Internal Flow in Pipe CFD Simulation: Laminar Vs. Turbulent FlowDescriptionThis project examines one of the most fundamental and important comparisons in all of CFD: laminar versus turbulent flow inside a pipe. Rather than simulating a single case, the same geometry is solved three times — once as laminar flow, once with the k-ε RNG turbulence model, and once with the k-ω standard model — with the results compared side by side.Understanding whether a flow is laminar (smooth, ordered layers) or turbulent (chaotic, vortex-dominated), and which turbulence model to apply, is the single most important modeling decision in CFD. The flow regime is determined by comparing the Reynolds number against its critical value, and in this project, the inlet velocity is used to control the regime directly: 0.0176 m/s produces laminar flow, while 0.334 m/s produces turbulent flow. As the opening project of the Gas & Petrochemical: Beginner CFD Training Package, this case builds the physical intuition on which every later simulation depends.MethodologyThe geometry is a symmetric 3-D half-pipe — a semi-cylinder with a radius of 0.015 m and a length of 1 m — designed in Design Modeler. Exploiting the symmetry plane cuts the computational domain, and therefore the solution cost, in half. A structured mesh of approximately 23,120 elements is generated for the domain.The simulation uses a pressure-based steady solver with SIMPLE pressure–velocity coupling and second-order discretization. The three cases are solved with identical numerical settings, changing only the viscous model: laminar, k-ε RNG, and k-ω standard. The lesson also explains the practical selection criteria for each turbulence model — k-ε RNG is well suited to curved geometries, transient flows, and HVAC problems, while k-ω standard performs better with adverse pressure gradients, flow separation, swirling flows, and aerodynamics.AnalysisAt the end of the solution process, contours of pressure, velocity, and turbulent kinetic energy are extracted on the pipe's symmetry plane and compared across all three regimes. Velocity and pressure plots along the central axis quantify the differences between the two turbulence models, showing how each treats the same flow conditions.The comparison makes the physical distinction between the regimes directly visible: the smooth parabolic development of the laminar case against the flatter, mixing-dominated profiles of the turbulent cases. Every CFD engineer must answer "is my flow laminar or turbulent, and which model should I use?" on every project — and by completing this lesson, you will have built that judgment from the ground up, along with the practical skills of symmetric geometry design, structured meshing, and multi-case comparative post-processing.

Steam Ejector ANSYS Fluent CFD Simulation TutorialDescriptionThis project simulates a steam ejector using ANSYS Fluent — a mechanical device with no moving parts that uses a primary (motive) steam jet to entrain and mix with a secondary fluid. Ejectors perform two essential jobs in process industries: creating vacuum for suction and mixing two fluid streams. They achieve both through continuous conversion between kinetic and pressure energy as the flow passes through a convergent-divergent nozzle.In this simulation, water vapor serves as the motive fluid driving the suction of a secondary stream. The flow accelerates beyond the speed of sound inside the device, making this a fully compressible, supersonic problem — and an ideal gateway into compressible flow modeling. Ejectors are found throughout the gas and petrochemical industries and beyond: refrigeration, vacuum systems, desalination, chemical processing, and power plants all rely on them.MethodologyThe 2-D ejector geometry, built around a convergent-divergent (de Laval) nozzle, is designed in Design Modeler, and a structured mesh of approximately 52,000 elements is generated — an efficient grid well suited to internal compressible flow.Because the flow is supersonic and density varies strongly with pressure, the simulation uses the density-based solver — the correct choice for compressible flows where the pressure and density fields are tightly coupled. The pressure difference between the primary and secondary inlets is defined so that the motive jet naturally generates the low-pressure region that drives the suction of the secondary fluid, rather than imposing the entrainment artificially. The Mach number governs the behavior throughout the device, with the flow passing through subsonic, sonic, and supersonic regimes as it traverses the nozzle.AnalysisAt the end of the solution process, contours of pressure, velocity, and Mach number are extracted to trace the complete energy conversion cycle inside the ejector: the motive steam accelerates through the converging section, reaches sonic conditions at the throat, and expands to supersonic speed in the diverging section, creating the low-pressure zone that draws in the secondary fluid.Downstream of the nozzle, the results show the mixing of the motive and secondary streams and their subsequent compression as the combined flow decelerates — the mechanism by which the ejector delivers its pumping effect without any moving parts. By completing this project, you will learn to set up the density-based solver for compressible flow, design and mesh a convergent-divergent nozzle, configure the inlet pressure difference that drives entrainment, and interpret Mach number fields to identify subsonic, sonic, and supersonic regions — skills that carry directly into nozzles, diffusers, supersonic airfoils, and any flow where compressibility matters.

