A new approach for artificial pollination in walnut trees: AirPoll.

Pollination is the first step in the plant's fruit development. Therefore, fruit setting does not occur without pollination. Some problems encountered in natural pollination cause pollination not to be achieved as desired and cause significant losses in yield and fruit quality. Artificial pollination applications with drones are the best way to solve these problems. In this study, the AirPoll artificial pollination machine, which performs artificial pollination through the air using drone technology, was developed and the operating success of the machine was tested in walnut gardens. In the experiment gardens, female flowers on 18 branches of 5 trees each in the artificially pollinated area with a drone and in the control area were marked with colored strings. Control trees were selected from a distance that would not be possible to transport pollen with a drone. As a result of the study carried out in 2020 and 2021, the average fruit setting rate in trees pollinated by drone was determined as 94.61%. In control trees, 32.33% fruit setting was achieved. Thus, it was determined that the productivity increase in artificial pollination with AirPoll was 62.28%. In addition, in the study, Computational Fluid Dynamics (CFD) simulation analysis was performed using ANSYS Fluent 2024 R1 software to predict the downward air flow and pollen distribution in the walnut tree crown. The analysis was carried out in 680 iterations using drone propellers at a rotation speed of 4500 rpm, 4 m/s airflow and a k-w viscous model. In the analysis, it was observed that the pollen was distributed homogeneously with the determined height and the created artificial pollination environment. Based on the results obtained from the simulations, a convergence criterion of 5e-3 for continuity and 1e-6 for speed, k, w was determined. Considering all the results, the ease of use of the developed AirPoll artificial pollination machine and the successful results obtained in field trials reveal the effectiveness of the AirPoll artificial pollination machine.

Development of idealized human aortic models for in vitro and in silico hemodynamic studies.

Background: The aorta, a central component of the cardiovascular system, plays a pivotal role in ensuring blood circulation. Despite its importance, there is a notable lack of idealized models for experimental and computational studies. Objective: This study aims to develop computer-aided design (CAD) models for the idealized human aorta, intended for studying hemodynamics or solid mechanics in both in vitro and in silico settings. Methods: Various parameters were extracted from comprehensive literature sources to evaluate major anatomical characteristics of the aorta in healthy adults, including variations in aortic arch branches and corresponding dimensions. The idealized models were generated based on averages weighted by the cohort size of each study for several morphological parameters collected and compiled from image-based or cadaveric studies, as well as data from four recruited subjects. The models were used for hemodynamics assessment using particle image velocimetry (PIV) measurements and computational fluid dynamics (CFD) simulations. Results: Two CAD models for the idealized human aorta were developed, focusing on the healthy population. The CFD simulations, which align closely with the PIV measurements, capture the main global flow features and wall shear stress patterns observed in patient-specific cases, demonstrating the capabilities of the designed models. Conclusions: The collected statistical data on the aorta and the two idealized aorta models, covering prevalent arch variants known as Normal and Bovine types, are shown to be useful for examining the hemodynamics of the aorta. They also hold promise for applications in designing medical devices where anatomical statistics are needed.

Computational fluid dynamics simulating of the FDA benchmark blood pump with different coefficient sets and scaler shear stress models used in the power-law hemolysis model.

Hemolysis is the most important issue to consider in the design and optimization of blood-contacting devices. Although the use of Computational Fluid Dynamics (CFD) in hemolysis prediction studies provides convenience and has promising potential, it is an extremely challenging process. Hemolysis predictions with CFD depend on the mesh, implementation method, coefficient set, and scalar-shear-stress model. To this end, an attempt was made to find the combination that would provide the most accurate result in hemolysis prediction with the commonly cited power-law based hemolysis model. In the hemolysis predictions conducted using CFD on the Food and Drug Administration (FDA) benchmark blood pump, 3 different scalar-shear-stress models, and 5 different coefficient sets with the power-law based hemolysis model were used. Also, a mesh independence test based on hemolysis and pressure head was performed. The pressure head results of CFD simulations were compared with published pressure head of the FDA benchmark blood pump and a good agreement was observed. In addition, results of CFD-hemolysis predictions which are conducted with scalar-shear-stress model and coefficient set combinations were compared with experimental hemolysis data at three operating conditions such as 6-7 L/min flow rates at 3500 rpm rotational speeds and 6 L/min at 2500 rpm. One of the combinations of the scalar-shear-stress model and the coefficient set was found to be within the error limits of the experimental measurements, while all other combinations overestimated hemolysis.

