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.

Spillway CFD SimulationDescriptionIn this project, a three-dimensional spillway is simulated using ANSYS Fluent to study how excess water is managed and released from a dam structure. Spillways serve as the outlet mechanism for a dam, designed to safely carry surplus water and floodwater from the reservoir side down to the downstream side once the water level rises past a defined threshold. Because agricultural water management relies heavily on dams and reservoirs for irrigation supply, understanding spillway hydraulics is directly relevant to agricultural and food engineering applications, where controlled water release protects both the structure and the farmland downstream.Several spillway designs exist, including ogee-shaped, stepped, side-channel, lotus, tunnel, and siphon spillways. This project focuses on simulating the flow behavior over one such spillway geometry using a multiphase approach, treating air as the primary phase and water as the secondary phase. In the model setup, the water column at the inlet reaches a height of 0.155 m, while the full model height is 0.306 m, and the dam structure itself has a height of 0.156 m.The three-dimensional geometry was built in Design Modeler, and the mesh was generated in ANSYS Meshing using an unstructured mesh strategy, resulting in a total of 698,691 elements.MethodologyThe two-phase air-water interaction is captured using the Volume of Fluid (VOF) multiphase model. Gravity is applied along the y-axis at a magnitude of -9.81 m/s² to correctly represent the driving force behind the water's downward flow over the spillway.ConclusionThe resulting flow contours confirm that the spillway performs as intended, allowing the water to pass over the structure smoothly. The highest flow velocities appear where the flow cross-section narrows, since the water accelerates as it's forced through the tighter geometry — a behavior consistent with what's expected in real spillway operation and useful for engineers designing water release systems for agricultural reservoirs.

Wind Tower with Qanat CFD SimulationDescriptionThis project uses ANSYS Fluent to simulate a wind tower system paired with a qanat, a traditional passive cooling arrangement rooted in the architecture of hot, arid regions. Passive ventilation techniques rely entirely on natural driving forces rather than mechanical equipment, and they generally fall into two categories: wind-driven systems, where airflow is generated by pressure differences, and buoyancy-driven systems, where temperature-induced density differences create natural convection. The wind tower and qanat combination sits in a hybrid category, drawing on both mechanisms at once.In this setup, a tall tower rises above the building and works together with an underground channel — the qanat — which acts as a natural cooling reservoir. When wind strikes the tall structure, it creates a pressure imbalance: high pressure builds on the windward face while a low-pressure zone forms behind it, driving suction that pulls air through the system. Meanwhile, inside the underground channel, incoming hot air passes over a body of cool water, picking up moisture and losing heat before rising into the building through the floor to condition the interior air.To keep the model manageable, the water surface inside the channel wasn't explicitly represented; instead, a fixed-temperature boundary condition of 278 K was applied to the channel walls to represent its cooling effect. The incoming hot air enters the channel at 0.2 m/s and 300 K. Window surfaces exposed to sunlight and outdoor heat were assigned a constant temperature of 298 K. The geometry, built in Design Modeler, consists of three connected components: the room, the tower, and the underground channel. Meshing was carried out in ANSYS Meshing using an unstructured approach, producing 402,198 cells.MethodologyThe simulation was run as a steady-state, time-independent case using a pressure-based solver in ANSYS Fluent. Because natural convection plays a central role here, buoyancy effects were captured by allowing air density to vary with temperature rather than treating it as constant — warmer, lighter air rises, which is what drives hot air out through the tower. This density variation was modeled using the incompressible ideal gas law, where density depends on temperature and operating pressure rather than on local pressure fluctuations, consistent with the assumption of constant pressure throughout the domain.ConclusionThe simulation produced temperature, pressure, and velocity contours in both 2D and 3D, along with velocity vector fields. The temperature results clearly show the cooling pathway: cool air drawn in through the underground channel and hot air escaping through the tower opening. The pressure field confirms the mechanism driving this exchange, with high pressure outside and lower pressure inside the room and tower pulling hot air upward and out. The velocity vectors trace this circulation clearly, showing cool air entering rapidly at floor level, circulating through the room, and exiting through the tower once conditioning is complete. Overall, the results confirm that this passive wind tower and qanat system successfully performs natural air conditioning without any mechanical input.

Project OverviewThis project presents an ANSYS Fluent simulation of time-dependent pulsatile blood flow through a simplified arterial bifurcation model.Geometry and MeshingThe fluid domain was created in Design Modeler, with mesh generation performed in ANSYS Meshing. An unstructured mesh containing 168,367 elements was employed for the computational domain.Boundary ConditionsBlood mass flow rates are specified as 0.001570178 kg/s at the inlet and 0.00078576 kg/s at each outlet. Inlet blood pressure is set at 250 Pa (approximately 1.87515 mmHg). For reference, physiological blood pressure in major human arteries typically ranges between 80 and 120 mmHg.Pulsatile Flow ImplementationThe pulsatile characteristics of blood flow are captured through a User-Defined Function (UDF), which modulates inlet velocity as a sinusoidal function of time, replicating the cardiac cycle’s rhythmic nature.Results and Clinical InsightsThe transient solver provides time-resolved flow data, with results presented at t = 0.162s, corresponding to peak systolic velocity. The simulation yields clinically relevant insights into arterial pathology susceptibility.High-Pressure Risk Zones: Pressure contour analysis at t = 0.16s reveals critical stress concentrations at the bifurcation apex, where flow streams diverge. Blood pressure reaches 125 Pa at this location—approximately half the inlet pressure—identifying this region as vulnerable to arterial wall rupture.Stenosis-Prone Regions: Wall Shear Stress (WSS) distribution analysis identifies areas susceptible to stenosis formation. Consistent with medical literature establishing low WSS as a stenosis predictor, the bifurcation apex exhibits minimal shear stress values, indicating heightened risk for atherosclerotic plaque development and subsequent arterial narrowing.

Baffle Cut Effect on Shell and Tube Heat Exchanger CFD SimulationDescriptionShell and tube heat exchangers are a workhorse of the chemical process industry, and this project investigates how baffle design affects their thermal performance. Baffles, typically made of a highly conductive metal, redirect the shell-side fluid flow to improve heat distribution across the shell, boosting overall heat transfer. The tradeoff is pressure drop: adding more baffles improves thermal performance but also increases the resistance the fluid has to work against, so the baffle cut geometry and spacing need to be chosen carefully rather than maximized blindly.This project simulates the baffle cut effect using ANSYS Fluent, treating the problem as a conjugated heat transfer case where the metal baffles themselves are modeled as solid domains participating directly in the heat exchange. Cool water enters the shell at 300 K with a flow rate of 0.5 kg/m³, equivalent to a velocity of 0.7 m/s, while the inner tubes are held at a constant wall temperature of 450 K to represent the hot side. Water's physical properties are defined as piecewise-linear functions of temperature rather than fixed constants, which improves the accuracy of the heat transfer prediction. The goal is to see how effectively the baffles' high thermal conductivity spreads heat away from the hot tubes into the surrounding fluid.Geometry & MeshThe geometry was built in Design Modeler with a shell measuring 600 mm in length and 90 mm in diameter. Six baffles, each 4 mm thick, are spaced 86 mm apart center-to-center, with a baffle cut of 36%. Seven tubes, each 20 mm in outer diameter, are arranged in a triangular pattern inside the shell with a 30 mm center-to-center spacing. The mesh was generated in ANSYS Meshing as an unstructured grid with 1,953,754 elements and a 30 mm element size, with a boundary layer mesh added near the walls to satisfy the Y-Plus requirements of the standard wall function.MethodologyThe case was solved as a steady-state problem using the pressure-based solver in ANSYS Fluent, with gravitational effects included. Turbulence was captured using the realizable k-ε model with standard wall functions. The SIMPLE algorithm handled pressure-velocity coupling, with standard spatial discretization for pressure and first-order upwind schemes for momentum, turbulent kinetic energy, turbulent dissipation rate, and energy. The shell wall was treated as adiabatic, while the tube wall was fixed at 450 K, and the outlet was set as a pressure outlet at 0 Pa gauge.ConclusionThe results show the shell-side water heating up from its 300 K inlet temperature to roughly 360 K at the outlet as it flows past the hot tubes. Heat transfer coefficient and total heat transfer rate both converge steadily as the solution progresses. By modeling the baffles and fluid together in a conjugated heat transfer setup, the results show that the baffles noticeably speed up temperature diffusion throughout the shell, raising the average fluid temperature and improving the heat transfer coefficient accordingly. The pressure contour at the exchanger's mid-plane shows a pressure drop of around 1 kPa, giving a clear picture of the tradeoff between the improved thermal performance and the flow resistance the baffles introduce.

Home Water Distiller CFD SimulationDescriptionThis project simulates a small-scale home water distiller using ANSYS Fluent, investigating one of the more accessible approaches to water desalination. The system relies entirely on heat transfer and phase change to produce clean water: a floor heater warms water until it evaporates, and because this steam carries none of the original salt, bacteria, or contaminants, cooling it back into liquid form yields pure freshwater. The steam travels through a spiral tube, where a fan cools the surrounding pipe walls, driving the vapor to condense back into fresh water on the other side.The device is built from three functional sections: an evaporator at the bottom where water turns to steam, a condenser at the top where that steam is cooled, and a spiral tube connecting the two that serves as the pathway for vapor transfer and the site where condensation actually occurs. To keep the model manageable, the heater and fan themselves weren't explicitly modeled; instead, fixed temperatures were assigned directly to represent their effects; the evaporator was held at 373 K, matching the saturation temperature of water, while the condenser was set to 363 K. The patch tool was used to define the initial water level inside the evaporator tank.Because the process unfolds over time as water evaporates and vapor condenses, the simulation was run as a time-dependent, unsteady case, allowing the rate of phase change and the resulting freshwater output to be tracked as the system evolves. The three-dimensional geometry was built in Design Modeler, and the model was meshed in ANSYS Meshing using an unstructured grid of 478,805 cells.MethodologyThree phases coexist in this system: liquid water, water vapor, and air, which serves as the coolant inside the condenser. Since these phases need to be tracked with distinct, clearly defined boundaries, the Volume of Fluid (VOF) model was used, with air set as the primary phase and both liquid water and water vapor treated as secondary phases. The Sharp interface option was applied to keep the boundary between phases crisp rather than smeared across a transition layer.The evaporation-condensation process between the water and vapor phases was captured through a mass transfer mechanism based on Lee's equations, which calculate phase-change rates based on the saturation temperature and the evaporation/condensation frequency coefficients. With the saturation temperature set at 373.15 K, any fluid temperature above this threshold triggers evaporation, while any temperature below it triggers condensation.ConclusionThe simulation produced contours of temperature, phase-change rate, and volume fraction for both water and vapor, captured on a mid-plane cross-section at the final second of the 10-second simulation, along with animations tracking how these quantities evolve over time. Plots of freshwater output — both the volume-averaged water fraction inside the system and the mass flow rate of freshwater leaving the condenser — were also generated.The temperature and mass transfer contours line up closely: wherever the fluid temperature drops below the saturation point, condensation occurs, shown by a negative phase-change rate. Inside the evaporator, water evaporates from its surface and rises as steam; once that steam reaches the cooler condenser tube, it condenses back into liquid, producing usable freshwater. The output plots confirm that freshwater production increases steadily over time as more condensation occurs, demonstrating that the desalination system's core evaporation-condensation cycle works as intended.

