DescriptionThis project simulates the HVAC system of an operating room using ANSYS Fluent, focusing on how the ventilation design manages contamination around the patient and surgical team. The system relies on laminar airflow to keep the room's air clean, with air entering from ceiling panels and moving downward through the space, sweeping contaminants, assumed to originate from the patient's body, toward the room's corners and eventually out through lower exhaust panels. A linear air curtain plays a key role in this process, forming a barrier that stops contaminated air from circulating back toward the patient once it has been displaced. The geometry, representing the hospital room and its HVAC layout, is built in Design Modeler and meshed in ANSYS Meshing with an unstructured grid of 4,137,570 cells.MethodologySince the central goal is tracking how pollutants, oxygen, nitrogen, and humidity distribute through the room simultaneously, the Species Transport model is used, solving a separate transport equation for each of these gas-phase components rather than treating the air as a single uniform species.AnalysisThe solution yields contours of velocity, pressure, temperature, and species mass fraction for both oxygen and contaminants, along with 2D velocity vector fields. The temperature contours confirm that incoming conditioned air successfully cools the region around the patient's body, which is a strong heat source, indicating the cooling function of the system is working as intended. The velocity vectors show fresh air descending from the ceiling panels and spreading sideways as it reaches the room, a pattern that initially carries contaminants toward the patient's surface before the air curtain intervenes, generating a strong flow barrier that redirects the contaminated air toward the room's sides and ultimately to the outlet panels rather than letting it return to the patient. The contaminant mass fraction contours track this same pattern closely, confirming that the HVAC system, once the air curtain effect is accounted for, effectively clears contaminants from the patient's vicinity and channels them out of the operating room.

DescriptionThis project simulates the steady-state breathing of a coronavirus patient inside a clean room using ANSYS Fluent.Hospital rooms require dedicated air conditioning systems capable of continuously supplying fresh air to dilute and remove contaminated air surrounding the patient, while also maintaining proper cooling and heating. In this simulation, a bedridden patient inside a hospital room is modeled as the source of coronavirus transmission, with the patient's mouth explicitly defined as the point of respiratory viral release.The patient's body surface is set to a temperature of 308 K, reflecting a common symptom of the illness. Fresh air supplied through the room's air purification system works to remove contaminated air and virus particles while simultaneously cooling the patient's body surface to maintain thermal comfort. To achieve this, several ceiling-mounted panels introduce fresh air at 294 K, with corresponding outlet panels positioned along the lower section of the side walls.The 3D geometry was created in Design Modeler and meshed using ANSYS Meshing, resulting in an unstructured mesh of 5,666,870 cells.MethodologyThis simulation employs the Discrete Phase Model (DPM) to represent the patient's respiration and the resulting release of coronavirus particles. When studying the behavior of discrete particles suspended within a continuous fluid medium, the solution approach shifts from Eulerian to Lagrangian — tracking individual particle trajectories rather than treating them as part of the continuous flow field.Since a coughing or breathing patient releases coronavirus particles as discrete entities into the surrounding air, this Lagrangian, particle-tracking approach is required. The DPM model is therefore used to define an injection representing virus particles released from the patient's mouth. These particles are modeled as inert, with a surface-type injection. Particle boundary conditions are set to escape when crossing domain boundaries, and to trap or reflect upon contact with wall surfaces.ConclusionThe simulation results yield velocity, temperature, and pressure contours, along with airflow pathlines for the ventilation system. These results show that fresh air entering through the ceiling panels circulates throughout the room before exiting through the outlet panels.Using the DPM approach, the released virus particles are tracked and shown to be carried by the ventilation airflow, ultimately directed toward the outlet panels and removed from the room.Thermal comfort is also evaluated using two key parameters: PMV (Predicted Mean Vote) and PPD (Predicted Percentage of Dissatisfied). PMV, derived from human thermal-response data across various experimental conditions, depends on variables such as air temperature, humidity, air velocity, and occupant activity level, and ranges from -3 to +3. PPD is calculated as an exponential function of PMV and reflects the expected percentage of occupants dissatisfied with the thermal environment. The results indicate that both PMV and PPD values fall within an acceptable range, confirming suitable thermal comfort conditions within the simulated room.