Computational fluid dynamics study on first reaction chamber of internal circulation anaerobic reactor.

This study aims to investigate the characteristics of gas-liquid-solid three-phase flow in an Internal Circulation (IC) anaerobic reactor during the treatment of wastewater. Through computational fluid dynamics (CFD) simulations of the gas-liquid-solid three-phase in the first reaction chamber and based on the anaerobic granule swarms drag coefficient model, the study investigates the effects of superficial liquid velocity and superficial gas velocity on granules distribution, uniformity index, gas holdup, flow velocities of each phase, and the dimensionless variance of residence time distribution. In addition, the relationship between the fully mixed superficial velocities of gas and liquid in the first reaction chamber is also determined.

Discovery of delayed gas production after implantation of a continuous-flow left ventricular assist device and a preliminary exploration of the mechanisms of its occurrence.

Objective: To characterize the gas production phenomenon in the animal model of left ventricular assist device (LVAD), and study its mechanism. Methods: An in vitro bubble precipitation experiment was conducted, and the blood samples of Parma spp. animals were divided into ordinary group and oxygen-enriched group according to whether they were oxygenated or not at the time of blood collection, and a static control group was set up respectively. Blood gases were drawn and analyzed before and after the experiment. Activate the pump, and the number of air bubbles in the loop was measured by ultrasound at different rotational speeds; CFD was applied to simulate the flow field in the blood pump, and pressure, fluid velocity vector and shear force diagrams were plotted, and a thrombus model was constructed and the flow field was simulated and plotted as a cloud diagram. Results: There was a statistical difference in the number of bubbles in the inflow and outflow tubes of the blood pump (P values of 0.04 and 0.023, respectively), and the number of bubbles in the outflow tubes of both groups was significantly higher than the number of bubbles in the inflow tubes. The number of bubbles in the tubes of both the oxygen-enriched and normal groups was significantly higher than that in the inflow group. In both the normal and oxygen-enriched groups, more gas was produced at higher speeds than at lower speeds. Blood gas analysis showed that the reduced gas composition in the blood was mainly oxygen. Flow field simulation results: the high rotation speed group had lower central pressure and greater scalar shear. The thrombus simulation group was more prone to turbulence, sudden pressure changes, and greater shear than the normal group. Conclusion: Blood gas production is associated with higher partial pressures of blood oxygen, higher rotation speed, and intrapump thrombosis, and the mechanism of pump gas production is degassing of dissolved gases rather than cavitation of water, and the gas released is most likely to have oxygen. The degassing phenomenon is an warning factor for pump thrombosis.

Allometric comparison of viral dynamics in the nasal cavity-nasopharyngeal mucus layer of human and rhesus monkey by CFD-HCD approach.

