Mastering Airfoil Thermal Management: Advanced CFD Simulation of Lateral Hole CoolingWelcome to the “Cooling of Airfoil Surface by Lateral Hole Air Inlets CFD Simulation” episode of our “THERMAL Engineers: INTERMEDIATE” course. This comprehensive module delves into advanced aerospace cooling techniques, focusing on the application of Computational Fluid Dynamics (CFD) in analyzing and optimizing airfoil surface cooling using lateral hole air inlets with ANSYS Fluent. Immerse yourself in this critical aspect of aircraft thermal management and learn how to enhance cooling efficiency in aerodynamic surfaces through powerful CFD techniques.Understanding the Pre-configured Airfoil Model with Lateral Cooling HolesBefore diving into the simulation specifics, we’ll explore the fundamental concepts of airfoil cooling.Principles of Airfoil Thermal ManagementDiscover the key design features that make lateral hole cooling systems crucial for maintaining optimal airfoil performance in high-temperature environments.Geometry and Configuration of Lateral Cooling HolesLearn about the critical elements that make up an effective lateral hole cooling system and how they contribute to enhanced heat dissipation.Analyzing the Interaction Between External Airflow and Cooling Air from Lateral InletsThis section focuses on the complex fluid dynamics involved in airfoil cooling:External Boundary Layer BehaviorGain insights into how the external airflow interacts with the airfoil surface and influences cooling effectiveness.Cooling Jet Dynamics and MixingUnderstand the behavior of cooling air as it exits the lateral holes and mixes with the external flow, and its impact on surface cooling.Implementing Appropriate Boundary Conditions for Airflow and Heat TransferDive into the specifics of setting up realistic simulation scenarios:External Flow ConditionsExplore how to define accurate freestream velocity, temperature, and pressure conditions for the external airflow.Cooling Air Inlet ParametersLearn to set appropriate flow rates, temperatures, and pressures for the cooling air entering through lateral holes.Configuring ANSYS Fluent for Fluid Flow and Conjugate Heat Transfer AnalysisIn this section, we’ll guide you through the process of preparing your CFD simulation:Mesh Generation Strategies for Airfoils with Cooling HolesMaster techniques for creating appropriate meshes that capture both the airfoil surface and the intricate geometry of lateral cooling holes accurately.Selecting Appropriate Physical Models for Aerospace CoolingLearn to choose and configure the right turbulence, heat transfer, and compressibility models for precise airfoil cooling simulation.Investigating Temperature Distributions Across the Airfoil SurfaceUnderstand how to analyze and interpret the key outputs of your simulation:Visualizing Surface Temperature PatternsDevelop skills in creating and interpreting temperature contours to understand the cooling effectiveness across different airfoil regions.Analyzing Thermal Boundary Layer DevelopmentLearn to evaluate the thermal boundary layer characteristics and their influence on overall cooling performance.Evaluating the Effectiveness of Lateral Hole Cooling in Reducing Surface TemperaturesThis section focuses on assessing the overall performance of the cooling system:Calculating Cooling Effectiveness ParametersDiscover methods for quantifying the cooling performance using standard effectiveness metrics used in aerospace thermal management.Comparing Different Cooling Hole ConfigurationsLearn to use CFD results to assess and optimize lateral hole patterns for improved cooling efficiency.Interpreting Results to Understand Cooling Performance Under Various ConditionsMaster the art of translating CFD data into practical design improvements:Analyzing the Impact of Flight Conditions on Cooling EffectivenessDevelop techniques for evaluating cooling system performance across different flight regimes and environmental conditions.Optimizing Blowing Ratios for Maximum Cooling EfficiencyLearn to determine the optimal cooling air flow rates relative to the external flow for enhanced thermal management.Practical Applications and Industry RelevanceConnect simulation insights to real-world engineering challenges:Airfoil Cooling in Gas Turbine EnginesExplore how CFD simulations can inform the design and optimization of cooling systems for turbine blades and vanes in aircraft engines.Thermal Management in Hypersonic Vehicle SurfacesUnderstand how to apply CFD analysis to address the extreme thermal challenges faced by hypersonic aircraft and spacecraft.Why This Module is Essential for Intermediate Thermal EngineersThis intermediate-level module offers a deep dive into advanced aerospace thermal management CFD simulation, a critical skill in modern aircraft design. By completing this simulation, you’ll gain valuable insights into:Advanced principles of airfoil cooling design and performance optimizationIntermediate CFD techniques for modeling complex fluid-thermal interactions in aerospace applicationsPractical applications of CFD analysis in enhancing aircraft component durability and performanceBy the end of this episode, you’ll have developed essential skills in:Setting up and running comprehensive airfoil cooling simulations with lateral hole inlets in ANSYS FluentInterpreting simulation results to assess cooling performance and identify potential improvementsApplying CFD insights to enhance thermal management strategies in various aerospace applicationsThis knowledge forms a crucial stepping stone for thermal engineers looking to specialize in aerospace thermal systems, providing a foundation for advanced studies in propulsion system cooling, aerothermodynamics, and innovative heat management solutions for next-generation aircraft.Join us on this exciting journey into the world of airfoil cooling CFD simulation, and take your next steps towards becoming an expert in advanced thermal engineering for cutting-edge aerospace applications!

Drying Seed Behavior in a Porous Medium — ANSYS Fluent CFD SimulationThis project investigates the drying process of seeds within a semi-cylindrical domain packed with seed particles, using ANSYS Fluent to capture the coupled heat and mass transfer occurring as hot air flows through the seed bed. The simulation tracks temperature, moisture distribution, and velocity fields within the porous seed zone to evaluate how the drying process evolves over time under given thermal and flow conditions, offering insight into drying efficiency, local heat transfer, and vapor concentration patterns.Geometry and MeshThe geometry was built using ANSYS SpaceClaim and DesignModeler. Taking advantage of symmetry, only half of the physical domain was modeled, with the bottom surface defined as a symmetry boundary to reduce computational cost. Two zones were defined: a fluid zone representing the drying air, and a seed zone treated as a porous medium with a porosity of 0.418, reflecting the physical packing of the seeds. The domain was discretized in ANSYS Meshing using a tetrahedral mesh of approximately 4.5 million cells, providing sufficient resolution to resolve the temperature and velocity gradients around the seed particles.Model and Solver SettingsA pressure-based transient solver was used to capture the time-dependent heat and mass transfer behavior, with gravity set to -9.81 m/s² in the Y-direction to correctly account for buoyancy. The energy equation was activated to model heat exchange between the hot air and the seed surfaces, and the RNG k-ε turbulence model was selected for its accuracy in capturing recirculating and swirling flows within porous media. The species transport model was enabled to track water vapor (H₂O) concentration, with air, H₂O, and wheat defined as the working materials. Pressure-velocity coupling was handled using the SIMPLEC algorithm, with a velocity inlet for the incoming hot air and a pressure outlet for the exiting flow. The transient formulation allowed the temperature and moisture fields within the seed zone to be monitored over time.ResultsThe temperature contours show a gradual rise across the seed bed, with values ranging from approximately 302.6 K to 303.1 K, indicating a gentle but effective drying process. The H₂O mass fraction contours show a progressive decrease in vapor concentration along the airflow path, confirming that moisture is being removed from the seed surfaces. Velocity streamlines show the air accelerating as it passes through the porous region, enhancing convective heat and mass transfer. Together, the flow, temperature, and species fields indicate that the airflow is well distributed through the porous bed, promoting uniform drying conditions throughout the domain. These results can help guide the optimization of airflow velocity, porosity, and inlet temperature for improved drying performance in industrial applications.

DescriptionThis project uses ANSYS Fluent to study how dust particles enter a room through windows and move/deposit inside. The 3D geometry (DesignModeler) represents a room with two windows and a chimney. Meshing (ANSYS Meshing) yields 42,061 elements. Because deposition evolves over time, the simulation is transient.Dust Particles MethodologyDust-laden air enters via the two window inlets at 0.25 m/s and exits through a pressure outlet at the chimney top. Particle transport and settling are modeled with a two-way coupled Discrete Phase Model (DPM) to capture interaction between the particles and the carrier flow. The laminar flow model is used for the continuous phase.ConclusionOutputs include 2D velocity contours, vectors, and streamlines, revealing airflow paths and dust motion. The wind-driven flow carries particles along the main stream, while recirculation zones promote enhanced deposition and sediment accumulation.

