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Biomechanics and Modeling in Mechanobiology

, Volume 18, Issue 6, pp 1759–1771 | Cite as

Implementation of a specific boundary condition for a simplified symmetric single-path CFD lung model with OpenFOAM

  • A. Pandal-Blanco
  • R. Barrio-Perotti
  • R. Agujetas-Ortiz
  • A. Fernández-TenaEmail author
Original Paper
  • 149 Downloads

Abstract

CFD modeling research about the lung airflow with a complete resolution and an adequate accuracy at all scales requires a great amount of computational resources due to the vast number of necessary grid elements. As a result, a common practice is to conduct simplifications that allows to manage it with ordinary computational power. In this study, the implementation of a special boundary condition in order to develop a simplified single conductive lung airway model, which exactly represents the effect of the removed airways, is presented. The boundary condition is programmed in the open-source software OpenFOAM®, and the developed source code is presented in the proper syntax. After this description, modeling accuracy is evaluated under different flow rate conditions typical of human breathing processes, including both inspiration and expiration movements. Afterward, a validation process is conducted using results of a Weibel’s model (0–4 generations) simulation for a medium flow rate of 50 L/min. Finally, a comparison against the proposed boundary condition implemented in the commercial code ANSYS Fluent is made, which highlights the benefits of using the free code toolbox. The specific contribution of this paper will be to show that OpenFOAM® developed model can perform even better than other commercial codes due to a precise implementation and coupling of the default solver with the in-house functions by virtue of the open-source nature of the code.