BACKGROUND AND OBJECTIVE: Viral respiratory infections stand as a considerable global health concern, presenting significant risks to the health of both humans and animals. This study aims to conduct a preliminary analysis of the time series of viral load in the nasal cavity-nasopharynx (NC-NP) of the human and rhesus macaque (RM). METHODS: Taking into account the random uniform distribution of virus-laden droplets with a diameter of 10 µm in the mucus layer, this study applies the computational fluid dynamics-host cell dynamics (CFD-HCD) method to 3D-shell NC-NP models of human and RM, analyzing the impact of initial distribution of droplets on the viral dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), estimating parameters in the HCD model based on experimental data, integrating them into simulations to predict the time series of viral load and cell counts, and being visualized. The reproductive number (R0) are calculated to determine the occurrence of infection. The study also considers cross-parameter combinations and cross-experimental datasets to explore potential correlations between the human and RM. RESULTS: The research findings indicate that the uniform distribution of virus-laden droplets throughout the whole NC-NP models of human and RM is reasonable for simulating and predicting viral dynamics. The visualization results offer dynamic insights into virus infection over a period of 20 days. Studies involving parameter and dataset exchanges between the two species underscore certain similarities in predicting virus infections between the human and RM. CONCLUSIONS: This study lays the groundwork for further exploration into the parallels and distinctions in respiratory virus dynamics between humans and RMs, thus aiding in making more informed decisions in research and experimentation.

Research on the technology of uniformly injecting nitrogen into the porous long pipes in the gob of the gob-side entry retaining mining mode with roof cutting and pressure relief.

The implementation of the Gob-Side Entry Retaining Mining Mode with Roof Cutting and Pressure Relief (GERRCPR) results in the gob connecting to the retaining roadway, creating an open space that causes significant air leakage and increases the risk of spontaneous combustion. A study was conducted during the implementation of the GERRCPR in the Xiaonan Coal Mine N1-1502 working face to investigate spontaneous combustion characteristics, along with fire prevention and extinguishing measures. To analyze gob airflow, Computational Fluid Dynamics (CFD) was employed to collect data on airflow conditions, O2 concentration, and temperature. Based on this, this study focuses on exploring the effects of nitrogen injection treatment under various rates and positions to optimize parameters for buried pipe nitrogen injection. Results indicated that within the GERRCPR, air leakage in the gob increased, leading to an increase in O2 concentration, expansion of the oxidation zone, and an elevated risk of spontaneous combustion. Air leakage primarily occurred from the retaining roadway and the working face near the intake-air roadway, peaking at a retaining roadway length of 500 m, with a flow rate of 226 m3/min. Following nitrogen injection treatment, the oxidation zone was significantly reduced, with optimal treatment achieved at a nitrogen injection depth of 70 m and a rate of 600 m3/h. Field monitoring data showed that the inertization measure of using porous long pipes, a nitrogen injection spacing of 30 m, and a nitrogen injection rate of 600 m3/h significantly decreased the O2 concentration within the gob. This reduction meets safety production requirements and outperforms the effectiveness of traditional buried-pipe nitrogen injection methods, thereby validating the simulation accuracy. Understanding the laws governing spontaneous coal combustion in the GERRCPR and enacting preventive measures for nitrogen injection can improve safety standards in mining operations. This proactive approach can effectively prevent spontaneous coal combustion accidents, resulting in substantial social benefits.

Understanding the flow behavior around marine biofilms.

In vitro platforms capable of mimicking the hydrodynamic conditions prevailing in natural aquatic environments have been previously validated and used to predict the fouling behavior on different surfaces. Computational Fluid Dynamics (CFD) has been used to predict the shear forces occurring in these platforms. In general, these predictions are made for the initial stages of biofilm formation, where the amount of biofilm does not affect the flow behavior, enabling the estimation of the shear forces that initial adhering organisms have to withstand. In this work, we go a step further in understanding the flow behavior when a mature biofilm is present in such platforms to better understand the shear rate distribution affecting marine biofilms. Using 3D images obtained by Optical Coherence Tomography, a mesh was produced and used in CFD simulations. Biofilms of two different marine cyanobacteria were developed in agitated microtiter plates incubated at two different shaking frequencies for 7 weeks. The biofilm-flow interactions were characterized in terms of the velocity field and shear rate distribution. Results show that global hydrodynamics imposed by the different shaking frequencies affect biofilm architecture and also that this architecture affects local hydrodynamics, causing a large heterogeneity in the shear rate field. Biofilm cells located in the streamers of the biofilm are subjected to much higher shear values than those located on the bottom of the streamers and this dispersion in shear rate values increases at lower bulk fluid velocities. This heterogeneity in the shear force field may be a contributing factor for the heterogeneous behavior in metabolic activity, growth status, gene expression pattern, and antibiotic resistance often associated with nutrient availability within the biofilm.