DescriptionThis project simulates the operation of an inhaler asthma spray using ANSYS Fluent.Shortness of breath is the distressing sensation of not being able to draw a full breath, often felt during physical activity, where the body seems unable to take in enough oxygen. Respiratory sprays are one of the common treatments. An inhaler is a portable device that holds a specific medication and lets the patient deliver it directly into the airways as they breathe in. A key advantage of this delivery method is that the drug goes mainly to the airways and lungs rather than being absorbed by other organs, which gives inhalers fewer side effects than oral or injectable medications.The geometry was created in Design Modeler. The model includes the spray device itself, with an internal hole that serves as the point where the drug is injected and dispersed, along with a surrounding computational zone that captures the region where the spray disperses. The domain was then meshed in ANSYS Meshing using an unstructured grid of 752,277 cells.Simulation MethodologyBecause the goal is to track individual drug particles, we use a Lagrangian approach, which follows each particle separately through a discrete space. This is handled with the Discrete Phase Model (DPM). An injection is defined to release the discrete particles: the injection is of the Surface type, the particles are Inert, and the release is unsteady, occurring over 0.1 seconds.Results & ConclusionAfter solving, we examined particle tracking at several time steps and generated an animation of the injection. Following the particles over time shows how the device disperses the medication and confirms that the spray is delivered effectively. The results indicate the simulation was set up and solved correctly.

Absorption of Ammonia into Water in a Packed Tower, VOF, ANSYS Fluent CFD Simulation TutorialDescriptionThis project simulates the absorption of ammonia from air inside an absorption tower using ANSYS Fluent.Absorption is a method of separating the components of a gas mixture by bringing it into contact with a liquid solvent. The process relies on the difference in solubility between the gas-phase components: where the gas meets the liquid solvent, one or more components transfer into the liquid phase and separate from the gas stream. In refineries, one common use of absorption towers is to strip ammonia out of a gas stream.In this model, an airflow carrying one percent ammonia enters at 0.43 m/s through a nozzle at the bottom of the tower chamber. At the same time, liquid water enters at 0.0332 kg/s through a nozzle at the top. As the ammonia-laden gas rises and contacts the descending water, the ammonia is absorbed into the water.The geometry was built in three dimensions in Design Modeler as a vertical absorption tower: the ammonia-air stream enters through the bottom nozzle and exits from the upper section, while the water enters through the top nozzle and exits from the lower section. The domain was meshed in ANSYS Meshing using an unstructured grid of 478,882 elements.Simulation MethodologyThe simulation uses the VOF multiphase model, with the air stream carrying one percent ammonia and liquid water acting as the solvent.Results & ConclusionAfter solving, we obtained two- and three-dimensional contours of pressure, velocity, turbulent (eddy) viscosity, density, and the volume fractions of water, air, and ammonia. The results show the ammonia transferring from the gas into the water stream, confirming that the water absorbs the ammonia and separates it from the gas.

Reverse Osmosis (RO) CFD Simulation, ANSYS Fluent TutorialDescriptionThis project simulates reverse osmosis using ANSYS Fluent.Reverse osmosis is one of the most widely used technologies in clean-water engineering, where it drives desalination and water-purification systems that turn seawater, brackish water, and contaminated supplies into potable water. Understanding how salt and impurities separate across the membrane is central to designing and improving these systems.Osmosis is a natural phenomenon in which a fluid tends to move from a region of lower concentration to one of higher concentration until the concentration on both sides is balanced. Imagine a semi-permeable membrane placed between pure and impure water. By osmosis, water moves toward the impure side until a pressure difference builds up across the membrane. This difference is called the osmotic pressure. If a pressure equal to the osmotic pressure is applied to the impure side, the fluid movement stops. If the applied pressure exceeds the osmotic pressure, the natural direction of flow reverses.Reverse osmosis desalination systems work on exactly this principle: a pressure beyond the osmotic pressure is applied across the semi-permeable membrane, and as the water passes through, salt and impurities are separated from it.This project is simulated in two parts. The first part looks only at fluid behavior driven by osmotic pressure. A closed chamber is modeled and divided into two sections by a barrier that is removed instantaneously. The left side holds a saltwater solution and the right side holds pure water. The goal is to observe how fluid moves between the two sections of different concentration, which illustrates the concepts of osmosis and osmotic pressure.Building on the first part, the second part studies the reverse osmosis desalination system itself. Here a membrane, modeled as a porous medium, is placed in the middle of the chamber. The water-and-salt mixture enters from the inlet on the left and moves toward the membrane. When the solution reaches the membrane, pure water passes through while the salt (the higher-concentration water) is trapped behind it.The geometry was built in two dimensions in Design Modeler as a simple rectangular chamber with a membrane between its two sections. The domain was meshed in ANSYS Meshing using a structured grid of 44,800 cells.Simulation MethodologyBecause an impure solution is used instead of a single pure fluid, a two-phase flow must be defined, so a multiphase model is used. Of the available options (VOF, Mixture, and Eulerian), the Eulerian model, which is the most complex of the three, is used here. Water is the primary phase and salt is the secondary phase dissolved in it, with a salt concentration of 0.02. Concentration is tracked through the volume fraction, and the solver handles the transport equations for that volume fraction.The membrane between the two sections is modeled as a porous medium, where the porosity (the ratio of empty space to total volume) sets its permeability. The simulation runs in two steps: in the first model the fluid moves naturally with no external forces, while the second model applies a driving boundary condition. Since the aim is to study how the system behaves over time, the solution is transient (time-dependent).Results & ConclusionAfter solving, two-dimensional contours of pressure and of the water and salt volume fractions were obtained. Because the solution is transient, the results are compared at different times to capture the system's behavior, and an animation of the change in dissolved-salt volume fraction was produced. Results were obtained for both simulation cases.In the first case (a closed chamber with no external boundary conditions), the left side initially holds water and salt while the right holds pure water. Over time, fluid moves from the higher-concentration side to the lower-concentration side and continues until both sides reach equilibrium. This movement occurs naturally, without external forces, and correctly reproduces the osmotic behavior of the fluid.In the second case, a porous membrane sits in the middle of the system. The water-and-salt solution is driven toward the membrane at a set velocity and pressure beyond the osmotic pressure, opposite to the natural osmotic direction. The results show pure water passing through the membrane while the dissolved salt is trapped behind it. This continues until a fully concentrated solution builds up behind the membrane and pure water collects beyond it. The pressure results also show the pressure difference across the system increasing over time. Together, these results confirm that the reverse osmosis system works correctly and successfully purifies the water.

This project simulates the airflow and heat transfer inside a Heller-type dry cooling tower — the indirect cooling system used in thermal power plants to reject heat from the working fluid (water) to ambient air without evaporative water loss. After leaving the condensers, the hot water is pumped through a ring of air-cooled heat exchangers; the tower draws cooling air through them by natural draft, created by the density difference between the warm air inside the tower and the cooler air outside.The study captures the core physics that governs tower performance: the temperature driving force. The larger the gap between the working fluid and ambient air, the stronger the natural draft and the better the cooling. This is also why these towers lose efficiency in summer — as ambient temperature rises, the driving force shrinks, forcing the plant to cut power output and increase water consumption to maintain cooling. The simulation lets you see and quantify that buoyancy-driven flow directly.Geometry & mesh: the model includes the cooling tower, the heat-exchanger (radiator) ring, the flow domain, and the air inlet, built and meshed in GAMBIT with an unstructured mesh of 1,343,988 cells.Setup: the case is solved steady with a pressure-based solver, with gravity enabled at −9.81 m/s² in the Y direction — essential, since the natural draft is entirely buoyancy-driven. Turbulence uses the standard k-ε model with standard wall functions, and the energy equation is on.Boundary conditions reproduce the natural-draft setup:Inlet: pressure inlet, 0 Pa gauge total pressure, normal to boundaryOutlet: pressure outlet, 0 Pa gauge, backflow temperature 303 KRadiator (heat exchanger): modeled as a heated surface at 318 K with a heat generation rate of 14,861.52 W/m³, and its shadow face at 313 KWalls: stationary, zero heat fluxSolution uses SIMPLE pressure–velocity coupling with first-order upwind discretization for momentum, energy, and turbulence (standard scheme for pressure and density), and standard initialization at 303 K.What the results show: contours of velocity, pressure, and temperature, along with flow streamlines through the tower. Together they reveal how air is drawn in through the heat exchangers, heats up, and rises through the tower — and how the temperature field across the radiator ring sets the cooling capacity available to the plant.You'll learn to: set up a buoyancy-driven natural-draft flow, represent a heat-exchanger ring with a heated radiator surface and heat generation rate, configure a steady pressure-based solver with the energy equation, and interpret cooling-tower performance from temperature and streamline fields.