Coronavirus Spread Due to a Cough in Open Air — ANSYS Fluent CFD Simulation TrainingThis project simulates the spread of coronavirus particles resulting from a human cough in open-air conditions, using ANSYS Fluent. When an infected person coughs, virus-laden particles disperse through the air and can potentially reach and infect a nearby healthy individual. Understanding this process, and determining the minimum safe distance needed to limit transmission, has become one of the most actively studied topics in CFD research, commonly referred to as social or physical distancing.The model consists of a human figure placed within a cube-shaped domain representing the open-air environment, with the mouth defined as the source of virus-carrying droplets. The 3-D geometry was created using SolidWorks and Design Modeler, and meshed in ANSYS Meshing with an unstructured mesh, refined further near the mouth region. The total element count is 584,587.MethodologyA two-way coupled Discrete Phase Model (DPM) is used to capture the unsteady behavior of the dispersed droplets and their interaction with the surrounding continuous airflow. The model accounts for stochastic collision, coalescence, and breakup of droplets. Droplets are injected at a temperature of 310 K, a velocity of 31.85 m/s, and a flow rate of 0.018 kg/s, released over a time interval of 0 to 0.1 s.Since droplet sizes vary, the Rosin-Rammler logarithmic distribution is used to define the diameter range, including the minimum, maximum, and mean diameters, the spread parameter, and the number of diameter classes per injection. The Species Transport model is enabled alongside the droplet model to capture droplet evaporation, meaning the airflow field around the patient is solved together with species mixing.ResultsThe simulation tracks the virus-laden particles over time, producing an animation that shows their release and gradual dispersal. Snapshots of the particle distribution at different time steps are also extracted. The results illustrate how the virus spreads during a cough event in open air, covering the period from 0.1 s to 1.75 s.

DescriptionCoronavirus (COVID-19) has been widely recognized as one of the most significant global health challenges, both due to its danger to human health and its high transmissibility between infected and healthy individuals.Coughing or sneezing without a mask is a primary mechanism for viral spread, which is why maintaining social distance has consistently been recommended by physicians as a key preventive measure. The elevator cabin represents a particularly high-risk environment in this context, since multiple people are often confined to a small space at minimal distance from one another, typically with limited ventilation.This project uses CFD methods in ANSYS Fluent to simulate the dispersion of coronavirus particles released by a coughing patient inside an elevator cabin. The computational domain models two individuals within the cabin: one representing an infected patient who coughs or sneezes, and the other positioned at a defined distance, representing a person potentially exposed to the released virus particles. The goal is to evaluate how effectively virus particles disperse within the confined elevator space and assess the likelihood of transmission to the second occupant.The cough is modeled as an injection of virus-laden water droplets expelled from the patient's mouth as they evaporate into the surrounding air. These droplets are released at a temperature of 310 K, a velocity of 31.85 m/s, and a mass flow rate of 0.018 kg/s, over an interval of 0 to 0.1 seconds. Since droplet diameter varies during propagation, a Rosin-Rammler logarithmic distribution is used to represent the range of particle sizes.The 3D geometry was built using SolidWorks and Design Modeler, and meshed in ANSYS Meshing with an unstructured mesh of 454,433 elements.CFD MethodologyThis simulation uses the Discrete Phase Model (DPM), which enables the study of a discrete particle mass suspended within a continuous fluid domain. Here, the virus-laden droplets released from the patient's mouth are treated as the discrete phase, while the airflow moving through the elevator's ventilation system represents the continuous phase.Several physical sub-models are applied to the discrete particles, including two-way turbulence coupling (capturing the mutual interaction between the continuous and discrete phases), stochastic collision (irregular droplet-to-droplet collisions), coalescence (droplet merging), and breakup (droplet disintegration). Based on this framework, parameters such as minimum, maximum, and mean droplet diameter determine the spread exponent and the number of diameter classes considered per injection.The droplet-based approach is applied alongside an activated species transport model, while the energy equation is enabled to account for temperature variation throughout the domain.ConclusionAt the end of the simulation, virus particle tracking is obtained at the final time step, based on residence time and particle diameter. An animation depicting virus dispersion and its gradual disappearance over time is also generated and included in the project deliverables.Additionally, three-dimensional contours are produced showing temperature distribution, the mass fraction of oxygen introduced by the ventilation system, and the spread of water droplets released during the cough — together illustrating how ventilation airflow influences the containment or dispersion of airborne viral particles within the elevator cabin.