Keywords

Boundary condition CFD Human airways OpenFOAM® Single airway Truncated branches 

Notes

Acknowledgements

Authors acknowledge that this work was partially funded by the Spanish Ministry of Economy, Industry and Competitiveness—Instituto de Salud Carlos III under Project “Estudio de la influencia de la geometría de las vías respiratorias en las patologías pulmonares obstructivas (PI17/01639)” and was supported by Universidad de Oviedo under Project “Desarrollo de nuevo material docente sobre flujos biológicos: simulación de vías áreas humanas. PINN-17-A-061.” and by Junta de Extremadura through Grant IB16119 (partially financed by FEDER).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Adler K, Brücker C (2007) Dynamic flow in a realistic model of the upper human lung airways. Exp Fluids 43(2–3):411CrossRefGoogle Scholar
  2. ANSYS Fluent Version 6.3.26. ©ANSYS Inc. (2006)Google Scholar
  3. ANSYS Gambit Version 2.4.6. ©ANSYS Inc. (2006)Google Scholar
  4. Ball CG, Uddin M, Pollard A (2008a) High resolution turbulence modelling of airflow in an idealised human extra-thoracic airway. Comput Fluids 37(8):943–964CrossRefGoogle Scholar
  5. Ball CG, Uddin M, Pollard A (2008b) Mean flow structures inside the human upper airway. Flow Turbul Combust 81(1–2):155–188CrossRefGoogle Scholar
  6. Beekmans JM (1965) The deposition of aerosol in the respiratory tract: I. Mathematical analysis and comparison with experimental data. Can J Physiol Pharmacol 43:157–172CrossRefGoogle Scholar
  7. Davies CN (ed) (1961) A formalized anatomy of the human respiratory tract. In: Inhaled particles and vapours. Symposium Publications Division, Pergamon Press, New York, pp. 82–87Google Scholar
  8. Fernández-Tena A (2014) Clinical applications of fluid dynamics models in respiratory disease. In: PhD thesis, University of Oviedo, Spain. http://digibuo.uniovi.es/dspace/handle/10651/29057?locale=en
  9. Fernández-Tena A, Fernández J, Álvarez E, Casan P, Walters DK (2017a) Design of a numerical model of lung by means of a special boundary condition in the truncated branches. Int J Numer Method Biomed Eng 33(6):e2830MathSciNetCrossRefGoogle Scholar
  10. Fernández-Tena A, Marcos AC, Martínez C, Keith Walters D (2017b) A new adaptive time step method for unsteady flow simulations in a human lung. Comput Methods Biomech Biomed Eng 20(8):915–917CrossRefGoogle Scholar
  11. Findeisen W (1935) Über das absetzenkleiner, in der luftsuspendierter teilchen in der menschlichen lunge bei der atmung. Pflüger’sArch Gesamte Physiol Menschen Tiere 236(1):367–379CrossRefGoogle Scholar
  12. Gemci T, Ponyavin V, Chen Y, Chen H, Collins R (2008) Computational model of airflow in upper 17 generations of human respiratory tract. J Biomech 41(9):2047–2054CrossRefGoogle Scholar
  13. Grosse S, Schroder W, Klaas M, Klockner A, Roggenkamp J (2007) Time resolved analysis of steady and oscillating flow in the upper human airways. Exp Fluids 42:955–970CrossRefGoogle Scholar
  14. Hegedűs CJ, Balásházy I, Farkas A (2004) Detailed mathematical description of the geometry of airway bifurcations. Respir Physiol Neurobiol 141(1):99–114CrossRefGoogle Scholar
  15. Hyatt RE, Wilcon RE (1963) The pressure-flow relationships of the intrathoracic airway in man. J Clin Invest 42(1):29–39CrossRefGoogle Scholar
  16. Kannan R, Guo P, Przekwas A (2016) Particle transport in the human respiratory tract: formulation of a nodal inverse distance weighted Eulerian-Lagrangian transport and implementation of the Wind-Kessel algorithm for an oral delivery. Int J Numer Method Biomed Eng 32(6):e2746CrossRefGoogle Scholar
  17. Kannan R, Chen ZJ, Singh N et al (2017a) A quasi-3D wire approach to model pulmonary airflow in human airways. Int J Numer Method Biomed Eng 33(7):e2838CrossRefGoogle Scholar
  18. Kannan R, Przekwas A, Singh N, Delvadia R, Tian G, Walenga R (2017b) Pharmaceutical aerosols deposition patterns from a dry powder inhaler: Euler Lagrangian prediction and validation. Med Eng Phys 42:35–47CrossRefGoogle Scholar
  19. Kannan R, Singh N, Przekwas A (2018a) A compartment-quasi-3D multiscale approach for drug absorption, transport, and retention in the human lungs. Int J Numer Method Biomed Eng 34(5):e2955MathSciNetCrossRefGoogle Scholar
  20. Kannan R, Singh N, Przekwas A (2018b) A quasi-3D compartmental multi-scale approach to detect and quantify diseased regional lung constriction using spirometry data. Int J Numer Method Biomed Eng 34(5):e2973CrossRefGoogle Scholar
  21. Kitaoka H, Takaki R, Suki B (1999) A three-dimensional model of the human airway tree. J Appl Physiol 87(6):2207–2217CrossRefGoogle Scholar
  22. Koullapis PG, Hofemeier P, Sznitman J, Kassinos SC (2017) An efficient computational fluid-particle dynamics method to predict deposition in a simplified approximation of the deep lung. Eur J Pharm Sci 113:132–144CrossRefGoogle Scholar
  23. Landahl HD (1950) On the removal of air-borne droplets by the human respiratory tract: I The lung. Bull Math Biophys 12(1):43–56MathSciNetCrossRefGoogle Scholar
  24. Nowak N, Kakade PP, Annapragada AV (2003) Computational fluid dynamics simulation of airflow and aerosol deposition in human lungs. Ann Biomed Eng 31(4):374–390CrossRefGoogle Scholar
  25. OpenFOAM (2017). OpenFOAM Foundation. 2(0)Google Scholar
  26. Sauret V, Goatman KA, Fleming JS, Bailey AG (1999) Semi-automated tabulation of the 3D topology and morphology of branching networks using CT: application to the airway tree. Phys Med Biol 44(7):1625CrossRefGoogle Scholar
  27. Sbirlea-Apiou G, Katz I, Caillibotte G, Martonen T, Yang Y (2007) Deposition mechanics of pharmaceutical particles in human airways. In: Hickey AJ (ed) Inhalation aerosols. Physical and biological basis for therapy, 2nd edn. Informa Healthcare USA, New York, pp 1–30Google Scholar
  28. Schmidt A, Zidowitz S, Kriete A, Denhard T, Krass S, Peitgen HO (2004) A digital reference model of the human bronchial tree. Comput Med Imaging Graph 28(4):203–211CrossRefGoogle Scholar
  29. Tawhai MH, Burrowes KS (2003) Developing integrative computational models of pulmonary structure. Anat Rec 275(1):207–218CrossRefGoogle Scholar
  30. Theunissen R, Riethmuller ML (2007) Particle image velocimetry in lung bifurcation models. In: Schröder A, Willert CE (eds) Particle image velocimetry. Topics in applied physics, vol 112. Springer, Berlin, pp. 73–101Google Scholar
  31. Walters DK, Luke WH (2010) A method for three-dimensional Navier-Stokes simulations of large-scale regions of the human lung airway. J Fluids Eng 132(5):051101CrossRefGoogle Scholar
  32. Weibel ER (1963) Geometric and dimensional airway models of conductive, transitory and respiratory zones of the human lung. In: Morphometry of the human lung, Springer, Berlin, pp 136–142Google Scholar
  33. Weller H, Tabor G, Jasak H, Fureby C (1998) A tensorial approach to computational continuum mechanics using object-oriented techniques. Comput Phys 12:620–631CrossRefGoogle Scholar
  34. Yang XL, Liu Y, Luo HY (2006a) Respiratory flow in obstructed airways. J Biomech 39(15):2743–2751CrossRefGoogle Scholar
  35. Yang XL, Liu Y, So RMC, Yang JM (2006b) The effect of inlet velocity profile on the bifurcation COPD airway flow. Comput Biol Med 36(2):181–194CrossRefGoogle Scholar
  36. Zhang Z, Kleinstreuer C (2011) Computational analysis of airflow and nanoparticle deposition in a combined nasal–oral–tracheobronchial airway model. J Aerosol Sci 42(3):174–194CrossRefGoogle Scholar
  37. Zhang Z, Kleinstreuer C, Kim CS (2008) Airflow and nanoparticle deposition in a 16-generation tracheobronchial airway model. Ann Biomed Eng 36(12):2095–2110CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Departamento de EnergíaUniversidad de OviedoOviedoSpain
  2. 2.Departamento de IMEMUniversidad de ExtremaduraBadajozSpain
  3. 3.Instituto Nacional de SilicosisOviedoSpain

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