What You'll BuildThis lesson walks you through a CFD simulation of a steam ejector — a mechanical device with no moving parts that uses a primary (motive) steam jet to suck in and mix with a secondary fluid. Ejectors perform two essential jobs: creating vacuum for suction and mixing two fluid streams, and they do it by continuously converting between kinetic and pressure energy as the flow passes through a convergent-divergent nozzle.In this project, you'll model water vapor as the motive fluid driving the suction of a secondary fluid, watching the flow accelerate beyond the speed of sound and observing how the vacuum-driven suction physically arises.What You'll LearnHow an ejector works — the physics of vacuum generation, fluid entrainment, and mixing through energy conversionWhy supersonic flow is fundamentally compressible, and how Mach number governs the behavior inside the deviceHow to design a 2-D convergent-divergent (de Laval) nozzle ejector geometry in Design ModelerHow to generate an efficient structured mesh (~52,000 elements) suited to internal compressible flowHow to set up the density-based solver — the correct choice for compressible and supersonic flows where density varies strongly with pressureHow to handle the pressure difference between primary and secondary inlets that drives the suction phenomenonHow to post-process pressure, velocity, and Mach number contours to trace where the flow goes subsonic, sonic, and supersonicHow to interpret the mixing and compression of the motive and secondary streams downstream of the nozzle throatWhy It MattersEjectors are everywhere — refrigeration, vacuum systems, desalination, chemical processing, and power plants. This lesson is your gateway to compressible flow modeling and the density-based solver, skills that carry directly into nozzles, diffusers, supersonic airfoils, and any flow where Mach number matters.

This project simulates film cooling on a gas turbine blade — the technique that lets turbine blades survive gas temperatures well above their material limits by holding a thin layer of cool air against the surface. The cooling air, bled from the compressor stage, is fed through internal channels and ejected through discrete holes to form a protective film over the blade.The study is set up as a conjugate heat transfer (CHT) problem: the fluid domain (hot gas and cooling air) and the solid blade are coupled at the walls, so heat conducts through the blade while the external hot gas and the internal/film cooling air exchange heat with it simultaneously. Turbulence is modeled with k-ω SST, which resolves both the near-wall film behavior and the free-stream mixing between cool and hot streams.Geometry is built in Design Modeler, meshed in ANSYS Meshing, then converted to a polyhedral mesh (~2.7 million cells) in ANSYS Fluent for better gradient resolution and faster convergence around the cooling holes.What the results show: pathlines trace the cooling air through the blade's internal channels and out through the film holes, where it forms a thin thermal barrier over the surface. Film thickness varies along the blade — thickest near the holes — and the film is turbulent, mixing with the hot gas downstream and progressively losing effectiveness. The simulation makes the core design trade-off visible: hole size, shape, spacing, count, and injection angle all control how well the film holds before the hot gas entrains it.You'll learn to: set up a coupled fluid–solid CHT model, mesh and inject through discrete cooling holes, choose and justify k-ω SST for film flows, and read film effectiveness from temperature fields and pathlines.

DescriptionThis project simulates airflow within a building’s double-skin façade (DSF) using ANSYS Fluent. In a DSF, solar-heated air rises due to buoyancy, providing passive heating and aiding ventilation/cooling inside the building.The 3D geometry (DesignModeler) is a rectangular cavity measuring 0.6 × 3.2 × 5 m, composed of a duct for airflow and a glazed section that absorbs solar heat. Openings include a 0.2 m rectangular inlet at the bottom of the glass wall and a 0.2 m outlet near the top. Meshing (ANSYS Meshing) yields 490,725 elements.MethodologyThe study evaluates buoyancy-driven circulation in the DSF cavity. The glass section is modeled with a volumetric heat generation of 6940 W/m³ to represent solar gain. Building walls are brick and subject to convection to the interior: T = 300 K, h = 23 W/m²·K (free convection).Supply air enters the façade at 304.55 K and atmospheric pressure. To capture buoyancy, air density follows the ideal gas law, and gravity = 9.81 m/s² is applied.ConclusionPost-processing provides 2D/3D pressure, velocity, and temperature contours, plus 2D/3D velocity vectors. The vectors show an upward flow in the cavity, confirming buoyancy-driven ventilation within the double-skin façade.

Mastering Hydraulic Structure Analysis: Ogee Spillway CFD Simulation for BeginnersWelcome to the “Ogee Spillway CFD Simulation” episode of our “HYDRAULIC Engineers: BEGINNER” course. This comprehensive module introduces civil engineers to the powerful world of computational fluid dynamics (CFD) applied to spillway design and analysis. Learn how to leverage ANSYS Fluent to simulate and analyze the complex flow characteristics of ogee spillways, a critical component in modern dam engineering and flood control systems.Understanding the Importance of Ogee Spillways in Hydraulic EngineeringBefore diving into the simulation specifics, let’s explore the fundamental concepts of ogee spillways and their significance in dam engineering.The Role of Spillways in Dam Safety and Flood ControlDiscover how spillways contribute to water level regulation and dam safety, and why understanding their hydraulic behavior is crucial for effective flood management.Advantages of Ogee-Shaped Spillways in Energy DissipationLearn about the unique characteristics of ogee spillways that make them highly efficient in dissipating energy and controlling water flow in dam structures.Introduction to ANSYS Fluent for Spillway AnalysisThis section focuses on familiarizing beginners with the ANSYS Fluent software environment:Navigating the ANSYS Fluent InterfaceGain insights into the basic layout and functionality of ANSYS Fluent, essential for efficient simulation setup and analysis of hydraulic structures.Understanding the CFD Workflow for Spillway SimulationsLearn the step-by-step process of setting up, running, and analyzing an ogee spillway CFD simulation in ANSYS Fluent.Setting Up a Basic Ogee Spillway ModelMaster the art of creating a simple simulation environment for spillway hydraulics:Defining Geometry and Mesh for Ogee Spillway SimulationsLearn techniques for creating a basic geometry representing an ogee spillway, along with appropriate meshing strategies for accurate flow analysis.Configuring Water Properties in ANSYS FluentExplore methods for defining and implementing the properties of water in your spillway flow simulation.Boundary Conditions for Spillway Flow ScenariosDive into the critical settings that ensure realistic representation of water flow over ogee spillways:Specifying Inlet and Outlet ConditionsUnderstand how to set up appropriate inlet flow rates and outlet pressure conditions that accurately represent spillway operation scenarios.Implementing Wall and Free Surface Boundary ConditionsLearn to define proper boundary conditions for the spillway surface and water-air interface to capture realistic flow behavior.Running Simple Simulations of Water Flow Over an Ogee SpillwayDevelop skills to execute and monitor your first ogee spillway CFD simulations:Setting Up Solver Parameters for Hydraulic SimulationsMaster the basics of configuring solver settings, including time-stepping and convergence criteria, suitable for spillway flow simulations.Monitoring Simulation Progress and Ensuring StabilityLearn techniques for tracking simulation progress and identifying potential issues during the solving process.Analyzing Basic Velocity Distributions and Pressure ProfilesDevelop expertise in extracting meaningful insights from your spillway simulations:Visualizing Water Flow Patterns Over the SpillwayMaster techniques for creating insightful visualizations of velocity fields and streamlines to understand flow behavior along the ogee profile.Interpreting Pressure Distributions on Spillway SurfacesLearn to analyze pressure profiles along the spillway surface, crucial for assessing hydraulic loads and potential cavitation risks.Understanding Energy Dissipation in Ogee SpillwaysExplore the fundamentals of energy dissipation, a key function of ogee spillways:Principles of Energy Dissipation in Hydraulic StructuresGain insights into how ogee spillways effectively dissipate energy from high-velocity flows, protecting downstream structures.Analyzing Energy Dissipation Patterns in CFD ResultsLearn introductory methods for identifying and interpreting energy dissipation characteristics in your simulation results.Practical Applications and Civil Engineering RelevanceConnect simulation insights to real-world spillway design challenges:Applying CFD Insights to Spillway Design and AnalysisExplore how the flow patterns and pressure distributions observed in CFD simulations can inform spillway design decisions and performance assessments.Understanding the Limitations of Beginner-Level SimulationsGain awareness of the simplifications in this introductory course and the potential for more advanced analyses in future studies.Why This Module is Essential for Beginner Hydraulic EngineersThis beginner-level module offers an introduction to the powerful world of CFD in hydraulic structure analysis. By completing this simulation, you’ll gain valuable insights into:Basic application of ANSYS Fluent for simulating water flow over ogee spillwaysEssential CFD techniques for capturing flow patterns and pressure distributions in spillway structuresPractical applications of CFD analysis in spillway design and performance evaluationBy the end of this episode, you’ll have developed foundational skills in:Setting up and running basic spillway flow simulations using ANSYS FluentInterpreting simulation results to assess hydraulic characteristics of ogee spillwaysApplying CFD insights to enhance understanding of spillway performance and inform design decisionsThis knowledge forms a solid foundation for civil engineers looking to integrate advanced computational methods into their hydraulic structure design and analysis toolkit, providing a springboard for more advanced studies in dam engineering and flood control systems.Join us on this exciting journey into the world of ogee spillway CFD simulation, and take your first steps towards becoming a proficient hydraulic engineer equipped with cutting-edge computational tools for spillway analysis and design!

