Optimal human respiratory simulation for exhaled gas based on CFD method

Human breathing is crucial for studying indoor environments and human health. Computational Fluid Dynamics (CFD) is a key tool for simulating human respiration. To enhance the accuracy of CFD simulations and reduce computation time, a new simulation strategy for human respiration is proposed in this paper. The effects of steady versus unsteady boundary conditions on simulation results were examined. For the unsteady boundary, sinusoidal exhalation velocities and non-inhalation gas were assumed, while the steady boundary involved constant velocities during both exhalation and inhalation phases. The jet center trajectory under different boundary conditions was analyzed and compared with experimental data. Additionally, variations in pollutant dispersion near the mouth under the two boundary conditions were discussed. Furthermore, the paper compared the calculation accuracy, calculation time and memory occupied by a single turbulence model or switching flow character models in human respiration simulation. Differences in exhaled gas vorticity and jet penetration depth across different flow models were identified. Finally, combined with the non-iterative algorithm, the optimal strategy of human respiration simulation was proposed. Results show that under the comprehensive consideration of calculation accuracy, calculation time and memory occupancy, using sinusoidal expiratory boundary conditions combined with the PISO algorithm, with the RNG k-ε model during expiratory phase, and switching into the laminar flow during inspiratory phase, is the optimal strategy of simulating human breathing.

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Publication Details

Journal
PLoS ONE
Published
2024-11-18
DOI
https://doi.org/10.1371/journal.pone.0313522
Citations
1
Primary Topic
Infection Control and Ventilation
Type
article
Field-Weighted Citation Impact
0.42

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article

Optimal human respiratory simulation for exhaled gas based on CFD method

Zhihe Su, Yanfeng Li, Chunlin Wang, Haidong Wang et al.
1 citations
PLoS ONE
Infection Control and Ventilation
0.42
article

Optimal human respiratory simulation for exhaled gas based on CFD method

Zhihe Su, Yanfeng Li, Chunlin Wang, Haidong Wang, Junmei Li
article en
1 citations

Abstract

Human breathing is crucial for studying indoor environments and human health. Computational Fluid Dynamics (CFD) is a key tool for simulating human respiration. To enhance the accuracy of CFD simulations and reduce computation time, a new simulation strategy for human respiration is proposed in this paper. The effects of steady versus unsteady boundary conditions on simulation results were examined. For the unsteady boundary, sinusoidal exhalation velocities and non-inhalation gas were assumed, while the steady boundary involved constant velocities during both exhalation and inhalation phases. The jet center trajectory under different boundary conditions was analyzed and compared with experimental data. Additionally, variations in pollutant dispersion near the mouth under the two boundary conditions were discussed. Furthermore, the paper compared the calculation accuracy, calculation time and memory occupied by a single turbulence model or switching flow character models in human respiration simulation. Differences in exhaled gas vorticity and jet penetration depth across different flow models were identified. Finally, combined with the non-iterative algorithm, the optimal strategy of human respiration simulation was proposed. Results show that under the comprehensive consideration of calculation accuracy, calculation time and memory occupancy, using sinusoidal expiratory boundary conditions combined with the PISO algorithm, with the RNG k-ε model during expiratory phase, and switching into the laminar flow during inspiratory phase, is the optimal strategy of simulating human breathing.

PLoS ONEVol. 19(11)
University of Shanghai for Science and Technology (CN), Chifeng University (CN), Beijing University of Technology (CN)
Natural Science Foundation of Inner Mongolia
Openalex Percentile: Top 31%
Infection Control and Ventilation
0.42
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