Objective: The purpose of this study was two-fold. First, we wanted to develop optimized strategies for evaluating publicly available CT datasets for use in simulated studies of airflow. Second, we sought to generate 3D models of the nasal cavity from these CT datasets and perform Computation Fluid Dynamics (CFD) modeling of airflow in the generated models. This work lays the groundwork for future investigations into studying diversity in airflow patterns and rates, analyzing microparticle and odorant deposition, how anatomical variations relate to difference in airflow and pathologies, and the role of comparative CFD analyses in surgical planning. Methods: 490 CT scans were downloaded from the CQ500 repository dataset and evaluated for effectiveness in building digital models of the nasal airway. Scans were digitally dissected and analyzed in 3D Slicer to determine eligibility for model creation and simulation. Eligible scans were further edited in 3D Slicer. All ethmoid air cells, sinuses and recesses were removed, as it was determined these areas do not contribute to the airflow we want to study. These reconstructed models made in 3D Slicer were further refined using the program MeshMixer. Once editing was completed in MeshMixer, STL files were imported back into 3D Slicer to be overlayed with the original STL file created directly from the CT scan to verify anatomical accuracy. Any inaccuracies were removed with further editing in MeshMixer. Models were then imported into Salome, where they were converted into “solid models” for CFD analysis. All CFD was performed in OpenFOAM, and results were visualized using ParaView. Results: Five models have been reconstructed and are being subjected to CFD analysis. To be a strong candidate for modeling, the CT scan had to include at least 230 slices, show all anatomy relevant to the study including turbinates, ethmoid air cells, the cribriform plate and olfactory region, nasal vestibules, and the nasopharynx, and display adequate detail. Of the five final models, four were deemed anatomically normal and all relevant anatomy was intact. One of the five models displayed a deviated septum. The four “normal variants” were used to predict normal patterns of airflow using Computational Fluid Dynamics (CFD). The results of these models compared with the deviated septum model show severe limitation of airflow in the deviated model, together with compensatory flow on the more open side of the airway. Relatively simple digital "corrections" were applied, which effectively recovered normal airflow. Conclusion: CT scans from the publicly available CQ500 repository dataset are effective in creating digital models of the nasal airway to be used for CFD. Computational modeling was shown to be a useful tool in understanding how nasal morphology contributed to the diversity in airflow patterns and rates, microparticle and odorant deposition. Thus far 5 CT scans out of 490 specimens were eligible for modeling; however, due to time constraints of the study, these were the only scans that were utilized. Four of these models were “normal variants” while the scan with a deviated septum was a “pathological variant.” One of the normal models is currently being run in the OpenFOAM CFD program and shows promising results with similar airflow patterns through both the left and right airways. All other models will also be analyzed in this CFD program and more rounds of CFD will be run to compare airflow dynamics for normal and pathological variants. This work provides insights into how a broad, comparative sample of subjects can be used in surgical planning and in understanding the physiological consequences of potential surgical interventions. Burrell College. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.