Offshore Pipeline Considering Hydrodynamic Force, ANSYS Fluent CFD Simulation TrainingDescriptionThis project simulates seawater flow around an offshore pipeline using ANSYS Fluent.Offshore pipelines are a core part of marine engineering, carrying oil, gas, and other resources across the seabed between platforms and shore. As seawater waves pass over these pipelines, they generate drag and lift forces on the pipe. To keep the line safe and stable, it must be positioned so that it experiences the lowest possible hydrodynamic loading, which makes this kind of analysis important for offshore pipeline design.The 2-D model was built in ICEM and consists of a rectangular seawater domain with a circular cross-section representing the pipe. Two key geometric parameters govern the study: the pipe diameter (D) and the gap between the bottom of the pipe and the seafloor (e), expressed through the e/D ratio. The pipe diameter is fixed at 0.4 m, and two cases are considered, e = 0.2 m and e = 0.1 m, giving e/D = 0.5 and e/D = 0.25. The seawater domain is 12 m long and 3.24 m high.The model was meshed in ICEM using a structured grid of 135,417 elements. To capture the flow accurately, the mesh is refined near the pipe: the circumference of the circular section is split into five segments, and the cells closest to the pipe are smaller and of higher quality.Simulation MethodologyThe main geometric variable in this study is the pipe-to-seafloor gap ratio (e/D). Because the seawater motion is wavy rather than steady, the inlet velocity is defined as a wave-flow equation through a UDF. Likewise, the pressure inside the seawater is measured relative to atmospheric pressure and varies with the wave motion, so the wave (ambient) pressure is also imposed through a UDF. In total, the inlet horizontal velocity, the relative wave pressure, the turbulent kinetic energy, and the turbulence dissipation rate are all defined as UDFs.The goal is to compare the hydrodynamic forces on the pipeline over one full wave period and identify the optimal configuration. The seawater wavelength (the distance between two wave peaks) is 163.20 m, with a corresponding period of 10.3 s, giving a wave angular frequency of 2π/Tw = 2π/10.3 ≈ 0.61 rad/s. The maximum velocity at a wave peak is 2.729 m/s, and k_m and ε_m denote the maximum turbulent kinetic energy and the maximum turbulence dissipation rate, respectively.In the wave-pressure equation, H is the wave height and d is the seawater depth. The term −z is the height of the water column at the point where the dynamic pressure is evaluated, and d − (−z) is the distance from that point down to the seabed.Results & ConclusionAfter solving, we obtained two-dimensional contours of velocity and pressure, along with two-dimensional velocity vectors, for both cases (e/D = 0.5 and e/D = 0.25). These results are taken at the final instant of the simulation (10.3 s), i.e., at the end of one complete wave period.We also obtained time-history graphs of the drag and lift hydrodynamic forces and of the drag and lift coefficients, again for both e/D cases. Comparing the two configurations shows how the pipe's distance from the seabed affects the hydrodynamic loading, which is what determines the optimal placement of the line.

The airfoil is the most fundamental geometry in all of aerodynamics — its shape governs the lift and drag that determine the performance of aircraft wings and turbine blades alike. In this project, you'll use ANSYS Fluent to study the airflow around a three-dimensional airfoil and learn to read the flow physics that engineers actually design around.You'll simulate an incompressible, isothermal airflow over a 0.5-meter NACA-type airfoil placed inside a wind tunnel domain, with a free-stream inlet velocity of 10 m/s. The mesh, built in ANSYS Meshing, is refined around the leading edge, the upper and lower surfaces, and the trailing edge to capture the boundary layer and wake accurately, while coarsening toward the far-field boundaries to keep the cell count efficient. The case is solved with a pressure-based, steady-state solver using the k–ω SST turbulence model.From the results, you'll learn to interpret the high-pressure stagnation region at the leading edge, the low-pressure suction zone on the upper surface that generates lift, and the pressure differential between the upper and lower surfaces that produces the net upward aerodynamic force. You'll also see how the velocity field accelerates over the suction side and develops a velocity deficit in the wake, where vortical structures and energy loss give rise to aerodynamic drag. Finally, you'll connect these flow features to the lift and drag coefficients and see why near-wall mesh refinement is essential for reliable predictions.By the end of this project, you'll be able to set up, solve, and analyze a complete external aerodynamics case in ANSYS Fluent — and understand the forces and losses behind the results, not just the contours.

This project simulates heat extraction from a geothermal reservoir using a single U-tube Downhole Heat Exchanger (DHE) — a U-shaped pipe set in a wellbore through which a working fluid circulates to draw heat out of the ground. It's a strong study in natural-convection-driven conjugate heat transfer, where the heat path runs from the surrounding ground, through the borehole fluid, and into the circulating tube water.The model is built from three coupled parts — the U-tube, the borehole, and the ambient geothermal reservoir — and is a scaled version of a real field case (which sits ~200 m underground). Here the ground zone and U-tube are scaled to 6 m and 3.2 m depth, with a 0.0875 m tube diameter inside a 0.35 m borehole, all within a 3 m ground cylinder. Geometry is built in Design Modeler and meshed in ANSYS Meshing as a polyhedral mesh (~1.75 million cells).The physics centers on free (natural) convection: the solid ground temperature is set as a linear function of depth using a Named Expression, so the borehole heats from the bottom up. Gravity is enabled, and water's thermal conductivity and heat capacity are defined as temperature-dependent via the polynomial method — the buoyancy that drives the whole problem depends on getting these property variations right. Turbulence uses the Realizable k-ε model with standard wall functions, and the flow is solved steady.What the results show: temperature and pressure contours plus velocity vectors for both the tube and borehole zones. Convective heat transfer raises the tube outlet temperature to 305.47 K. The velocity vectors reveal the mechanism clearly — a vortex forms at the bottom of the hole, intensifying turbulence and heat transfer; water near the hot wall warms, loses density, and rises, then cools and sinks, completing the natural-convection loop that feeds heat into the tube.You'll learn to: set up buoyancy-driven natural convection, define depth-dependent solid temperatures with Named Expressions, model temperature-dependent fluid properties, and run a coupled solid–fluid heat exchange case.

This project analyses the thrust and lift generated by a rotating propeller and its effect on an aircraft fuselage using ANSYS Fluent, with the Mesh Motion (moving mesh) technique as the central theme. A propeller converts the rotational power of an engine into thrust: its twisted blades act like small rotating wings, producing an aerodynamic force that can be resolved into a component along the aircraft axis (the propulsive thrust) and a component in the plane of the blades (the torque). Reproducing this behaviour in CFD requires the propeller region to physically rotate within the simulation, and the moving-mesh approach is what makes that possible — it is the core of the methodology.The aircraft and propeller geometry was designed in SolidWorks and imported into ANSYS Meshing for grid generation and boundary naming. The mesh was first built with tetrahedral elements and then converted to a polyhedral mesh within Fluent, which yields fewer cells and higher quality: the element count is 3,812,519 for the tetrahedral mesh and 692,023 for the polyhedral mesh.The model is divided into two zones, rotational and stationary, which is the defining structure of a mesh-motion simulation. A cylindrical rotating domain sized at 1.12 propeller diameters surrounds the impeller and is meshed more finely, reflecting the greater importance of the blade region to the results. This rotating domain sits inside the fixed outer zone, and the two are connected through an interface that transfers flow quantities between them. The Mesh Motion method makes the rotating domain physically spin about the impeller axis, directly capturing the propeller's rotation, and a transient solver is used to resolve the resulting time-dependent flow.To scale the simulation correctly, the advance ratio is used as the governing similarity parameter. With an impeller diameter of 0.0532 m and a rotational speed of 1800 rpm (30 rad/s), an advance ratio of J = 1.225 corresponds to a flow velocity of 2 m/s. These conditions provide a consistent basis for simulating the propeller across different scales by holding the advance ratio fixed.The results yield the drag and lift on the fuselage together with the thrust and torque on the propeller, presented in the accompanying diagrams, along with contours, vectors and flow lines that reveal the flow physics around the aircraft and blades. The study shows that, by respecting the advance ratio for each propeller, working points can be defined through the relationship between flow velocity and rotational speed. For a fully rigorous match, additional criteria are needed — in particular the Reynolds number based on both the impeller speed and the flow velocity — and a valid scaled simulation requires that the computed Reynolds number exceed the critical value for that propeller. On that basis the model can represent real propeller operating points. As a study in moving-mesh modelling, the project demonstrates how splitting the domain into rotating and stationary zones joined by an interface, combined with a transient solver, captures the genuine rotation of a propeller and the thrust, torque and aerodynamic loads it produces.

