Air Freshener Spray Device CFD SimulationDescriptionThis project presents the numerical simulation of an air freshener device using ANSYS Fluent software.An air freshener is used to spray aromatic substances and is commonly found in rooms, homes, and public places. The mechanism of these devices is based on spraying: an aromatic liquid is stored inside the chamber of the air freshener and is then dispersed into the surrounding space through spraying.The geometry of the project is modeled using Design Modeler software. The geometry represents an air freshener, and the computational zone includes a sample box surrounding the device to study the spraying process within this space. The model is then meshed using ANSYS Meshing software. The mesh is unstructured, and the number of cells is equal to 357,758.MethodologyIn this project, the spraying of fragrant particles is simulated, which requires the use of the Lagrangian approach. According to this approach, the particles are tracked individually within a discrete space. For this purpose, the Discrete Phase Model (DPM) is used, and an injection is defined to disperse the discrete particles.The injection type is conical, and the particle type is droplet. The particle injection is unsteady and occurs over a period of 0.4 seconds. To make use of droplet injection, the Species Transport model is also defined, with three volumetric species: oxygen, nitrogen, and water vapor.ConclusionAfter the solution, particle tracking is studied at different points in time, and an animation of the particle injection is also obtained. By examining the particles over time, the spraying process of the device can be analyzed. The results show that the simulation is performed correctly and that the device sprays the fragrant particles effectively.

Description: This project simulates the operation of an asthma inhaler spray using ANSYS Fluent, targeting the mechanics of drug delivery for patients experiencing shortness of breath. Since inhalers deliver medication directly into the airways rather than through systemic absorption, they offer an advantage in reduced side effects compared to oral or injectable treatments, and CFD provides a way to visualize exactly how the spray disperses upon release.Methodology: The geometry, built in Design Modeler, includes the inhaler device with an internal orifice where the drug is injected, along with a surrounding computational zone representing the space into which the spray disperses; the domain is meshed in ANSYS Meshing with an unstructured grid of 752,277 cells. Because the goal is to track individual drug particles rather than treat the spray as a continuous phase, the simulation uses a Lagrangian approach through the Discrete Phase Model, with a surface-type injection releasing inert particles over an unsteady 0.1-second interval.Analysis: Particle tracking is examined across several time steps, supported by an animation of the injection sequence, showing how the medication disperses from the device over time. The results confirm that the spray is delivered effectively into the surrounding domain and validate that the injection setup and solution were configured correctly.

DescriptionThis project simulates the dispersion of human cough virus particles inside a coffee shop using ANSYS Fluent, investigated through CFD analysis. Studying how respiratory droplets travel through an indoor space is a core biomedical and public-health concern, since it informs how airborne infections spread in crowded environments and how such spaces might be made safer.The model was built in 3D using Design Modeler, with the computational domain representing the interior of a coffee shop. Meshing was performed in ANSYS Meshing using an unstructured grid, with the curvature method applied to refine the mesh in grid-sensitive regions; the total cell count is 4,578,388. Because of the time-dependent nature of the problem, a transient solver was used.MethodologyHere, ANSYS Fluent simulates the human cough virus particles using a two-way coupled Discrete Phase Model (DPM), in which the particles and the surrounding air influence one another. Following this injection definition, the virus particles are physically expelled from the patient's mouth as water droplets that evaporate in the surrounding air.These droplets have a temperature of 310 K, a velocity of 31.85 m/s, and a mass flow rate of 0.018 kg/s, released over the interval from 0 s to 0.1 s. The droplet diameter is not constant during propagation; instead, the Rosin-Rammler logarithmic distribution is used to characterize the range of droplet sizes. Through this method and its associated formulation, the minimum, maximum, and average diameters, the spread (exponential) parameter, and the number of diameter classes per injection are all defined. The droplet (drop) mode is applied together with the activated Species Transport model, allowing the evaporation of the droplets to be captured.The discrete-phase boundary conditions are defined as follows: the patient's mouth is set to Escape, meaning particles pass through this boundary; the surfaces of people's bodies and all the table and chair walls use the wall-film mode; and the floor uses the Trap mode, so that particles reaching it are captured and accumulate there. The simulation is unsteady, run over a 3 s interval with a time step of 0.01 s. The RNG k-epsilon model, together with the energy equation, was enabled to resolve the turbulent flow and compute the temperature distribution throughout the domain.AnalysisOn completion of the solution, the virus particle tracking at the final second of the simulation was obtained, based on the residence time of the particles. An animation of the virus dispersion and its gradual disappearance over time was also exported, showing how the droplets spread through the coffee shop and where they ultimately settle — offering insight into airborne transmission risk and the distribution of contamination across surfaces within an indoor public space.

