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Evaluation of computational fluid dynamics models for predicting pediatric upper airway airflow characteristics

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Abstract

Computational fluid dynamics (CFD) has the potential for use as a clinical tool to predict the aerodynamics and respiratory function in the upper airway (UA) of children; however, careful selection of validated computational models is necessary. This study constructed a 3D model of the pediatric UA based on cone beam computed tomography (CBCT) imaging. The pediatric UA was 3D printed for pressure and velocity experiments, which were used as reference standards to validate the CFD simulation models. Static wall pressure and velocity distribution inside of the UA under inhale airflow rates from 0 to 266.67 mL/s were studied by CFD simulations based on the large eddy simulation (LES) model and four Reynolds-averaged Navier–Stokes (RANS) models. Our results showed that the LES performed best for pressure prediction; however, it was much more time-consuming than the four RANS models. Among the RANS models, the Low Reynolds number (LRN) SST k-ω model had the best overall performance at a series of airflow rates. Central flow velocity determined by particle image velocimetry was 3.617 m/s, while velocities predicted by the LES, LRN SST k-ω, and k-ω models were 3.681, 3.532, and 3.439 m/s, respectively. All models predicted jet flow in the oropharynx. These results suggest that the above CFD models have acceptable accuracy for predicting pediatric UA aerodynamics and that the LRN SST k-ω model has the most potential for clinical application in pediatric respiratory studies.

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Funding

This research is supported by the Interdisciplinary Research Foundation of HIT.

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Authors and Affiliations

  1. School of Energy Science and Engineering, Harbin Institute of Technology, Harbin, China

    Yicheng Chen, Weihua Cai, Biao Li & Yijun Zhao

  2. Department of Clinical Dentistry, Section for Oral and Maxillofacial Radiology, University of Bergen, Bergen, Norway

    Xin Feng & Xieqi Shi

  3. Faculty of Medicine, University of Oslo, Oslo, Norway

    Xin Feng

  4. Department of Oral Maxillofacial Radiology, Faculty of Odontology, Malmö University, Malmö, Sweden

    Xieqi Shi

  5. School of Energy and Power Engineering, Northeast Electric Power University, Jilin City, China

    Weihua Cai

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  1. Yicheng Chen
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Appendices

Appendix 1

In this study, experimental errors of pressure were tested by time-averaged and repeated measurements. The pressure sensors were set to measure for 1 min and collect the data every 50ms. Each measurement yielded 1200 data points and averaged as a pressure result (see Fig. 11). Two ranges were applied during experiments, the equipment error is ±1% F.S. and drawn by dash lines. More than 98% of data points were within the error range and proved the reliability of the equipment.

Fig. 11
figure 11

Time-varied pressure in experiments

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To reduce manual error, data were measured five times under the same working condition. The manual errors at different flow rates were as follows (see Table 1), which were much smaller than the equipment error.

Table 1 Manual errors in measurements (Pa)
Full size table

Appendix 2

In the PIV measurement of this study, a two-dimensional flow field was measured using polystyrene microspheres (20 μm) as tracers for visualization. A standard PIV system from Dantec was applied. Some parameters of key components in the PIV system were introduced as follows: the double-pulsed Nd-YAG (YAG-yttrium aluminum garnet) lasers with an output of 200 mJ/pulse; the CCD camera (FlowSense 4MEO Model-81C92) with a resolution of 2048 × 2048 pixels. Here, the area of PIV image was larger than the size of experimental setup to obtain the whole flow field in the convection cell. The interrogation area was set to be 32 × 32 pixels (with 50% overlap in each direction). The mean density of particles was 1.05 g/cm3, and their Stokes number was about 0.0088. The thickness of laser light was about 1.0 mm. The time gap between two subsequent image pairs was about 25 ms. Here the diameter of particles was only about 1.25 pixels in the image. The diameter of the particle was close to the estimated pixel size, and the displacements of particles tended to be biased towards integer values in PIV results. The measurements were affected by peak locking. The system error of the image analysis by peak locking was about 0.03 pixels. Therefore, the uncertainty analysis of velocity in PIV measurement was 2%.

The postprocessing of PIV data includes cross-correlation, universal detection, coherence filter, average filter, and moving average validation. The cross-correlation and universal detection adopt recommended settings, but the limited radius to be 30 pixels in the Coherence filter. Set the box size of the average filter and moving average validation was 3 × 3 and 5 × 5, separately. Time-varying velocity measurement at fixed points is shown in Fig. 12.

Fig. 12
figure 12

Time-varying velocity at fix point. The vmean = 0.41, vmax = 0.43, and vmin = 0.39

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Chen, Y., Feng, X., Shi, X. et al. Evaluation of computational fluid dynamics models for predicting pediatric upper airway airflow characteristics. Med Biol Eng Comput 61, 259–270 (2023). https://doi.org/10.1007/s11517-022-02715-9

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