Urban Heat Island (UHI) CFD Simulation on a Real Urban Zone, ANSYS Fluent TrainingDescriptionThis project simulates airflow and heat transfer over a real urban area — the Auckland University of Technology (AUT) campus in Auckland, New Zealand — to study the Urban Heat Island (UHI) effect using ANSYS Fluent.Urban heat island and pedestrian comfort are central concerns in urban planning. As cities grow denser and taller, buildings reshape local wind patterns and trap heat, creating uncomfortable or even unsafe conditions at street level. CFD lets planners predict wind speed and temperature around a real building layout while the design is still on the drawing board, so problem areas can be identified and fixed before anything is built.The study has two goals: to map pedestrian wind comfort and flag locations where wind speed exceeds 3.8 m/s, and to check outdoor thermal comfort, where the aim is to keep the campus area below 295 K.The real building footprints were extracted from Google Earth Pro, and the corresponding geometry and building volumes were reconstructed in ANSYS Design Modeler as a main domain (the campus itself) surrounded by a larger subdomain that captures the incoming wind.Simulation MethodologyThe analysis is carried out in two parts: wind comfort and thermal comfort.For the wind-comfort study, note that Auckland's airflow is predominantly from the southwest, shifting toward the northeast in summer as the high-pressure belt moves south, and that coastal areas are consistently windier than sheltered inland ones. Using representative wind conditions for the site, the model resolves the wind field around the buildings and evaluates it against standard pedestrian wind-comfort criteria.For the thermal-comfort study, solar loading is applied with the Discrete Ordinates (DO) radiation model, set from the site's geographic coordinates for mid-February at 1 p.m. The building surfaces are assigned a representative heat flux, the ground surface is fixed at 286.15 K, and the incoming free-stream air is set to 288.15 K, based on local meteorological data. Together these drive the temperature field that forms the urban heat island.Results & ConclusionThe velocity contours show that buildings directly exposed to the wind experience speeds up to about 30 km/h (≈ 8.3 m/s) at some points, well above the pedestrian-comfort limit. As the air moves into the passages between buildings it slows to around 10 km/h (≈ 2.8 m/s), which is comfortable, and it is damped further in the rear rows of buildings.On the thermal side, the incident solar radiation over the domain ranges from about 1560 to 1700 W/m²; taller buildings absorb more, while the passages between them receive less because of shading. Temperature contours were extracted at several heights above the ground: at 0.5 m the average is about 287.46 K with a local peak of 295.77 K, at 1 m the average rises to 287.96 K (peak 292.58 K), at 1.5 m the field changes little, and at 2 m the peak reaches 294.87 K. The warm zones between buildings come from heat rejected by the surrounding surfaces — the signature of the urban heat island — and one low but wide building (the meeting hall) cools more slowly and holds the highest roof temperatures.Overall, the wind-comfort criterion is exceeded in several exposed areas, particularly toward the suburbs, so the study points to mitigations such as windbreaks or added vegetation and greater spacing between closely packed buildings to avoid narrow, high-speed street canyons. The thermal-comfort target of 295 K is met across almost the entire campus at pedestrian height, with only a small, localized area reaching it, so heat is not expected to cause meaningful hardship for people using the campus.

Gas Flare, Two-Step Air–Methane Mechanism Combustion, ANSYS Fluent CFD Simulation TutorialDescriptionThis project simulates combustion in a gas flare, using a two-step methane–air mechanism, in the presence of a crosswind, with ANSYS Fluent.This case is a clear example of a reacting flow, where the fluid motion and the chemistry are solved together: the flow carries fuel and air into the flame, the combustion reactions release heat and change the gas composition, and the resulting temperature and density fields feed back into the flow. Modeling this coupling is exactly what the reacting-flow (species transport) approach is built for.A gas flare is a combustion device used in industrial facilities such as oil and gas refineries and at production wells, particularly on offshore platforms, to safely burn off natural gas.The 3-D geometry was built in Design Modeler. Because the flare is symmetric, only half of it is modeled to cut the computational cost, with a symmetry boundary condition applied. The flare has a cylindrical body with four outlet ducts and sits inside a computational domain that carries the wind flow; this domain is likewise halved along the symmetry plane. The model was meshed in ANSYS Meshing with 1,546,925 elements.Simulation MethodologyGas flares burn the natural gas released during oil extraction. During extraction, natural gas accumulates above the oil in the reservoir. Collecting and storing this gas is preferable, but where that is not possible it is flared. Burning the gas in a flare avoids uncontrolled, hazardous release, and converting methane to carbon dioxide before it reaches the atmosphere is less harmful than releasing the methane directly.To capture the chemistry, the species transport model is used with volumetric reactions enabled, and the eddy-dissipation model estimates the reaction rate. A methane–air mixture burns through a two-step mechanism: first methane and oxygen react to form carbon monoxide (and water), then the carbon monoxide combines with oxygen to form carbon dioxide. Air enters the domain at 0.2 m/s and 300 K, and the fuel enters at 0.1 m/s and 300 K. The realizable k-ε model and the energy equation are enabled to solve the turbulent flow and compute the temperature distribution.Results & ConclusionAfter solving, two- and three-dimensional contours of pressure, temperature, velocity, and the mass fraction of each modeled species were obtained, with the two-dimensional contours shown on the geometry's symmetry plane.The species mass-fraction contours confirm that the reaction takes place: the carbon dioxide and carbon monoxide contours show these products being generated, while the methane contour shows the hydrocarbon being consumed as the reactant. The contours also show that the crosswind carries the combustion products, such as carbon dioxide and carbon monoxide, away from the flare and disperses them into the surrounding environment.

Slat and Flap Devices Effects on an Aircraft Wing, ANSYS Fluent TrainingDescriptionThis project simulates the airflow around a 3-D aircraft wing fitted with a slat on the leading edge and a flap on the trailing edge, using ANSYS Fluent.This is a representative compressible-flow case. Near the wing the air moves fast enough that its density can no longer be treated as constant, so the flow must be solved with a density-based solver and the air modeled as an ideal gas whose density varies with pressure and temperature. Capturing these density changes — and the pressure field they produce over the wing — is exactly what the Compressible Flow model is built for.A flap is a small aerodynamic surface on the trailing edge of the wing used to increase lift. Lift is what holds the aircraft up against its weight, and it grows with speed. When the aircraft slows down — as during takeoff and landing — lift drops, so it must be recovered: the flap rotates about its hinge at the trailing edge and increases the effective area of the wing exposed to the airflow. A slat is the equivalent device on the leading edge; it rotates about its hinge to increase the wing's contact area with the air, and it also raises drag, which helps the aircraft land more slowly.The 3-D geometry was built in Design Modeler as a wing with a leading-edge slat and a trailing-edge flap. The model was meshed in ANSYS Meshing using an unstructured grid of 5,658,021 elements.Simulation MethodologyBecause the flow is compressible, the simulation uses a density-based solver with the ideal-gas law for density, and it is run as steady with gravity neglected. Turbulence is handled with the Spalart-Allmaras model, and the energy equation is enabled to resolve the temperature field.For boundary conditions, the inlet is a velocity inlet with components of 271.958 m/s along x and 40.79 m/s along y (a combined freestream of about 275 m/s at a small angle of attack) and a temperature of 305.5 K. The outlet is a pressure outlet at 0 Pa gauge, the wing, flap, and slat are walls with zero heat flux, and a symmetry plane bounds the domain. The flow and the turbulence transport variable are both discretized with a second-order upwind scheme, and the solution is initialized from the inlet conditions.Results & ConclusionAfter solving, two-dimensional contours of pressure, temperature, velocity, Mach number, and density were obtained on a plane cutting through the flow adjacent to the wing, along with path lines and velocity vectors on the same plane and a pressure contour over the wing surface.The contours clearly show the variation of velocity, density, and pressure around the wing. Comparing the pressure contour on the upper and lower surfaces reveals the pressure difference between them, which is the source of the lift force that counteracts the aircraft's weight.

