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.

Borehole Flow, ANSYS Fluent CFD Simulation TrainingDescriptionThe interaction between flowing fluids and the surrounding formation inside a borehole is a fundamental concern in upstream hydrocarbon operations, where drilling provides the principal access to subsurface reservoirs. This project simulates liquid–solid two-phase flow in a vertical wellbore using ANSYS Fluent, with the objective of characterizing how soil grains detach from the borehole wall and become entrained in the fluid stream — a process of direct relevance to wellbore stability and solids production in oil and gas wells.The physics captured here underlies several critical drilling phenomena: sand production, which erodes downhole and surface equipment and plugs the wellbore; hole enlargement caused by excessive wall scouring; and cuttings transport, which determines how effectively the drilling fluid cleans the hole. Understanding the conditions under which a formation begins to fail under imposed flow is essential for designing safer wells and better solids-control strategies.MethodologyThe simulation employs the Eulerian multiphase model, with water as the primary (continuous) phase and soil grains as the secondary (dispersed) phase. This formulation is appropriate for particle-laden flows in which the dispersed-phase volume fraction exceeds roughly ten percent — characteristic of the slurry-type regimes encountered in drilling and in petrochemical particulate processing.The computational domain is reduced to a representative cylindrical sector of the wellbore to limit computational cost. Water enters the central region of the well at 1.6 m/s together with soil particles at 1 m/s. Turbulence is modeled with the standard k–ε model with standard wall functions and the dispersed turbulence multiphase treatment, and the case is solved with an unsteady, pressure-based solver that resolves the evolving flow field and phase distribution over time.AnalysisAt the end of the solution process, contours of phase volume fraction and velocity are extracted for both phases. The results show that a portion of the soil grains is liberated from the borehole wall and joins the fluid stream, while some fluid simultaneously penetrates into the formation. This behavior demonstrates the governing condition for solids detachment: the shear stress generated at the fluid–solid interface exceeds the cohesive adhesion holding the soil grains together.These findings carry direct engineering implications. Identifying the threshold at which interfacial shear overcomes grain cohesion provides a physical basis for predicting sand production; the same fluid–formation interaction governs wellbore stability, where controlled flow preserves wall integrity while excessive scouring promotes instability; and the computed volume-fraction and velocity fields inform the assessment of drilling-fluid carrying capacity and cuttings transport. By completing this project, you will learn to set up an Eulerian liquid–solid simulation, apply the dispersed turbulence treatment, and interpret phase-distribution results in the context of real drilling and completion operations.

Tank Discharge CFD Simulation, Ansys Fluent TrainingDescriptionThis project simulates the gravitational discharge of water through a multi-tank system using ANSYS Fluent. Tank discharge and transfer operations are a daily reality in gas and petrochemical plants, where liquids move between storage vessels under gravity through interconnected piping. The simulation employs the Volume of Fluid (VOF) multiphase model to capture the two-phase (water–air) flow dynamics and the evolving free surface as water drains from one tank and fills the next.The system consists of three interconnected tanks: a rectangular primary tank (229.4 mm × 157.7 mm) serving as the initial water reservoir, an octagonal secondary tank with uniform side lengths of 51.3 mm providing intermediate storage, and a rectangular tertiary tank (229.4 mm × 100 mm) acting as the final collection vessel. The design also includes air circulation pathways that maintain atmospheric pressure balance during discharge — a subtle but essential feature of real transfer systems.MethodologyThe two-dimensional geometry, including the three tanks and their connecting pipe network, is created in Design Modeler. An unstructured mesh of 15,310 elements is generated in ANSYS Meshing, providing adequate resolution for the free-surface dynamics and flow transitions between the tanks.The case is solved in transient mode with a pressure-based solver, with gravity applied at −9.81 m/s² along the y-axis as the driving force of the discharge. The VOF homogeneous model governs the two-phase flow, with air and water as the Eulerian phases; sharp interface modeling with interfacial anti-diffusion ensures accurate free-surface tracking, and the implicit formulation with implicit body force treatment provides solution stability. The flow is treated as laminar, appropriate for the low Reynolds numbers of gravitational discharge.The numerical setup uses SIMPLE pressure–velocity coupling, the PRESTO! scheme for pressure, second-order upwind for momentum, and the compressive scheme for volume fraction to keep the interface sharp. After standard initialization, the primary tank region is patched with a water volume fraction of 1. The solution advances with adaptive time stepping between 1×10⁻⁵ s and 0.001 s over 10,000 time steps to capture the complete discharge process.AnalysisAt the end of the solution process, contours of volume fraction, pressure, and velocity magnitude are extracted along with streamline patterns, tracking the discharge as it evolves in time. The results show the progressive transfer of water from the primary tank into the secondary tank, followed by overflow into the tertiary tank once the intermediate storage capacity is exceeded.The volume fraction contours clearly illustrate the free-surface evolution, with the VOF model capturing the interface deformation as water passes through the connecting pipes and fills the downstream tanks. The velocity and streamline results reveal the flow patterns inside each tank, while also demonstrating the role of the air circulation pathways in maintaining pressure equilibrium and preventing vacuum formation. By completing this project, you will learn to set up a transient VOF free-surface simulation, patch initial phase distributions, apply adaptive time stepping, and interpret discharge behavior in multi-vessel systems — insights directly applicable to pipe sizing, tank design, and venting requirements in industrial transfer operations.

