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

Chen, YichengSchool of Energy Science and Engineering, Harbin Institute of Technology, Harbin, China (author)

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.

Feng, XinDepartment of Clinical Dentistry, Section for Oral and Maxillofacial Radiology, University of Bergen, Bergen, Norway; Faculty of Medicine, University of Oslo, Oslo, Norway (author)
Shi, Xie-QiMalmö universitet,Odontologiska fakulteten (OD),Department of Clinical Dentistry, Section for Oral and Maxillofacial Radiology, University of Bergen, Bergen, Norway(Swepub:mau)ak3972 (author)
Cai, WeihuaSchool of Energy Science and Engineering, Harbin Institute of Technology, Harbin, China; School of Energy and Power Engineering, Northeast Electric Power University, Jilin City, China (author)
Li, BiaoSchool of Energy Science and Engineering, Harbin Institute of Technology, Harbin, China (author)
Zhao, YijunSchool of Energy Science and Engineering, Harbin Institute of Technology, Harbin, China (author)
School of Energy Science and Engineering, Harbin Institute of Technology, Harbin, ChinaDepartment of Clinical Dentistry, Section for Oral and Maxillofacial Radiology, University of Bergen, Bergen, Norway; Faculty of Medicine, University of Oslo, Oslo, Norway (creator_code:org_t)

In:Medical and Biological Engineering and Computing: Springer61:1, s. 259-2700140-01181741-0444

By the author/editor: Chen, Yicheng; Feng, Xin; Shi, Xie-Qi; Cai, Weihua; Li, Biao; Zhao, Yijun

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