DescriptionThis project simulates the water flow through a Francis hydraulic turbine using ANSYS Fluent. As a cornerstone of hydroelectric power generation, a water turbine is a turbomachine that converts the kinetic energy of flowing water — or the potential energy stored in a head (height) difference — into mechanical rotational motion, which is subsequently transformed into electrical power by a coupled generator.The Francis turbine is one of the most widely deployed turbine types in power plants because the arrangement of its blades allows it to harness kinetic and potential energy simultaneously, making it highly effective across a broad range of head and flow conditions.In operation, water first enters the volute (spiral casing), whose circular geometry imparts a rotational (swirling) component to the incoming flow. This swirl ensures the fluid strikes the blades at the correct angle, maximizing operational efficiency. The flow is then delivered at a controlled rate to the runner blades, where the momentum of the water drives the runner and produces useful mechanical work. Finally, the water exits the runner in an axial direction.In the present case, water enters the turbine's inner chamber at a mass flow rate of 1.996 kg/s, with the runner blades rotating at 158 rpm.MethodologyThe rotation of the blades is modeled using the Multiple Reference Frame (MRF) approach, also known as frame motion. In this method, the fluid region surrounding the blades is assigned a rotational motion, while the blades themselves are held stationary relative to that rotating frame — effectively reproducing the rotational flow field around the runner without physically moving the mesh.The geometry was built in Design Modeler and consists of two main components: fixed walls carrying stationary vanes at fixed angles, and moving walls carrying the rotating vanes.Meshing was performed in ANSYS Meshing using an unstructured grid of 4,653,160 elements, with local refinement applied near the blades to better capture the flow behavior in these critical regions.ConclusionOn completion of the solution, two- and three-dimensional contours of pressure, velocity, path lines, and velocity vectors were extracted. As expected, the peak velocity occurs in the immediate vicinity of the rotating blades. A full set of performance results can be derived from the simulation, including a pressure drop of approximately 2.3 × 10³ Pa across the turbine.

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.

Mastering Solar Chimney Design: Advanced CFD Simulation for Heat Transfer EngineersWelcome to the "Solar Chimney CFD Simulation" episode of our Heat Transfer Engineers: Intermediate course. This module offers an in-depth study of buoyancy-driven flows, applying Computational Fluid Dynamics (CFD) in ANSYS Fluent to analyze and optimize solar chimneys. Approached through a heat transfer lens — coupling conduction, convection, and radiation — the module explores this passive ventilation technology and shows how to improve thermal efficiency in sustainable building design.We begin with the fundamentals of the solar chimney itself. You will examine the physical mechanisms that set air in motion, centered on the stack effect and thermally induced buoyancy, and study the essential components of an effective system, including the solar collector, the air channel, and the outlet.The module then turns to boundary conditions and how to capture buoyancy effects realistically. You will learn to represent the solar energy absorbed at the chimney surfaces — the surface heating that drives the buoyant flow — and to define the atmospheric conditions and pressure differentials needed to reproduce natural ventilation.With the physics established, we walk through the CFD setup in ANSYS Fluent. This covers meshing strategies that resolve both the large-scale chimney structure and the fine detail of the airflow channels, along with the selection and configuration of appropriate turbulence, heat transfer, and buoyancy models for an accurate solution.Next, you will learn to interpret the key outputs of the simulation. This includes generating and reading temperature contours to understand thermal stratification and heat distribution within the chimney, as well as analyzing velocity vector fields to judge how effectively the buoyancy-driven ventilation performs.The module also links solar input directly to chimney performance. You will carry out a parametric study to quantify how variations in solar radiation influence flow rates and temperature fields, and simulate the chimney under different diurnal and seasonal conditions to assess how performance shifts throughout the day and across the year.Finally, you will translate the CFD data into practical design improvements — quantifying ventilation rates and thermal efficiency across configurations, and using the results to refine key parameters such as chimney height, width, and inclination angle. These insights are connected to real engineering challenges, from integrating solar chimneys into eco-friendly architecture to improving natural ventilation in industrial facilities and large structures.Why This Module Matters for Intermediate Heat Transfer EngineersThis intermediate module provides a focused study of passive ventilation driven by heat transfer — an increasingly valuable skill in sustainable building design. By completing it, you will deepen your understanding of natural convection and buoyancy-driven flow, strengthen your CFD techniques for modeling coupled thermal-fluid interactions in tall structures, and learn to apply CFD analysis to raise the efficiency of passive ventilation systems.By the end of the episode, you will be able to set up and run complete solar chimney simulations in ANSYS Fluent, interpret the results to evaluate ventilation performance and identify improvements, and apply those insights to enhance solar chimneys and similar passive systems. This knowledge forms a key stepping stone for heat transfer engineers moving toward specialization in sustainable building technologies, laying the groundwork for advanced work in passive cooling, natural ventilation, and energy-efficient building design.

DescriptionThis project investigates steady airflow (ventilation) within a storage container room containing two internal walls, simulated using ANSYS Fluent and studied through CFD analysis. Proper ventilation of this kind is a core HVAC concern, since maintaining a consistent airflow is essential for effective cooling and air distribution in storage environments.The three-dimensional geometry was created in Design Modeler, and meshing was performed in ANSYS Meshing. A structured mesh was used, comprising 115,635 elements.MethodologyHere, ANSYS Fluent is used to examine steady airflow through a storage container room fitted with two walls. Such container rooms are commonly used to store perishable industrial goods, which must be kept under continuous, steady airflow to ensure adequate cooling and ventilation.In this study, the airflow is simulated within a 0.5 × 0.5 × 1 m chamber containing two walls positioned across the flow path, representing the storage enclosure. Air enters the domain at a velocity of 5 m/s and accelerates to a maximum of roughly 20 m/s after passing over the second wall, as a result of the constricting geometry.The standard k-epsilon turbulence model, together with the energy equation, was enabled to resolve the turbulent flow field and compute the temperature distribution throughout the domain.ConclusionOnce the solution converged, two-dimensional contours of pressure, velocity, and streamlines were obtained. As shown, the inlet air velocity of 5 m/s rises to a peak of about 20 m/s owing to the geometry of the enclosure.The normal force exerted on the domain walls is 15.8526 N. Intense turbulence is observed in the region between the two walls, where the turbulent kinetic energy reaches values as high as 2 J/kg.

When water spills over an ogee overflow and discharges into a pond, the way it behaves depends heavily on whether the flow runs as a free surface or under pressure. Capturing that difference is essential for designing spillways and overflow structures that handle their intended flow safely. In this project, you'll use ANSYS Fluent to simulate water flowing over an ogee spillway into a pond, comparing two distinct flow regimes side by side.The model is built in two dimensions in ANSYS DesignModeler as an ogee overflow leading into a pond, and two separate cases are studied. In the first, the flow is a free surface reaching the overflow at a defined height with a flow rate of 140 kg/s; in the second, the water flows under pressure with a flow rate of 420 kg/s. The geometry is also configured in two variants — one that includes an upstream region before the overflow and one that omits it — and the inlet is split into separate water-flow and airflow sections. Meshing is carried out in ANSYS Meshing using a semi-structured grid, with roughly 20,100 elements for the free-flow case and 16,400 for the pressure-flow case.Because both cases involve a moving interface between air and water, a two-phase Volume of Fluid (VOF) model is used, with air defined as the primary phase and water as the secondary phase. From the results, you'll examine 2-D contours of pressure and velocity along with the volume-fraction field that reveals the free surface and the path of the water into the pond. You'll also obtain a plot of static pressure along the flow direction for both models, allowing a direct comparison between the free-surface and pressurized regimes.By the end of this project, you'll be able to set up a two-phase free-surface flow in ANSYS Fluent using the VOF model, configure and compare multiple flow scenarios on a single hydraulic structure, and interpret the results to understand how overflow conditions change pressure and velocity behavior.

