key: cord-274732-mh0xixzh
authors: Faizal, W.M.; Ghazali, N.N.N; Khor, C.Y.; Badruddin, Irfan Anjum; Zainon, M.Z.; Yazid, Aznijar Ahmad; Ibrahim, Norliza Binti; Razi, Roziana Mohd
title: Computational fluid dynamics modelling of human upper airway: a review
date: 2020-06-26
journal: Comput Methods Programs Biomed
DOI: 10.1016/j.cmpb.2020.105627
sha: 
doc_id: 274732
cord_uid: mh0xixzh

BACKGROUND AND OBJECTIVE: Human upper airway (HUA) has been widely investigated by many researchers covering various aspects, such as the effects of geometrical parameters on the pressure, velocity and airflow characteristics. Clinically significant obstruction can develop anywhere throughout the upper airway, leading to asphyxia and death; this is where recognition and treatment are essential and lifesaving. The availability of advanced computer, either hardware or software, and rapid development in numerical method have encouraged researchers to simulate the airflow characteristics and properties of HUA by using various patient conditions at different ranges of geometry and operating conditions. Computational fluid dynamics (CFD) has emerged as an efficient alternative tool to understand the airflow of HUA and in preparing patients to undergo surgery. The main objective of this article is to review the literature that deals with the CFD approach and modeling in analyzing HUA. METHODS: This review article discusses the experimental and computational methods in the study of HUA. The discussion includes computational fluid dynamics approach and steps involved in the modeling used to investigate the flow characteristics of HUA. From inception to May 2020, databases of PubMed, Embase, Scopus, the Cochrane Library, BioMed Central, and Web of Science have been utilized to conduct a thorough investigation of the literature. There had been no language restrictions in publication and study design of the database searches. A total of 117 articles relevant to the topic under investigation were thoroughly and critically reviewed to give a clear information about the subject. The article summarizes the review in the form of method of studying the HUA, CFD approach in HUA, and the application of CFD for predicting HUA obstacle, including the type of CFD commercial software are used in this research area. RESULTS: This review found that the human upper airway was well studied through the application of computational fluid dynamics, which had considerably enhanced the understanding of flow in HUA. In addition, it assisted in making strategic and reasonable decision regarding the adoption of treatment methods in clinical settings. The literature suggests that most studies were related to HUA simulation that considerably focused on the aspects of fluid dynamics. However, there is a literature gap in obtaining information on the effects of fluid-structure interaction (FSI). The application of FSI in HUA is still limited in the literature; as such, this could be a potential area for future researchers. Furthermore, majority of researchers present the findings of their work through the mechanism of airflow, such as that of velocity, pressure, and shear stress. This includes the use of Navier–Stokes equation via CFD to help visualize the actual mechanism of the airflow. The above-mentioned technique expresses the turbulent kinetic energy (TKE) in its result to demonstrate the real mechanism of the airflow. Apart from that, key result such as wall shear stress (WSS) can be revealed via turbulent kinetic energy (TKE) and turbulent energy dissipation (TED), where it can be suggestive of wall injury and collapsibility tissue to the HUA.

Breathing, known as ventilation, is the process of taking air into and expelling it from the lungs, often by consuming oxygen and discharging carbon dioxide from the lungs. The respiratory system comprises the nose and mouth, and all the way through the respiratory tract: airways and lungs. Lungs are the main organs of the respiratory system, in which it works to exchange oxygen and carbon dioxide during breathing. The nose and mouth are used for breathing, where the air would then enter the respiratory system, along the throat (pharynx), and across the voice box (larynx). Food and drink are blocked from entering the airways during swallowing because the passage to the larynx is lined with a small flap of tissue called the epiglottis, which will spontaneously close upon swallowing. The upper airway is situated at the lower end of the trachea, which refers to the airway segment within the nose or mouth and the main carina. The segment between the trachea and the mainstem bronchi is the central airways. The lower conducting airways, such as the main, lobar, and segmental bronchi, are different from the upper airway in a sense that the latter has no collateral ventilation. As such, any obstruction of the upper airway or central airways can be fatal, be it an acute obstruction (occurrence within minutes) or a chronic one (development in weeks or months). Asphyxia and fatality may be resulted from any clinically significant obstruction that take place along the site of the upper airway; this is where recognition and treatment can be lifesaving. The upper airways are segregated into four sections: the nose (functional during nasopharyngeal breathing) and the mouth (functional during oropharyngeal breathing), the pharynx, the larynx, and the trachea. Due to the parallel anatomic arrangement of the nose and the mouth, they seldom become an upper airway obstruction, unless in the event of massive facial trauma. Central airway obstruction is a branch of the upper airway obstruction, which includes trachea and mainstem bronchi.

In recent years, the study of human upper airway (HUA) has become one of the interesting research areas. Some studies were focused on the flow characteristics, computer modeling, and fluidstructure interaction between the airflow and the soft tissue of the upper airway. Human upper airway plays a crucial role in delivering the inhaled air from the nasal passages to the lungs, which is a part of the body's breathing mechanism. The unique anatomical structure and functional properties of the soft tissue of the upper airways (e.g., mucosa, cartilages, and neural and lymphatic tissues) significantly influence the airflow characteristics and have a crucial effect to the conduction of air to the lower airways. Figure 1 shows the anatomy of human upper airway, which begins with the nasal cavity and continues over the nasopharynx and oropharynx to the larynx [1] . The function of the upper airway is not limited to only deliver air to the lung, but it ensures normal phonation, digestion, humidification, olfaction, and warming of inspired air [2] . Therefore, the better understanding of human upper airway may enhance the clinical application of anatomical structure and improve the physiological knowledge of the respiratory system for a medical practitioner.

