Splitter Erosion CFD Simulation Training using DPM by ANSYS FluentDescriptionA splitter is a device used to uniformly distribute incoming fluid flow through outlets of matching shape and size. Beyond evenly dividing flow rate, splitters can incorporate filtration to remove impurities and improve outlet gas purity. Impurities such as sand and various metal oxides can cause progressive erosion on equipment surfaces over time, making erosion analysis on transmission pipelines and flow-distribution equipment a critical engineering concern.Using the Discrete Phase Model (DPM), this study examines how impurities within a working fluid affect erosion on a gas splitter body. The impurity-laden gas enters vertically at 5 m/s and is directed through three outlet nozzles. Impurity distribution, concentration, adsorption, and reflection behavior within the installed filters were analyzed using ANSYS Fluent, applying multiple erosion models to accurately predict erosion effects under varying operating conditions.The splitter geometry features three outlet nozzles, with a mainstream inlet diameter of 1.6 cm and outlet nozzle diameters of 0.3 cm. Filter fins measuring 2.5 cm in length are positioned inside the geometry, built using Design Modeler. The domain was meshed in ANSYS Meshing using an unstructured grid of 2,728,426 elements, with curvature and proximity refinement applied near the fins, and boundary layer meshing along the walls to satisfy turbulence model Y+ requirements.MethodologyThe governing equations were solved using ANSYS Fluent's pressure-based, steady-state solver, with gravitational effects excluded. Discrete phase particles were tracked using RANS with a Lagrangian reference frame, where particle inertia is balanced against the forces acting on each particle. Given the high-speed internal flow within the domain, natural gas density was treated as constant, with relevant thermodynamic properties — viscosity, thermal conductivity, and impurity density — defined accordingly.Key simulation parameters included:Natural gas properties: density of 0.65 kg/m³, viscosity of 0.00013 kg/m·sImpurity particles: density of 1600 kg/m³, uniform diameter of 0.15 mm, total flow rate of 0.04627 kg/sDPM settings: 10 continuous-phase iterations per DPM step, maximum step tracking of 50,000, trapezoidal tracking scheme, spherical drag law, and stochastic turbulent dispersion via the Discrete Random Walk modelParticle tracking outcome: of 24,900 tracked particles, 8,202 were trapped and 16,695 escapedBoundary conditions: 5 m/s velocity inlet, 0 Pa gauge pressure outlet, trap condition on fin walls, escape condition on external domain wallsTurbulence model: Realizable k-ε with enhanced wall treatmentSolution methods: SIMPLE pressure-velocity coupling, standard pressure discretization, second-order upwind for momentum, and first-order upwind for turbulent kinetic energy and dissipation rateFour erosion models were evaluated: the Generic model (broadly applicable, given sand is a common impurity across most cases), the Finnie model (empirically based, suited to malleable materials and sensitive to collision angle and velocity), the Oka model (accounts for wall hardness, making it well-suited to transmission pipe erosion analysis), and the McLaury model (intended for suspended solids in water, and found unsuitable for this particular case).AnalysisErosion contours across all applicable models consistently showed that particle impact on the upper wall — driven by high fluid velocity — produces greater erosion than other regions, with the outlet nozzle walls also experiencing elevated erosion. Oka erosion diagrams were monitored throughout the solution process to help assess convergence behavior.Impurity concentration contours revealed that near the splitter's outlet, high downstream velocity combined with a reduced cross-sectional area made particle exit difficult, leading to particle accumulation and increased impurity concentration in that region. However, the filter fins were shown to enhance impurity particle adsorption, as reflected in the trap-versus-escape particle tracking results.