DescriptionThis study investigates water flow over the blades of a Horizontal Axis Water Turbine (HAWT) using ANSYS Fluent, with the goal of examining the velocity and pressure distribution across the blade surfaces. Turbines of this kind are central to marine and hydrokinetic energy engineering, where they harness the kinetic energy of moving water to generate power.Two regions are defined around the blades: a cylindrical zone immediately surrounding them, and a larger domain enclosing that cylinder. In the outer domain, the water behaves as an ordinary free stream, while in the inner cylindrical region the rotational motion of the blades induces a swirling, rotational flow.Several assumptions underpin the simulation. The analysis is steady-state, since the turbine is of the horizontal-axis type and time therefore has no bearing on the drag and lift forces. A pressure-based solver is used, and gravitational force is neglected.MethodologyThe model was built in 3D, with the blade cross-section based on an S814 airfoil whose coordinates were taken from the Airfoil Tools website and exported as a text file. Because the airfoil section scales up or down along the blade span, Excel was used to define the coordinates at each spanwise station. Each section was then drawn in SOLIDWORKS at the appropriate angle and position and imported into Design Modeler to construct the blades and turbine shaft. Within Design Modeler, the rotational water region around the blades and the larger free-stream domain were both created.Meshing was performed in ANSYS Meshing using an unstructured grid. To improve accuracy, a boundary-layer mesh was applied to the blade surfaces, and the final cell count reached 4,270,222.The rotation of the blades is modeled using the Frame Motion (MRF) method. The turbine blades rotate at 191 rpm while the surrounding water is treated as stationary; under this approach, the blades are held fixed and the water region around them is assigned a rotating frame turning at the same 191 rpm about the Z-axis. Because the simulation is steady-state, the Mesh Motion option is disabled — it applies only when time-dependent effects must be captured, whereas here the objective is simply to impose the rotational speed on the blades.The solution setup is summarized below:Viscous model — SST k-omegaBoundary conditions — velocity inlet at 1 m/s; pressure outlet at 0 Pa gauge; all walls set as stationarySolution methods — SIMPLE pressure-velocity coupling; second-order upwind discretization for pressure, momentum, turbulent kinetic energy, and turbulent dissipation rateInitialization — standard method, with an initial velocity of −1 m/s in the Z-directionConclusionOn completion of the solution, the velocity and pressure distributions over the turbine blades can be examined in detail through the corresponding contours. These results reveal how the water loads the blade surfaces and how the rotational flow develops within the cylindrical zone, providing the basis for evaluating the hydrodynamic performance of the horizontal-axis water turbine.

Mastering Fuel Injector Dynamics: Advanced CFD Simulation Using VOF Multiphase ModelWelcome to the "Injector CFD Simulation" episode of the "Multi-Phase Flow: Beginner" course. This module introduces the fundamentals of multi-phase flow analysis within fuel injectors, a critical component across combustion systems in automotive, aerospace, and energy applications. You'll learn to apply the Volume of Fluid (VOF) multiphase model in ANSYS Fluent to simulate and interpret the complex fluid interactions occurring inside an injector.Understanding the VOF Model for Injector SimulationThis section covers the core principles behind the VOF approach as applied to fuel injection. You'll explore how the method captures the dynamic interface between liquid fuel and surrounding gas within the injector's confined internal geometry, and see how injector simulations are used across automotive fuel systems, aerospace propulsion, and industrial combustion processes.Exploring the Injector GeometryHere you'll become familiar with the pre-configured injector model, examining the key geometric features of a realistic injector design along with the mesh characteristics needed to accurately resolve the liquid-gas interface within its narrow internal passages.Implementing Boundary ConditionsThis section walks through defining realistic operating conditions for the simulation, including appropriate pressure, velocity, and fluid property settings at the fuel inlet, as well as proper representation of the surrounding gas phase and wall boundaries within the injector.Fine-Tuning VOF Parameters for Interface TrackingYou'll learn how to select and configure the VOF scheme for stable, accurate interface capture within the injector's complex internal geometry, along with how to incorporate surface tension and turbulence effects that govern fluid behavior during injection.Analyzing Volume Fraction Distribution and Flow PatternsThis section develops your ability to interpret multi-phase flow behavior through contours and animations showing the spatial distribution of liquid fuel and gas, alongside quantitative assessment of velocity profiles, pressure distributions, and spray characteristics at the nozzle exit.Investigating Injector Design and Operating ConditionsYou'll examine how injection pressure influences flow behavior and phase distribution, and how CFD results can guide nozzle geometry optimization to improve atomization and spray quality.Interpreting Results for Performance AnalysisThis section focuses on extracting meaningful insights from the steady-state simulation results, including techniques for evaluating injector efficiency, flow uniformity, and potential cavitation risk, and relating these findings back to real-world injector performance.Practical Applications and Industry RelevanceThe module closes by connecting these simulation skills to real engineering challenges — from optimizing fuel injection systems for improved engine performance to supporting the development of cleaner, more efficient combustion technologies.Why This Module MattersBy completing this episode, you'll gain a working understanding of the Volume of Fluid method and its application to multi-phase flows in confined geometries, along with practical CFD skills for simulating liquid-gas interaction and interface dynamics in high-pressure injection systems. You'll finish equipped to set up and run injector simulations using the VOF model in ANSYS Fluent, interpret results for flow characteristics and phase distribution, and apply these insights to broader multi-phase engineering problems — forming a solid foundation for further study in combustion systems, spray dynamics, and fuel injection technology.

DescriptionThis project simulates a parabolic trough reflector using ANSYS Fluent. A parabolic trough reflector is a type of solar energy collector — a cornerstone technology of concentrated solar power (CSP) — consisting of a long, parabolic mirror that focuses incoming sunlight onto a receiver tube positioned along the focal line of the parabola. The concentrated solar energy heats a working fluid flowing through the tube, which can then be used to generate steam and, in turn, produce electricity via a turbine.The geometry was created in SpaceClaim, modeling a 3 m long section of the system. It was then meshed in ANSYS Meshing using polyhedral elements, with a total count of 1,330,520.MethodologyTo simulate the problem, the Discrete Ordinates (DO) radiation model was employed together with a laminar flow model. Because the installation is located in Egypt, the longitude, latitude, and time zone were set according to the site's real geographic data so that the solar load could be represented accurately.ResultsWater enters the tube at 25 °C, with the ambient temperature also set to 25 °C; consequently, solar radiation is the only mechanism available to raise the water temperature. The water flows at a velocity of 0.1 m/s, and at the simulated time the solar irradiation is 968 W/m². The software reports an outlet temperature of 26.45 °C, corresponding to a rise of roughly 1.5 °C along the tube.The contours clearly show how the parabolic reflector concentrates most of the incoming irradiation onto the receiver tube. In addition, the reflector plate surface reaches a temperature of 36 °C.

Twin Screw Pump — ANSYS Fluent CFD SimulationThis project analyzes the operation of a twin-screw pump using ANSYS Fluent. A twin-screw pump is a positive displacement device, transferring a fixed volume of fluid per cycle based on the rotational speed and pitch of its screws. As the two screws turn, they form enclosed chambers that move along the axial direction, creating a vacuum at the inlet and positive pressure at the outlet. This double-chamber arrangement allows the pump to handle fluids of both high and low viscosity with minimal pulsation.More specifically, the pump consists of two counter-rotating screw rotors that turn toward each other, trapping fluid in the space between their threads. As the screws rotate, this trapped volume progressively shrinks, compressing the fluid and driving it toward the outlet.In this study, the pump is used to handle a highly viscous fluid—glycerin. The rotation of the screws draws glycerin into the domain, increases its pressure, and pushes it toward the outlet.The geometry was designed in SolidWorks and refined in ANSYS Design Modeler, consisting of two rotating zones (the screw rotors) and one stationary zone (the pump housing). The model was meshed in ANSYS Meshing using an unstructured mesh, totaling 1,184,161 cells.MethodologyRather than applying rotation directly to the rotor geometry, the rotational motion is imposed on the surrounding fluid through a dedicated computational zone defined in the cell zone conditions. This is achieved using the Mesh Motion method, with a rotational velocity of 3 rad/s. Pressure boundary conditions are applied at the inlet and outlet, since fluid movement through the pump is driven entirely by pressure differences rather than forced flow.ResultsThe simulation produces 2D and 3D contours of velocity and pressure, along with streamlines around the rotors. The results show that fluid motion through the pump is governed by pressure gradients: rotor rotation generates suction at the inlet and pushes the fluid toward the outlet, with the streamlines clearly illustrating the rotational flow pattern inside the pump.

DescriptionThis project simulates pollution diffusion within a street canyon using ANSYS Fluent. In the model, an urban district is defined as the computational domain: two parallel rows of building blocks are created, and the space enclosed between them forms what is known as a street canyon, or urban canyon. Understanding airflow and pollutant behavior in these canyons is a central concern of urban planning, since the form and dimensions of a canyon strongly influence how urban heat and airborne gases are dispersed through the city.The model was built in 3D using Design Modeler. The computational domain measures 36 m long, 24 m wide, and 8 m high, and contains two parallel rows of simplified geometric building blocks. To reduce computational cost, the domain is kept limited in extent, with symmetry boundary conditions applied around the perimeter of the urban area. Meshing was performed in ANSYS Meshing, producing 1,938,659 elements.MethodologyThe study examines the quantity and distribution of pollutants within the canyon space, and the Species Transport model was used to carry out the simulation.Two gaseous species are defined: air and a pollutant. The pollutant has a specific heat capacity of 1100 J/kg·K and a molecular weight of 77.49064 kg/kmol, while air has a specific heat capacity of 1006.43 J/kg·K and a molecular weight of 28.966 kg/kmol.All pollutants are assumed to originate within the street canyon. To represent this, two grooves are modeled on the canyon floor to act as pollution sources, and a source term of 0.011 kg/m³·s is applied for the pollutant species in this contamination region. Initially the urban area contains only air, after which pollutant generation begins.At the inlet boundary, only pure air enters under a velocity inlet condition. The inlet velocity is defined as a function of position across the inlet section, so a velocity profile is supplied through a UDF. The air temperature is set to 300 K. The RNG k-epsilon model, together with the energy equation, was enabled to resolve the turbulent flow field and compute the temperature variation throughout the domain.ConclusionOn completion of the solution, three-dimensional contours of pressure gradient, velocity, temperature gradient, air mass fraction, and pollutant mass fraction were obtained, along with two-dimensional contours of velocity, air mass fraction, and pollutant mass fraction.As the results show, air pollution builds up from within the street canyon. Two- and three-dimensional velocity vectors were also extracted, and depending on the flow behavior inside the canyon, a vortex or recirculating rotation of the flow appears between the building rows.

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.