DescriptionThis project simulates two-phase ejector flow using ANSYS Fluent, modeling liquid and vapor ammonia moving through a device that functions as a fluid-dynamic pump with no moving parts other than an inlet control valve. An ejector works by directing a high-pressure primary fluid through a nozzle so that its jet entrains a lower-pressure secondary fluid and carries it into a region of higher discharge pressure, the same principle behind steam ejectors that deliver water to a boiler using the boiler's own steam instead of a mechanical pump. In this case, liquid ammonia enters the primary inlet at 9 MPa and 393 K, and this high-pressure jet induces vapor ammonia to be drawn through and exit the ejector alongside the liquid. The geometry is designed and meshed in Gambit with a structured grid of 11,808 elements.MethodologyTurbulence is resolved with the realizable k-epsilon model and standard wall functions, and the energy equation is active to capture the thermal effects tied to the phase change and high operating pressure. The VOF multiphase model tracks the interface between ammonia vapor and ammonia liquid so their interaction can be resolved directly. The simulation is steady, uses a pressure-based solver, and neglects gravity. The outlet is a pressure outlet at 0 Pa gauge and 312 K, walls are stationary with zero heat flux except for a coupled wall segment, and the solution uses SIMPLE for pressure-velocity coupling, PRESTO! for pressure discretization, a compressive scheme for volume fraction, second-order upwind for momentum and energy, and first-order upwind for the turbulence quantities, with hybrid initialization.AnalysisThe results include contours of pressure, temperature, and velocity throughout the ejector. These fields characterize how the high-pressure primary ammonia jet entrains and accelerates the secondary vapor stream, and how the two phases interact as they move through the nozzle and mixing sections toward the discharge, the core behavior that determines an ejector's effectiveness as a passive pumping device in ammonia-handling process systems.

DescriptionThis project simulates erosion in a 90-degree pipe elbow (knee) using ANSYS Fluent, investigated through CFD analysis. Erosion in bends is a critical concern in the gas and petrochemical industry, where pipelines routinely transport fluids carrying entrained solid particles over long distances.In a straight run of pipe, fluid impurities pose little problem. The difficulty arises when the flow changes direction: the suspended solid particles, owing to their inertia, cannot follow the fluid streamlines through the turn. This causes the particles to decouple locally from the carrier fluid and strike the pipe wall, gradually wearing away the material — the phenomenon known as erosion.In practice, industrial fluids are almost never pure, so erosion is an unavoidable challenge in pipeline transport. Flow turbulence intensifies the effect: the more turbulent the flow, the greater the momentum carried by the particles, and the harsher their impact on the wall. These impacts are most severe wherever the flow changes direction, which is exactly why erosion is concentrated at bends and elbows. Beyond turbulence, several other factors govern the extent and pattern of erosion, including particle size, particle mass flow rate, the redirection of the particle path, the number of wall impacts, and the overall flow rate.Because erosion tends to occur precisely where the flow redirects, fittings and joints — particularly elbows — are the primary locations examined when assessing erosion in a pipeline network.MethodologyEvery simulation begins by defining the computational domain. Although the geometry is a single elbow joint, it was subdivided into separate sections to enable a structured mesh, with each segment meshed individually so that boundary-layer settings could be applied precisely.The 3D geometry was created in ANSYS Design Modeler, and meshing was performed in ANSYS Meshing. The domain was split into four parts, each meshed with a structured grid. A structured mesh offers faster and more accurate CFD solutions — an advantage that matters greatly for erosion studies, since the boundary layer, path lines, and particle tracking must all be resolved cleanly as the flow travels through the bend. The final mesh contains 4,319,695 elements.Because the mesh was sufficiently fine, the Enhanced Wall Treatment method was used in place of standard Wall Functions for near-wall modeling, providing higher accuracy at the boundaries. The Discrete Phase Model (DPM) was applied to represent the solid particles carried within the pipeline.ConclusionAn important consideration is that the upstream pipe length must be long enough for the flow to fully develop before reaching the elbow; otherwise, the particle distribution entering the bend may yield unrealistic results. The velocity magnitudes of both the fluid and the particles can be readily examined from the corresponding contours.In addition, the particle concentration and — most importantly — the erosion contour are presented, offering a comprehensive picture of erosion behavior in pipeline bends.

