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Insights from 3D modeling and fluid dynamics in COVID-19 pneumonia

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Abstract

We address the lack of research regarding aerodynamic events behind respiratory distress at COVID-19. The use of chest CT enables quantification of pneumonia extent; however, there is a paucity of data regarding the impact of airflow changes. We reviewed 31 COVID-19 patients who were admitted in March 2020 with varying severity of pulmonary disease. Lung volumes were segmented and measured on CT images and patient-specific models of the lungs were created. Incompressible, laminar, and three-dimensional Navier-Stokes equations were used for the fluid dynamics (CFD) analyses of ten patients (five mild, five pneumonia). Of 31 patients, 17 were female, 18 had pneumonia, and 2 were deceased. Effective lung volume decreased in the general group, but the involvement of the right lung was prominent in dyspnea patients. CFD analyses revealed that the mass flow distribution was significantly distorted in pneumonia cases with diminished flow rate towards the right lung. In addition, the distribution of flow parameters showed mild group had less airway resistance with higher velocity (1.228 m/s vs 1.572 m/s) and higher static pressure values at airway branches (1.5112 Pa vs 1.3024 Pa). Therefore, we conclude that airway resistance and mass flow rate distribution are as important as the radiological involvement degree in defining the disease severity.

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Abbreviations

A :

Cross-sectional area of inlet (m2)

D eq :

Equivalent diameter (m)

p :

Pressure (Pa)

p static :

Static pressure (Pa)

p Total :

Total pressure (Pa)

q :

Dynamic pressure, 1/2ρV2

Q :

Volume flow rate (m3/s)

Re :

Reynolds number, U∞c/ν

V mean :

Mean velocity (m/s)

\(\overset\rightharpoonup V\) :

velocity vector [m/s] 

ρ :

Density (kg/m3)

ν :

Kinematic viscosity (m2/s)

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Authors and Affiliations

  1. Otorhinolaryngology, Head and Neck Surgery Department, Ankara University Medical School, Ankara, Turkey

    M. Kürşat Gökcan

  2. Ankara Üniversitesi KBB Hastalıkları Anabilim Dalı, İbni Sina Hastanesi Ek bina K-2, 06100, Sıhhiye, Ankara, Turkey

    M. Kürşat Gökcan

  3. Department of Aerospace Engineering, Faculty of Engineering, Middle East Technical University, Ankara, Turkey

    D. Funda Kurtuluş

  4. Department of Infectious Diseases and Clinical Microbiology, Ankara Bilkent City Hospital, Ankara, Turkey

    Adalet Aypak

  5. Department of Radiology, Ankara Bilkent City Hospital, Ankara, Turkey

    Murathan Köksal & Sarper R. Ökten

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  1. M. Kürşat Gökcan
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  2. D. Funda Kurtuluş
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  3. Adalet Aypak
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  4. Murathan Köksal
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Gökcan, M.K., Kurtuluş, D.F., Aypak, A. et al. Insights from 3D modeling and fluid dynamics in COVID-19 pneumonia. Med Biol Eng Comput 62, 621–636 (2024). https://doi.org/10.1007/s11517-023-02958-0

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