DescriptionThe Lockheed Martin F-35 Lightning II is an American family of single-seat, single-engine, all-weather stealth multirole combat aircraft, also capable of electronic warfare and intelligence, surveillance, and reconnaissance missions.Notably, the F-35 can reach speeds of around 500 m/s, placing the surrounding airflow firmly in the supersonic regime. Since full-scale wind tunnel experiments at these conditions are costly in both time and money, CFD solvers are frequently used for initial evaluation. This project studies the supersonic, compressible flow around an F-35 aircraft. The geometry consists of a 20 m F-35 positioned inside a 150 m wind tunnel.The mesh contains 7,182,542 elements. In terms of quality, a maximum skewness of 0.79 with an average of 0.22 is satisfactory for this problem. To resolve the boundary layer accurately, 25 prism layers were added adjacent to both the wind tunnel walls and the aircraft body. The mesh was generated in ANSYS Meshing and subsequently converted to a polyhedral mesh within ANSYS Fluent, reducing the count to 1,845,364 elements while preserving the same quality. As with any numerical study, the first step in the modeling was the creation of the CAD geometry.MethodologyAir is treated as a compressible ideal gas, and a Mach number of 2.0 is reached at the maximum speed of 544 m/s. Solving this problem requires the flow equations to be handled in their differential form.A non-isothermal, compressible ideal-gas condition was assumed inside the wind tunnel; consequently, the energy equation was solved alongside the flow and turbulence equations. The governing mass and momentum equations are written in their standard conservative form for compressible flow.ConclusionThe drag force and the shock profiles were obtained over the course of the study. After the solution converged, the results were examined through post-processing. As a check on convergence, the drag value was monitored throughout the solution iterations: the solution was deemed converged once the drag force settled to a constant value and the residuals dropped below 10⁻⁶.The results are then presented for the pressure and velocity fields. The shock profile is visible in both the pressure and Mach number contours, while the velocity field is shown through both contours and streamlines to give deeper insight into the flow. The temperature gradient and its variation across different locations are also presented, since the temperature rise is an important factor in compressible aerodynamic calculations. Finally, the drag force was calculated at 181.66 kN, a reasonable value for a 20 m aircraft with the stated specifications.

Wide-Edge (Broad-Crested) Spillway — ANSYS Fluent CFD Simulation TrainingIntroductionA wide-edge (broad-crested) spillway is a cascading structure with a long horizontal crown aligned with the flow direction, such that the error arising from the hydrostatic pressure distribution can be neglected thanks to the acceleration of the radial flow. These spillways operate so that the upstream flow is subcritical while the flow over the spillway itself becomes supercritical, creating a flow-control section above the crown. One characteristic of these structures is that, a short distance from the crown, the flow lines run nearly parallel.In this type of spillway, the crest is wide and substantial relative to the other dimensions. The crowns may be wide, horizontal, or follow a specific curvature. Although they can be used to measure discharge, they serve most often as dam spillways — and sometimes as the dam itself, when water is allowed to pass through — and can store large volumes of water when needed.Project DescriptionThis project investigates the flow inside a wide-edge spillway using ANSYS Fluent. There is a deliberate elevation difference between the main channel and the sub-channel, in part to store a portion of the flowing water. The RNG k-epsilon model solves the turbulent flow equations, while the multiphase VOF model captures the two phases of water and air within the open channel. Water enters the channel at a mass flow rate of 65 kg/s and passes into the second channel after striking the middle section of the spillway.Geometry & MeshThe geometry was created in ANSYS Design Modeler and meshed in ANSYS Meshing using a structured grid, for a total of 981,900 elements.MethodologySeveral key assumptions underpin the model. The simulation uses a pressure-based solver and is run as steady-state, so the results do not vary with time. Gravity is applied at −9.81 m/s² in the Y direction.Turbulence is modeled with the RNG k-epsilon model using standard wall functions, and the two phases — air as the primary phase and water as the secondary phase — are handled with the VOF approach. Water enters through a mass-flow inlet (65 kg/s) defined as an open-channel boundary, with a free-surface level of 0.08 m, a bottom level of 0 m, and density interpolation taken from the neighboring cell. The outlets are set as pressure outlets, and the walls are treated as stationary.For the solution methods, pressure–velocity coupling uses the SIMPLE scheme. Pressure is discretized with PRESTO! and momentum with second-order upwind, while volume fraction, turbulent kinetic energy, and turbulent dissipation rate all use first-order upwind. The solution is initialized with the standard method: gauge pressure 0 Pa, velocity 0 m/s, turbulent kinetic energy 1 m²/s², turbulent dissipation rate 1 m²/s³, and water volume fraction 0.ResultsThe water volume fraction contour shows that, because of the height difference and the absence of any inlet flow in the sub-channel, the water volume fraction takes nonzero values in the upper part of the sub-channel. Once the solution is complete, 3D contours of pressure, velocity, volume fractions, and related quantities are extracted and presented.

DescriptionThis project simulates a lumen blood vessel using coupled Fluid-Structure Interaction (FSI) and a non-Newtonian blood model in ANSYS Fluent, with the results examined through CFD analysis. Because blood is a shear-thinning fluid whose viscosity changes with the local strain rate, a non-Newtonian treatment is essential for capturing the flow behavior realistically inside the vessel.The three-dimensional geometry was created in SpaceClaim. The computational domain is 164 mm long, 262 mm high, and 5 mm wide. Meshing was performed in ANSYS Meshing, producing a total of 356,794 elements. Owing to the pulsatile nature of the problem, a transient solver was used.MethodologyHere, a blood vessel together with its wall is simulated in ANSYS Fluent. The solver's intrinsic FSI module was enabled so that displacement of the vessel wall could be captured in response to the flow.The inlet boundary condition was defined as a pulsatile velocity using a UDF, while the outlet was defined as a pulsatile pressure, also supplied through a UDF. The blood itself was modeled as a non-Newtonian fluid using the Carreau model, which reproduces the shear-thinning drop in viscosity as the shear rate increases. A laminar model was enabled to solve the fluid equations.ConclusionOn completion of the solution, three-dimensional contours of wall displacement and von Mises stress were obtained. As the results show, the blood flowing through the vessel exerts stress on the vessel walls, deforming them and demonstrating the two-way coupling between the pulsatile non-Newtonian flow and the compliant vessel structure.

DescriptionThis project simulates pollution transport in a meandering river using ANSYS Fluent, investigated through CFD analysis. Water pollution is the contamination of water bodies — usually the result of human activity — in a way that harms their legitimate uses. Such pollution prevents a body of water from delivering the ecosystem services it would otherwise provide, and it is broadly classified as either surface water pollution or groundwater pollution.The model was built in 3D using Design Modeler. The river's width at the inlet is 14.035 m, and the pollutant enters through two circular profiles, each 3 m in diameter. Meshing was performed in ANSYS Meshing, producing 762,433 elements. Because of the time-dependent nature of the problem, a transient solver was used.MethodologyThis study employs the VOF (Volume of Fluid) multiphase model to solve the two-phase flow field. To represent the free surface of the river, the open channel option within the multiphase module was enabled, allowing the air–water interface and the gravity-driven surface flow to be captured accurately.Pollutant enters the river through two circular inlet profiles near its start and then diffuses into the water. Because the pollutant is less dense than water, it accumulates at the river's surface, and the flow carries it downstream, spreading the contamination along the channel.The Realizable k-epsilon viscous model with scalable wall functions was used to resolve the turbulent flow. Pressure-velocity coupling was handled with the SIMPLE scheme. A second-order upwind scheme was applied to the momentum equations, while a first-order upwind scheme was used for the turbulent kinetic energy and turbulent dissipation rate. Water enters the domain at 35 m/s, and the pollutant enters at 5 m/s.ConclusionOnce the solution was complete, contours of velocity, pressure, pollutant volume fraction, water volume fraction, eddy viscosity, and streamlines were extracted and presented across different time steps.As the results show, the pollutant enters the river through the two circular inlet profiles and gradually diffuses across the water surface over time. Driven by the river's flow, the pollution spreads along the free surface and ultimately leads to widespread contamination of the channel.

DescriptionThis project simulates combustion in a gas flare system using ANSYS Fluent, investigated through CFD analysis. The model was built in 3D using Design Modeler. Owing to the symmetrical structure of the flare and to reduce computational cost, only a 120-degree segment of the geometry was modeled.The flare has a cylindrical structure situated within a cylindrical computational domain. Several distinct sections — steam, gas flow, and pilot — are defined at the tip of the flare. Meshing was performed in ANSYS Meshing, producing 1,043,138 elements.MethodologyA flare system, or gas flare, is a combustion device used in industrial facilities such as oil and gas refineries and at oil and gas production wells, particularly on offshore platforms, to safely burn off surplus hydrocarbon gases. The Species Transport model was used to carry out this simulation.The reacting mixture is defined as an n-butane–air blend consisting of nine gaseous species: C₄H₁₀, O₂, CO₂, H₂O, H₂, CH₄, C₂H₆, C₃H₈, and N₂. The volumetric reaction model was activated to enable the chemical reactions and, in turn, the combustion process, which is represented by five distinct chemical reactions.At the flare tip, a stream of hydrocarbon gas enters the environment at a flow rate of 0.09259 kg/s. Simultaneously, a methane flow from the pilot and a steam flow from the steam inlet — both at velocities of 2.479 m/s — enter the domain to ignite the mixture. The standard k-epsilon model was used to solve the turbulent flow equations, together with the energy equation to compute the temperature variation within the combustion region.ConclusionOn completion of the solution, three-dimensional contours of velocity and of the mass fraction of each modeled gas species were obtained.For instance, examining the three-dimensional contour of carbon dioxide clearly shows that the combustion reaction and the resulting production of CO₂ are taking place. As the results also demonstrate, the mass fractions of the fuel species decrease with distance from the fuel inlet, while the mass fractions of the reaction products correspondingly increase along the same direction — confirming the progress of combustion through the domain.

