# Forward Computational Modeling of Respiratory Airflow
## 6 CFD Validation
The velocity magnitude obtained for region \(e\) from the computational simulation was 19% higher than that in the in vitro experiment. In region \(i\), the velocity profile was positively skewed for the in vitro experiment whereas it was centralized for the in silico study, although both studies show that the contour was more influenced by the flow from the right lungs (regions \(a\), \(b\), \(c\), \(d\), and \(e\)). Nonetheless, all other regions analyzed had similar velocity contours and magnitudes for the in vitro and in silico studies. Figure 6: Flow velocity contours measured experimentally by PIV (**a**) and computed from a CFD model (**b.1**,**b.2**), using boundary conditions from the in vitro study. (**a**) The in-plane velocity was evaluated in the coronal plane at the center of the geometry (z = 0). Velocities at the outlets (labeled  
numerically) and segments (labeled alphabetically) are shown. **(b.1)** Velocity magnitudes in coronal section were derived from the CFD model using a healthy boundary condition. Flow streamlines are displayed with arrows highlighting areas of flow separation and recirculation. **(b.2)** Axial velocity magnitudes and directions (vectors) are shown across branches a-i. Color bar indicates the velocity spectrum, with red corresponding to maximum velocity and blue corresponding to minimum velocity. From reference [75], with permission. Functional CT imaging techniques can also be used to validate computational fluid dynamic simulations of lung airflow more accurately [77; 78; 79; 80]. Xenon-enhanced CT imaging is a specialized CT imaging technique that utilizes xenon (Xe), a dense noble gas, as a contrast agent during imaging [34]. By introducing xenon into the airway during imaging, this technique enhances the CT intensity voxel values, enabling the tracking of airflow. Dynamic contrast enhancement occurs during the Xe wash-in process, allowing for improved visualization and analysis of airflow dynamics [78]. While there have been no direct studies comparing xenon-enhanced CT imaging and computational simulations of airflow in the lungs, both techniques have independently been used to evaluate gas transport in the lungs. With the advancement in high-speed computing in respiratory mechanics and access to CT scanners, future studies may be carried out to explore the integration of xenon-enhanced CT imaging and computational simulations of airflow in the lungs. MRI with hyperpolarized gases is another functional imaging technique that can be used to validate the computational fluid dynamic simulations of the lung. While MRI has limited ability to depict lung anatomical details because of its long acquisition time and the lungs' low tissue and low proton density, it can still be optimized and serve as an alternative for CFD validation. Similarly to xenon-enhanced CT imaging, hyperpolarized (i.e., helium or xenon gas) MRI provides a way to visualize the distribution of airflow in the lungs without ionizing radiation [81]. The validation of CFD using hyperpolarized MRI for CFD has also been explored [79; 80]. These validation techniques play a pivotal role in guiding researchers in the development of accurate and reliable computational models, enabling deeper insights into complex airflow phenomena in the lungs.