Nasal airway obstruction (NAO) is one of the most common symptoms in the human respiratory system and causes a considerable financial burden to both individuals and society. Currently, NAO detection is troublesome to achieve, which can influence the accuracy and effectiveness of clinical surgery. Although several objective measurement techniques are currently available, they are found to be inconsistent with patients’ sensations. In recent years, computational fluid dynamics (CFD) has become a novel technique to objectively assess nasal airflow by simulating the nasal airflow of NAO patients. Existing literature has also shown the potential to correlate relevant CFD parameters with patients’ sensations. Nevertheless, there is still debate on the numerical setup for an accurate solution and the CFD parameter to correlate with the subjective measurement. Based on these existing issues, we mainly investigated the boundary configuration (with and without the external nose), the usage of turbulence/laminar models, the usage of steady-state/transient solvers, and briefly discussed the potential of using unilateral pressure drop ratio as a parameter for NAO detection. 

To begin with, including the external nose in the nasal airflow simulation is recommended in the nasal airflow simulations. The external nose configuration can affect the flow direction through the nostrils and downstream flow distributions. However, the static (and total) pressure drop only shows a 4% difference compared to the commonly-used plane-truncated boundary configuration. Furthermore, using the laminar model is sufficient for the nasal airflow simulations concerning the static pressure drop prediction. The laminar model shows a difference lower than 15% in static pressure drop compared to the experimental values on 3D-printed nasal airway models. We stress the caution of using the 𝑘 − 𝜔 model in the nasal airflow simulations because it tends to overpredict the turbulent viscosity ratio near the inlet unphysically. Moreover, steady-state simulations can also reasonably predict nasal airflow. We observed unsteady effects when comparing the steady-state simulation with the transient simulation with a constant flow rate and the transient simulation with a sinusoidal flow-rate-versus-time profile representing the real-life breathing cycle. Nevertheless, the steady-state simulation achieves an accurate prediction in static pressure drop, with a difference lower than 6% compared to the tested transient simulations. The steady-state simulation can also perfectly match transient simulations in the velocity profile of the recirculation zones and require a much lower computational cost. Last but not least, we also tested the possibility of using the unilateral static pressure drop ratio for NAO detection using CFD. However, we note that future studies should make corrections to account for the nasal cycle effect for NAO detection. 

Overall, we conclude that including the external nose and using the laminar simulations with the steady-state solver can give an acceptable prediction for the nasal airflow, especially concerning the static pressure drop prediction. We also state that applying CFD in the nasal airflow shows the potential for NAO detection, although future studies may consider making some corrections to include the nasal cycle effect.