Nanofluid Porous Mixer for Enhanced Heat Transfer — ANSYS Fluent CFD SimulationDescriptionThis project investigates the mixing of hot (303 K) and cold (293 K) nanofluid streams, comparing two configurations: one using 28 mixers and another using 54 mixers, each modeled as a porous medium. The aim is to assess how the mixer arrangement influences the blending of the two streams and the resulting heat transfer.The geometries were drawn in SpaceClaim and meshed in ANSYS Meshing. Two geometries are considered: the first has 2 rows of mixers, and the second has 4 rows. Both meshes are unstructured, built with the triangular method, comprising 87,501 cells for case 1 and 83,180 cells for case 2. The domain has two inlets, both with a velocity of 0.1 m/s; the upper inlet is at 293 K and the lower inlet at 303 K.MethodologyA coupled algorithm was used for pressure-velocity coupling, and the Realizable k-epsilon model with standard wall functions was selected as the turbulence model. The nanofluid is treated as a single-phase fluid with modified thermophysical properties — density, viscosity, specific heat, and thermal conductivity — calculated as functions of the nanoparticle volume fraction using the standard nanofluid property correlations. The mixers are represented as aluminum porous media with a permeability of 1.The porosity is defined as the ratio of the void volume (Vv) to the total volume (Vt). Based on the dimensions of the problem, the porosity is 0.937 for the 2-row case and 0.875 for the 4-row case.ConclusionContours and vectors of velocity, static pressure, and temperature were obtained. As the figures show, the velocity contour is more uniform in the 2-row case. This can be attributed to the smaller number of mixer blocks and, consequently, the smaller variation in velocity gradient caused by the flow striking their sharp edges. The maximum and average velocities are higher in the 4-row case, indicating that although the 4-row case has more separation zones, the separations are more substantial in the 2-row case.The pressure readings show more negative values in the 2-row case, which is consistent with Bernoulli's principle. Pressure is more positive at the top of the domain, where the temperature is lower than elsewhere. The temperature contours indicate that the maximum and average temperatures are essentially the same for both cases; however, in the 4-row case the geometry produces a wider range of temperature variation with more gradual changes.Finally, the temperature is plotted along the centerline of each geometry. The diagram shows that in the 4-row case the temperature is higher at the center of the domain, a result of the greater number of separation zones enhancing the local mixing.

DescriptionThe use of porous media inside tubular structures has become a key strategy for sound absorption. This technique takes advantage of the inherent properties of porous materials to dissipate sound energy, thereby reducing noise pollution and improving the acoustic environment within the tube.In this project, we simulate the phenomenon of sound absorption inside a pipe, where a porous medium serves as a silencer. The primary aim is to measure two key parameters across a wide range of frequencies:Transmission Loss (TL) — the reduction in sound power as sound travels through the pipe filled with the porous medium. It is a critical quantity in many engineering applications where noise reduction is required.Sound Pressure Level (SPL) — the pressure deviation from ambient atmospheric pressure produced by a sound wave. Here, the interest lies in understanding how the SPL varies over a broad frequency range as sound propagates through the porous medium inside the pipe.The geometry was created in ANSYS SpaceClaim, and the computational domain was then divided into separate cell zones in ANSYS Meshing, generating 1,209,174 polyhedral cells.MethodologyTo achieve this, the Ffowcs Williams–Hawkings (FW-H) acoustic model was employed. This model is well regarded for its ability to accurately predict the acoustic behavior of a system, making it a suitable choice for the present simulation. The study aims to build a deeper understanding of how sound behaves under these conditions — insight with significant relevance to fields such as acoustical engineering and environmental noise control.The inlet and outlet are placed 200 mm and 500 mm from the silencer, respectively. Air enters the tube at a velocity of 5 m/s. The flow equations are first solved in steady-state form; the acoustic equations are then introduced and the solution continued in an unsteady (transient) manner.ConclusionAs the air enters the pipe, it must pass through the porous medium, which produces a marked pressure drop owing to the complex internal structure of the porous material. A stagnation point forms on the porous wall, and the velocity increases sharply in accordance with Bernoulli's equation. Both effects are visible in the figures below.From an acoustic standpoint, a comparable behavior is observed. To fulfill the study's objectives, three receivers were positioned within the domain: one placed 100 mm before the porous medium, and the other two placed 200 mm and 400 mm downstream of the porous silencer. When interpreting the results, note that the reference acoustic pressure is set to 2 × 10⁻⁵ Pa, so all reported values are relative to this reference level.

This project simulates non-premixed combustion in a 2-D combustion chamber, where air and hydrocarbon fuel enter through two separate inlets and react to release the fuel's chemical energy as heat. It's a foundational study in reacting-flow CFD — the configuration that describes most real burners, furnaces, and gas-turbine combustors, where fuel and oxidizer are deliberately kept apart until they meet in the reaction zone.The key modeling choice is the non-premixed (mixture-fraction) approach within Fluent's Species Transport framework. Instead of tracking every reaction rate directly, the model solves transport equations for the mixture fraction — the local mass fraction originating from the fuel stream — and reads the resulting species and temperatures from pre-computed chemistry. This is what makes non-premixed combustion both efficient and stable: the chemistry is folded into the mixture fraction, so you model the mixing and let the thermochemistry follow. By definition, fuel and oxidizer enter through independent paths and do not premix before reaching the chamber.Setup: an air stream (N₂ at mass fraction 0.767, O₂ at 0.233) enters at 300 K and 1.19 kg/s, while a pure CH₄ (methane) stream enters at 300 K and 0.019 kg/s through a separate inlet. Geometry is built in Design Modeler and meshed in ANSYS Meshing as an unstructured mesh (11,202 cells).What the results show: contours of pressure, temperature, velocity, and density, plus mass-fraction fields for O₂, CH₄, H₂O, CO₂, N₂, CO, and C₂H₆, along with in-chamber pathlines. The fields confirm a properly anchored combustion reaction: methane and air react where the streams meet, consuming reactants and producing CO₂, H₂O, and intermediates like CO — and the temperature field maps the flame and hot-product zone exactly where the mixture fraction is near stoichiometric.You'll learn to: set up Species Transport with the non-premixed mixture-fraction model, define separate fuel and oxidizer inlets with realistic compositions and flow rates, and read flame structure and product formation from temperature and species contours.

Color Spraying on a Wall with Conical Injection — ANSYS Fluent CFD SimulationDescriptionThis project simulates color (paint) spraying onto a wall using a conical injection in ANSYS Fluent. The discrete phase is modeled with a one-way coupled DPM approach, in which the continuous phase influences the particles but the particles do not feed back on the flow. The injection is of the cone type, with a particle velocity of 10 m/s and a cone angle of 30 degrees.Geometry & MeshThe 3D geometry was created in SpaceClaim. The computational domain is 3 m long, 3 m wide, and 4 m high. The mesh was generated in ANSYS Meshing using an unstructured grid, with a total of 254,934 cells.Several assumptions underpin the simulation: the solver is pressure-based, the simulation is unsteady (time-dependent), and the effect of gravity is neglected.MethodologyThe problem setup is summarized below:Viscous model — laminarDiscrete phase — enabled, with unsteady particle tracking; the injected material is the color spray, the particle type is inert, and the injection type is a coneBoundary conditions — the side wall and back wall are stationary, with the discrete phase condition set to escape; the top wall is stationary, with the discrete phase condition set to trapSolution methods — SIMPLE pressure-velocity coupling; second-order discretization for pressure, second-order upwind for momentum, and first-order upwind for the modified turbulent viscosityInitialization — standard methodConclusionIn this simulation, the spray paint deposited on the wall is modeled using an injector that introduces the particles in a conical pattern. The cone angle governs the spread and range of motion of the particles, determining how they disperse from the nozzle and where they ultimately strike the wall — with the trap condition capturing the particles that reach the target surface and the escape condition allowing them to exit elsewhere in the domain.

Master Check Valve CFD Simulation with Dynamic Mesh in ANSYS FluentStep into the world of advanced fluid dynamics with this comprehensive episode on Check Valve CFD Simulation using the Dynamic Mesh capabilities of ANSYS Fluent. This hands-on tutorial forms the third chapter of our Dynamic Mesh Training Course and is designed to raise your simulation skills to a professional level.In this practical session, you will learn to simulate the intricate flow behavior of a check valve in ANSYS Fluent. The episode guides you through the entire workflow — from geometry creation to result analysis — with a particular focus on the dynamic mesh techniques that make this kind of simulation possible.The episode begins with the fundamentals of check valve dynamics, covering the mechanics of unidirectional flow, the opening and closing behavior of the valve, and the coupled interaction between the fluid flow and the valve's motion. From there, it moves into model setup and meshing, where you will create the geometry in Design Modeler, apply meshing techniques in ANSYS Meshing, and build an understanding of the computational domain — a pipe fitted with a check valve.With the model in place, the focus shifts to the dynamic mesh implementation itself. Here you will learn to apply the Six Degrees of Freedom (6-DOF) solver, define the rotational motion of the valve, and set up deforming mesh zones that adapt as the valve moves. The episode then explores several advanced techniques to enrich the simulation, including setting up multiphase flow with the Volume of Fluid (VOF) model, capturing time-dependent flow behavior, and using Execute Commands to control the inflow conditions.The methodology follows a clear, step-by-step approach so that every aspect of the simulation is understood: configuring the dynamic mesh model for valve motion, defining rigid body motion for the valve, implementing multiphase flow for water and air, and selecting appropriate solver settings for transient analysis. In the results and analysis stage, you will learn to interpret and visualize the outcomes — analyzing the water mass fraction contours, creating animations of the valve movement and flow behavior, and verifying that the check valve mechanism operates correctly.Why This Episode Is EssentialBy working through this episode, you will gain practical experience with a real-world engineering problem, master the application of dynamic mesh techniques in complex flow scenarios, deepen your understanding of multiphase flow simulation, and develop skills that transfer readily to a wide range of industrial valve simulations.Who Should Watch This Episode?This episode is ideally suited to mechanical and hydraulic engineers, CFD specialists looking to broaden their skill set, researchers in fluid dynamics and valve design, and students pursuing advanced studies in computational engineering.Take Your CFD Skills to the Next LevelOn completing this episode, you will be equipped to simulate complex valve dynamics problems, apply dynamic mesh techniques across a variety of engineering scenarios, analyze and optimize check valve designs, and strengthen your multiphase flow simulation capabilities in ANSYS Fluent.Don't miss this opportunity to master Check Valve CFD Simulation with Dynamic Mesh in ANSYS Fluent. Enroll now and transform your CFD skills for real-world applications!