This project simulates free-surface flow through an open channel using ANSYS Fluent, with the open-channel flow model as its central theme. An open channel is a waterway — natural or artificial — used to convey water for purposes such as transport, service-water supply and irrigation; in effect, an engineered version of a river. Canals of this kind are widely used in industry, from water-transmission systems to air ducts, and their shape and dimensions are dictated by their intended use. The defining feature of such flows, and the core of this study, is the presence of a free surface between water and the air above it, which must be tracked accurately as the flow develops.The configuration studied here is an open channel with a 180° bend and a side outlet. Water enters the canal at a mass flow rate of 45 kg/s, and partway through the bent section a set of obstacles reduces the flow pressure and diverts a portion of the incoming water into the side outlet — representing water drawn off to irrigate an adjacent farm. The aim is to understand how the bend, the obstacles and the side outlet together govern the flow distribution, pressure field and water level within the channel.The geometry was created in Gambit and meshed in ANSYS Meshing with an unstructured grid of 178,093 cells.Because two phases — water and air — are present with a sharp, well-defined interface between them, the simulation uses a multiphase approach built on the Volume of Fluid (VOF) model. VOF is the natural choice for open-channel flow precisely because the phase boundary is distinct: it tracks the fraction of each cell occupied by water versus air and so resolves the free surface directly. To set up the problem, the initial water level is specified, with water filling the channel up to a depth of 0.15 m and air occupying the region above it.After solving, the simulation yields contours of velocity, pressure and the volume fraction of water and air. The pressure field shows elevated pressure in the lower part of the channel, where the water column stands to its defined level, consistent with the expected hydrostatic behaviour. The volume-fraction contours correctly capture the stratified arrangement of the two phases — water occupying the lower portion of the channel and air flowing above it — confirming that the VOF model reproduces the free surface faithfully. As a study in open-channel flow modelling, the project demonstrates how the VOF method can represent a stratified water–air system with a defined free surface to analyse flow diversion, pressure distribution and water levels in practical canal and irrigation applications.

Forced Convection of a Non-Newtonian Nanofluid in a Tube, Paper Numerical Validation, ANSYS Fluent TrainingDescriptionThis project simulates the forced-convection heat transfer of a non-Newtonian nanofluid flowing through a horizontal tube under constant wall heat flux, using ANSYS Fluent. It reproduces and validates against the reference paper "Modeling of forced convective heat transfer of a non-Newtonian nanofluid in the horizontal tube under constant heat flux with computational fluid dynamics."The defining feature of this case is the non-Newtonian flow model. A Newtonian fluid has a single, constant viscosity, but many real fluids do not — their apparent viscosity changes with the local shear rate. Here the working fluid is water carrying Al₂O₃ nanoparticles together with xanthan: the aluminium-oxide particles make it a nanofluid, while the xanthan makes it non-Newtonian, so its viscosity is no longer constant and cannot be described by Newton's law. Capturing this shear-dependent viscosity is exactly what the non-Newtonian flow model does, and this tube flow is a clean setting to demonstrate it.Rather than treating the nanofluid as a multiphase mixture, it is defined as a single new material with effective thermophysical properties taken from the paper: density 1126.384 kg/m³, specific heat 3700.264 J/kg·K, and thermal conductivity 0.615 W/m·K. Its non-Newtonian viscosity is described with the Herschel-Bulkley model — a yield-stress fluid that only begins to flow once a threshold stress is exceeded, after which it follows a power law. The model parameters are a power-law index of 0.149, a yield stress of 2.92 Pa, and a critical shear rate of 58.4 s⁻¹, all from Table 1 of the paper, at a 4% nanofluid concentration.The 2-D geometry was built in Design Modeler as a horizontal tube 1.2 m long and 0.00475 m in diameter. Because it is symmetric about its centerline, it is modeled as axisymmetric. The domain was meshed in ANSYS Meshing using a structured grid of 40,000 elements.Simulation MethodologyThe simulation uses a pressure-based, steady solver with gravity neglected, a laminar viscous model, and the energy equation enabled. The flow is studied at two Reynolds numbers, 900 and 1600. Because the fluid is non-Newtonian, the inlet velocity for each case is computed from the generalized Reynolds-number definition given in the paper, giving 1.2698 m/s for Re = 900 and 1.7327 m/s for Re = 1600. The nanofluid enters at 295 K, and the tube wall carries a constant heat flux of 8846.4 W/m². Pressure-velocity coupling uses SIMPLE, with second-order discretization for pressure, momentum, and energy.Paper Validation & ResultsValidation follows Figure 3-a of the paper, which plots the convective heat transfer coefficient (h) against Reynolds number at a dimensionless station of x/D = 147 (with D = 0.00475 m). The heat transfer coefficient is evaluated from Equation 9 of the paper using the applied heat flux (8846.4 W/m²) together with the wall temperature (Tw) and the fluid bulk temperature (Tf), extracted at that station from the wall and from a line through the tube.The simulation matches the paper closely at both Reynolds numbers:CasePresent simulationPaperErrorh at Re = 9001676.1 W/m²·K1700 W/m²·K≈ 1.4%h at Re = 16001846.8 W/m²·K1750 W/m²·K≈ 5.5%The agreement is strong on both counts that matter: the error magnitude stays within about 5.5%, and the behavior is reproduced correctly — the heat transfer coefficient rises with Reynolds number, exactly as in the reference. Two-dimensional temperature and velocity contours are also obtained at both Reynolds numbers along the mid-section of the tube.

Pollution Spread in a Stagnant River, ANSYS Fluent TrainingDescriptionThis project simulates the entry and spread of a pollutant into a stagnant river using ANSYS Fluent.The core of this case is open-channel flow — flow in a channel or river whose upper surface is open to the atmosphere and free to deform, rather than being fully enclosed by walls. In open-channel problems the position and shape of the free surface is part of the solution, and gravity governs how the water and anything riding on it settle and move. A river receiving a discharge is a natural example: the pollutant enters at the surface and spreads across it, so tracking that free surface is essential, which is exactly what the open-channel (free-surface VOF) approach is built for.The application itself is an environmental one. Water pollution from industrial waste is a serious concern: chemical by-products discharged into rivers endanger aquatic life and can enter the human food chain through it, causing disease. Simulating how a pollutant disperses once it reaches a river helps predict how far and how fast contamination travels.The 3-D geometry was built in Design Modeler, with a river inlet width of 11.05 m. The domain was meshed in ANSYS Meshing with 161,562 elements, and because the spreading process evolves in time, a transient solver is used.Simulation MethodologyThe two phases — water and pollutant — are handled with the VOF multiphase model. The pollutant enters through a non-uniform profile partway along the river and diffuses into the water. Because its density is lower than that of water, it floats and spreads along the free surface. Turbulence is solved with the realizable k-ε model using scalable wall functions, pressure-velocity coupling is SIMPLE, and momentum and the volume fraction use second-order upwind discretization. The river water itself is initially stagnant, and the pollutant enters at 8 m/s.Results & ConclusionAfter solving, contours of velocity, pressure, and pollutant volume fraction were obtained. The results show the pollutant progressively diffusing into the river water over time, with the pressure near the pollutant inlet higher than elsewhere. The cross-sectional pressure contour also shows pressure increasing with depth, as expected for a body of water under gravity.

Diesel Fuel Combustion in a Gas Turbine Combustion Chamber — ANSYS Fluent CFD Simulation TrainingThis project simulates the combustion of diesel fuel inside the combustion chamber of a gas turbine system using ANSYS Fluent, with the full case analyzed through CFD post-processing.The combustion chamber works as follows: air enters from the space surrounding the chamber, passes through a bladed diffuser duct where it becomes turbulent, and then enters the dedicated combustion space to mix more effectively with the fuel. The fuel, meanwhile, is injected into the chamber through a nozzle and mixes with the incoming air, allowing combustion to take place. The fuel used is diesel (C₁₆H₂₉), which reacts with the airflow.The combustion reaction involves four species — diesel, hydrogen, oxygen, and carbon — so the Species Transport model is used to define the gaseous species, together with the volumetric reaction model to govern the reaction between them. Air enters the chamber at a velocity of 3 m/s and a temperature of 300 K, while diesel is sprayed into the chamber interior at 4 m/s and 300 K. The aim of the study is to investigate the mass fractions of the reactants and the combustion products.The 3D geometry was created in Design Modeler and meshed in ANSYS Meshing using an unstructured grid, for a total of 3,488,057 cells.MethodologyThe Species Transport model is used to analyze the combustion process, and the energy equation is activated to compute the temperature changes throughout the domain.ResultsOnce the solution is complete, 2D and 3D contours of pressure, temperature, velocity, and the mass fractions of diesel, oxygen, carbon dioxide, and water vapor are obtained.The contours show that the fuel mixes well with the oxidizer and that combustion takes place, with its products clearly visible. The temperature is very high in parts of the combustion chamber, and the results show that the combustion flame is well formed.