DescriptionThis project investigates COVID-19 airborne transmission risk within a classroom setting using ANSYS Fluent, carried out through a full CFD analysis.COVID-19 remains one of the most significant global health challenges, both due to its impact on human health and its high transmissibility between infected and healthy individuals. Breathing without a mask in an enclosed public space can transmit the virus to nearby occupants, which is why maintaining proper social distance has been a central recommendation from health professionals. In settings such as university lecture halls or school classrooms, the short distances between seated students can meaningfully increase the risk of transmission from an infected individual to those seated nearby.This project simulates the breath of virus-carrying students within a classroom, with the goal of evaluating how effectively the room's ventilation system removes contaminated air. The modeled ventilation setup includes several fresh-air inlets positioned along the classroom ceiling, along with outlet panels located at the base of the side walls to allow stale air to exit.The geometry was created in Design Modeler, representing a classroom populated with chairs, each occupied by a modeled student. For every student, a dedicated surface represents the mouth as the source of breathing and viral emission. The model was meshed using ANSYS Meshing, producing an unstructured mesh of 2,745,511 cells.MethodologyThis simulation uses the Discrete Phase Model (DPM), which allows a mass of particles to be studied discretely within a continuous fluid — in this case, air. The discrete phase representing virus particles is defined within a steady-state solver framework.Once the discrete phase model is activated, the injection parameters are defined to specify the type and behavior of the particles introduced into the classroom. Virus particles are modeled as inert, with a surface-type injection applied at each student's mouth.Boundary conditions for the discrete phase are set to Escape at the classroom's outer boundaries, allowing particles to exit the domain, while Trap conditions are applied to the students, chairs, and classroom walls, causing particles to accumulate upon contact with these surfaces.ConclusionThe simulation results include particle tracking of virus particles based on a 60-second residence time, along with 2D and 3D temperature and air velocity contours, and 3D flow pathlines throughout the classroom.The findings indicate that the installed ventilation system is poorly suited to the classroom environment and actually increases the risk of virus transmission — the virus particles are shown to disperse widely throughout the room's interior rather than being effectively removed, suggesting that the ventilation mechanism inadvertently helps sustain airborne viral presence within the space.