Pollution Ventilation in a Subway — DescriptionThis project simulates pollution ventilation in a subway station using ANSYS Fluent, with a focus on the station's air-conditioning system. A subway station is one of the busiest of public spaces and is therefore readily exposed to pollution. In this model, a polluting gas is defined with a molecular weight of 77.5 kg/kmol and a specific heat capacity of 1100 J/kg·K.The subway doors are treated as the sources of pollutant, since they are the points where passengers gather. The air-conditioning system is positioned on the ceiling of the station and uses its suction power to draw pollutants out into the surrounding environment. The pollutant gas enters the station at a velocity of 0.1 m/s and is carried outside by the vacuum pressure.The Species Transport model is used for the simulation, with two species defined — the air already present inside the station and the pollutant gas that enters it. The model solves the transport equations for each species. The suction at the ceiling outlet panels is defined using the Exhaust Fan boundary condition, in which a pressure jump draws the pollutant gas out of the environment. This pressure jump is set to 1,000,000 Pa.Geometry & MeshThe geometry was drawn in Design Modeler and represents a subway station comprising a subway line with rails.Setup & SolutionViscous model — RNG k-epsilon with standard wall functionsSpecies — Species Transport with 2 volumetric species (air and pollutant gas)Energy — enabledBoundary conditions — Inlet-Doors: velocity inlet at 0.1 m/s, 300 K, pollutant mass fraction 1; Inlet-Subway: pressure inlet at 0 Pa gauge, 300 K, pollutant mass fraction 0; Outlet-Panels: exhaust fan at 0 Pa gauge with a 1,000,000 Pa pressure jump; Walls: stationary, with zero heat fluxMethods — SIMPLE pressure-velocity coupling; second-order discretization for pressure; first-order upwind for momentum, turbulent kinetic energy, turbulent dissipation rate, pollutant, and energyInitialization — standard method, with an initial velocity of 0 m/s, temperature of 300 K, and pollutant mass fraction of 0ConclusionOn completion of the solution, 3D contours of pressure, velocity, and pollutant mass fraction were obtained. The pollutant contour indicates that the station's air-conditioning system performs effectively: although pollutants enter through the subway doors, they do not spread into the station space. The powerful ceiling-mounted fans of the air-conditioning system draw the contaminated air out to the external environment, keeping the station interior clear — demonstrating the role of the exhaust fan in maintaining air quality within the station.

DescriptionIn this project, we present a simulation of an Airfoil exposed to the airflow via ANSYS software.Since the airfoil is exposed to airflow, an interaction occurs between the wind blowing and the airfoil structure. First, the airflow exerts a volume force on the airfoil's body by hitting it. Subsequently, displacement or deformation appears on the airfoil, which can lead to the airflow being affected. Therefore, we intend to perform a numerical simulation of the airfoil as a Fluid-Structure Interaction (called FSI).The interaction between fluid and structure can be implemented as:One-way FSITwo-way FSIIn this project, we aim to analyze both the effect of fluid on the structure and the effect of the structure on the fluid. So, we choose Two-way FSI, which is a more accurate and realistic but more complex approach.We modeled the geometry via Design Modeler software. The computational domain is a sample space of the surrounding air that includes both fluid and solid domains. There is a solid airfoil structure within the fluid environment, which is considered fixed from the center.We meshed the computational domain via ANSYS Meshing software. The mesh is of an unstructured type, and approximately 56,000 cells have been generated.MethodologyFluid-structure interaction can be performed in two general methodologies:In the ANSYS Workbench environment, using an external solver (specifically, system coupling)Only in the Fluent solver (in the form of an intrinsic FSI).In this project, we implemented a two-way FSI in the ANSYS workbench environment.For two-way FSI with an external solver, three main steps are required:Simulation of the fluid domain from the model using the Fluent solverSimulation of the solid domain from the model using the Transient Structural solverDefinition of the Data Transfer between the fluid and structural solvers using the System Coupling toolFor utilizing the system coupling, we define two data transfers:In the form of Forces to the interface wall (from the fluid solver to the structural solver)In the form of Displacements of the interface wall (from the structural solver to the fluid solver)Since we were analyzing two-way FSI and considering the effect of the structure's displacement on the adjacent fluid, we used the Dynamic Mesh model. In other words, we establish a connection between the fluid and structure calculations with the System Coupling option. Then, for defining a deforming mesh, we enabled the smoothing and remeshing methods.In addition, because of the aerodynamic nature of the airfoil and the very high airflow velocity, we considered a density-based solver.ResultsWe analyzed the results in two fluid and solid approaches:In Fluent, we studied the behavior of airflow. For this, we obtained the distributions of the pressure and velocity of air. The results show that the airflow collides with the airfoil body at high speed and, as a result, exerts a hydraulic force on the airfoil structure.In Structural Transient, we studied the behavior of the airfoil body under the influence of the applied forces of the airflow. For this, we obtained the distribution of the deformation, von Mises stress, and elastic strain. The results confirm that the airflow affects the airfoil structure and, as a result, it undergoes displacements relative to the fixed center.In conclusion, we can claim that we carried out the simulation project of an airfoil correctly and acceptably by using the two-way FSI method.

DescriptionThis research presents a numerical investigation of the fluid dynamics and heat transfer in a two-phase immersion (submerged) cooling system. The subject of the study is a set of chips with interposer components mounted vertically on a printed circuit board (PCB), submerged in the dielectric coolant HydroFluoroEther (HFE)-7100. The main objective is to understand how the physical architecture and geometry of these components influence the flow paths of bubbles, the phenomenon of bubble coalescence, and the extent of vapor coverage on the chip surfaces.The geometry was created in SpaceClaim, ensuring an accurate representation of the PCB and its components. A structured mesh was generated in ANSYS Meshing, comprising more than 19,000 elements to enable reliable numerical simulation of this configuration.A transient solver was used, since the problem requires tracking changes in the volume fraction of the two phases over time. Gravity was included in the model, fixed at −9.81 m/s² in the Y direction.MethodologyThe PCB was modeled in ANSYS Fluent. The evaporation and condensation mass transfer mechanisms were captured using a multiphase VOF (Volume of Fluid) model, which resolves the interface between the liquid coolant and the vapor phase as boiling occurs. The dielectric coolant was assigned a saturation temperature of 339 K. The turbulent flow was solved using the standard k-epsilon model together with the energy equation, allowing the temperature distribution throughout the domain to be computed.ConclusionThe study examines the fluid dynamics and heat transfer in a two-phase immersion cooling system featuring vertically mounted chips and an interposer component. Using a computational model based on the finite volume method and the VOF approach, it investigates how the interposer component affects the deflection of bubble streamlines, the coalescence of bubbles on the heated chips, and the overall cooling rate.The results show that the interposer component can significantly influence chip heat transfer, with the evaporation–condensation mass transfer at the phase interface governing how heat is removed from the chip surfaces. The work highlights the importance of electronic system topology in the efficiency of two-phase cooling and offers valuable guidance for designing electronic systems that achieve effective thermal management.

DescriptionThis project simulates combustion in the presence of an electrohydrodynamic (EHD) field using ANSYS Fluent. A simple combustion chamber is designed, into which airflow and fuel enter axially. The fuel, C₁₀H₂₂ (decane), enters through the central section, with the airflow surrounding it.The study is carried out in two stages. First, ordinary combustion between air and fuel is investigated; then the same combustion is performed in the presence of an EHD field. Applying EHD causes the fluid to become electrically charged, and the motion of the ionized particles or molecules — together with their interaction with the electric field and the surrounding fluid — is studied. The combustion reaction is modeled using the Species Transport model, with C₁₀H₂₂ and O₂ defined as reactants and CO₂ and H₂O as products.Airflow enters the chamber at 447 K with a velocity of 5 m/s, while fuel enters at 300 K with a velocity of 0.01 m/s. The EHD model is used to impose the effect of the electric field on the chamber's performance: a current density of 40 A/m² is applied at the inlet and outlet boundaries, with a positive charge defined on the inlet boundary and a negative charge on the outlet boundary.Geometry & MeshThe geometry was created as a 3D model in Design Modeler. The computational domain is a horizontal cylindrical combustion chamber; fuel enters through a narrow inner tube, and airflow enters around this tube. Meshing was performed in ANSYS Meshing using an unstructured grid, producing 1,000,658 cells.Setup & SolutionSeveral assumptions underpin the simulation: a pressure-based solver is used, the simulation is steady, and the effect of gravity is neglected.Viscous model — standard k-epsilon with standard wall functionsSpecies — Species Transport with 5 volumetric species (C₁₀H₂₂, O₂, CO₂, H₂O, N₂) and volumetric reactionsEnergy — enabledPotential (electric field) — enabledBoundary conditions — Inlet-Air: velocity inlet at 5 m/s, 447 K, O₂ mass fraction 0.21, current density −40 A/m²; Inlet-Fuel: velocity inlet at 0.01 m/s, 300 K, C₁₀H₂₂ mass fraction 1, current density 0 A/m²; Outlet: pressure outlet at 0 Pa gauge, current density 40 A/m²; Inner Wall: stationary, coupled thermal condition; Outer Wall: stationary, zero heat flux, current density 0 A/m²Methods — Coupled pressure-velocity coupling; second-order for pressure; second-order upwind for momentum, species mass fraction, and energy; first-order upwind for turbulent kinetic energy and turbulent dissipation rateInitialization — standard method, with 0 Pa gauge pressure, O₂ mass fraction 0.21, velocity 5 m/s, temperature 447 K, and potential 0ConclusionOn completion of the solution, 2D and 3D contours of temperature, velocity, pressure, and the mass fraction of each species (CO₂, C₁₀H₂₂, O₂, N₂, and H₂O) were obtained. These results are presented in two modes — without EHD and with EHD — so that the effect of the electric field can be assessed through direct comparison.The contours show that when EHD is applied to the combustion chamber, more energy is delivered to the species, producing higher product temperatures. This rise in the temperature of the reacting species accelerates the combustion reaction. Furthermore, examination of the reaction products indicates that combustion in the presence of EHD proceeds with higher quality, demonstrating how the electric field can be used to enhance combustion performance.