Ionic Polymerization-Based Synthesis of Bioinspired Adhesive Hydrogel Microparticles with Tunable Morphologies from Microfluidics.

Shape-anisotropic hydrogel microparticles have attracted considerable attention for drug-delivery applications. Particularly, nonspherical hydrogel microcarriers with enhanced adhesive and circulatory abilities have demonstrated value in gastrointestinal drug administration. Herein, inspired by the structures of natural suckers, we demonstrate an ionic polymerization-based production of calcium (Ca)-alginate microparticles with tunable shapes from Janus emulsion for the first time. Monodispersed Janus droplets composed of sodium alginate and nongelable segments were generated using a coflow droplet generator. The interfacial curvatures, sizes, and production frequencies of Janus droplets can be flexibly controlled by varying the flow conditions and surfactant concentrations in the multiphase system. Janus droplets were ionically solidified on a chip, and hydrogel beads of different shapes were obtained. The in vitro and in vivo adhesion abilities of the hydrogel beads to the mouse colon were investigated. The anisotropic beads showed prominent adhesive properties compared with the spherical particles owing to their sticky hydrogel components and unique shapes. Finally, a novel computational fluid dynamics and discrete element method (CFD-DEM) coupling simulation was used to evaluate particle migration and contact forces theoretically. This review presents a simple strategy to synthesize Ca-alginate particles with tunable structures that could be ideal materials for constructing gastrointestinal drug delivery systems.

Analysis of the airflow features and ventilation efficiency of an Ultra-Clean-Air operating theatre by qDNS simulations and experimental validation.

Ultra-Clean-Air (UCA) operating theatres aim to minimise surgical instrument contamination and wound infection through high flow rates of ultra-clean air, reducing the presence of Microbe Carrying Particles (MCPs). This study investigates the airflow patterns and ventilation characteristics of a UCA operating theatre (OT) under standard ventilation system operating conditions, considering both empty and partially occupied scenarios. Utilising a precise computational model, quasi-Direct Numerical Simulations (qDNS) were conducted to delineate flow velocity profiles, energy spectra, distributions of turbulent kinetic energy, energy dissipation rate, local Kolmogorov scales, and pressure-based coherent structures. These results were also complemented by a tracer gas decay analysis following ASHRAE standard guidelines. Simulations showed that contrary to the intended laminar regime, the OT's geometry inherently fosters a predominantly turbulent airflow, sustained until evacuation through the exhaust vents, and facilitating recirculation zones irrespective of occupancy level. Notably, the occupied scenario demonstrated superior ventilation efficiency, a phenomenon attributed to enhanced kinetic energy induced by the additional obstructions. The findings underscore the critical role of UCA-OT design in mitigating MCP dissemination, highlighting the potential to augment the design to optimise airflow across a broader theatre spectrum, thereby diminishing recirculation zones and consequently reducing the propensity for Surgical Site Infections (SSIs). The study advocates for design refinements to harness the turbulent dynamics beneficially, steering towards a safer surgical environment.

Evaluation on the performance of a swirling-type hydrodynamic separator using physical and numerical models.

