Background: Abdominal aortic aneurysms (AAAs) present a formidable public health concern due to their propensity for localized, anomalous expansion of the abdominal aorta. These insidious dilations, often in their early stages, mask the life-threatening potential for rupture, which carries a grave prognosis. Understanding the hemodynamic intricacies governing AAAs is paramount for predicting aneurysmal growth and the imminent risk of rupture. Objective: Our extensive investigation delves into this complex hemodynamic environment intrinsic to AAAs, utilizing comprehensive numerical analyses of the physiological pulsatile blood flow and realistic boundary conditions to explore the multifaceted dynamics influencing aneurysm rupture risk. Our study introduces novel elements by integrating these parameters into the overall context of aneurysm pathophysiology, thus advancing our understanding of the intricate mechanics governing their evolution and rupture. Methods: Conservation of mass and momentum equations are used to model the blood flow in an AAAs, and these equations are solved using a finite volume-based ANSYS Fluent solver. Resistance pressure outlets following a three-element Windkessel model were imposed at each outlet to accurately model the blood flow and the AAAs’ shear stress. Results: Our results uncover elevated blood flow velocities within an aneurysm, suggesting an augmented risk of future rupture due to increased stress in the aneurysm wall. During the systole phase, high wall shear stress (WSS) was observed, typically associated with a lower risk of rupture, while a low oscillatory shear index (OSI) was noted, correlating with a decreased risk of aneurysm expansion. Conversely, during the diastole phase, low WSS and a high OSI were identified, potentially weakening the aneurysm wall, thereby promoting expansion and rupture. Conclusion: Our study underscores the indispensable role of computational fluid dynamic (CFD) techniques in the diagnostic, therapeutic, and monitoring realms of AAAs. This body of research significantly advances our understanding of aneurysm pathophysiology, thus offering pivotal insights into the intricate mechanics underpinning their progression and rupture, informing clinical interventions and enhancing patient care.

Complementing computational fluid dynamics (CFD) simulations with machine learning algorithms is becoming increasingly popular as the combination reduces the computational time of the CFD simulations required for classifying, predicting, or optimizing the impact of geometrical and physical variables of a specific study. The main target of drug delivery studies is indicating the optimum particle diameter for targeting particular locations in the lung to achieve a desired therapeutic effect. In addition, the main goal of molecular dynamics studies is to investigate particle–lung interaction through given particle properties. Therefore, this study combines the two by numerically determining the optimum particle diameter required to obtain an ideal striking velocity magnitude (velocity at the time of striking the alveoli, i.e., deposition by sedimentation/diffusion) and impact time (time from release until deposition) inside an acinar part of the lung. At first, the striking velocity magnitudes and time for impact (two independent properties) of three different particle diameters (0.5, 1.5, and 5 μm) are computed using CFD simulations. Then, machine learning classifiers determine the particle diameter corresponding to these two independent properties. In this study, two cases are compared: A healthy acinus where a surfactant layer covers the inner surface of the alveoli providing low air–liquid surface tension values (10 mN/m), and a diseased acinus where only a water layer covers the surface causing high surface tension values (70 mN/m). In this study, the airflow velocity throughout the breathing cycle corresponds to a person with a respiratory rate of 13 breaths per minute and a volume flow rate of 6 l/min. Accurate machine learning results showed that all three particle diameters attain larger velocities and smaller impact times in a diseased acinus compared to a healthy one. In both cases, the 0.5-μm particles acquire the smallest velocities and longest impact times, while the 1.5-μm particles possess the largest velocities and shortest impact times.