Gas Particle Movement Through the Nozzle, CFD Simulation Tutorial by ANSYS FluentDescriptionThis simulation models gas-particle movement through a convergence-divergence nozzle using a two-way DPM model in ANSYS Fluent, with the nozzle operating under grossly overexpanded conditions.Convergence-divergence (also known as convergent-divergent or de Laval) nozzles are designed to accelerate flow from subsonic to supersonic speeds, with the narrowing throat section followed by a diverging outlet. When the exit pressure of such a nozzle is significantly lower than the surrounding ambient pressure, the flow is described as overexpanded — a condition that produces complex shock structures, flow separation, and pressure oscillations downstream of the throat. Understanding particle behavior under these conditions is particularly important in the gas and petrochemical industry, where nozzles of this type are widely used in gas transport, flow metering, and pressure-letdown applications, and where entrained solid or liquid particles can significantly affect equipment performance and erosion behavior.The 3D geometry was built using Design Modeler, and the domain was meshed in ANSYS Meshing with an unstructured grid totaling 16,245,216 cells.MethodologySeveral assumptions were applied to simulate this model: a pressure-based solver was used, only fluid behavior was examined (heat transfer was not simulated), and gravitational effects were ignored.Key simulation settings included:Viscous model: Realizable k-epsilon with scalable wall functionsPhases: air as the primary phase, gas particles as the discrete phase, using an explicit formulationBoundary conditions: velocity inlet at 5 m/s with an initial gauge pressure of 448,000 Pa and discrete phase escape condition; pressure outlet with 0 Pa supersonic gauge pressure and discrete phase escape condition; stationary wall with standard wall motionSolution methods: phase-coupled pressure-velocity coupling, PRESTO! for pressure discretization, and first-order upwind schemes for momentum, specific dissipation rate, and volume fractionInitialization: hybrid method, with a water velocity of 52 m/s in the y-direction and particle velocity initialized to 0 m/s in all directionsAnalysisThe results yield two-dimensional and three-dimensional contours of velocity, static enthalpy, and turbulence kinetic energy. The simulation illustrates how gas particles enter the nozzle from the inlet and travel through its convergent-divergent geometry, revealing how the nozzle's overexpanded shock structure and pressure distribution influence particle velocity under the given simulation conditions.

Diesel-Air Mixture Flow with Fuel Droplet Evaporation, CFD TrainingDescriptionThis project investigates the complex dynamics of a diesel-air mixture using ANSYS Fluent, focusing on the interaction between dispersed fuel droplets and the continuous air phase, along with the impact of droplet evaporation on the resulting flow field — analysis directly relevant to engine combustion and fuel spray characterization. The 3D geometry represents a simplified cylindrical combustion chamber, built in SpaceClaim, and meshed in ANSYS Meshing using a grid of over 2 million elements to ensure sufficient resolution for capturing the complex flow phenomena involved.MethodologyA pressure-based, transient solver was used, with the standard k-epsilon turbulence model and standard wall functions applied to capture the flow's turbulent behavior. The energy equation was enabled to resolve the temperature field. The Species Transport model with Eddy-Dissipation for turbulence-chemistry interaction was used to model the mixing of diesel and air. The Discrete Phase Model (DPM) simulated the movement and evaporation of fuel droplets as discrete entities, with two-way coupling enabled to capture the interaction between the dispersed phase (droplets) and the continuous phase (air). Temperature-dependent latent heat effects were also included to account for the energy consumed during droplet evaporation.Droplet evaporation was permitted throughout the domain, capturing the associated mass transfer process. Diesel fuel was injected from the center of the inlet, with air entering from the surrounding region, and the simulation ran for 5 seconds to capture the mixture's transient behavior.ConclusionThe simulation results provide detailed insight into the diesel-air mixture's flow behavior: Density contours show higher mixture density near the injection point, decreasing as the diesel jet spreads and mixes with the surrounding air — reflecting diesel's higher density relative to air. Mass fraction contours for diesel (C10) show the fuel jet extending into the chamber, with concentration progressively decreasing as mixing occurs, while nitrogen mass fraction contours show a corresponding decrease near the injection point as the diesel jet displaces the surrounding air.Static temperature contours reveal a significant temperature rise where the diesel jet interacts with the air, indicating heat release associated with combustion. Turbulence intensity is elevated near the jet, reflecting strong mixing activity, while velocity magnitude contours show peak velocity at the jet core, gradually decreasing as the jet spreads — illustrating momentum transfer from the injected diesel. Particle trajectories from the DPM model illustrate the dispersion and evaporation of individual fuel droplets within the chamber, with smaller droplets evaporating faster and exhibiting shorter residence times. Particle residence time visualizations further show how time spent within the domain varies depending on droplet size and injection location.