Hydrodynamic separators are commonly used to control the total suspended solid concentration in stormwater before being discharged to natural water bodies. The separator studied in this paper, featuring a swirling flow generated by tangential inlet and outlet connections, was analyzed for its sediment removal efficiency in relation to sediment and flow rates. For the separator studied in this paper, the numerical model showed that the flow field was favorable for the sediments to gather at the center and settle. A higher flow rate or a smaller sediment diameter corresponded to a lower removal rate and vice versa. The dimension improvement for increasing the sediment removal rate was also studied. It was found that increasing the diameter of the separator showed a higher sediment removal rate compared with corresponding increase in the height of the separator. A dimensionless parameter J was proposed to assess the sediment removal rate of a separator, which may be used for designing and optimizing such a device. The removal rate is positively correlated with the J value. When the J value reaches 0.5 or above, the sediment removal rate exceeds 80%, which is a good initial target value for designing this type of separator.

Computational fluid dynamics of bladder voiding using 3D dynamic MRI.

Over the last couple of decades, image-based computational fluid dynamics (CFD) has revolutionized cardiovascular research by uncovering hidden features of wall strain, impact of vortices, and its use in treatment planning, as examples, that were simply not evident in the gold-standard catheterization studies done previously. In the work presented here, we have applied magnetic resonance imaging (MRI)-based CFD to study bladder voiding and to demonstrate the feasibility and potential of this approach. We used 3D dynamic MRI to image the bladder and urethra during voiding. A surface mesh processing tool was developed to process the bladder wall prior to executing a wall-motion driven CFD simulation of the bladder and urethra. The obtained flow rate and pressure were used to calculate urodynamic nomograms, which are currently used in the clinical setting to assess bladder voiding dysfunction. These nomograms concluded that our healthy volunteer has an unobstructed bladder and normal contractility. We calculated the work done to void the bladder and propose this as an additional quantitative metric to comprehensively assess bladder function. Further, we discuss the areas that would improve this relatively new methodology of image-based CFD in urodynamics.

CFD simulation study and experimental analysis of indoor air stratification in an unventilated classroom: A case study in Spain.

Health problems and respiratory diseases are associated with poor indoor air ventilation. We investigated the air quality inside a classroom-laboratory where no ventilation is provided. The case of study, consisting of an internal enclosure, is located at the Escuela Técnica Superior de Edificación (ETSEM) of Madrid (Spain). The high height favours air stratification which is analysed in terms of temperature and CO2 spatial distribution. Temperature, air humidity, atmospheric pressure and CO2 concentration measurements were taken in time at three different height locations. A CFD numerical model was established to analyse air quality. Flow circulation is derived by solving full 3D Navier - Stokes governing equations, coupled with the thermal problem. The diffusion problem of the CO2 produced by the inner occupants is then derived from the kinematics solution. Three scenarios were taken into account: occupants seated (1), standing (2), half seated, half standing (3). Results clearly show the air stratification as a result of density gradient, which is in turn determined by temperature difference between the occupants and the surrounding air. Temperature prediction maximum relative error is contained to 3.5 %. As expected, CO2 concentration increases over time, reaching maximum values depending on the configuration considered and height location.

Towards a Customizable, SLA 3D-Printed Biliary Stent: Optimizing a Commercially Available Resin and Predicting Stent Behavior with Accurate In Silico Testing.

Inflammation of the bile ducts and surrounding tissues can impede bile flow from the liver into the intestines. If this occurs, a plastic or self-expanding metal (SEM) stent is placed to restore bile drainage. United States (US) Food and Drug Administration (FDA)-approved plastic biliary stents are less expensive than SEMs but have limited patency and can occlude bile flow if placed spanning a duct juncture. Recently, we investigated the effects of variations to post-processing and autoclaving on a commercially available stereolithography (SLA) resin in an effort to produce a suitable material for use in a biliary stent, an FDA Class II medical device. We tested six variations from the manufacturer's recommended post-processing and found that tripling the isopropanol (IPA) wash time to 60 min and reducing the time and temperature of the UV cure to 10 min at 40 °C, followed by a 30 min gravity autoclave cycle, yielded a polymer that was flexible and non-cytotoxic. In turn, we designed and fabricated customizable, SLA 3D-printed polymeric biliary stents that permit bile flow at a duct juncture and can be deployed via catheter. Next, we generated an in silico stent 3-point bend test to predict displacements and peak stresses in the stent designs. We confirmed our simulation accuracy with experimental data from 3-point bend tests on SLA 3D-printed stents. Unfortunately, our 3-point bend test simulation indicates that, when bent to the degree needed for placement via catheter (~30°), the peak stress the stents are predicted to experience would exceed the yield stress of the polymer. Thus, the risk of permanent deformation or damage during placement via catheter to a stent printed and post-processed as we have described would be significant. Moving forward, we will test alternative resins and post-processing parameters that have increased elasticity but would still be compatible with use in a Class II medical device.