Heat transfer between inhaled hot/cool air and the lung surface within the human respiratory system is an intriguing topic that has not received enough attention. The lung can be considered an in vivo heat exchanger, balancing the inhaled air temperature by lowering the hot air temperature and increasing the cool air temperature. The current work studies the unsteady and incompressible airflow motion and heat transfer during inhalation between the surface of the lungs (37 °C) and the inhaled cool air (25 °C) in one case and inhaled hot air (43 °C) in another. Computerized tomography scan (CT-scan) images of the lung of a 39-year-old male patient were processed to generate the airway geometry consisting of 16 generations. The geometry was further modified in UG NX 12.0, and the mesh generation was carried out using Ansys Meshing. The shear stress transport (SST) k−ω turbulent model was employed in Ansys Fluent 20.2 to model the air/lung convective volume heat transfer utilizing a realistic breathing velocity profile. Temperature streamlines, lung volume temperatures, surface heat flux, and surface temperatures on all 16 generations were produced for both cases during the breathing cycle of 4.75 s. Several conclusions were made by studying and comparing the two cases. First, heat transfer between inhaled hot or cool air and the lung surface mainly occurred in the first few generations. Second, airflow temperature patterns are dependent on the inlet breathing velocity profile. Third, the lung volume temperature change directly correlates with the temperature difference between air and the lung surface. Finally, the surface heat flux strongly depended on the heat transfer coefficient. The density, viscosity, thermal conductivity, and specific heat of hot/cool air affected the Reynolds number, Nusselt number, heat transfer coefficient, and surface heat flux.

Following the emergence of many blood transfusion-associated diseases, novel passive cell separation technologies, such as microfluidic devices, are increasingly designed and optimized to separate red blood cells (RBCs) and white blood cells (WBCs) from whole blood. These systems allow for the rapid diagnosis of diseases without relying on complicated and expensive hematology instruments such as flow microscopes, coagulation analyzers, and cytometers. The inertia effect and the impact of intrinsic hydrodynamic forces, the Dean drag force (F_D), and the inertial lift force (F_L) on the migration of particles within curved and complex confined channels have been explored theoretically, computationally, and experimentally. This study aimed to optimize the dimensions of a microfluidic channel for fast particle propagation and separation. Several spiral geometries with different cross-sections were tested using computational fluid dynamics (CFD) to separate two particle types representing RBCs and WBCs. The chosen three geometries consist of a single inlet, two outlets, and three spiral turns, each having a different cross-sectional height (120, 135, and 150 µm). Particle separation was successfully achieved in the 135 µm-height microchannel, while other microchannels demonstrated mixed particle types at the outlets.

Utilizing a computed tomography (CT)‐based realistic nasal‐to‐lung respiratory tract model allows for a comprehensive investigation of the transport and deposition of nasal sprayed aerosols. This study has three main objectives: first, to determine the optimal mesh that achieves the quickest convergence for computational fluid‐particle dynamics (CFPD) simulations of a nasal‐to‐lung nasal respiratory tract by assessing the performance of different element types, sizes, and prism boundary layers; second, to design and validate a numerical method to compare grid data with different mesh structures and densities for simulation result validation; and finally, to observe and analyze fluid‐particle dynamics in the respiratory tract to aid in the development of nasal sprayed medications. This study involves reverse‐engineering a realistic and anatomically accurate respiratory tract model from CT scans. Results reveal that the optimal numerical approach for minimum calculation time is the polyhedral hybrid mesh with four boundary prism layers and the SIMPLE pressure‐velocity coupling scheme. Furthermore, observations of particle dynamics reveal that the vocal cords' location contains a concentration site of deposited small aerosols due to the turbulent airflow in the region. The optimal diameters of nasal sprayed aerosols to target each region are concluded in the end.

Complementing computational fluid dynamics (CFD) simulations with machine learning Algorithms (MLA) is becoming increasingly popular as the combination reduces the computational time of the CFD simulations required for classifying, predicting, or optimising the impact of geometrical and physical variables of a specific study. The current study aims to numerically determine the optimum particle diameter required to obtain an ideal striking velocity magnitude and impact time inside an acinar part of the lung. At first, the striking velocity magnitudes and time for impact (two independent properties) of three different particle diameters ( [[EQUATION]] ) are computed using CFD simulations. Then, machine learning classifiers determine the particle diameter corresponding to these two independent properties. In this study, two cases are compared: A healthy acinus where a surfactant layer covers the inner surface of the alveoli providing low air-liquid surface tension (LST) values [[EQUATION]] ), and a diseased acinus where only a water layer covers the surface causing high surface tension (HST) values [[EQUATION]] ). In this study, the airflow velocity throughout the breathing cycle corresponds to a person with a respiratory rate of 13 breaths per minute and a volume flow rate of [[EQUATION]] . Results showed that all three particle diameters attain larger velocities and smaller impact times in a diseased acinus than in a healthy one. In both cases, the [[EQUATION]] particles acquire the smallest velocities and longest impact times, and the [[EQUATION]] particles possess the largest velocities and shortest impact times.