Nano Fluid Heat Transfer in a Porous Heat Exchanger, ANSYS Fluent CFD Simulation TutorialDescriptionThis project simulates the heat transfer of a nanofluid flowing through a porous-medium heat exchanger using ANSYS Fluent.The core of this case is the nanofluid itself. A nanofluid is a base fluid, such as water, carrying suspended nanoparticles. Those particles raise the fluid's effective thermal conductivity, so a nanofluid can move more heat than the base fluid alone — which is why nanofluids are attractive in heat-exchanger and cooling applications. Rather than resolving individual particles, the nanofluid is treated as a single fluid with modified thermophysical properties, and this porous heat exchanger is a practical setting to demonstrate the heat-transfer benefit it provides.The exchanger uses a porous medium as its heat-transfer core. Porous media contain many small pores and passages that greatly increase the internal surface area available for heat exchange, which is why they appear across industry — in crude-oil production, building insulation, and heat-recovery exchangers, among others. Here the nanofluid flows through this porous section and exchanges heat with it.The geometry was built in ANSYS Design Modeler and meshed in ANSYS Meshing, using a structured grid for the upstream and downstream sections and an unstructured grid for the middle (porous) region, for a total of 1,901,882 cells.Simulation MethodologyThe incoming working fluid is a nanofluid, defined through its effective properties, and the energy equation is enabled to resolve the temperature field. The flow enters at 1.63 m/s; the porous core is held at 343 K and the tube wall at 293 K, setting up the temperature difference that drives the heat transfer.Results & ConclusionAfter solving, contours of pressure, velocity, and temperature were obtained, along with streamlines and velocity vectors. The temperature contours clearly show the heat exchange taking place, particularly within the porous region, and the velocity vectors follow the pores and passages of the porous medium as the nanofluid works its way through it.

Here's the version with headers added:Air Compressor Acoustics Analysis in ANSYS FluentIntroductionThis project investigates the aeroacoustics and noise generation mechanisms of airflow within a four-row multistage axial flow compressor using ANSYS Fluent. As engine manufacturers continue to prioritize noise reduction as a key design objective, understanding the acoustic contribution of individual engine components has become an essential first step toward developing effective noise mitigation strategies. This simulation focuses specifically on quantifying and visualizing the sound power generated by the rotor and stator stages of a compressor assembly, building on prior turbomachinery flow analysis of the same geometry.Geometry and MeshThe three-dimensional compressor geometry was constructed in Design Modeler, comprising two rotor rows and two stator rows, with each row containing 22 airfoil-section blades. The rotor blades feature aerodynamic deflection, while the stator blades remain horizontal without deflection. Taking advantage of the geometry's rotational symmetry, only a single blade passage from each rotor and stator row was modeled, with periodic boundary conditions applied to the lateral surfaces to represent the full annular assembly while substantially reducing computational cost. The domain was discretized in ANSYS Meshing using an unstructured mesh totaling 972,354 elements, providing adequate resolution for capturing the flow and acoustic phenomena around the blade rows.MethodologyThe acoustic analysis was performed using the Broadband Noise Sources model within Fluent, applied on top of the underlying turbomachinery flow solution obtained with periodic boundary conditions. Reference acoustic properties were defined consistent with standard air conditions: a density of 1.225 kg/m³, a sound speed of 340 m/s, and a reference acoustic power of 1×10⁻¹² W, forming the basis for computing acoustic power level contours throughout the domain.Results and ConclusionResults indicate that the rotor rows are the dominant source of noise generation within the compressor stage, exhibiting substantially higher acoustic power levels in the corresponding contour plots compared to the stator rows. Contours of the linearized Euler equations further illustrate how sound waves propagate through the gap region between successive rotor and stator rows, offering insight into the spatial distribution and directivity of noise transmission within the multistage compressor.

Vortex Combustion Chamber Simulation in ANSYS FluentIntroductionThis project simulates the combustion reaction occurring inside a vortex combustion chamber using ANSYS Fluent. Combustion is a chemical process between a combustible material and an oxidizing agent, resulting in the release of heat and the transformation of raw materials, typically accompanied by light in the form of a flame or glow. While combustion is fundamentally a form of oxidation reaction, its rapid reaction rate, substantial heat release, and associated temperature rise and flame formation place it in a distinct category of chemical processes. The vortex combustion chamber represents a new generation of liquid-fuel internal combustion engine design, in which a specific injector arrangement generates a swirling vortex flow. This vortex enhances cooling and improves mixing of the propulsion components within the chamber, enabling complete combustion to be achieved in a smaller chamber volume.Geometry and MeshThe combustion chamber geometry was designed and meshed within GAMBIT, using an unstructured mesh totaling 379,535 cells.MethodologyThe combustion process was analyzed using the species transport model, with a mixture of air and methane serving as the fuel. The Eddy-Dissipation method was employed to capture the chemical-turbulent interaction of the combustion reactants, and the NOx prediction model was activated, using the temperature method for the turbulence-chemistry interaction mode. The ideal gas equation was used to account for density variations resulting from temperature changes within the chamber.Results and ConclusionContours of velocity, pressure, temperature, and species mass fraction were generated in both 3D and 2D, clearly capturing the formation of the combustion flame and the resulting temperature distribution within the chamber. Velocity vectors reveal a high degree of flow turbulence throughout the domain, and the overall contour results confirm that the combustion chamber's performance has been accurately captured by the simulation.

DescriptionThis project simulates the delivery of an asthma spray into human lungs using ANSYS Fluent. The 3D geometry—built in SpaceClaim—represents a simplified lung model with a 50 cm inlet diameter. The mesh (ANSYS Meshing) contains 3,734,238 elements. Given the time-dependent nature of inhalation and particle motion, a transient solver is used.Asthma Spray MethodologyA one-way coupled Discrete Phase Model (DPM) tracks aerosol particles moving through a continuous air phase. Air enters at 5 m/s, with gravity set to −9.81 m/s² along the z-axis. Particles (diameter 100 µm) are introduced via a surface-velocity injection at the inlet. Turbulence is resolved with the realizable k–ε model. Particle trajectories inside the lung domain are computed and visualized to assess transport and deposition behavior.ConclusionPost-processing provides 2D and 3D contours of velocity and pressure, along with an animation of particle tracks throughout the lungs, illustrating the spray’s distribution following inhalation.

Master High-Speed Aerodynamics: Bullet (HPBT) Movement CFD Simulation with Dynamic Mesh in ANSYS FluentDive into the cutting-edge world of supersonic aerodynamics with our advanced tutorial on “Bullet (HPBT) movement CFD Simulation by Dynamic Mesh”. This essential episode in our “ANSYS Fluent: All Levels” course offers a comprehensive exploration of high-speed projectile dynamics, crucial for ballistics experts, aerospace engineers, and CFD specialists in defense and sporting industries.Unlock Advanced CFD Techniques for Supersonic Flow AnalysisLearn to harness the power of ANSYS Fluent to simulate and analyze complex supersonic flows around moving objects. This tutorial provides a detailed approach to modeling Hollow Point Boat Tail (HPBT) bullet movement using dynamic mesh techniques, essential for understanding shock wave formation and aerodynamic performance at high speeds.Key Learning Objectives:- Master the setup of 2D HPBT bullet models in ANSYS Design Modeler - Develop proficiency in unstructured mesh generation for dynamic simulations - Understand the application of dynamic mesh and density-based solvers in ANSYS Fluent - Analyze supersonic flow characteristics and shock wave formation around moving bulletsComprehensive Simulation Setup and MethodologyGain hands-on experience in configuring and executing a professional-grade CFD simulation for high-speed projectile movement, covering all aspects from geometry creation to advanced flow visualization.1. Precise 2D Geometry and Mesh Generation- Create optimized 2D models of HPBT bullets using ANSYS Design Modeler - Implement unstructured meshing strategies with ANSYS Meshing for dynamic remeshing - Optimize mesh quality for accurate supersonic flow simulations (13,040 elements)2. ANSYS Fluent Configuration for Dynamic Supersonic Simulation- Set up density-based solver for compressible, transient flow scenarios - Configure dynamic mesh model for bullet movement at Mach 2.531 - Implement ideal gas properties for air to model compressibility effects3. Advanced Data Analysis and Visualization Techniques- Extract and interpret pressure, temperature, and velocity contours - Analyze shock wave formation and propagation behind the moving bullet - Evaluate mesh deformation and regeneration during bullet movementReal-World Applications and Industry RelevanceThis tutorial is crucial for professionals and researchers in:Ballistics and ammunition designAerospace engineering for supersonic flightDefense industry projectile developmentSporting and hunting equipment optimizationKey Simulation Outcomes and Aerodynamic Insights1. Supersonic Flow Analysis- Interpret pressure and velocity distributions around the moving HPBT bullet - Understand shock wave formation and its impact on bullet performance2. Dynamic Mesh Behavior Evaluation- Analyze mesh deformation and regeneration techniques in moving object simulations - Assess the effectiveness of dynamic meshing in capturing transient flow phenomena3. Compressibility Effects Assessment- Evaluate the impact of air compressibility on flow behavior at supersonic speeds - Understand the importance of density-based solvers in high-speed aerodynamicsElevate Your CFD Skills in High-Speed Aerodynamics SimulationBy completing this specialized tutorial, you’ll gain:Cutting-edge skills in applying CFD to complex supersonic flow problemsProficiency in setting up and analyzing dynamic mesh simulations in ANSYS FluentDeep understanding of shock wave physics and compressible flow dynamicsInsights into optimizing projectile designs for improved aerodynamic performanceWho Should Take This Advanced TutorialBallistics engineers specializing in ammunition designAerospace engineers focused on supersonic aerodynamicsCFD analysts working on defense and sporting equipmentGraduate students in high-speed aerodynamics or computational fluid dynamicsDon’t miss this opportunity to significantly advance your CFD simulation skills in high-speed aerodynamics. Enroll now in our “ANSYS Fluent: All Levels” course and master the art of simulating bullet movement with dynamic mesh techniques in ANSYS Fluent!

