Elsevier

Building and Environment

Volume 94, Part 2, December 2015, Pages 504-515
Building and Environment

Investigation of flow pattern in upper human airway including oral and nasal inhalation by PIV and CFD

https://doi.org/10.1016/j.buildenv.2015.10.002Get rights and content

Highlights

  • An idealized- and realistic-human airway model were main subjects in this study.

  • We conducted CFD and PIV to investigate the flow pattern in both models.

  • Inhalation mode were applied in oral and nasal cavity to examine the effect of inlet conditions.

  • The prediction and actual result of a flow field shown a good agreement.

Abstract

Breathing is one of the most essential processes in the human body. The basic functions of breathing are to exchange gases (supplying oxygen from ambient air and removing carbon dioxide from the blood) and also to exchange heat and moisture through mucous surfaces of the airway. During an average lifetime, human beings experience significant exposure to indoor air and countless of contaminants/particles via inhalation. In this study, experimental and numerical results of flow fields in a realistic respiratory model were obtained. Flow patterns in a realistic replica model of the human respiratory tract were investigated with particle image velocimetry (PIV) under three constant breathing conditions; 7.5, 15 and 30 L/min. Computational fluid dynamics (CFD) analyses were conducted on turbulent models with boundary conditions corresponding to the experimental models. We used four RANS turbulence models to predict airflow in a realistic human airway model: two low Reynolds (Re) number-type k–ε turbulence models, RNG k–ε model, and the SST k–ω model. The CFD results were compared with PIV data and showed relatively good agreement in trachea region in all cases.

Section snippets

Practical implications

Various types of computer simulated persons (CSPs) have been developed for conducting indoor environmental analyses. Recently, a CSP with a respiratory tract from the nostril inlet to the bronchial tubes has been reported. To validate computational models of a respiratory tract, there are two types of measurements: in vivo and in vitro. Though early observations were mainly made in vivo, these measurements have many limitations due to the complicated structure and small size of nasal passages.

Creation of the in vitro experiment model

Original respiratory tract data were obtained using a Toshiba 64 multi-detector row computed tomography (MDCT) scanner. The subject was a nonsmoking Asian male volunteer, 35 years old, with a body mass index (BMI) of approximately 21. The CT scans produced 785 slices of the respiratory tract. The images are stored as standard Digital Imaging and Communications in Medicine (DICOM) data, a format commonly used for the transfer and storage of medical images. Fig. 1 shows the combined CT images of

Idealized human airway model

The complexity of the physical model was also reduced with an idealized computational geometry. A corresponding CFD analysis with low Re type k–ε model (LR-ANK) was also conducted. The measurement and prediction results of the average air velocity magnitudes distributions by PIV and CFD are shown in Fig. 8. A relatively simple flow pattern was observed in both the PIV experiment and CFD prediction.

The mean velocities at cross line S1, S2, S3 in Fig. 8. (4), (5), (6) were normalized by inlet

Discussion

In the PIV measurements, the total error in the estimation of a single displacement vector can be expressed as a sum of the bias error and the measurement uncertainty. Each displacement vector is associated with a certain degree of over- or under-estimation error, i.e., bias error, plus some degree of random error or measurement uncertainty. Bias errors include the correlation mapping error and the conversion error resulting from the conversion of the pixel spacing to dimensional measurements.

Conclusions

We introduced a basic model that was not intended to reflect the exact behavior of the human airway but that incorporated sufficient geometrical characteristics to allow a realistic comparison. It should be noted that a rigid-wall model was developed and measured. The lack of cyclic wall motion in both experimental and computational models is a significant limitation in the representation of air flows. This study provides additional contributions in validating the use of CFD analyses to

Acknowledgments

Dr. Shin-ichiro Aramaki (Kyushu University), Mr. Masato Yamashita (Kyushu University) and Mr. Kota Hirase (Kyushu University) provided valuable suggestions and support for the PIV measurements, for which the authors are deeply appreciative.

This project was partially supported by a Grant-in-Aid for Scientific Research (JSPS 21676005).

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