DescriptionThis project uses ANSYS Fluent to simulate pigging oil flow inside a pipeline, a core operational process in gas and petrochemical pipeline engineering. A "pig" (Pipeline Inspection Gauge) is a device used inside pipelines for inspection, cleaning, and separating different fluid batches. Because a pig acts as an obstruction to flow, it introduces a pressure drop across its body — a key flow assurance concern this simulation investigates. The model examines fluid behavior around a stationary pig and the resulting pressure drop on either side, under two inlet oil velocities (0.9 m/s and 1.9 m/s).MethodologyThe 2D geometry, consisting of a pipeline with a simple pig inside it, is built in DesignModeler and meshed in ANSYS Meshing using an unstructured grid of 5,789 elements. The simulation uses a pressure-based, transient solver, run for 90 seconds with a 0.03 second time step, with gravity neglected. Turbulence is modeled using the standard k-epsilon model with standard near-wall treatment. The VOF multiphase model defines two fluid phases — gas-oil and petro — using implicit formulation with sharp interface modeling to track the boundary between them.Boundary conditions specify a velocity inlet (0.9 or 1.9 m/s) with a petro volume fraction of 1 and gas-oil volume fraction of 0, a pressure outlet at 0 Pa gauge, and stationary walls for both the pipeline and pig surfaces. The solution uses the SIMPLE scheme for pressure-velocity coupling, PRESTO for pressure discretization, second-order upwind for momentum, a compressive scheme for volume fraction, and first-order upwind for turbulence quantities, with standard initialization at zero gauge pressure and zero petro volume fraction.ConclusionResults include 2D contours of pressure, velocity, and phase volume fraction for both inlet velocity cases, evaluated at the final second of simulation. These results characterize the pressure drop and flow disruption caused by the pig, directly informing pipeline pigging operations and pressure loss management in oil and gas transport systems.

Surface Injection Using DPM — ANSYS Fluent CFD SimulationDescriptionThis project explores surface injection using the Discrete Phase Model (DPM) in ANSYS Fluent — a powerful tool for particle-laden flows across many industries. Using the Lagrangian approach, you'll simulate and analyze the behavior of particles injected from a surface, a technique essential to spray systems, combustion processes, and particle transport. Surface injection is one of the most basic ways to release particles into a domain, making it a natural first injection technique to master. Within the DPM: Beginner CFD Training Package, this project introduces the first and simplest injection method, building directly on the DPM interface lesson toward the spray and application cases that follow.MethodologyThe workflow begins with creating an optimized 3D cubic domain with surface-injection inlets in ANSYS Design Modeler, then building a structured mesh in ANSYS ICEM refined for accurate particle-flow resolution, with a final count of 92,809 elements. The simulation uses a pressure-based solver for incompressible flow, with a transient analysis and the Discrete Phase Model enabled for particle tracking, together with the gravitational effects and surface-injection parameters that govern how the particles enter and move through the domain. The DPM lies at the heart of the setup: rather than treating the particles as a continuum, it follows each particle individually along its trajectory — the Lagrangian approach that makes DPM so well suited to dispersed, particle-laden flows.AnalysisPost-processing focuses on extracting and interpreting the results: the 3D trajectories of the injected particles, how they disperse under the influence of gravity, and the injection statistics and evolution of the particle cloud over time through animated results. From these you gain a practical sense of how to tune injection parameters for a given application. The techniques are directly relevant to spray-system design, combustion and fuel injection, environmental particle dispersion, and pharmaceutical aerosol delivery — anywhere predicting where injected particles travel and how they spread is central to good design. By the end of this project, you'll be able to set up a surface-injection DPM simulation, apply gravitational and injection parameters, track particles with the Lagrangian approach, and interpret particle trajectories and cloud evolution — while understanding the advantages of Lagrangian tracking over Eulerian methods.

