# Forward Computational Modeling of Respiratory Airflow
## 6 CFD Validation  
Accuracy and reliability of computational simulations for gas and blood flow in the lungs rely on the robustness of the numerical techniques for solving the equations of motion and continuity, as well as the appropriateness of the imposed boundary conditions. Validation of such in silico simulations becomes crucial for researchers to ensure the credibility of their computational designs and assumptions. Among such validation techniques, in vitro, in vivo, and ex vivo experiments are often used for assessing and validating the fidelity of computational models used to simulate airflow mechanics within the lungs. By conducting experiments on physical lung models (in vitro), utilizing lung tissue samples (ex vivo), or functional imaging (in vivo), researchers can quantitatively evaluate the similarity between experimental measurements and computational predictions, thereby establishing a validation framework for computational simulations. [PERSON] et al. [73] validated computationally simulated human nasal airflow under breathing flow rates of 180, 560, and 1100 mL s\({}^{-1}\) with an in vitro experimental setup utilizing the right nasal cavity of a healthy male adult obtained from [74]. For all simulations, the inlet and outlet boundary conditions were set to match the experimental flow conditions. The numerical results of velocity profiles and turbulence intensities were obtained using the laminar model, RANS (k-\(\epsilon\), standard k-\(\omega\), SST k-\(\omega\), and RSM), LES, and DNS models. For both laminar and all RANS models, second-order spatial discretization schemes were used to solve pressure and momentum, and SIMPLEC was used for pressure-velocity coupling. For the LES model, the central difference scheme was used to solve pressure and momentum, and PISO was used for pressure-velocity coupling. For the DNS model, an in-house immersed-boundary-method-based solver was utilized to solve the Navier-Stokes equations, and an Adams-Bashforth and implicit Crank-Nicolson scheme were utilized to discretize the convective and diffusive terms respectively. Although there was irregularity in the flow field, the laminar model achieved good agreement with experimental results under restful breathing condition (assumed at 180 mL s\({}^{-1}\)) and performed better than the RANS models. As the breathing flow rate increased and turbulence was attained, the RANS models achieved more accurate predictions, but performed worse than LES and DNS. Both the LES and DNS models provided accurate predictions of the nasal airflow under all flow conditions, but were computationally expensive. [PERSON] et al. [75] utilized an extended model (idealized) of the lung airway structure to assess and validate airflow sensitivity to healthy and diseased lung conditions. The airway model included the upper airway, the central airway, and the distal airway up to the sixth generation. The upper airway was reconstructed from a CT image scan while the central and distal airways were based on the Horsfield model [12; 75; 76]. The same geometric model was utilized for in vivo (3D printed) and in silico studies. Airflow was simulated in both the in vivo and in silico studies under steady exhalation conditions with the total airflow rate at 770 m s\({}^{-1}\), corresponding to flow rate under moderate activity. RANS model was utilized to solve for turbulence in the flow field for the in silico simulation. Particle Image Velocimetry (PIV) was implemented to capture flow fields and flow measurements were obtained from the PIV images. The velocity contours obtained from PIV and computational simulation measurement were shown to be similar with slight regional variations in velocity magnitudes (Figure 6). The PIV measure and computed velocity contours were evaluated at regions \(a\)-\(i\). Regions \(e\) and \(i\) did not agree, although the velocity profile in both conditions for region \(e\) was asymmetric.