y et al (2008) Computational model of airflow in upper 17 generations of human respiratory tract. J Biomech 41:2047–2054Google Scholar
  12. 12.
    Rochefort L, Vial LR, Fodil et al (2007) In vitro validation of computational fluid dynamic simulation in human proximal airways with hyperpolarized 3He magnetic resonance phase-contrast velocimetry. J Appl Physiol 102:2012–2023Google Scholar
  13. 13.
    Bokov P, Mauro B, Revel MP et al (2010) Lumen areas and homothety factor influence airway resistance in COPD. Respir Physiol Neurobiol 173(1):1–10CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.Sino-Dutch Biomedical and Information Engineering SchoolNortheastern UniversityShenyangChina
  2. 2.Radiology DepartmentShengjing Hospital of China Medical UniversityShenyangChina

Personalised recommendations