Combustion Chamber by DPM Spray, CFD Simulation ANSYS Fluent TrainingDescriptionThis project simulates a combustion chamber incorporating the Discrete Phase Model (DPM) using ANSYS Fluent, with the 3D geometry built in SpaceClaim and the mesh generated in ANSYS Meshing. The mesh was initially constructed using tetrahedral elements, then converted into a polyhedral mesh within Fluent itself. Given the nature of this problem, the simulation was run using a transient solver to capture the time-dependent development of the spray and combustion process.MethodologyThis project simulates a gas turbine combustion chamber — a configuration commonly used in jet engine applications — using the Discrete Phase Model alongside the Species Transport model. The effect of injecting sprayed benzene combusting particles is captured using the Eddy-Dissipation combustion approach together with its volumetric sub-model, with the Species Transport model enabled to represent the combustion reaction itself. Both airflow and fuel enter the domain through their respective inlet boundaries at a velocity of 3 m/s, with turbulence resolved using the SST k-omega model and the energy equation enabled to capture temperature variation as combustion progresses.ConclusionResults include contours of temperature and chemical species mass fraction, along with particle track visualizations. The benzene fuel reaches the nozzle as sprayed particles and is carried into the combustion chamber at high velocity, where combustion occurs within the nozzle before the resulting high-speed, high-temperature flow enters the chamber itself. The accompanying animation reveals that the flame front advances more slowly than the fuel penetration, with the injected fuel outpacing the flame as it moves through the domain.

Combustion Chamber CFD Simulation with Combusting Particle, ANSYS Fluent TrainingDescriptionThis project simulates a combustion chamber involving combusting particles using ANSYS Fluent, with the 3D geometry designed in SpaceClaim and the domain meshed in ANSYS Meshing using a structured grid totaling 125,000 cells. Given the nature of this problem, the simulation was run using a transient solver to capture the time-dependent behavior of the combustion process as it develops within the chamber.MethodologyGiven the continued industrial reliance on coal and the growing importance of developing cleaner coal combustion technologies, accurately modeling coal combustion characteristics remains an important area of study. This simulation uses a two-way Discrete Phase Model (DPM) for particle tracking, with anthracite — the highest-calorific-value type of coal — used as the injected material, while the Species Transport model with its volumetric sub-model was enabled to capture the combustion reaction itself. Particles were injected into the domain at a velocity of 1 m/s and a temperature of 308 K over a 1-second injection period, with turbulence resolved using the standard k-epsilon model and the energy equation enabled to capture temperature variation throughout the domain as combustion progresses.ConclusionResults include 2D and 3D contours of temperature along with particle track visualizations, confirming that effective combustion occurred within the chamber. The average chamber temperature reached 3765.30 K, a result consistent with the high calorific value expected from anthracite combustion and indicative of a well-sustained reaction throughout the injection period.

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.

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.

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.