Non-invasive fractional flow reserve estimation in coronary arteries using angiographic images.

Coronary artery disease is the leading global cause of mortality and Fractional Flow Reserve (FFR) is widely regarded as the gold standard for assessing coronary artery stenosis severity. However, due to the limitations of invasive FFR measurements, there is a pressing need for a highly accurate virtual FFR calculation framework. Additionally, it's essential to consider local haemodynamic factors such as time-averaged wall shear stress (TAWSS), which play a critical role in advancement of atherosclerosis. This study introduces an innovative FFR computation method that involves creating five patient-specific geometries from two-dimensional coronary angiography images and conducting numerical simulations using computational fluid dynamics with a three-element Windkessel model boundary condition at the outlet to predict haemodynamic distribution. Furthermore, four distinct boundary condition methodologies are applied to each geometry for comprehensive analysis. Several haemodynamic features, including velocity, pressure, TAWSS, and oscillatory shear index are investigated and compared for each case. Results show that models with average boundary conditions can predict FFR values accurately and observed errors between invasive FFR and virtual FFR are found to be less than 5%.

Fast flow field prediction of pollutant leakage diffusion based on deep learning.

Predicting pollutant leakage and diffusion processes is crucial for ensuring people's safety. While the deep learning method offers high simulation efficiency and superior generalization, there is currently a lack of research on predicting pollutant leakage and diffusion flow field using deep learning. Therefore, it is necessary to conduct further studies in this area. This paper introduces a two-level network method to model the flow characteristics of pollutant diffusion. The proposed method in this study demonstrates a significant enhancement in flow field prediction accuracy compared to traditional deep learning methods. Moreover, it improves computational efficiency by over 800 times compared to traditional computational fluid dynamics (CFD) methods. Unlike conventional CFD methods that require grid expansion to calculate all operation conditions, the deep learning method is not confined by grid limitations. While deep learning methods may not entirely replace CFD methods, they can serve as a valuable supplementary tool, expanding the versatility of CFD methods. The findings of this research establish a robust foundation for incorporating deep learning methods in addressing pollutant leakage and diffusion challenges.

Assessment and design of filters and masks against COVID-19 via modeling and simulations.

This study aimed to evaluate and design masks against viruses, especially SARS-CoV-2 associated with COVID-19. A continuum filtration model was developed where the rate of particle deposition and "sticking" on the filter fibers is a critical term in the mass transfer, together with permeation velocity, filter porosity, tortuosity, and Brownian diffusion. CFD simulations of the airflow during respiration lead to the recommendation that the filter permeability should be above 4 × 10-11 m2 to direct the airflow for effectiveness against virus particles; otherwise, low filter permeabilities cause the unfiltered air to flow preferentially through the leak gaps between the mask and the headform. Different mask filters with microstructural and geometry data from the literature are assessed via filtration simulations for breathability and filtration efficiency. The results demonstrate that a surgical mask of 25% porosity, pore size of 150 µm and permeability of 4.4 × 10-11 m2 can achieve 100% minimum efficiency while demonstrating high breathability, complying with the criteria of FFP3, N95, and surgical Class II and IIR masks. Selected cotton and synthetic cloths as well as electrospun fiber layers are predicted to comply with FFP2, N95, and surgical mask Class II and IIR standards.