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.

DescriptionThis project simulates the airborne transmission of coronavirus particles among airplane passengers via breathing, using ANSYS Fluent, in response to the well-documented risk that close passenger spacing on aircraft poses for disease spread. The computational domain represents an airplane cabin with rows of seats, one passenger modeled per seat, and each passenger's mouth defined as a surface source for exhaled breath and virus-laden droplets. Since maintaining physical distance is difficult in a cabin, the goal is to characterize how far and how effectively breath-borne virus particles travel between nearby passengers under the aircraft's actual ventilation conditions. The geometry is built in 3D in SpaceClaim and meshed in ANSYS Meshing with an unstructured grid of 1,316,384 elements.MethodologyVirus-laden droplets are modeled with a density of 1000 kg/m³, specific heat of 1680 J/kg·K, viscosity of 0.000172 kg/m·s, and surface tension of 0.03 N/m, released from each passenger's mouth during breathing. Since the goal is tracking a discrete population of droplets moving through the continuous cabin airflow, the Discrete Phase Model (DPM) is used, with the particles defined as inert and injected as a surface injection through each passenger's mouth inlet, at a diameter of 0.000001 m, temperature of 308 K, velocity of 0.05 m/s, and flow rate of 0.0000221 kg/s. The cabin's ventilation is represented in detail: fresh air enters from ceiling vents at 2.36 m/s and 292.65 K, from side vents at 0.3 m/s and 292.65 K, and from under-seat vents at 0.59 m/s and 292.65 K, while spent air exits through two lower-side outlets held at atmospheric pressure.AnalysisThe solution yields particle tracking based on residence time, along with 3D temperature and velocity contours throughout the cabin. These results show the virus-laden particles leaving the mouth and being picked up by the surrounding ventilation flow, tracing how the cabin's air circulation pattern carries exhaled droplets toward or away from neighboring passengers. This confirms the model captures its intended purpose: showing how the interaction between passenger breathing and the aircraft's specific airflow pattern governs the pathway and residence time of virus-carrying particles in an enclosed cabin environment.

Description: Face shields have become a widely adopted protective measure in situations where maintaining full social distance isn't practical, but their actual effectiveness at blocking respiratory droplets during ordinary conversation is not always intuitive from visual inspection alone. This study uses computational fluid dynamics to examine that question directly, simulating how virus-laden particles expelled during speech behave when a face shield is present, and whether it successfully intercepts those particles before they can reach another person nearby. The scenario is deliberately set at a distance closer than standard social-distancing guidelines recommend, representing a realistic close-proximity interaction such as a conversation at a counter or a brief face-to-face exchange, in order to test the shield's protective capability under a challenging rather than ideal scenario.Methodology: The 3D domain (1.6 m × 2 m × 2.6 m), built in Design Modeler, represents two individuals facing each other at 80 cm, with one designated as infected and their mouth acting as the source of viral particles during speech. The domain is meshed in ANSYS Meshing with 724,076 elements, and given the time-dependent nature of particle dispersion, a transient solver is used with a 0.001 s time step. The Discrete Phase Model tracks inert particles of 1 µm diameter released at body temperature (310 K) from the mouth over 0-20 seconds, driven by a sinusoidal velocity profile peaking at 0.33 m/s with flow rate scaled proportionally; an escape condition is set at the mouth to allow emission, while a trap condition on the shield surface captures incoming particles, and the RNG k-epsilon turbulence model is used alongside the energy equation to resolve flow and temperature behavior.Analysis: Particle tracking visualizations across the 20-second simulation, colored by residence time and velocity, show particles accumulating on the shield's inner surface rather than reaching the second individual, confirming that the shield effectively intercepts droplets expelled during speech. These results support the shield's role as a protective barrier in close-proximity interactions where maintaining full social distance isn't practical.