An abnormal breathing during sleep can be classified in various forms and conditions, such as sleep apnea, sleep-disordered breathing (SDB), sleep apnea-hypopnea syndrome (SAHS), and sleeprelated breathing disorder (SRBD), all of which arise from different etiologies [3] . Many researchers are concerned about the importance of abnormal breathing during sleep, to which they have proposed different efficient methods to detect or monitor the breathing performance of the patient [4] . On top of that, the scholars have proposed other efficient ways to treat the abnormal sleep breathing patient via skeletal surgical approach [5] , volumetric tongue reduction [6] , and using oral appliances to improve the their sleep breathing experience [7] , [8] . Figure 1: The human upper airway is described as the area of airway between the nose (nasal cavity) and the mouth (oral cavity), and the main carina at the lower end of the trachea [5] .

The primary physiological objective of sleep is the patency of the upper airway. The relaxation of throat muscle narrows the airway and causes the failure of upper airway patency, which leads to the obstructive sleep apnea (OSA) and its sequelae [9] . Moreover, the protective upper airway is weak during sleep and the relaxation of the soft tissues can easily lead to the upper airway collapse [10] .

OSA can be classified as a multifactorial disease, which involves a complex interplay of the upper airway anatomy, alone and/or a combination of neuromuscular control mechanism with other pathophysiologic factors (e.g., respiratory arousal threshold and loop gain) [11] . Dynamic tongue movement and tongue thickness may impair the respiratory control of an OSA patient. The factors that contribute to the OSA vary individually. Thus, an understanding of the OSA mechanism is important to achieve the goal of individualized and targeted therapy for patients [12] . However, some restrictions such as lack of information on the flow behavior and upper airway collapse can result in only 50% operation success rate [13] . Therefore, the knowledge and understanding of the flow properties of upper airway are practically important for medical practitioners and surgeons to accurately locate the obstruction in OSA patients [14] , [15] .

Surgical correction of OSA syndrome is one of the alternative methods to tackle the obstruction of the upper airway [16] . The understanding of a three-dimensional (3D) airway anatomy is crucial for the surgical correction of OSA because it involves a number of parameters such as internal airflow velocity, wall shear stress, and pressure drop [17] . The advanced cone-beam computed tomography (CBCT) scan and automated computer analysis were used to facilitate the visualization of the 3D upper airway in identifying the abnormal airway condition and their response to surgery. Besides that,

preoperative studies provide knowledge and information on specific airway obstruction, which can grant precise surgical treatment for OSA patients by focusing on the specific region [18] . For example, 75% to 100% success rate of operation for maxillomandibular advancement (MMA) was reported in the surgical correction of OSA syndrome [19] .

In recent years, the computational fluid dynamics (CFD) method has been widely employed to analyze the airflow in both healthy and diseased human conducting airways [20] . Most studies have focused on the pollutant transport and drug delivery in respiratory systems, whereas others focused on sleep-disordered breathing [21] . During breathing, the airflow characteristics through the human respiratory tract are very complex. The airflow can be in the laminar, transitional, or turbulent condition. The geometry and boundary conditions are the main factors that greatly affect the airflow in human respiratory tracts [22] . Therefore, these factors must be considered in the CFD study of respiratory flow. Sometimes, the simplified airway geometries are considered in the CFD analysis due to the limitations of computing memory and time. With the advancements in medical imaging techniques, unique and realistic geometries of the respiratory tract can be reconstructed from the scanned images, to which they can be converted into a CFD model; this also applies for both extraand intra-thoracic airways [23] .

The CFD analysis is able to provide clear visualization and its results can cover the lack of information from experiments such as the flow properties and flow pattern generated inside the human upper airway [24] . Moreover, the rapid development of new computational and numerical methods using powerful hardware can now shorten the computation time, allowing researchers to study the critical airflow of the upper airway prior to conducting the surgery [25] . This makes CFD a vital and useful tool for predicting the various flow properties in the human upper airway by computationally solving flow equations [26] . Thus, this review article focuses on the challenges that arise in analyzing the airway mechanisms via CFD approach, and critical process, including modeling and its mathematical background that are related to the study of the human upper airway (HUA). In addition, the various steps involved (i.e., preprocessing and post-processing) in solving the CFD analysis are investigated. Lastly, the application of CFD method in the analysis of the human upper airway, which has been carried out by previous scholars, is also discussed in this article.

As mentioned beforehand, the upper and central airways are the parts of a human breathing airway. These airways can be afflicted by respiratory diseases, which can distress or impair organs and structures that are related to breathing, such as the nasal cavities, the pharynx (also known as throat), the larynx, the trachea (or windpipe), the bronchi and bronchioles, the tissues of the lungs, and the respiratory muscles of the chest cage. There are many causes of respiratory illnesses and diseases; some of them are through infection, smoking of tobacco or breathing in secondhand tobacco smoke, asbestos, radon, as well as various types of air pollution. Asthma, chronic obstructive pulmonary disease (COPD), pneumonia, pulmonary fibrosis, lung cancer [27] , and the newest pandemic-causing The aim of this work was to evaluate the effect of inlet velocity profile on the flow features in blocked airways along the branches of a human lung. Evaluation of bifurcation flow in a human lung was deemed vital to give a better prediction on the particle deposition in drug therapy and inhalation toxicology. This is done through the use of fully 3D

incompressible laminar Navier-Stokes equations and continuity equation, while the CFD solver was used to solve the unstructured tetrahedral meshes.

Ali et al. [31] Respiratory disease (lung airways)

CFD and particle dynamics framework on a specific lung model of a patient were described under unsteady flow conditions, which can enhance the knowledge of particle transport and deposition in the airways. Essentially, this can be a useful guide for future targeted drug delivery studies.

Jinxiang et al. [32] Respiratory obstructive diseases (bronchial tumor)

Potential sites of diseases were investigated in this work,

where disease severity was determined to help formulate a targeted drug delivery plan for the treatment of the disease.

CFD was employed to provide visualization of the unique lung structure in assessing the exhaled aerosol distribution.

CFD is also sensitive to the varying airway structures.