DescriptionThis project simulates airflow over a dimpled rotating cylinder using ANSYS Fluent software. A cylindrical object is placed inside a rectangular channel. The airflow enters the channel at a horizontal velocity of 0.45 m/s and collides with the cylindrical body.The cylinder rotates about its central axis at an angular velocity of 20 radians per second (rad/s), so a moving wall must be defined. For this reason, the fluid simulation domain is divided into two parts: the rotating region, which contains the cylinder rotating at a constant angular velocity, and the surrounding fluid region, which is the interior of the rectangular channel outside the cylinder.The cylinder wall features dimples whose protruding side faces the inside of the cylinder and whose recessed side faces the outside. The aim of the study is to investigate the pressure distribution and the rotational phenomena around the rotating cylindrical wall, since the presence of dimples on the cylinder surface influences the behavior of the fluid.The geometry of the present model is three-dimensional and is designed using SOLIDWORKS software. The meshing is performed with ANSYS Meshing software. The mesh type is unstructured, and the number of elements is equal to 1,064,903.Dimpled MethodologyA cylindrical wall is created in the form of an interface, that is, a common surface shared between two regions that allows the fluid to flow across its boundary. Around this wall, a dedicated flow region in the shape of a hollow cylinder is defined to represent the rotating cylinder. The Frame Motion (MRF) method is then used to simulate this inner cylindrical region, which rotates at the same angular velocity as the main cylinder.Dimpled ConclusionAt the end of the solution process, contours of pressure, velocity, and turbulent kinetic energy are obtained. Using the MRF method, the cylinder can be assumed stationary while the surrounding airflow is treated as rotating at the same rotational speed of 20 rad/s around the central axis of the cylinder. The contours clearly show the velocity and pressure distributions within the domain.

Sea Robot Motion Immersed in Water (Dynamic Mesh) — ANSYS Fluent CFD SimulationDescriptionThis project presents a CFD simulation of a sea robot moving through water using the Dynamic Mesh technique — the essential method for problems where a body physically moves through the fluid domain and the computational cells must change shape and position over time. In this project, the robot (modeled as a cube) starts on one side of the domain and travels toward the inlet against an oncoming water stream, letting you study the pressure buildup ahead of it and the wake region trailing behind. Within the Marine Engineering: Beginner CFD Training Package, this project introduces dynamic mesh for a body in motion, marking the step from fixed-geometry and free-surface cases toward genuine moving-body simulation.MethodologyThe 2D moving-body domain is designed in Design Modeler and meshed in ANSYS Meshing with roughly 30,010 elements. Because the location and shape of the computational cells change as the body moves, a Dynamic Mesh is mandatory, and a transient solver is required. Smoothing and remeshing work together to maintain high-quality elements as the body advances, preventing the mesh degradation that causes solver errors, with the mesh regenerated at a remeshing interval of every 50 iterations. A prescribed velocity profile is imposed on the moving body — 3 m/s in the X-direction over 0–3 seconds — while the surrounding flow is set up with an inlet water velocity of 1.5 m/s using the standard k-ε turbulence model.AnalysisPost-processing produces velocity, pressure, and turbulent-viscosity contours along with streamlines, revealing the elevated stagnation pressure ahead of the robot and the wake region trailing behind it. From these results you can study how the moving body loads the surrounding water and how its wake develops over time. Dynamic Mesh is the gateway to simulating real motion — submarines, AUVs, valves, pistons, projectiles, and store separation — and mastering smoothing and remeshing here equips you for an entire class of moving-body CFD problems. By the end of this project, you'll be able to set up a transient dynamic-mesh simulation, configure smoothing and remeshing to preserve mesh quality, prescribe the motion of a body through a fluid, and interpret the pressure and wake fields it produces.