Tank Charge (2-Phases), CFD Simulation Ansys Fluent TrainingDescriptionThis project models the filling — or "charge" — of a tank between two equal-height reservoirs using ANSYS Fluent. As water advances from one reservoir into the air-filled one, the two fluids exchange places: water flows in while air rises out, until the connected system settles into balance. The two-phase VOF approach captures this water–air interaction, reflecting the kind of phase separation and transfer operations that are common in chemical and petrochemical processing.What makes this case distinctive is its driving mechanism. Both vents are held at atmospheric pressure, so the transfer is driven purely by gravity and the pressure imbalance between the reservoirs rather than by a forced inlet velocity — a natural transfer problem rather than a pumped one, and a direct complement to the tank discharge project earlier in this package.MethodologyThe geometry consists of two 2-D reservoirs, each 1.25 × 2.5 m, built in Design Modeler and meshed in ANSYS Meshing with a structured grid of 32,510 cells.The case is solved as a pressure-based, transient simulation with gravity enabled at −9.81 m/s² along the Y direction. The water and air are tracked with the VOF model using two phases (air as primary, water as secondary), with a sharp interface and implicit formulation. Turbulence is modeled with the realizable k-ε model and standard wall functions. Both the inlet and outlet vents are set to 0 Pa gauge pressure, leaving gravity as the sole driver of the transfer.Pressure–velocity coupling uses the Coupled scheme, with PRESTO! for pressure discretization and the Compressive scheme for the volume fraction to keep the interface crisp. The case is initialized with the water region patched to a volume fraction of 1, then advanced with a 0.001 s time step over 10,000 steps.AnalysisAt the end of the solution process, 2-D contours of volume fraction, pressure, velocity, and turbulent kinetic energy are generated, along with an animation of the transfer process. The animation shows the mechanism clearly: air rises and escapes as the water advances into the air-filled tank, the two phases continuously exchanging places through the connected system.After several seconds of simulated time, the system approaches hydrostatic balance — equal pressure at equal elevations across the two connected reservoirs — confirming that the transfer reaches equilibrium exactly as expected from first principles. By completing this project, you will be able to set up a transient gravity-driven VOF simulation, configure pressure-vent boundaries for a natural transfer process, patch initial phase distributions, and interpret how a two-phase system evolves toward hydrostatic equilibrium.