Qingtao et al. [33] Lung cancer The changes in structural and functional of the tracheobronchial tree post-lobectomy was assessed via the CFD to depict the airflow characteristics of the wall pressure, airflow velocity, and lobar flow rate.

Long et al. [34] Pulmonary fibrosis (lung disease)

The respiratory system was assessed through the use of CFD method and compared with the pulmonary acinus mechanics and functions in healthy.

The airflow in the human upper airway is complex and time dependent. The airflow undergoes transition from laminar to turbulent, and vice versa within a second [35] . The complex geometry of the human upper airway results in curve streamlines, recirculation or vortex regions, secondary flow, and jet flow. In order to study the human upper airway, the laminar-turbulent transition flow with complex geometry can be analyzed via experimental and computational approaches. Both methods require a similar first approach, which is the preparation of the geometry model for a human upper airway [36] . The geometry model can be generated from the images captured or scanned from the medical imaging process, such as CT scan and MRI, among others [37] .

In the experimental method, a prototype of the human upper airway is fabricated based on the unique geometry of the upper airway of a patient, and the experiments are performed on this physical prototype [38] . The prototype of the upper airway is typically fabricated according to the actual dimension or that in a scale model, such as reduced-or enlarged-scale model. The operating parameters are considered in the experiments to evaluate the velocity, pressure, and flow profile of an upper airway. Several factors, such as time and cost, are required for the study, as well as the availability of facility, and measurement device that needs to be taken into consideration in the experimental works [39] . Furthermore, other errors, such as measurement and human errors, may influence the accuracy of data collected from the upper airway experimental setup. Many scholars had performed experimental analysis on various upper airways from different patients to identify the accurate causes, and to propose the response for surgery and operation [40] . Pirnar et al. [41] investigated a clear physical interpretation by developing a simplified experimental system. The 

Computational fluid dynamics method is an alternative way of analyzing and solving a complicated fluid flow problem. Although the CFD is widely applied in the engineering field, it can also be an extremely powerful tool in the biomedical research field. CFD solves the governing equations of fluid flow while advances the solution via space and time [42] . Fernández-Parra et al. In addition, Koullapis et al. [46] had studied the efficiency of computational fluid-particle dynamics approach for the prediction of deposition in a simplified approximation of the deep lung.

Moreover, the availability of computational resources for solving numerical algorithm also makes it possible to govern equations using a specific numerical method [47] . The combination of conventional coupled CFD-DEM model is also able to solve fluid-structure interaction (FSI)

problems. The integration of a dynamic meshing approach allows the FSI simulation to resolve the flow structure surrounding the large free-moving object. Commercial software platform, such as ANSYS FLUENT, with user-defined functions (UDFs) are used to simulate the analysis due to its capability in handling complex geometries of the model and solving the dynamic meshing [48] , [49] .

Recently, the FSI simulation analysis of the upper airway was adopted to explain the mechanism of pharyngeal collapse and snoring [50] . Various CFD commercial software tools use different discretization methods (e.g., finite difference, finite element, and finite volume methods) for solving the governing equations. ANSYS FLUENT is a popular software for CFD code, which has been widely used in the simulation and research of the human upper airway. In the CFD analysis, ANSYS

FLUENT separates the analysis into three main stages, which are: (i) preprocessing, (ii) solver; and (iii) post-processing. However, to predict the flow accurately in the study of the upper airway, the selected numerical method must have the capability to simulate the low-Reynolds number turbulence model in a complex geometry [51] . The CFD model requires validation to ensure the reliability of the predicted results as reference.

Model validation is a very important step in any simulation analysis to ensure the models are performing as expected or mimics the real condition. Similarly, model validation in biomechanical modeling of the human upper airway with clinical data gains the confidence of users in utilizing the CFD computations. The rapid growth of interest in biomechanical modeling of the human upper airway since the 1990s has provided a better understanding and explanation of its physiology and pathophysiology. In the CFD model validation, the experimental data or clinical data benchmark is usually used to validate the simulation results [52] , [53] . 15 in x-z plane. This bifurcation model was then further validated by Mihai et al. [56] in their CFD pharyngeal airflow study. Various turbulence models (e.g., LES and RANS) were employed to simulate the turbulent airflow behavior in a bifurcation model. The simulation results were compared with Zhao and Lieber's experiment [55] , as shown in Figure 5 . Comparing CFD results with available experimental data is a popular technique to validate the CFD model or analysis. A good agreement between the experimental and simulation results indicates the strong capability of CFD model to reflect the reality or real condition. CFD validation is not only limited to the results, but it is also applicable for the methodology. The CFD methodology is compared with the experimental flow data to ensure the airway has the similar conditions before it can be extended for parametric study using the same model [57] , [58] . Table 2 summarizes the differences between experimental and computational approaches for various applications.  Limited range of problem and constraint of operating conditions.  Difficult to modify the experimental set up once manufactured or fabricated.  Require a large number of measuring instruments in the data collection.  Experimental works are not constrained by the complexity of the problem.

 Slow, sequential and single purpose for the experimental work.

 Simplification is done on the physical system.  Simulation data in no limit in term of space and time.  Cost only for initial investment of commercial software.  Applicable for any range of problem virtually and for realistic operating conditions.  Computational model or domain can be modified easily here.  Various models and tools are available for calculation and data collection.  The computational approach is constrained by the build in mathematical models and require user defined functions, especially for complex system.  Fast computing, parallel and multi-purpose in computational approach.