Non-Newtonian Blood Pulsatile Flow in a Vein — ANSYS Fluent CFD Simulation TrainingThis project simulates non-Newtonian, pulsatile blood flow through a vein using ANSYS Fluent, with the full case analyzed through CFD post-processing.The working fluid is blood, a non-Newtonian fluid. Non-Newtonian fluids are those whose viscosity changes with shear rate, meaning they have no single fixed viscosity. In such fluids the relationship between shear stress and applied strain rate is nonlinear, so no constant viscosity coefficient applies. The simulation is run as transient over 0.5 s, and a User-Defined Function (UDF) is applied to model the pulsing of the blood flow. Because blood flow is not steady but pulsed, the velocity is prescribed as a periodic function through the UDF code.The geometry was created in Gambit. The model consists of a main cylindrical vessel and two smaller branch vessels of reduced size and diameter — one branching at a 90-degree angle and the other with a 45-degree curvature. It has one inlet section and two outlet sections.Meshing was performed in ANSYS Meshing using an unstructured grid, for a total of 397,388 cells.MethodologyThe working fluid is blood, with a density of 1050 kg/m³. Because blood is non-Newtonian, its viscosity is described using the Carreau model with appropriate parameters.Newtonian fluids maintain a constant viscosity under applied force, whereas non-Newtonian fluids exhibit variable viscosity, of which there are several types. Time-dependent non-Newtonian fluids fall into two categories: rheopectic fluids, such as printer ink and cream, whose viscosity increases over time under load, and thixotropic fluids, such as honey, whose viscosity decreases as force is applied. Time-independent non-Newtonian fluids divide into three groups: dilatants, such as starch and clay, whose viscosity depends only on the magnitude of the applied force; pseudoplastics, such as greases, paints, soaps, and ketchup, whose viscosity is inversely related to the applied force; and Bingham fluids, such as toothpaste and silica nanocomposites, which require a threshold stress before they begin to flow.In this simulation, blood is treated as a pseudoplastic non-Newtonian fluid defined by the Carreau model. This model spans a wide range of fluid behavior by fitting a curve that matches both Newtonian and shear-thinning (pseudoplastic) responses.ResultsAfter the solution is complete, contours of pressure and wall shear stress are obtained at several time instants. The results confirm that the flow inside the vessel is fully pulsatile, since the pressure varies over time. They also show that pressure and wall shear stress are correlated: as the pressure inside the vessel rises, the wall shear stress increases accordingly.

Aorta, Non-Newtonian Pulsating Blood Flow — ANSYS Fluent CFD SimulationDescriptionThis project studies non-Newtonian pulsating blood flow in the aorta using ANSYS Fluent. The aorta geometry is obtained from a real CT scan, provided as an STL file that must be repaired before meshing — a workflow representative of patient-specific biomedical CFD. Blood is a non-Newtonian fluid whose apparent viscosity changes with shear rate, and the aorta's pulsatile flow, curvature, and branching make it a rich, realistic case for studying how such a fluid behaves in a large vessel. Within the Non-Newtonian Flow: Beginner CFD Training Package, this project builds on the earlier blood-flow case by moving to a larger, geometrically complex vessel reconstructed from real medical imaging.MethodologyThe aorta geometry is obtained from a CT scan, and tools such as SpaceClaim, ICEM CFD, and Design Modeler can be used to repair it; here ICEM CFD was used to fix the geometry and generate the mesh. The mesh was first generated with the octree method using five layers of prism cells at a ratio of 1.2, then improved with the Delaunay method, giving a final count of 457,864 cells. A UDF defines the pulsatile inlet velocity. The non-Newtonian behavior of blood is captured with the Carreau model, in which viscosity depends on the shear rate, defined by the zero-shear viscosity (µ₀), the infinite-shear viscosity (µ∞), the power index (n), and the relaxation time (λ). No energy equation is included, so temperature is neglected. The solver is transient, the flow is turbulent, and the density is constant at 1060 kg/m³, with a no-slip condition on the inner surface of the vessel wall. The UDF used to define the pulsating inlet velocity is provided.AnalysisThe results illustrate the inlet velocity and pressure drop over the pulse cycle, with the maximum velocity occurring at 0.15 s. The wall shear stress (WSS) contours show the maximum values in the aorta sections of smaller diameter, while the static-pressure contours show that at the beginning of the blood pumping, the pressure is highest at the entrance of the branches. When suction occurs at 0.4 s, it has the greatest impact on the inlet section of the aorta. Animation files of pressure and shear stress are included to reveal the pulsatile behavior and give a clearer understanding of the flow. By the end of this project, you'll be able to repair a real STL geometry from medical imaging, generate a prism-layer mesh, apply the Carreau non-Newtonian model with a UDF-defined pulsatile inlet, and interpret the velocity, pressure, and wall-shear-stress fields that characterize pulsatile blood flow in the aorta.