Spiral Magnetic Separator CFD Simulation Using ANSYS FluentIntroductionThis study investigates the performance of a spiral magnetic separator using computational fluid dynamics to understand the complex interactions between fluid flow, magnetic particles, and an applied magnetic field within the separator. Water enters the domain from the upper boundary carrying both magnetic particles and SiO2 particles, while an applied magnetic field, represented through user-defined functions for the Bx, By, and Bz components, influences the trajectory of the magnetic particles and enables their separation from the non-magnetic SiO2 particles. By combining turbulence modeling, discrete phase modeling, and magnetohydrodynamics, this research provides valuable insight into the separation efficiency and flow behavior characteristic of magnetic separation systems.Geometry and MeshThe geometry consists of a spiral-shaped separator with multiple turns, designed in ANSYS SpaceClaim and meshed in ANSYS Meshing to promote effective particle separation along the spiral flow path. The simulation was conducted using a steady-state, pressure-based solver in ANSYS Fluent to capture the coupled flow and particle behavior throughout the domain.MethodologyTurbulent flow within the separator was resolved using the Realizable k-epsilon model with standard wall functions. A two-way coupled Discrete Phase Model was implemented to simulate the behavior of both magnetic and SiO2 particles, capturing the interaction between the particles and the continuous water phase. Group injection was defined for both particle types, with diameter distributions specified using the Rosin-Rammler model. The Magnetic Induction MHD method was enabled with a DC field type to simulate the effects of the applied magnetic field on both the flow and particle trajectories, with several user-defined functions implemented to define the source terms for the Bx, By, and Bz magnetic field components.Results and ConclusionThe magnetic field components exhibit alternating positive and negative regions along the spiral path, with By ranging from -1.5347×10⁻¹⁵ to 1.7298×10⁻¹⁵ T, Bz ranging from -1.0879×10⁻¹⁴ to 8.7105×10⁻¹⁶ T, and Bx displaying a more complex distribution between -3.79×10⁻¹⁵ and 4.40×10⁻¹⁵ T. Static pressure within the separator ranges from -0.84606 to 4.6414 Pa, with higher pressures concentrated near the outer walls of the spiral, while velocity magnitude varies from 0 to 0.13735 m/s, with higher velocities observed near the inner walls. Particle tracks reveal a polydisperse mixture with diameters ranging from 1.00×10⁻⁴ to 2.96×10⁻⁴ m, experiencing static pressures between -8.94380 and 9.69623 Pa as they travel through the domain. Pathlines colored by Bx and velocity magnitude illustrate the complex spiral flow pattern, with velocities along the pathlines reaching up to 0.181 m/s in the upper turns of the spiral. The particle tracks further indicate a gradual separation of particles based on their magnetic properties and size, with larger and more strongly magnetic particles tending to concentrate toward the outer walls of the spiral. These results confirm the effectiveness of the spiral design in creating an extended separation path, where the combined variation in magnetic field strength and flow velocity along the spiral drives progressive particle segregation based on each particle's position within the separator.

Electric Field Effect on Nanofluid Heat Transfer (EHD) — ANSYS Fluent CFD SimulationDescriptionThis project uses ANSYS Fluent to investigate the effect of an electric field on nanofluid heat transfer in an N-shaped cooling pipe, applying the EHD (Electrohydrodynamic) module coupled with the DPM (Discrete Phase Model). A potential difference is established between the pipe shell (positive) and a central wire (negative), driving charged aluminum nanoparticles through the coolant to enhance heat transfer from the hot pipe walls. Cool water enters the pipe and absorbs heat from walls held at 390 K, with the outlet temperature rise used to evaluate the effect of the particles and electric field on cooling performance. Within the Magnetohydrodynamics & Electrohydrodynamics (MHD & EHD): All Levels CFD Training Package, this project opens the Electrohydrodynamics block, introducing the electric field effect as the EHD counterpart to the magnetic-field nanofluid cases.MethodologyThe 3D geometry is built in SpaceClaim, with an inlet, outlet, hot wall zone, an inner wall representing the central wire, and an outer wall representing the pipe shell. The domain is meshed in ANSYS Meshing using an unstructured grid of 2,966,928 elements and 720,300 nodes. The EHD model is combined with DPM to simulate the current generated between the positive and negative poles and its effect on heat transfer from the walls. Aluminum nanoparticles are modeled as inert solid particles with a diameter of 0.00001 m, a charge density of 23, and a total flow rate of 1e-20 kg/s, using the DPM model with interaction with the continuous phase. The energy equation is enabled to resolve the temperature distribution, and the results are compared between a case with particles and electric field versus a baseline case without them.AnalysisTemperature contours show more uniform heat distribution in the case with particles and electric field, with the average domain temperature rising by 0.1 K (310.43 K vs. 310.31 K) and the average outlet temperature rising by 0.5 K (316.59 K vs. 316.16 K) compared to the baseline. Velocity contours also show a more uniform flow field in the particle-laden case, indicating that the electric field's influence on the charged nanoparticles measurably improves cooling performance and heat distribution uniformity. By the end of this project, you'll be able to couple the EHD module with the Discrete Phase Model, drive charged nanoparticles through a coolant with an applied electric field, run a comparative study against a baseline without the field, and interpret the temperature and velocity fields that reveal how electrohydrodynamic effects enhance nanofluid heat transfer.