The mechanical properties of the upper airway are important to define the obstructive events in detail, such as the relaxation of the soft tissue during sleep [61] . A better understanding of the mechanism involved in the obstructive upper airway can be achieved via experimental and numerical studies [62] . A particular numerical simulation method, CFD, is the best approach to describe the 

Preprocessing is the initial step of the CFD simulation analysis, comprising the identification of domain interest, creating a domain for the upper airway, and mesh generation on the domain. The preprocessing step is not limited to the study of upper airway, rather, it is also widely applied in the CFD analysis of various engineering problems. In this step, modeling goals, such as the initial assumptions of the airflow (e.g., steady and unsteady flow) and the model (e.g., laminar and turbulent), that are used to describe the airflow must be clear on the given problem of the upper airway. Moreover, the degree of accuracy, computation time, and simplification also needs to be considered in the initial step of the CFD simulation process. Essentially, the preprocessing step of the CFD analysis includes the following:

(i) Identifying the domain of interest: Figure 6 shows the schematic diagram of human upper airway in the anatomy of the nasal airway. The regions of the nasal airway were divided into nine different cross sections, and defined based on the nomenclature and magnitude of the airflow velocity [63] . The selection of specific section was considered according to the interest of the study. were generated close to the wall region for better numerical stability and accuracy. Figure 8 shows the boundary layer meshing in the study of the upper airway using a 2D nonuniform mesh [63] . The boundary layer meshing was expected to provide better stability during the simulation. Moreover, the grid independence test was required to determine the best number of cells or elements of a domain. The optimal number of elements reduces the computational resources, such as computation time while providing accurate predictions of the airflow through the simulation. In the grid independence test, different numbers of elements were considered for the domain, as shown in Figure 9 [65]. Once the elements had achieved the optimal number, the further increase in the elements had less than 1% of variation in the result. Thus, the step to determine the optimal number of elements for a domain is necessary to obtain reliable and accurate CFD results. On the other hand, the upper airways were simplified into a 2D model to simulate the fluidstructure interaction (FSI) phenomenon of the soft palate in the pharynx using more than one turbulent model as a solver [70] . The multi-block structured grid was applied to the 2D model to keeping intact its unique feature of the patient [72] . The medical images captured from the CT, highresolution computed tomography (HRCT), and MRI are imported and processed by the image processing software (e.g., 3D-Doctor, Mimics, and 3D Slicer) before the computational domain is created [73] . Figure 12 depicts the typical steps to construct an accurate model of the upper airway, as well as that of other models (e.g., bone and tissue) [74] . The CT scan data were chosen for the image processing because they have a good contrast between bones and soft tissues compared with the MRI data. The volumetric data was constructed in the image processing step, while the Gaussian filters and edge detection were applied to remove the image noise. The specific region of bone is identified in this step, while the segmentation step separates the details of the bony structures. For example, the ligaments, tissues, cartilages, and bony structures, such as tibia and femur, are identified and defined in the image via a region-growing algorithm that is typically built in the algorithm of the commercial software. The defined regions were then created to form a profile series. Then, the detailed model (i.e., accurate model) was generated and meshed based on the profile series.

To generate an accurate model, the basic element used in generating the model is voxels. A voxel represents a value on a regular grid in three-dimensional space. Voxels of the tissue with fixed dimensions provide the coordinates and some attributes that characterize the position of the tissue.

From the voxels, the surface of the model is then generated using the graphic rendering technique.

The marching cubes algorithm is applied to extract the polygons from the volumetric data. The boundary voxels and surface triangulation are generated to represent the 3D object [75] . Finally, the vessel reconstruction process/meshing step is carried out to discretize the complex model into finite volumes or finite elements. Smaller volumes or elements are generated to resolve the complex flow in the accurate model. Apart from that, the specific region of the elements is used to define the boundary condition to compute the inlet velocity. The optimal number of meshing elements can be determined from the study of grid sensitivity to ensure the accuracy of the model. Thus, a two-equation RANS model, which is the standard k-ε (SKE) model, is preferred in the simulation study of the human upper airway [77] .

Three turbulence models were compared with previous studies, which are realizable k-ε (RKE) model [78] , the k-ω turbulence model [79] , and its shear stress transport (SST) variant, k-ω SST [80] .

Menter et al. [81] and Langtry et al. [82] had developed the four-equation transition SST model, which is more demanding compared with the two-equation model. Different models have its own wall mesh treatment and mesh resolution, as shown in Figure 9 [83]. The wall functions are typically employed in cases where a flow separation is not to be expected. Distance from the wall to the adjacent layer (y p ) is different for k-ε and k-ω models. Therefore, the dimensionless wall distance (y + ) is applied to assess the mesh resolution near the wall, as defined in Equation 1:

where, u τ is the friction velocity based on wall shear stress and air density; and ν is the kinematic viscosity. Figure 14 shows the possible combination of computational models and techniques in the modeling of a turbulent flow [92] . The specific flow modeling depends on its turbulence length scale and specific computational techniques to tackle the physics of turbulent flow.

Any interface tracking method (ITM) can be straightforwardly combined with the LES model due to its very small resolvable length scale. The combined methods are able to facilitate the interface exchange terms while delivering the time-dependent interfacial kinematics of the model. Moreover, the LES model has become a rather popular and extremely powerful tool in turbulence modeling [93] .

In addition, LES is not only limited to the turbulent flow analysis, but it is also applicable for various analyses, such as in aeroacoustics, combustion, gas turbine, and many other engineering areas. nasal resistance. They used this model to explain the regional dynamic airway narrowing phenomenon during the expiration in the upper airway of a child with OSA ( Figure 15 ). Figure 15 shows the inspiration pressure, velocity, magnitude, turbulence kinetic energy, and expiration pressure in Caudal and lateral views, and midline sagittal plane. The CFD results demonstrated that the low and negative pressure zones were attributed by the area of restriction, which is related to the cause of sleep apnea. Jeong et al. [63] carried out a numerical investigation on the aerodynamic force in HUA of patients with OSA using CFD analysis. Their simulation results revealed that the area of restriction in the velopharynx region had induced a turbulent jet flow in the pharyngeal airway. This situation caused higher shear stress and pressure forces surrounding the velopharynx (Figure 17 ). In addition, an accurate approach was proposed by Mihai et al. [56] to tackle the airflow situations that occur in the human airway. Steady RANS and LES approaches were applied to the flow modeling. Both modeling approaches yielded different airflow characteristics and static pressure distributions on the human airway walls. The maximum narrowing region of retropalatal pharynx had caused velocity changes and pressure variations in both modeling approaches, as clearly shown in Figure 17 . CFD approach can provide valuable preliminary information to a surgeon on the geometrical reconstruction of the human upper airway prior to performing the surgery. Sittitavornwong et al. [97] analyzed the anatomical airway changes to predict maxillomandibular advancement (MMA) surgery.

