Face masks and respirators are used to filter inhaled air, which may contain airborne droplets and high particulate matter (PM) concentrations. The respirators act as a barrier to the inhaled and exhaled air, which may change the nasal airflow characteristics and air-conditioning function of the nose. This study aims to investigate the nasal airflow dynamics during respiration with and without an N95 respirator driven by airflow through the nasal cavity to assess the effect of the respirator on breathing conditions during respiration. To achieve the objective of this study, transient computational fluid dynamics simulations have been utilized. The nasal geometry was reconstructed from high-resolution Computed Tomography scans of a healthy 25-year-old female subject. The species transport method was used to analyze the airflow, temperature, carbon dioxide (CO2), moisture content (H2O), and temperature distribution within the nasal cavity with and without an N95 respirator during eight consecutive respiration cycles with a tidal volume of 500 ml. The results demonstrated that a respirator caused excessive CO2 inhalation by approximately 7× greater per breath compared with normal breathing. Furthermore, heat and mass transfer in the nasal cavity was reduced, which influences the perception of nasal patency. It is suggested that wearers of high-efficiency masks that have minimal porosity and low air exchange for CO2 regulation should consider the amount of time they wear the mask.

We investigated the flow characteristics and heat transfer during nasal breathing in the complete human upper airway through the respiratory cycle using transient numerical simulations. We postulate the complete airway from the nasal cavity to the trachea most accurately represents dynamic airflow patterns during inhalation and exhalation as they are likely to be affected by downstream anatomical structures. We constructed a 3-Dimensional model from a healthy adult computed tomography (CT) scan. Computational Fluid Dynamics simulations were performed with Ansys Fluent software using the Stress-Blended Eddy Simulation (SBES) turbulence model, looking at airflow patterns, velocity, mucosal temperature and humidity (H₂O fraction). We simulated one-and-a-half breathing cycles (5.65 seconds) and discarded the first inhalation cycle to avoid start-up effects. The results demonstrated that airway geometry structures, including the turbinates, the soft palate and the glottic region, affect the flow patterns differently during inspiration and expiration. It also demonstrated phenomena not seen in steady flow simulations or those without the lower respiratory tract geometry, including the nasopharyngeal temperature imprint during inhalation, the nasopharyngeal jet during exhalation and the flow structures of the larynx and laryngeal jet. The inclusion of the exhalation phase demonstrates airflow pre-conditioning before inhalation, which we postulate contributes to achieving alveolar conditions. Alveolar temperature and humidity conditions are not achieved by the nasal cavity alone, and we demonstrate the contribution of the nasopharynx and larynx to air conditioning. Including the complete airway with realistic anatomy and using transient airflow modelling provided new insights into the physiology of the respiratory cycle.

Unmanned Aerial Vehicles (UAVs) integrated with lightweight visual cameras hold significant promise in renewable energy asset inspection and monitoring. This study presents an AI-assisted soiling detection methodology for inspecting solar photovoltaic (PV) panels, using UAV-captured RGB images. The proposed scheme introduces an autonomous end-to-end soiling detection model for common types of soiling in solar panel installations, including bird droppings and dust. Detecting soiling, particularly bird droppings, is critical due to their pronounced negative impact on power generation, primarily through hotspot formation and their resistance to natural cleaning processes such as rain. A dataset containing aerial RGB images of PV panels with dust and bird droppings is collected as a prerequisite. This study addresses the unique challenges posed by the small size and indistinct features of bird droppings in aerial imagery in contrast to relatively large-sized dust regions. To overcome these challenges, we developed a custom model, named SDS-YOLO (Soiling Detection System YOLO), which features a Convolutional Block Attention Module (CBAM) and two dedicated detection heads optimized for dust and bird droppings. The SDS-YOLO model significantly improves detection accuracy for bird droppings while maintaining robust performance for the dust class, compared with YOLOv5, YOLOv8, and YOLOv11. With the integration of CBAM, we achieved a substantial 40.2% increase in mean Average Precision (mAP50) and a 26.6% improvement in F1 score for bird droppings. Dust detection metrics also benefited from this attention-based refinement. These results underscore the CBAM’s role in improving feature extraction and reducing false positives, particularly for challenging soiling types. Additionally, the SDS-YOLO parameter count is reduced by 24%, thus enhancing its suitability for edge computing applications.

The movement of the respiratory walls has a significant impact on airflow through the respiratory tract. The majority of computational fluid dynamics (CFD) studies assume a static geometry which may not provide a realistic flow field. Furthermore, many studies use Reynolds Averaged Navier-Stokes (RANS) turbulence models that do not resolve turbulence structure. Combining the application of advanced scale-resolving turbulence models with moving respiratory walls using CFD will provide detailed insights into respiratory flow structures. This study simulated a complete breathing cycle involving inhalation and exhalation in a nasal cavity to trachea geometry that incorporated moving glottis walls. A second breathing cycle was simulated with static glottis walls for comparison. A recently developed hybrid RANS-LES turbulence model, the Stress-Blended Eddy Simulation (SBES), was incorporated to resolve turbulent flow structures in fine detail for both transient simulations. Transient results were compared with steady-state RANS simulations for the same respiratory geometry. Glottis motion caused substantial effects on flow structure through the complete breathing cycle. Significant flow structure and velocity variations were observed due to glottal motion, primarily in the larynx and trachea. Resolved turbulence structures using SBES showed an intense mixing section in the glottis region during inhalation and in the nasopharynx during expiration, which was not present in the RANS simulations. Transient simulations of a realistic breathing cycle uncovered flow structures absent in simulations with a constant flow rate. Furthermore, the incorporation of glottis motion impacted airflow characteristics that suggest rigid respiratory walls do not accurately describe respiratory flow. Future research in respiratory airflow should be conducted using transient scale-resolving models in conjunction with moving respiratory walls to capture flow structures in detail.