DescriptionIn this project, we present a simulation of a Blood Vessel via ANSYS Fluent software.Since the vessel is exposed to blood flow, an interaction occurs between the blood flowing and the vessel structure. First, the blood flow exerts a force on the vessel's body by hitting it. Subsequently, displacement or deformation appears on the vessel, which can lead to the blood flow being affected. Therefore, we intend to perform a numerical simulation of the blood vessel as a Fluid-Structure Interaction (called FSI).The interaction between fluid and structure can be implemented as:One-way FSITwo-way FSIIn this project, we aim to analyze both the effect of fluid on the structure and the effect of the structure on the fluid. So, we choose Two-way FSI, which is a more accurate and realistic but more complex approach.We modeled the geometry via Spaceclaim software. The computational domain is a sample space of a vascular system with a simple construction. We considered the blood vessel as a horizontal cylinder with a solid layer surrounding the fluid region.We meshed the computational domain via ANSYS Meshing software. The mesh is of an unstructured type, and approximately 56,000 cells have been generated.MethodologyFluid-structure interaction can be performed in two general methodologies:In the ANSYS Workbench environment, using an external solver (specifically, system coupling)Only in the Fluent solver (in the form of an intrinsic FSI).In this project, we implemented a two-way FSI in the ANSYS Fluent environment. In other words, the Fluent solver performs both fluid and solid calculations simultaneously.For two-way FSI in Fluent solver, the Structure model is utilized. The structural model can be implemented in two ways:Linear elasticity: The deformation is proportional to the applied force. In this case, the deformations are usually small, and the calculation process is faster.Nonlinear elasticity: The deformation is not necessarily proportional to the applied force. In this case, the deformations are usually large, and the calculation process is more complex and time-consuming.In this project, we considered fluid-structure interaction in the form of a Linear Elasticity state.Since we were analyzing two-way FSI and considering the effect of structural displacement on the adjacent fluid, we used the Dynamic Mesh model. In other words, we establish a connection between the fluid and structural calculations with the Intrinsic FSI option. Then, we enabled the smoothing and remeshing methods to define a deformable mesh.In addition, for defining blood flow in a pulse-mode, we used a user-defined function (UDF) so that the flow has a variable velocity with respect to time.ResultsWe analyzed the results in two fluid and solid approaches:In a fluid view, we studied the behavior of blood flow. For this, we obtained the distributions of the pressure and velocity of blood. The results show that the blood flow collides with the vessel body at pulsatile speed and, as a result, exerts a hydraulic force on the vessel structure.In a solid view, we studied the behavior of the vessel body under the influence of the applied forces of the blood flow. For this, we obtained the distribution of the von Mises stress and displacements (in all directions). The results confirm that the blood flow affects the vessel structure and, as a result, it undergoes deformation relative to the initial state.In conclusion, we can claim that we carried out the simulation project of a blood vessel correctly and acceptably by using the two-way FSI method.