They considered a 3D geometrical reconstruction of the airway and investigated the fluid dynamics before and after the surgery using the CFD approach. After the MMA surgery, the dimension of the airway increases, thus reducing the airway resistance and improving the pressure effort of OSAS. The MMA surgery is expected to overcome OSA problems for the patients. Therefore, the study of human airway before and after the MMA surgery becomes a focus in this research area. Moreover, Sittitavornwong et al. [98] extended their research on the effect of changes in soft tissues post-MMA surgery. The dimension of the human airway for OSA patient increases after the MMA surgery ( Figure 18 ). Pre-and post-surgery results were compared (i.e., pressure distribution and shear stress)

in Figure 19 . The comparison study showed a significant improvement in the pressure distribution in the airway. Apart from that, the investigation of mandibular advancement splint (MAS) was reported by Zhao et al. [99] using the fluid-structure interaction method. Chang et al. [100] also used FSI to evaluate the effect before and after the maxillomandibular advancement surgery. In the pre-and posttreatment of OSA, the CFD is a popular approach, in which it can reveal the airflow characteristics and flow variables after the treatment via the unsteady flow analysis. FSI method is considered when displacement and vibration of the soft tissue or membrane such as the trachea are involved. The function of trachea is to regulate the pressure during breathing, coughing, or sneezing. The deformations and stresses of the trachea induced by these ventilation conditions can be computed via the FSI method. Severe muscular membrane deformation leads to critical issue for the physiological function of trachea [101] . Based on the literature review, the capability of FSI method is validated and recognized in various studies. FSI may demonstrate realistic and accurate results on the flow patterns in the study of HUA [106] , [107] . It is also applicable for the analysis of global and local flow features of the nasal cavity [23] . Comparison between preoperative and postoperative pressure distributions along the upper airways [98] .

Recently, the CFD approach has emerged as a virtual surgical concept and reference for medical practitioners and surgeons by providing the simulation analysis. The CFD result is used for surgical planning and as reference for decision-making procedures in the diseased airways (Table 3) .

Therefore, the most effective tailored treatment plan can be done by medical personnel to prevent the recurrence of OSA disease. This procedure allows surgeons to modify the airway model, including the geometry reconstruction and boundaries (Figure 20 ) based on a certain approach or a series of surgical procedures [105] . In addition, ultra-low-dose computed tomography (CT) scan data were used to predict the treatment outcome on children with sleep-disordered breathing using the CFD approach [106] . The excessive internal pressure forces may lead to the collapse of the upper airway [107] , which can be observed in the CFD analysis. The prediction of CFD is consistent with the clinical parameters and data. Thus, it can be a useful tool to evaluate the surgical effects that are associate with OSA problems [108] . Table 3 : Four types of virtual surgery analyses using functional imaging and CFD [105] .

Modifications to baseline (B) airway boundaries on axial scans (baseline and virtual surgeries S1-S4 boundaries are shown in Figure 19 ) S1

Baseline airway boundaries changed on crosssectional planes A, B, C, and D. S2

Baseline airway boundaries changed on crosssectional planes D, E, F, and G. S3 (= S1 + S2)

Baseline airway boundaries changed on crosssectional planes A-G. This surgery removes both the constrictions in the airway.

Baseline airway boundaries changed on crosssectional planes D-G. Similar to S2. Airway lumen enlarged compared with the baseline, but not as much as it did in S2. Additionally, CFD is used to test and prove the hypothesis model for the treatment of OSA.

The CFD model is used to correlate with the treatment response after the adenotonsillectomy surgery (AT) by measuring the apnea-hypopnea index (AHI). A decrease in AHI indicates the success of the hypothesis model in the proposed virtual surgery. CFD, MRI, and physiological data of ten obese children were used to calculate the air pressure and velocity before and after the AT surgery [109] .

The correlation between the AT treatment response with other parameters (i.e., pressure-flow ratio, local air pressure drops, geometrical changes, and minimum surface pressure) were considered in the analysis. Moreover, in vitro experiment was used to investigate the airflow distribution in the human airway [110] . The numerical and in vitro experimental models of airway were reproduced by the CT data, and the experimental model was created by a 3D printer. The particle image velocimetry (PIV)

technique was used to measure the velocity and the in vitro data were useful for the validation of numerical simulation results. The DNS-LBM model was adopted to assess the flow properties in the upper airway, including the consideration of nasal cavity, pharynx, larynx, and trachea [89] .

The CFD approach has been used to quantify the glottis motion, cyclic flow, and its effect towards the respiratory dynamics. Liu et al. [111] had conducted the study of airflow dynamics in an obstructed realistic HUA. The focus was put on the continuous inspiration and expiration with varied respiratory intensities. The above-mentioned authors had proposed a theoretical guidance for the treatment of respiratory diseases. A 3D airway model was developed, with a time-varying glottal aperture. Several aspects, such as vortex topologies, shear stress, flow resistance, and breathing conditions, were considered in their studies. With the aid of CFD, the new clinical information can be incorporated into the CFD model to aid in the treatment of tracheomalacia [26] . The passive or active relationship of aerodynamic forces and the airway motion was categorized. Passive relationship indicates the direction of pressure force, and the airway motion is concurrent. The opposite direction for both the pressure force and airway motion is categorized as an active relationship. In heavy breathing and several pathological conditions, the consideration of airway movement is crucial in the simulation analysis. However, FSI models can tackle this airway problem.