DescriptionThis project simulates cigarette smoke dispersion inside a smoking room using ANSYS Fluent, with an exhaust fan mounted on the upper wall and six air intakes at the base of the walls that draw in air due to the resulting negative room pressure. The room contains three occupants: a smoking woman seated on a bench near the domain's center, positioned relatively close to the exhaust fan's direction, a standing woman holding a cigarette in front of her, and a non-smoking man conversing with them nearby. The goal is to understand how air vortices shape smoke distribution in different parts of the room, which in turn informs where seating like a couch or bench should ideally be placed. The 3D geometry is built in Design Modeler and meshed in Fluent Meshing with a polyhedral grid of 965,187 elements.MethodologyCigarette smoke, treated as composed of four constituent materials, is modeled using both the Species Transport model and the Discrete Phase Model (DPM) together, capturing both the gas-phase dispersion and the discrete particulate behavior of the smoke. The exhaust fan on the ceiling is set to a negative pressure of -10 Pa to drive the room's ventilation, the energy equation is active to resolve temperature, and turbulence is handled with the standard k-epsilon model.AnalysisThe solution yields 2D and 3D contours of temperature, pressure, velocity, and smoke mass fraction, with 2D contours extracted on YZ and XZ planes through the room's center, along with time-resolved smoke pathlines. These pathlines show clearly how the combined intake and exhaust airflow shapes the smoke's movement through the room. The results indicate that seating placed closer to the exhaust fan's direction experiences a lower likelihood of air vortex formation, making that positioning preferable for reducing smoke exposure at seated locations. It's worth noting that smoke in this simulation originates only from the cigarette tips themselves; smoke inhaled and re-exhaled by occupants is not modeled, even though it could be a meaningful contributor to smoke distribution in real occupied spaces.

DescriptionPerforated plates feature patterns of holes, slots, or decorative shapes and are widely used in industrial applications such as filters, silencers, radiator grilles, ventilation, and separator plates. In CFD, the porous jump condition is used to model a thin "membrane" with known velocity and pressure-drop characteristics; typical uses include representing the pressure drop through screens and filters, and modeling radiators when heat transfer is not of concern. Perforated louvers, a common example, are generally used indoors to allow air to move from one area to another.This project uses ANSYS Fluent to simulate a porous-jump perforated-plate louver. A series of fluid flows is introduced into a rectangular duct, within which a porous region is created to study the flow behavior. The inlet and outlet use velocity inlet and pressure outlet conditions, respectively, with an inlet velocity magnitude of 1.5 m/s. The duct measures 1 × 1 × 10 m.Geometry & MeshThe 3D geometry was created in Design Modeler. Meshing was performed in ANSYS Meshing using a structured grid throughout the domain, with 85,760 elements.MethodologyThe simulation uses a steady, pressure-based solver with the RNG k-ε turbulence model to capture the shear and recirculation generated by the perforated plate (porous jump). Air is the working fluid, and the Discrete Phase Model is enabled with one-way coupling to track inert, uniformly sized particles without feedback to the continuous phase. The boundary conditions are a velocity inlet of 1.5 m/s, a pressure outlet at 0 Pa gauge, and stationary no-slip walls. The porous-jump thickness is set to 0.003 m, with the specified pressure-jump coefficient applied to represent the resistance of the plate — the heart of the model, since it reproduces the pressure drop across the perforated plate without resolving each individual hole. The DPM settings use Escape at the inlet and outlet and Reflect at the walls. The case is initialized with Standard Initialization (computed from the inlet) and advanced with a time scale factor of 1.ConclusionOn completion of the solution, the flow field around the perforated plate can be examined in detail. The velocity vectors show how the flow aligns as it passes through the perforated plate, and the pressure results reveal the drop imposed by the porous-jump region.This behavior reflects a common practical need: piping systems include numerous fittings — bends, valves, tees, enlargements, and contractions — and fluids passing through them often emerge maldistributed, which can be undesirable. Perforated plates are a frequently used means of homogenizing the flow, in addition to their other flow-control applications. The simulation demonstrates how the porous-jump approach efficiently captures the resistance and flow-conditioning effect of such a plate, making it a practical tool for evaluating perforated plates and similar thin flow-resistance elements.