IntroductionThis report presents the computational fluid dynamics (CFD) simulation of the Switchblade 300 drone using ANSYS Fluent. The primary objective was to analyze the fluid dynamics around the geometry under the specified conditions of motion and environmental parameters.The geometry of the Switchblade 300 was created in SpaceClaim. Using ANSYS Meshing, a non-conformal mesh of approximately 40 million tetrahedral cells was initially generated. This dense mesh provided high resolution of the flow features around the geometry and was well suited to capturing the intricate details of the flow.However, the initial 40,000,000-element mesh presented significant computational challenges. To optimize the simulation, the mesh type was converted to polyhedral, which dramatically reduced the element count from 40,000,000 to 7,000,000 while preserving the necessary resolution. Figure 2 shows the mesh configuration, highlighting the non-conformal mesh regions essential for accurately capturing the fluid interactions.MethodologyThe simulation was performed in ANSYS Fluent, making use of its Mesh Motion capability to analyze the flow characteristics of the rotating geometry. The setup specified a velocity inlet of 28.05 m/s, an angle of attack of 5 degrees, and a rotational speed of 5000 RPM. The k-ω SST turbulence model was selected for its effectiveness in predicting boundary layer separation and handling complex flow dynamics. For the numerical methods, the SIMPLE algorithm was used for pressure-velocity coupling, and standard initialization was applied to set the initial flow conditions within the solution domain.ResultsVelocity Contour — Presented in Figure 3, the velocity contour depicts the flow around the Switchblade 300, identifying the regions of high- and low-speed flow that arise from the angle of attack and the rotation of the geometry.Pressure Contour — Shown in Figure 4, the pressure contour illustrates the varying pressure distribution across the geometry, which is critical for identifying the aerodynamic forces — such as lift and drag — acting on the drone.ConclusionThe simulation results provide a detailed understanding of the aerodynamic performance of the Switchblade 300, offering significant insight into how rotational speed, inlet velocity, and angle of attack influence the overall flow behavior. These findings support the optimization of the design and operating parameters to enhance performance. Further simulations under varied conditions are recommended to explore additional operational scenarios and improve predictive accuracy.

Advanced Mixing Tank CFD: Mastering the MRF Method in ANSYS FluentElevate your turbomachinery simulation skills with our in-depth tutorial on “MRF Method, Mixing Tank CFD Simulation by ANSYS Fluent”. This essential episode in our “Turbomachinery: All Levels” course offers a comprehensive guide to applying the Multiple Reference Frame (MRF) method in complex mixing tank scenarios.Cutting-Edge Simulation Techniques for Rotating MachineryDelve into the world of advanced Computational Fluid Dynamics (CFD) as we explore the intricacies of simulating a mixing tank using the MRF method. This tutorial provides a step-by-step approach to modeling complex fluid behaviors in rotating systems using ANSYS Fluent, an industry-leading CFD software.Key Learning ObjectivesMaster the application of the Multiple Reference Frame (MRF) methodUnderstand fluid dynamics in multi-zone rotating systemsDevelop proficiency in ANSYS Fluent for advanced turbomachinery simulationsAnalyze and interpret critical flow parameters in complex mixing scenariosComprehensive Simulation Workflow and MethodologyLearn to set up and execute a professional-grade CFD simulation for a mixing tank, covering all aspects from geometry creation to result interpretation.1. Advanced Geometry and Mesh Generation- Creating detailed 3D models using ANSYS Design Modeler - Implementing sophisticated meshing strategies with ANSYS Meshing - Optimizing mesh quality for high-fidelity results (229,177 unstructured elements)2. ANSYS Fluent Setup for MRF Simulation- Configuring multiple zones for the MRF method - Setting up steady-state analysis with k-ε turbulence model - Defining boundary conditions for a 500 rpm impeller rotation in a stationary tank3. Advanced Analysis and Visualization Techniques- Extracting and interpreting pressure, velocity, and turbulent intensity contours - Analyzing vortex formation and fluid behavior in multi-zone systems - Understanding the impact of impeller rotation on fluid dynamics across different zonesReal-World Applications and Industry RelevanceThis tutorial is crucial for professionals and researchers in:Chemical and process engineeringPharmaceutical mixing processesFood and beverage industryWastewater treatment and environmental engineeringKey Simulation Outcomes and Insights1. Pressure Distribution Analysis- Observe pressure variations around the impeller - Understand pressure effects on mixing efficiency in different zones2. Velocity Profile Examination- Analyze flow speed patterns, particularly behind the impeller - Correlate velocity distributions with mixing effectiveness in rotating and stationary zones3. Turbulence Intensity Evaluation- Visualize turbulence patterns throughout the mixing tank, especially near the impeller - Assess the impact of turbulence on mixing performance in different regions4. Vector Flow Analysis- Examine water flow vectors around the impeller - Understand vortex formation and its impact on mixing efficiencyEnhance Your Turbomachinery Simulation ExpertiseBy completing this tutorial, you’ll gain:Advanced skills in applying the MRF method to complex rotating machinery problemsProficiency in setting up and analyzing multi-zone CFD simulations in ANSYS FluentDeep understanding of fluid dynamics in mixing tanks with rotating impellersInsights into optimizing mixing processes for various industrial applicationsWho Should Take This TutorialProcess engineers specializing in mixing and blending technologiesCFD specialists focusing on complex rotating machineryGraduate students in chemical, mechanical, or environmental engineeringR&D professionals in fluid dynamics and industrial mixing processesDon’t miss this opportunity to advance your CFD simulation skills and gain a deeper understanding of complex turbomachinery applications. Enroll now in our “Turbomachinery: All Levels” course and master the art of mixing tank simulation using the MRF method in ANSYS Fluent!

DescriptionThis project simulates Eulerian two-phase flow in a moving-wall cylinder using ANSYS Fluent, investigated through CFD analysis. The system consists of two fluids: water as the primary fluid, together with a secondary fluid (with a density of 2610 kg/m³ and a viscosity of 0.0026 kg/m·s).The two-phase flow enters the chamber in the shape of a hollow cylinder. Water enters the system at a velocity of 0.629 m/s with a volume fraction of 0.67, while the secondary fluid enters at 0.099 m/s with a volume fraction of 0.23, under a relative pressure of 1,379,000 Pa.The 3D geometry was created in Design Modeler. It consists of two concentric cylinders — an outer and an inner cylinder — with the two-phase fluid flowing through the annular space between the outer and inner walls; the inlet and outlet take the form of hollow circles. Meshing was performed in ANSYS Meshing using an unstructured grid, producing 11,880 elements.MethodologyThe Eulerian multiphase model is used to represent the flow of the two fluids through the system, treating each phase as an interpenetrating continuum with its own set of governing equations. The outer wall of the cylinder is stationary, while the inner wall is a moving wall rotating about the central axis of the cylinder at 30 rpm.The model employs the standard k-omega turbulence model with the shear-flow correction option, together with the dispersed turbulence model for the multiphase flow.ConclusionThis study investigates the effect of the rotating inner wall on the Eulerian multiphase turbulent flow.On completion of the solution, two- and three-dimensional contours were obtained for pressure (for the mixture), velocity (for both the water phase and the secondary-fluid phase), the volume fraction of water and of the secondary fluid, and the path lines of each phase.The two-dimensional contours are presented in two planes: the YZ section and the XY section. The YZ section is defined along the central axis of the cylinder, while the XY section is taken perpendicular to the central axis at distances of 4, 9, and 13.716 m (the outlet) from the inlet — allowing the development of the two-phase flow to be tracked along the length of the cylinder.

Water Infiltration into a Porous Concrete BlockDescriptionThis project simulates multiphase flow inside a porous cube using ANSYS Fluent. The main objective was to analyze the behavior of air and water within a porous medium using the Volume of Fluid (VOF) model. The simulation was carried out under transient, pressure-based conditions to observe how water interacts with air under a specified inlet pressure. The work proceeded through four main stages: geometry creation, meshing, solver setup, and post-processing to visualize the flow and pressure distributions.Geometry and MeshThe geometry was created in ANSYS Design Modeler as a cube measuring 0.15 m × 0.15 m × 0.15 m, with the inlet area defined as 0.0038472951 m². The geometry was then imported into ANSYS Meshing, where a structured hexahedral mesh of approximately 1 million elements was generated. This mesh type was chosen for its accuracy and numerical stability in capturing multiphase interactions, and it maintains adequate cell density near the boundaries, effectively representing the cube.MethodologyThe simulation was performed in ANSYS Fluent using a pressure-based, transient solver. The standard k–ε turbulence model was selected to account for turbulent effects. Multiphase flow was modeled with the Volume of Fluid (VOF) approach, with air defined as the primary phase and water as the secondary phase. A porous zone was included in the domain, with an assumed particle diameter (Dp) of 0.0005 m. The pressure inlet boundary condition was set to 500,000 Pa, driving water into the cube. The SIMPLE algorithm was applied for pressure-velocity coupling to ensure stability and convergence over the 15-second simulation period.ConclusionThe results reveal the formation and interaction of the air and water phases within the cube over time. The VOF contours show the distribution of the water volume fraction, with water gradually rising through the porous region while displacing the air, and the air volume fraction plots highlight the interface separating the two phases. The velocity contours indicate that the maximum velocity occurs near the inlet region, while the upper portion of the cube remains largely stationary. The pressure distribution decreases gradually from the inlet toward the outlet, confirming the expected flow behavior through the porous medium. Overall, the simulation successfully demonstrates transient multiphase fluid interaction within a porous cube domain.

Radiation Heat Transfer in a Computer Room — ANSYS Fluent CFD SimulationDescriptionThis project simulates the air conditioning of a computer room containing four computers using ANSYS Fluent. The model represents a computer room with several distinct heat sources and was built in 3D using SpaceClaim. Because the geometry is symmetric, only one-quarter of the room is modeled to reduce computational cost.Meshing was performed in ANSYS Meshing, producing 809,037 elements.MethodologyIn this simulation, steady airflow enters the domain through several inlets at the bottom of the room and exits through several outlets in the ceiling, with radiation heat transfer taken into account. This air-conditioning approach is widely used in office environments; it offers greater energy efficiency because the flow rises naturally through the density difference and buoyancy body force rather than being forced mechanically.Fresh air enters the computational domain at a velocity of 0.61254 m/s and a temperature of 291.8 K. One of the room's four main walls is subjected to a constant heat flux of 194 W/m². The remaining heat sources include a laptop and a simulator, with heat fluxes of 153.25 W/m² and 90.56 W/m², respectively.The Realizable k-epsilon model is used to solve the turbulent flow equations. The energy equation is enabled to compute the temperature variation within the domain, and the ideal gas model is used to capture the change in air density with temperature. Most importantly, the Surface-to-Surface (S2S) radiation model is employed to simulate the radiative heat exchange between surfaces inside the domain.ConclusionThe mixture mass flow rate at the computer room outlet is 0.568 kg/s. Air density reaches its minimum on the surfaces subjected to heat flux: as the fluid temperature rises, its density falls, and the resulting upward buoyant force acts on the fluid volume. As a consequence, the air density decreases progressively with height up the room.High temperatures of around 327 K are observed on the laptop surfaces and the hot walls. Intense turbulence appears near the hot wall and above the simulator, a direct result of the high heat fluxes assigned to the laptop, the simulator, and the hot wall.