Recently, the CFD analysis of bifurcation junctions of respiratory tract have been considering the key parameters of the wall turbulence effects, such as local Reynolds number (y + ), wall shear stress (WSS), turbulent energy dissipation (TED), and turbulent kinetic energy (TKE) [112] . Local

Reynolds number (y + ) plays a significant role in calculating the wall turbulence and other related parameters. For example, wall shear stress is used as an indicator to determine the wall injury level in the respiratory tract. The upper and lower connecting airways were considered on the respiratory airway tract without omitting it, which extended from the nasal to the terminal bronchioles. Valuable information obtained from the CFD results, such as airflow pattern, can contribute to the baseline data to provide a complete understanding of respiratory physiology [23] and airway collapsibility [113] .

Lastly, the CFD applications in simulating the airflow of the upper airway and respiratory are helpful and can be used as a guideline for proposing a surgical approach of the medical treatment to increase the cure rate. It is also beneficial in attaining a better understanding of the mechanism, treatment, and prevention of OSA. Table 4 summarizes the CFD study and comparison of relevant literature in the software and solver used. Wang et al. [114] had also discussed on the relationship between the relation of obstructive sleep apnea and adenotonsillar hypertrophy in children with dissimilar weight status. On the other hand, Vinha et al. [115] had explored the relationship between the dimensions of the palate and pharynx pre-and post-surgery that can affect patients with obstructive sleep apnea. Software/Solver SC/Tetra solver (version 12; Software Cradle) was adopted to simulate the airflow. FSI simulations were run using the SC/Tetra Abaqus module (version 12.0; Software Cradle) to assess the flexible region of the airway.

The correlation between cross-sectional area after maxillomandibular advancement surgery with pressure, velocity, airway volume, and resistance.

Post-MMA surgery, it was learned that there was an increase in airway volume, but a decrease in pressure drop, maximum airflow velocity, and airway resistance for both patients. These results were given by the simulation results of CFD and FSI. FSI simulation portrayed a section of marked airway deformation in both patients prior to the surgery. However, this deformation was considered inconsequential after both patients had undergone the surgery. 2 Liu et al. [102] Software/Solver Fluent 17.2 (ANSYS) was utilized for the computation of the airway flow equations. The ANSYS mechanical simulation software was able to decipher the transient structural equations in the evaluation of the soft tissue deformation and loading.

Variation of geometry along the airway with pressure and velocity in determination of location obstruction in upper airway.

Identification of areas prone to collapse and precipitate apneic episode was achieved from observation of the tips of the soft palate and the tongue. Consequently, the result was able to rationalize the mechanism of velocity and pressure distribution in the upper airway. 3

Jinxiang et al. [116] Software/Solver ANSYS Fluent (Ansys, Inc.) was used to solve for the mass and momentum conservation equations by taking into account the airway complexity, while the computational meshes were created via the ANSYS ICEM CFD (Ansys, Inc.)

The relationship and significance of tidal breathing and glottis motion with regard to the airflow features and energy expenditure in an image-based human upper airway model.

Glottis motion on airflow demonstrated aperture and cyclic flow of both altered laryngeal jet instability and vortex generation through main flow speed variations. 4

Alister et al. [26] Software/Solver STAR-CCM+ 11.04.012 was employed in the calculation of Navier-Stokes equations to assess the air pressure and velocity along the domain, which was bounded by moving walls.

The researcher investigated the correlation between wall geometry from MRI and airflow (inhale and exhale) in the upper airway.

Outcomes of CFD simulations are enhanced through rapid breathing maneuver and integrating the airway movement. There was a 19.8% increase in peak resistance that took place in earlier breath. There was also a 19.2% decrease in overall pressure loss, whereas the proportion of flow in the mouth had elevated by 13.0% in the airway.

Vivek at al. [112] Software/Solver Finite volume method (FVM) based on CFD solver (ANSYS Fluent 15) was utilized to computationally simulate the aerosol deposition in the image according to the respiratory tract model.

Investigate the correlation between turbulence model and near-wall function.

The findings of this work will assist researchers in selecting the optimal range of local Reynolds number (y + ) and turbulence model in simulating the right flow features in a human respiratory system. 6

Yidan et al. [23] Software/Solver The ANSYS Fluent Meshing (ANSYS Inc., Lebanon, NH) was used to mesh the airway model with polyhedral elements. The airflow was assumed to be an incompressible flow (i.e., having constant air density), and a no-slip boundary condition was established at the walls. All simulations were numerically performed via the ANSYS Fluent v17.0 (ANSYS Inc., Lebanon, NH).

Determination of the correlation and importance relation between large-to-small conducting airways and inhale mass flow rate (inspiration breathing).

The outcome showed that secondary flow currents were observed in the larynxtrachea segment and left main bronchus, whereas the airflow was much smoother with no secondary flow currents for the terminal conducting airway in the right lower lobe. 8

Omid et al. [117] Software/Solver The solid and fluid domains had employed an unstructured mesh, at which it was produced via the ANSA software by BETA CAE Systems USA Inc. Alya highperformance computational mechanics code, a multiphysics code, was also used to solve the FSI problems.

Relationship between deformation and collapse of the upper airway when breathing.

Findings showed that the sleeping position, gravity, and stiffness of the soft tissues (i.e., utilized in this work as a proxy for neuromuscular effects) were the key aspects of an upper airway collapse.