Particle-Laden Flow in a Micro-Bearing (DPM) — ANSYS Fluent CFD Simulation TrainingDescriptionIn certain industries — such as Microelectromechanical Systems (MEMS) and microfiltration — micro-channels and micro-bearings with flow thicknesses on the micrometer scale can carry particles on the nanometer scale. Under these conditions, the effect of the carried particles on the frictional force acting on the channel or bearing wall becomes very important. This project uses ANSYS Fluent and the Discrete Phase Model (DPM) to investigate how this wall frictional force differs with and without the presence of particles. The particles are spherical anthracite grains 400 nm in diameter, and the two-way interaction between the particles and the fluid is taken into account.The geometry consists of two concentric cylinders with diameters of 100 and 70 micrometers, drawn in ANSYS SpaceClaim. The outer cylinder rotates while the inner cylinder remains stationary. The mesh was generated in ANSYS Meshing and comprises 51,840 hexahedral elements — a density that resolves the flow dynamics, turbulence effects, and particle distribution within the domain. The element size is kept larger than the particle size to ensure proper particle tracking.MethodologyA pressure-based, transient solver is used to track the particle injection over time and capture the interaction between the particles and the fluid. The fluid filling the gap between the cylinders is Polyalphaolefin (PAO) 68. The SST k-omega turbulence model is adopted for its effectiveness in capturing the complex flow that develops around the rotating cylinder, and a suitable wall function resolves the near-wall region — essential for accurately representing the fluid–particle interactions near the walls and the resulting frictional force. Appropriate particle-dynamics forces and models are also included to improve the accuracy of the DPM settings.Results & ConclusionThe simulation yields the following results:The frictional force on the inner cylinder wall increases over time, while the frictional force on the outer cylinder wall decreases over time.In both the with-particle and without-particle cases, the frictional force on the outer cylinder wall exceeds that on the inner cylinder wall. This is likely because the outer cylinder is the rotating one, and the frictional force is proportional to the velocity gradient.The wall frictional force is higher when particles are present, because the particles raise the effective viscosity of the medium and thereby increase the friction.The plots show that, for the inner cylinder, the problem reaches steady state after 27 ms without particles and after 34 ms with particles. For the outer cylinder, steady state is reached after 11 ms without particles and after 20 ms with particles. Overall, then, the case without particles reaches steady state after 27 ms, while the case with particles reaches steady state after 34 ms.

Decomposition of MgO with Argon Gas for Magnesium Particle Production — ANSYS Fluent SimulationIntroductionThermal decomposition, or thermolysis, is a chemical breakdown driven by heat. The decomposition temperature of a substance is the temperature at which it chemically breaks apart. Such reactions are typically endothermic, since energy is required to sever the chemical bonds within the compound. In line with the equation below, the decomposition of magnesium oxide is an endothermic reaction, and here the process is driven by preheating the system with argon gas:MgO(s) → Mg(s) + O₂(g)This project presents a Computational Fluid Dynamics (CFD) simulation of a magnesium–oxygen (Mg–O) thermal reaction using ANSYS Fluent. The aim is to investigate the coupled interactions between fluid flow, heat transfer, and chemical reaction within a specialized reactor geometry. A clear understanding of these processes is essential for optimizing the design and operation of Mg–O-based energy systems, which hold promise for clean energy production and storage.The geometry was created in ANSYS Design Modeler and meshed in ANSYS Meshing, producing a structured grid of 53,760 elements. This level of refinement provides a good balance between computational accuracy and efficiency.MethodologyA steady-state, pressure-based solver was used together with the SST k-omega turbulence model. Reaction modeling was handled with the Species Transport model coupled to the Eddy-Dissipation turbulence-chemistry interaction. The Discrete Phase Model (DPM) was activated to capture particle behavior, with droplet-type particles evaporating from the MgO-particle phase into the MgO-fluid phase.For the boundary conditions, argon gas together with MgO particles is injected from the right inlet, while argon gas alone enters from the left inlet.ConclusionThe CFD simulation of the Mg–O thermal reaction offers valuable insight into the coupled processes occurring inside the reactor. The key findings are as follows:Static Pressure — The pressure field ranges from −1.893 to 2.994 Pa, with higher values near the walls and lower values in the central region, a distribution that promotes reactant mixing.Temperature — Temperatures span 300–700 K, peaking in the lower chamber and at the outlet, which marks the primary reaction zone.Velocity — Velocity magnitudes range from 0 to 2.199 m/s, with complex flow patterns and recirculation zones that enhance mixing and boost reaction rates.Species Distribution — The Mg mass fraction (0–0.06) is highest in the lower chamber, coinciding with the high-temperature regions. The MgO-fluid mass fraction (0–0.1) peaks in the central chamber, illustrating product formation and transport. The O₂ mass fraction (0–0.039) is inversely correlated with the Mg concentration, confirming the progress of the reaction.Together, these results demonstrate the interplay between fluid dynamics, heat transfer, and chemical reaction. The reaction is most intense in the lower chamber, where significant recirculation strengthens mixing, and the formation and distribution of the MgO-fluid product are clearly observed.