An efficient procedure for the blood flow computer simulation of patient-specific aortic dissections.

In this work we present a novel methodology for the numerical simulation of patient-specific aortic dissections. Our proposal, which targets the seamless virtual prototyping of customized scenarios, combines an innovative two-step segmentation procedure with a CutFEM technique capable of dealing with thin-walled bodies such as the intimal flap. First, we generate the fluid mesh from the outer aortic wall disregarding the intimal flap, similarly to what would be done in a healthy aorta. Second, we create a surface mesh from the approximate midline of the intimal flap. This approach allows us to decouple the segmentation of the fluid volume from that of the intimal flap, thereby bypassing the need to create a volumetric mesh around a thin-walled body, an operation widely known to be complex and error-prone. Once the two meshes are obtained, the original configuration of the dissection into true and false lumen is recovered by embedding the surface mesh into the volumetric one and calculating a level set function that implicitly represents the intimal flap in terms of the volumetric mesh entities. We then leverage the capabilities of unfitted mesh methods, specifically relying on a CutFEM technique tailored for thin-walled bodies, to impose the wall boundary conditions over the embedded intimal flap. We tested the method by simulating the flow in four patient-specific aortic dissections, all involving intricate geometrical patterns. In all cases, the preprocess is greatly simplified with no impact on the computational times. Additionally, the obtained results are consistent with clinical evidence and previous research.

Experimental Tests and Numerical Analysis of Aerodynamic Properties of the Composite-Made Jet-Propelled Aerial Target.

The design of an aircraft's internal structure, and therefore the appropriate choice of material type, is a direct function of the performed tasks and the magnitude and type of the acting loads. The design of a durable aircraft structure with appropriate stiffness and lightness requires knowledge of the loads that will be applied to the structure. Therefore, this paper presents the results of an aerodynamic experimental test and numerical analysis of a newly designed jet-propelled aerial target. The experimental tests were carried out in a low-speed wind tunnel for a wide range of angles of attack and sideslips. Moreover, they were performed for various configurations of the airplane model. In addition, the results of the experimental test were supplemented with the results of the numerical analysis performed using computational fluid dynamics methods. During numerical analysis, specialized software based on solving partial differential equations using the Finite Volumes Method was used. This article presents the methodology of the conducted research. The results of the aerodynamic analysis are presented in the form of diagrams showing the aerodynamic force and moment components as a function of the angle of attack and sideslip. In addition, qualitative results of the flow around the plane have been presented. The results obtained prove that the adopted methods are sufficient to solve these types of problem. The aerial system was positively verified during the qualification tests of the system at the Polish Air Force training range and finally received the type certificate.

Influencing factors of particle deposition in the human nasal cavity.

Objective: To review the existing literature on the application of computational fluid dynamics methods to study nasal particle deposition and to summarize and analyze the factors affecting nasal particle deposition in order to provide theoretical references for the development of future transnasal drug delivery devices and the prevention of respiratory-related diseases. Data Source: PubMed and CNKI databases. Methods: A search of all current literature (up to and including February 2023) was conducted. Search terms related to the topic of factors influencing nasal particle deposition were identified, and queries were conducted to identify relevant articles. Results: Both the properties of the particles themselves and the environmental conditions external to the particles can affect particle deposition in the nasal cavity, with particle deposition showing a positive correlation with particle size, particle density, and airflow velocity, with increasing subject age leading to a decrease in deposition, and with the relationship between airflow temperature and humidity still requiring more research to further explore. Conclusions: With the popularity of computational fluid dynamics, more and more scholars have applied computational fluid dynamics technology to explore the influence of different parameters on particle deposition. By summarizing and analyzing the influence law of various factors on deposition, it can provide a theoretical basis for the future development and application of transnasal drug delivery devices and the prevention of respiratory-related diseases, which makes a significant contribution to the optimization of clinical disease prevention and treatment. Level of Evidence: NA.