This article presents review on the experimental and numerical method such as, computational fluid dynamics approach, and its application in the analysis of human upper airway (HUA), including the fluid-structure interaction. The experimental study of HUA had utilized the prototype, scale, and in vitro models to measure the velocity, pressure, and flow profiles. Nevertheless, limited facilities and measurement devices are some of the constraints in the experimental study. A 3D printing technique was adopted to create the prototype based on the medical images (e.g., MRI and CT scan data), whereby the model was able to retain the actual dimension, geometry, and unique features of the upper airway of each patient. The computational approach is an alternative method used in the HUA research, as it is able to solve the complex phenomena in HUA. Various turbulence models (e.g., RANS, k-ε, k-ω, LES, and DNS) are available in the commercial software to simulate the airflow characteristics and properties. Most studies used commercial software to carry out the CFD analysis on HUA. This is due to the capability of CFD to be integrated with the structural analysis software to perform a fluid-structure interaction simulation in tackling the HUA problems that involve movement, motion, and deformation of soft tissue or airway wall. This work discusses the approach and modeling conditions (e.g., heavy or light breathing, and inhale or exhale), and region of interest (e.g., geometry parameters, consider the nasal cavity, pharynx, larynx, and trachea). It is reported that the increase in dynamic pressure leads to development of secondary flow. The larynx-trachea segment and left main bronchus is found to be occupied by secondary flow currents. Furthermore, CFD is used to study the airflow characteristics of before and after adenotonsillectomy and maxillomandibular advancement surgeries. Nevertheless, numerous researchers have analyzed the airflow mechanism by exhibiting the velocity, pressure, and shear stress to demonstrate the findings of their work. For example, Navier-Stokes equations are used to provide visualization of the actual mechanism of the airflow. This is because the technique is able to demonstrate meaningful results through its description of the real fluid flow mechanism and via the expression of turbulent kinetic energy (TKE) in their outcomes.

Turbulent kinetic energy (TKE) and turbulent energy dissipation (TED) can provide important findings, such as wall shear stress (WSS), to which it can be suggestive of a wall injury, as well as collapsibility tissue of the human upper airway. Additionally, this approach can give the analysis and detect the airway collapse during breathing. The airway collapse is caused by the posterior wall of the trachea bulging forward or tongue collapse during sleep, fully or partly blocking an airway that causes obstruction while exhalation and inspiration. The virtual surgical concept helps surgeons and medical practitioners to plan and determine decision-making procedures in the treatment of diseased airways.

In addition, the literature demonstrates that the researchers were able to validate the computational model through clinical and in vitro experimental data.

Laminar and turbulent flow calculations through a model human upper airway using unstructured meshes

Functional Anatomy and Physiology of Airway

Techniques to assist selection of appropriate therapy for patients with obstructive sleep apnea

New Approaches to Diagnosing Sleep-Disordered Breathing

Skeletal Surgery for Obstructive Sleep Apnea

Volumetric Tongue Reduction for Obstructive Sleep Apnea

The impact of continuous positive airway pressure on heart rate variability in obstructive sleep apnea patients during sleep: A meta-analysis

Respiratory effort during sleep apneas after interruption of long-term CPAP treatment in patients with obstructive sleep apnea

Mechanical properties of the upper airway

The nose, upper airway, and obstructive sleep apnea

Submental Ultrasound Measurement of Dynamic Tongue Base Thickness in Patients with Obstructive Sleep Apnea

Classification techniques on computerized systems to predict and/or to detect Apnea: A systematic review

Nasal Obstruction Considerations in Sleep Apnea

Obstructive Sleep Apnea: The Role of Gender in Prevalence, Symptoms, and Treatment Success

Obstructive Sleep Apnea: A Standard of Care That Works

Sleep, Snoring, and Surgery: OSA Screening Matters

The Effects of Noncontinuous Positive Airway Pressure Therapies on the Aerodynamic Characteristics of the Upper Airway of Obstructive Sleep Apnea Patients: A Systematic Review

Maxillary, mandibular, and chin advancement: Treatment planning based on airway anatomy in obstructive sleep apnea

Maxillomandibular advancement for treatment of obstructive sleep apnea syndrome: A systematic review

A review of the implications of computational fluid dynamic studies on nasal airflow and physiology

Prevalence of obstructive sleep apnea in the general population: A systematic review

Development of Human Airways Model for CFD Analysis

Detailed computational analysis of flow dynamics in an extended respiratory airway model

The Challenges of Precision Medicine in Obstructive Sleep Apnea

Obstructive Sleep Apnea: Emphasis on Discharge Education After Surgery

Assessing the relationship between movement and airflow in the upper airway using computational fluid dynamics with motion determined from magnetic resonance imaging

Strategies to Increase Physical Activity in Chronic Respiratory Diseases

Computational fluid dynamics can detect changes in airway resistance in asthmatics after acute bronchodilation

Numerical modeling of particle deposition in the conducting airways of asthmatic children

The effect of inlet velocity profile on the bifurcation COPD airway flow

Topological analysis of particle transport in lung airways: Predicting particle source and destination

CFD modeling and image analysis of exhaled aerosols due to a growing bronchial tumor: Towards non-invasive diagnosis and treatment of respiratory obstructive diseases

Structural and functional alterations of the tracheobronchial tree after left upper pulmonary lobectomy for lung cancer

Characterization of air flow and lung function in the pulmonary acinus by fluid-structure interaction in idiopathic interstitial pneumonias

Airway evaluation in obstructive sleep apnea

Upper airway imaging in pediatric obstructive sleep apnea syndrome

Correlation between severity of sleep apnea and upper airway morphology based on advanced anatomical and functional imaging

Experimental methods for flow and aerosol measurements in human airways and their replicas

Computed Tomography Characterization and Comparison With Polysomnography for Obstructive Sleep Apnea Evaluation

Sleep-Related Breathing Disorders and Quality of Life

Effect of airway surface liquid on the forces on the pharyngeal wall: Experimental fluid-structure interaction study

Numerical investigation of airflow, heat transfer and particle deposition for oral breathing in a realistic human upper airway model

Use of computational fluid dynamics to compare upper airway pressures and airflow resistance in brachycephalic, mesocephalic, and dolichocephalic dogs

Computational analysis of airflow dynamics for predicting collapsible sites in the upper airways: Machine learning approach

Computational fluid dynamics simulation of full breathing cycle for aerosol deposition in trachea: Effect of breathing frequency

An efficient computational fluid-particle dynamics method to predict deposition in a simplified approximation of the deep lung

Modeling congenital nasal pyriform aperture stenosis using computational fluid dynamics