DescriptionOne of the drawbacks of gasoline fuel is that its temperature drops during cold seasons or in cold locations. As the gasoline cools, sediments and gummy deposits form first; then, as the temperature continues to fall, the heavier hydrocarbon molecules begin to freeze and solidify. To prevent gasoline from freezing, the fuel temperature in the storage tank must be raised. One effective way to do this is to circulate a hot fluid through pipes inside the tank. Helical (spiral) tubes are particularly attractive in space-constrained situations, since they provide greater heat transfer within a given volume.The 3D geometry was created in Design Modeler. The model consists of two main parts: the fuel tank and an internal spiral tube carrying the hot water flow. Meshing was performed in ANSYS Meshing using an unstructured grid, with a total of 511,821 cells.MethodologyThis project simulates a gasoline fuel tank containing a single-pass spiral tube that runs through the tank. The inner tube carries water at a temperature higher than that of the gasoline, transferring heat to the fuel and thereby raising its temperature to prevent freezing inside the tank. To capture the phase change of the gasoline, the Solidification and Melting module is used to model the phase change material (PCM). The simulation is transient.ConclusionThe greatest degree of melting occurs in the region immediately surrounding the helical tube, where the hot water delivers the most heat. As the heating process continues, the liquid mass fraction steadily increases throughout the tank. The study was conducted over a limited time period, and the results presented here correspond to the end of the simulation.

What You'll BuildThis lesson walks you through a CFD simulation of Steam Methane Reforming (SMR) — the most widely used industrial process for producing hydrogen from hydrocarbon fuels. In an SMR plant, methane reacts with steam over a catalyst to produce hydrogen, carbon monoxide, and carbon dioxide through a set of endothermic reactions, with the required heat supplied by a burner in a surrounding heating chamber.In this project, you'll model a sleeve-type SMR reactor, capturing both the catalytic reforming reactions inside the tubes and the combustion that supplies their heat — a genuinely multi-physics chemical engineering problem.What You'll LearnThe chemistry behind Steam Methane Reforming and why it's central to hydrogen productionHow to design an SMR plant geometry — heating chamber plus reforming tubes — in Design ModelerHow to generate a large unstructured mesh (~1.65 million elements) for a complex multi-zone reactorHow to set up the Species Transport model to track multiple chemical species (H₂, CO, CO₂, CH₄, O₂)How to define multiple volumetric reactions — three reforming reactions inside the tubes and one combustion reaction in the thermal chamberHow to model a porous medium as a catalyst inside the reforming tubes, coupling reacting flow with porous-zone behaviorHow to handle endothermic reactions and the heat coupling between the burner and the reforming tubesHow to post-process mass fraction contours of each species to verify methane consumption and hydrogen productionHow to interpret results to confirm the reactor is operating correctlyWhy It MattersHydrogen is central to clean energy, ammonia synthesis, and refining. The skills here — multi-reaction Species Transport coupled with catalytic porous zones — transfer directly to catalytic converters, fuel reformers, chemical reactors, and combustion systems across the process industries.

Profile Macro: Advanced UDF for Pressure Profiles in ANSYS FluentWelcome to the ninth chapter of our comprehensive User-Defined Function (UDF) Training Course. This module focuses on implementing the Profile Macro to create realistic pressure distributions in urban CFD simulations using ANSYS Fluent.Project Overview: Urban Area Air Pressure SimulationIn this advanced CFD simulation, we model air pressure distribution in a simplified urban environment, considering the natural pressure variation with altitude. This project demonstrates the power of User-Defined Functions in creating realistic boundary conditions for complex environmental simulations.Key Simulation Components3D geometry modeling of urban area using Design ModelerStructured meshing with 118,400 cells via ANSYS MeshingCFD simulation using ANSYS Fluent with custom UDF implementation for pressure profileMethodology: Implementing Profile Macro in UDFOur approach leverages ANSYS Fluent’s UDF capabilities to define a height-dependent pressure profile at the inlet boundary. The core of this simulation lies in the custom implementation of atmospheric pressure variation using a User-Defined Function.Pressure Profile Modeling TechniquesCustom pressure function based on height (Y-coordinate)Implementation of DEFINE_PROFILE macro for advanced boundary condition definitionIntegration of atmospheric pressure model into urban flow simulationUDF Implementation and Simulation ProcessThe User-Defined Function plays a crucial role in setting up realistic inlet conditions for the urban airflow simulation. We’ll guide you through the process of writing and integrating the UDF into your ANSYS Fluent simulation.Step-by-Step UDF IntegrationWriting the custom pressure profile functionImplementing the DEFINE_PROFILE macroCompiling and loading the UDF into ANSYS FluentSetting up the inlet boundary condition with the custom pressure profileResults Analysis and VisualizationAfter running the simulations, we conduct a thorough analysis to evaluate the effectiveness of our custom UDF in creating realistic pressure distributions within the urban environment.Performance Metrics and VisualizationPressure contours at inlet boundary and cross-sectional planesVertical pressure profile plots at inlet and central domain locationsComparison of UDF-generated profiles with theoretical atmospheric modelsAdvanced Insights: Enhancing Urban Flow SimulationsThis simulation provides valuable insights into the importance of accurate pressure profiles in urban CFD simulations, with applications ranging from city planning to pollution dispersion studies.Applications and Benefits of Custom Pressure ProfilesEnhanced accuracy in predicting urban airflow patternsImproved simulation fidelity for tall building aerodynamicsAbility to model complex atmospheric conditions in urban environmentsFuture Directions and Research OpportunitiesThe techniques learned in this module open up numerous possibilities for advanced CFD research and urban planning applications. Consider exploring:Integration of temperature and density variations in atmospheric profilesDevelopment of time-dependent atmospheric boundary conditionsApplication to large-scale urban heat island effect studiesBy mastering the Profile Macro and UDF implementation in ANSYS Fluent, you’re equipped to tackle complex environmental flow problems with unprecedented control over boundary conditions. This knowledge is invaluable for CFD professionals looking to simulate and optimize urban environments, assess building ventilation, or study pollutant dispersion in cities.

Master Particle Transport Simulation in Bent Pipes with ANSYS CFXDive into the intricate world of multiphase flow dynamics with our comprehensive tutorial on “Particle Transport in Bent Pipe” using ANSYS CFX. This essential episode in our “ANSYS CFX: All Levels” course offers an in-depth exploration of particle-fluid interactions, crucial for engineers in various industries dealing with particle-laden flows.Unlock Advanced CFD Techniques for Multiphase Flow AnalysisLearn to harness the power of ANSYS CFX to simulate and analyze complex particle transport phenomena in bent pipes. This tutorial provides a detailed approach to modeling particle-fluid interactions, essential for optimizing pipeline designs, preventing erosion, and enhancing overall system efficiency.Key Learning Objectives:- Master the setup of 3D bent pipe models in SpaceClaim for particle transport simulations - Develop proficiency in unstructured mesh generation for complex geometries with multiphase flow - Understand the application of Fully Coupled Particle Transport Fluid Morphology - Analyze particle distribution, pressure drop, and flow patterns in bent pipesComprehensive Simulation Setup and MethodologyGain hands-on experience in configuring and executing a professional-grade CFD simulation for particle transport in bent pipes, covering all aspects from geometry creation to advanced flow visualization.1. Precise 3D Geometry and Advanced Mesh Generation- Create optimized 3D models of bent pipes using SpaceClaim - Implement unstructured meshing strategies with ANSYS Meshing for accurate multiphase flow simulation - Optimize mesh quality for complex geometries (139,461 elements)2. ANSYS CFX Configuration for Particle Transport Simulation- Set up Fully Coupled Particle Transport Fluid Morphology for accurate particle-fluid interaction modeling - Configure Scalable k-Epsilon turbulence model for robust flow prediction - Implement High Resolution schemes for Advection and Turbulence Numerics3. Advanced Data Analysis and Visualization Techniques- Extract and interpret velocity, pressure, and turbulence kinetic energy distributions - Analyze particle volume fraction to understand particle distribution and accumulation - Evaluate flow patterns and vortex formation using streamlines and vector plotsReal-World Applications and Industry RelevanceThis tutorial is crucial for professionals and researchers in:Oil and gas pipeline engineeringChemical process industryWater treatment and distribution systemsPneumatic conveying systems designKey Simulation Outcomes and Engineering Insights1. Particle Distribution Analysis- Interpret the complex patterns of particle distribution throughout the bent pipe - Understand the mechanisms of particle accumulation and potential erosion hotspots2. Flow Dynamics Evaluation- Analyze velocity patterns and vortex formation in the bent section - Assess the impact of pipe geometry on pressure drop and flow characteristics3. Performance Optimization- Evaluate the effectiveness of the pipe design in transporting particles - Understand the relationship between flow conditions and particle behaviorElevate Your CFD Skills in Multiphase Flow SimulationBy completing this specialized tutorial, you’ll gain:Cutting-edge skills in applying CFD to complex particle transport problemsProficiency in setting up and analyzing multiphase flow simulations in ANSYS CFXDeep understanding of particle-fluid interaction mechanisms in bent pipesInsights into optimizing pipe designs for improved particle transport and reduced erosionWho Should Take This Advanced TutorialPipeline engineers in oil, gas, and chemical industriesProcess engineers dealing with particle-laden flowsCFD specialists focusing on multiphase flow simulationsGraduate students in chemical or mechanical engineering studying multiphase flowsDon’t miss this opportunity to significantly advance your CFD simulation skills in particle transport analysis. Enroll now in our “ANSYS CFX: All Levels” course and master the art of simulating particle transport in bent pipes using ANSYS CFX!