DescriptionThis project simulates the flow over a NACA 0012 airfoil using ANSYS Fluent, with compressible flow as the central modelling theme. At the freestream conditions studied here, the air can no longer be treated as incompressible — density varies appreciably with pressure and temperature across the flow field — so the simulation is built around a compressible-flow formulation, making it a clear illustration of how that class of flow model is set up and solved. The airfoil is the cross-sectional shape of a lifting surface such as an aircraft wing, a wind-turbine blade or a helicopter rotor, and the aerodynamic behaviour of a given design depends strongly on its profile, which is why different airfoils are selected for different applications. The geometry is defined by familiar parameters: the chord line, the leading and trailing edges, and the angle of attack — the angle between the chord and the oncoming flow direction. In this case the angle of attack is 5°, so the incoming velocity is resolved into a horizontal component of cos5° ≈ 0.996 and a vertical component of sin5° ≈ 0.087. The objective is to examine the airflow behaviour and the pressure distribution around the airfoil and to study the resulting lift and drag forces.MethodologyThe geometry is created in Design Modeler and meshed in ANSYS Meshing with a structured grid of 35,000 cells. Because the flow is compressible, a density-based solver is used — the appropriate choice when density variations are coupled tightly to the pressure and energy fields, as they are in high-speed aerodynamics. For compressible flow, the Mach number must be specified in the boundary conditions; it is the ratio of the flow speed to the local speed of sound (for reference, the speed of sound in air at 25 °C is about 343 m/s). Airfoil simulations of this kind require a far-field boundary condition with the Mach number prescribed for the surrounding flow, set here to 0.6 — firmly in the subsonic-but-compressible regime where compressibility effects are significant and cannot be neglected.AnalysisThe solution produces two-dimensional contours of pressure, velocity, temperature, density and Mach number, together with streamlines around the profile. The results show the highest pressure at the leading edge, where the flow stagnates on direct contact with the airfoil, and the strongest pressure drop along the upper surface. This pressure difference between the upper and lower surfaces is what generates lift. The velocity field mirrors the pressure field exactly, as expected: regions of highest pressure coincide with the lowest velocity, and regions of lowest pressure with the highest velocity — the classic inverse relationship that underlies airfoil aerodynamics, here captured within a fully compressible treatment that also resolves the accompanying temperature and density variations.

DescriptionThis project models a wind tunnel and a specific body placed inside it using ANSYS Fluent, with the goal of investigating the drag force acting on the body. The wind tunnel is one of the most widely used aerodynamic testing tools in use today, enabling experiments on structures such as airfoils, aircraft, and static bodies to study aerodynamic behavior and visualize flow patterns. Wind tunnels can also be used for free-fall tests, examining how airflow affects a falling object, and since forces such as drag significantly influence a body's behavior, quantifying them accurately is essential — a task CFD is well suited to.MethodologyThe geometry is created in ANSYS Design Modeler and meshed in ANSYS Meshing using an unstructured grid totaling 179,542 elements. A density-based (compressible flow) solver is used in this simulation, with the energy equation activated to capture the compressible-flow physics across a range of inlet Mach numbers.AnalysisThe solution produces contours of velocity, pressure, temperature, and related quantities across the studied Mach numbers. The velocity vectors reveal the formation of separation vortices behind the body, and as a result of this separation, flow turbulence in the wake is substantial — noticeably greater than in the rest of the computational domain, illustrating how body geometry drives the wake dynamics that underlie drag generation.