Solar Radiation Effect on a Gasoline Tank SimulationDescriptionThis project simulates the effect of solar radiation on a gasoline storage tank using ANSYS Fluent — an important safety and storage problem, since overheating fuel raises its vapor pressure and increases evaporation risk. The simulation captures how sunlight heats the tank and its contents, and how a protective coating layer can mitigate that effect.The model consists of a cylindrical fuel tank placed inside an external airflow domain, and the study is comparative: the same case is run twice, once with a bare tank and once with an insulating coating layer, to quantify directly how the coating acts as a thermal barrier. Beyond fuel storage, the workflow developed here applies wherever sunlight drives thermal behavior — building heat loads, solar collectors, and vehicle cabins alike.MethodologyThe 3-D cylindrical gasoline tank and its surrounding external flow domain are designed in Design Modeler, and an unstructured mesh of 1,084,362 cells is generated in ANSYS Meshing. The comparative study uses two geometries: the bare tank, and the tank wrapped in a 0.003 m coating layer.Radiation is modeled with the P1 radiation model — well suited to this case thanks to its low computational cost and its ability to handle scattering and optically thick media. Solar ray tracing and the Solar Load model are activated to apply realistic thermal loading from the sun, with the solar calculator configured for a specific location and time: longitude 36.2605°, latitude 59.6168°, time zone +4.5, at 13:08 on day 17 of month 8. The external flow boundary conditions define air striking the tank at 10 m/s with a temperature of 318.15 K.AnalysisAt the end of the solution process, 2-D and 3-D temperature contours at the final time step are extracted and compared between the two configurations. The results show how solar radiation heats the tank surface and raises the temperature of the gasoline inside — and how the coating layer interrupts that heat path, acting as a radiation barrier that keeps the stored fuel significantly cooler.The comparison delivers a clear engineering conclusion: a thin insulating coating is an effective passive protection measure against solar heating of fuel storage, directly reducing vapor-pressure buildup and evaporation losses. By completing this project, you will learn to set up the P1 radiation model with solar ray tracing and the Solar Load calculator, define location- and time-specific solar inputs, combine external-flow convection with radiation loading, and run a two-geometry comparative study — a workflow that transfers directly to any design where sunlight governs the thermal response.

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.

Gas Sweetening Hydrodynamic, ANSYS Fluent CFD Simulation TutorialDescriptionThis project simulates the hydrodynamic behavior inside a gas sweetening unit using ANSYS Fluent. Gas sweetening is a critical process in the natural gas industry, removing hydrogen sulfide, carbon dioxide, mercaptans, and other contaminants from sour gas streams before transportation and end use. Treating sour gas is essential for two reasons: hydrogen sulfide and carbon dioxide are severely corrosive to pipeline infrastructure, and hydrogen sulfide is highly toxic to human health.The study concentrates exclusively on the hydrodynamics of the process — the flow interaction and contact between the gas stream and the amine solution — rather than the chemical absorption mechanisms themselves. Water serves as a substitute for the amine material in this hydrodynamic analysis. Understanding how the two streams meet, mix, and distribute inside the vessel forms the foundation of contact efficiency in real sweetening operations, where chemical absorption occurs at the gas–liquid interface.MethodologyThe three-dimensional geometry of the sweetening equipment, including the inlet configurations for both the gas and amine streams, is constructed in Design Modeler. An unstructured mesh of 2,168,649 elements is generated in ANSYS Meshing, providing adequate resolution for the multiphase flow interactions inside the vessel.The case is solved as a steady-state, pressure-based simulation with gravity applied at −9.81 m/s² in the vertical direction. The two-phase environment is defined with the VOF multiphase model using two Eulerian phases (gas and water) with dispersed interface modeling, and turbulence is handled with the RNG k-epsilon model with standard wall functions.The amine stream enters through a velocity inlet at 0.3 m/s with a water volume fraction of 1, while the gas stream enters through its own inlet with a water volume fraction of 0. Both the gas and amine outlets are defined as pressure outlets at 0 Pa gauge, and the equipment walls carry the no-slip condition. The numerical setup uses SIMPLE pressure–velocity coupling, the PRESTO! scheme for pressure, second-order upwind for momentum, and first-order upwind for the turbulence and volume fraction equations, starting from standard initialization.AnalysisAt the end of the solution process, two-dimensional and three-dimensional contours of pressure, velocity, and phase volume fraction are extracted for both the gas and water phases. The results show that the gas and amine streams collide after navigating through the internal flow barriers of the equipment — the key hydrodynamic event of the process.This collision demonstrates the amine current's ability to redirect portions of the gas flow toward the equipment outlet, revealing the mixing zones and contact patterns that would govern absorption efficiency in an actual sweetening operation. The velocity and pressure contours identify the flow distribution and potential areas for equipment optimization — insights essential to designing efficient gas–liquid contact systems. By completing this project, you will learn to set up a VOF simulation with dual inlet streams, model gas–liquid contact hydrodynamics, and interpret phase distribution results in the context of industrial gas treatment equipment.

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.