Lumen Blood Vessel (Non-Newtonian) — ANSYS Fluent CFD SimulationDescriptionThis project simulates a lumen blood vessel using coupled Fluid-Structure Interaction (FSI) together with a non-Newtonian blood model in ANSYS Fluent. Because blood is a shear-thinning fluid whose viscosity changes with the local strain rate, a non-Newtonian treatment is essential for capturing the flow behavior realistically inside the vessel — and because the elastic vessel wall deforms under the pulsating flow, the case couples the fluid and structural response. Within the FSI: Beginner CFD Training Package, this project builds on the pulsatile blood-vessel case by adding non-Newtonian blood behavior, giving a more physically realistic biomedical FSI problem.MethodologyThe three-dimensional geometry was created in SpaceClaim, with a computational domain 164 mm long, 262 mm high, and 5 mm wide, meshed in ANSYS Meshing to a total of 356,794 elements. Owing to the pulsatile nature of the problem, a transient solver was used. A blood vessel together with its wall is simulated in ANSYS Fluent, with the solver's intrinsic FSI module enabled so that the displacement of the vessel wall could be captured in response to the flow. The inlet boundary condition was defined as a pulsatile velocity through a UDF, while the outlet was defined as a pulsatile pressure, also supplied through a UDF. The blood itself was modeled as a non-Newtonian fluid using the Carreau model, which reproduces the shear-thinning drop in viscosity as the shear rate increases, and a laminar model was enabled to solve the fluid equations.AnalysisOn completion of the solution, three-dimensional contours of wall displacement and von Mises stress were obtained. As the results show, the blood flowing through the vessel exerts stress on the vessel walls, deforming them and demonstrating the two-way coupling between the pulsatile non-Newtonian flow and the compliant vessel structure. From these results you can evaluate how the pulsating blood loads the vessel wall, where the stress and deformation concentrate, and how the shear-thinning viscosity shapes the flow. By the end of this project, you'll be able to set up a coupled FSI simulation with a compliant vessel wall, apply the Carreau non-Newtonian model with UDF-defined pulsatile inlet and outlet conditions, and interpret the wall-displacement and von Mises stress fields that characterize biomedical fluid-structure interaction.

DescriptionThis project presents a computational fluid dynamics (CFD) simulation based on the reference study "Analysis of Computational Fluid Dynamics and Particle Image Velocimetry Models of Distal-End Side-to-Side and End-to-Side Anastomoses for Coronary Artery Bypass Grafting in a Pulsatile Flow" by Shintani et al. (Circulation Journal, 2018).The objective was to reproduce and validate the hemodynamic behavior of two coronary artery bypass grafting (CABG) configurations: the distal-end side-to-side (deSTS) and end-to-side (ETS) anastomoses. Using ANSYS Fluent, the simulation results were benchmarked against the reference data to confirm the numerical methodology's accuracy. The study examined steady, laminar, incompressible flow conditions to evaluate velocity profiles and wall shear stress (WSS) distributions, verifying the CFD approach's reliability for modeling physiological blood flow through coronary bypass geometries.Geometry and MeshThe geometry was built in ANSYS Design Modeler using coronary artery and graft dimensions consistent with those described by Shintani et al. The model featured a circular graft connected to the host artery at a physiologically realistic angle, capable of representing both the ETS and deSTS configurations.An unstructured tetrahedral mesh was generated in ANSYS Meshing to accurately resolve flow gradients near the anastomotic junction, consisting of approximately 2,049,525 elements. Fine mesh resolution near the vessel wall enabled precise evaluation of velocity distribution and wall shear stress, ensuring mesh-independent, high-quality results suitable for validation.Model and Solver SettingsThe fluid was modeled as laminar, incompressible, and Newtonian, with blood-like properties: a density of 1060 kg/m³ and a dynamic viscosity of 0.004 Pa·s. An inlet velocity of 0.0097 m/s was applied, along with a pressure outlet boundary condition set to 0 Pa gauge pressure. Vessel walls were treated as rigid with no-slip conditions.Simulations were run using ANSYS Fluent's pressure-based solver under steady-state conditions, with convergence defined as all residuals falling below 1×10⁻⁵ — ensuring numerically stable, accurate predictions of the velocity field and wall shear stress distribution across the anastomotic region.ResultsThe CFD results showed strong agreement with the data reported by Shintani et al., confirming the reliability of the numerical approach. Velocity profiles closely matched the reference comparison at both evaluated sections, correctly capturing flow behavior at the anastomotic junctions.The dimensionless wall shear stress comparison likewise validated the model, with low WSS regions identified near the graft's heel and distal end — areas typically associated with disturbed flow — while higher WSS values appeared along regions of direct flow impingement. These results confirm the CFD model's ability to accurately capture the complex hemodynamic behavior characteristic of coronary bypass flow.