Coupling CFD-DEM with dynamic meshing: A new approach for fluid-structure interaction in particle-fluid flows

Modelling nasal high flow therapy effects on upper airway resistance and resistive work of breathing

Computational fluid-structure interaction simulation of airflow in the human upper airway

Simulation of pharyngeal airway interaction with air flow using low-re turbulence model

Numerical simulation of stent deployment within patient-specific artery and its validation against clinical data

Clinical Data Are Essential to Validate Lung Ultrasound

Modelling the human pharyngeal airway: Validation of numerical simulations using in vitro experiments

Steady inspiratory flow in a model symmetric bifurcation

Large Eddy simulation and Reynolds-Averaged Navier-Stokes modeling of flow in a realistic pharyngeal airway model: An investigation of obstructive sleep apnea

Validation of computational fluid dynamics methodology used for human upper airway flow simulations

Validation of airway resistance models for predicting pressure loss through anatomically realistic conducting airway replicas of adults and children

Large Eddy Simulation of the pharyngeal airflow associated with Obstructive Sleep Apnea Syndrome at pre and postsurgical treatment

A review of CFD methodology used in literature for predicting thermo-hydraulic performance of a roughened solar air heater

Assessment of upper airway mechanics during sleep

Experimental and numerical investigation on inspiration and expiration flows in a three-generation human lung airway model at two flow rates

Numerical investigation on the flow characteristics and aerodynamic force of the upper airway of patient with obstructive sleep apnea using computational fluid dynamics

Interaction between a simplified soft palate and compressible viscous flow

A CFD (computational fluid dynamics) based heat transfer and fluid flow analysis of a solar air heater provided with circular transverse wire rib roughness on the absorber plate

Development of a realistic human airway model

Novel imaging techniques using computer methods for the evaluation of the upper airway in patients with sleepdisordered breathing: A comprehensive review

Flow-induced oscillation of collapsed tubes and airway structures

A computational study of the respiratory airflow characteristics in normal and obstructed human airways

Recommendations for simulating microparticle deposition at conditions similar to the upper airways with two-equation turbulence models

In silico investigation of sneezing in a full real human upper airway using computational fluid dynamics method

Magnetic resonance sleep studies in the evaluation of children With obstructive sleep apnea

Imaging the Upper Airway in Patients with Sleep Disordered Breathing

Image-based computational fluid dynamics modeling in realistic arterial geometries

Image processing, geometric modeling and data management for development of a virtual bone surgery system

Computational modeling and validation of human nasal airflow under various breathing conditions

The numerical computation of turbulent flows

A new k-ϵ eddy viscosity model for high reynolds number turbulent flows

Turbulent modeling for CFD

Two-equation eddy-viscosity turbulence models for engineering applications

A correlation-based transition model using local variables -Part I: Model formulation

A correlation-based transition model using local variables -Part II: Test cases and industrial applications

Comparison of Reynolds Averaging Navier-Stokes (RANS) turbulent models in predicting wind pressure on tall buildings

Direct numerical simulation of developed compressible flow in square ducts

Direct numerical simulations of droplet condensation

Evaluation of pressure oscillations by a laboratory motor

Direct numerical simulation of flow over a slotted cylinder at low Reynolds number

A fast algorithm for Direct Numerical Simulation of turbulent convection with immersed boundaries

On locating the obstruction in the upper airway via numerical simulation

CFD analysis of drift eliminators using RANS and LES turbulent models

Advances and challenges of applied large-eddy simulation

Status and future developments of Large-Eddy Simulation of turbulent multifluid flows (LEIS and LESS)

Large-eddy simulation: Past, present and the future

Computational fluid dynamics modeling of the upper airway of children with obstructive sleep apnea syndrome in steady flow

Fluid-structure analysis of microparticle transport in deformable pulmonary alveoli

Numerical simulation of soft palate movement and airflow in human upper airway by fluid-structure interaction method

Evaluation of Obstructive Sleep Apnea Syndrome by Computational Fluid Dynamics

Computational fluid dynamic analysis of the posterior airway space after maxillomandibular advancement for obstructive sleep apnea syndrome

Simulation of upper airway occlusion without and with mandibular advancement in obstructive sleep apnea using fluid-structure interaction

Fluid structure interaction simulations of the upper airway in obstructive sleep apnea patients before and after maxillomandibular advancement surgery

Modeling of the fluid structure interaction of a human trachea under different ventilation conditions

Study of the upper airway of obstructive sleep apnea patient using fluid structure interaction

Computational fluid dynamics simulation of the upper airway response to large incisor retraction in adult class I bimaxillary protrusion patients

A review of fluid-structure interaction simulation for patients with sleep related breathing disorders with obstructive sleep

Planning human upper airway surgery using computational fluid dynamics

Functional respiratory imaging as a tool to assess upper airway patency in children with obstructive sleep apnea

Computational fluid dynamics for the assessment of upper airway response to oral appliance treatment in obstructive sleep apnea

Large Eddy Simulation of flow in realistic human upper airways with obstructive sleep

Computational fluid dynamics endpoints for assessment of adenotonsillectomy outcome in obese children with obstructive sleep apnea syndrome

Investigation of flow pattern in upper human airway including oral and nasal inhalation by PIV and CFD

Numerical Investigation of Flow Characteristics in the Obstructed Realistic Human Upper Airway

Capturing the wall turbulence in CFD simulation of human respiratory tract

Computational fluid dynamics analysis of Huvulopalatopharyngoplasty in obstructive sleep apnea syndrome

Correlations between obstructive sleep apnea and adenotonsillar hypertrophy in children of different weight status

Effects of surgically assisted rapid maxillary expansion on the modification of the pharynx and hard palate and on obstructive sleep apnea, and their correlations

Effects of glottis motion on airflow and energy expenditure in a human upper airway model

Impact of sleeping position, gravitational force & effective tissue stiffness on obstructive sleep apnoea