Series Fans CFD Simulation Using MRF Method in ANSYS FluentIntroductionThis project investigates the steady-state airflow behavior between two 3-bladed series fans rotating at an angular velocity of 300 rpm using ANSYS Fluent, employing the Multiple Reference Frame (MRF) method to capture the rotational effects of the fan blades on the surrounding flow field.Geometry and MeshThe three-dimensional geometry of the dual fan assembly was designed in SpaceClaim, and the domain was meshed using ANSYS Meshing, resulting in a total element count of 1,914,000.MethodologyThe rotation of the fans generates air suction at the inlet boundary, with a volumetric flow rate of 2.95755 m³/s. Along the domain centerline, air velocity reaches values up to 25 m/s, while the maximum velocity in the entire domain, 47.05 m/s, occurs downstream of the first fan. Turbulent flow behavior throughout the domain was resolved using the RNG k-epsilon turbulence model.Results and ConclusionTwo- and three-dimensional contours of pressure, velocity, velocity vectors, and streamlines were generated to characterize the flow field. Based on the calculated Fluent data, the air mass flow rate at the inlet equals 3.62019 kg/s. A comparison of the pressure drop across each fan reveals that the first fan produces a pressure drop roughly twice that of the second fan, at 500 Pa and 230 Pa, respectively. Negative gauge pressure is observed downstream of both fans, with the region downstream of the first fan reaching a value five times lower than that of the second fan, at -500 Pa compared to -100 Pa. Consistent with the higher pressure drop, the velocity magnitude downstream of the first fan is also higher, at 28 m/s, compared to 12 m/s downstream of the second fan, confirming that the first fan experiences a more significant aerodynamic loading within the series configuration.

FSI Method for Water Turbine CFD Simulation in ANSYS FluentIntroductionThis study investigates the water flow around a vertical water turbine using an unsteady, transient CFD simulation in ANSYS Fluent. The turbine blades are assumed to be affected by the passing fluid flow, such that the fluid impedes forces on the turbine body, causing deformation and resizing of the blade structure. Since the problem involves the simultaneous solution of both fluid and solid domains, a Fluid-Structure Interaction (FSI) approach is employed, coupling the fluid flow solution with a Transient Structural analysis through system coupling. The simulation is solved using a pressure-based, transient solver, with gravitational effects neglected.Geometry and MeshThe three-dimensional model was designed in Design Modeler, consisting of a large cubic fluid domain with the water turbine positioned inside as the solid domain. The domain was discretized using an unstructured mesh generated in ANSYS Meshing, resulting in a total element count of 523,202.MethodologyTurbulent flow behavior was resolved using the standard k-epsilon viscous model with standard wall functions applied for near-wall treatment. The dynamic mesh approach, incorporating smoothing and remeshing methods, was coupled with a system coupling dynamic mesh zone to capture the two-way interaction between the fluid flow and the deforming turbine structure. At the inlet, a velocity-inlet boundary condition was applied with a velocity magnitude of 1.5 m/s, while a pressure-outlet condition with 0 Pa gauge pressure was set at the outlet. The turbine blades and fixed surfaces were defined as stationary walls. The SIMPLE algorithm was used for pressure-velocity coupling, with second-order upwind discretization applied to pressure and momentum, and first-order upwind discretization applied to turbulent kinetic energy and turbulent dissipation rate. The simulation was initialized using the standard initialization method with an x-velocity of 1.5 m/s.Results and ConclusionThe coupled FSI solution captures the dynamic interaction between the water flow and the turbine blade structure, allowing the deformation behavior of the blades under fluid loading to be evaluated alongside the surrounding flow field characteristics, providing insight into the structural response of the turbine under realistic unsteady hydrodynamic conditions.

Horizontal Fluidized Bed Simulation for Soil Drying in ANSYS FluentIntroductionSolid particles, when sufficiently small, can behave like a fluid under vertical flow conditions—a phenomenon known as fluidization. By applying an upward flow to a bed of fine particles, the drag force exerted by the fluid on the particles can balance the gravitational force acting on them, causing the particles to levitate and exhibit fluid-like behavior. This phenomenon is widely used in industrial applications, including increasing residence time for burning waste biofuels and supporting catalytic processes in petroleum production. This project simulates a horizontal fluidized bed designed to dry incoming soil containing moisture. Soil enters the bed at a mass flow rate of 1 kg/s with a water mole fraction of 0.1, while air flows in from the bottom at 1.7 m/s and 393 K, driving evaporation of the moisture contained within the soil as it passes through the fluidized bed.Geometry and MeshThe geometry is two-dimensional, with a fluidizer height of 0.5 m and a length of 2 m. It was designed in SpaceClaim and meshed using ANSYS Meshing with a structured mesh totaling 22,000 elements.MethodologyThe simulation was solved as unsteady, with gravity activated to capture its effect on the soil particles and the energy equation enabled to resolve thermal behavior. The Eulerian multiphase approach was used to model the two-phase system, with each phase containing two species defined through the species transport model. The primary phase is a gas composed of air and water vapor, while the secondary phase is granular, with a particle size of 0.0018 m, containing soil and liquid water as its constituent species. Mass transfer between liquid water and vapor was captured using the evaporation-condensation method with the Lee model. Viscous effects were modeled using the standard k-ε model with standard wall functions, chosen for its robustness and relatively low computational cost.Results and ConclusionContours of temperature and species mass concentration illustrate the progressive evaporation of soil moisture within the fluidized bed, showing a decrease in liquid water concentration accompanied by a corresponding increase in vapor concentration. The air temperature drops to the saturation temperature of water by the outlet, while the soil temperature remains essentially constant throughout the bed, indicating that the heat lost by the gas phase is consumed by the evaporation process rather than by heating the soil. The gas-phase pressure drop across the fluidized bed was calculated as 284.8 Pa. Key quantitative results are summarized below:LocationArea-weighted avg. vapor concentration [kg/m³]Mass-weighted avg. vapor concentration [kg/m³]Temperature [K]Inlet gas0.00170393Outlet gas0.01081.85×10⁻⁵372.4Inlet soil0.00587.92373Outlet soil0.016141.91373.37These results confirm that the horizontal fluidized bed effectively facilitates moisture removal from the soil through convective drying, with the coupled heat and mass transfer behavior consistent with the physical expectations of an evaporation-driven fluidization process.

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

Mixing Tank CFD Simulation Using Mesh Motion Method in ANSYS FluentIntroductionThis project simulates the performance of a mixing tank using ANSYS Fluent. The closed tank contains water, and an impeller rotates at 500 rev/min, generating a substantial vortex at the center of the tank. This product represents the fourth episode of the Turbomachinery Training Course.Geometry and MeshThe three-dimensional geometry of the mixing tank was designed in Design Modeler and meshed using ANSYS Meshing, resulting in an unstructured mesh with 209,328 elements.MethodologyThe Mesh Motion method was enabled to capture the rotational movement of the impeller. This approach requires two distinct zones connected through an interface: a rotating zone containing the impeller, which moves independently, and a surrounding stationary zone. The simulation was solved as unsteady, with the k-epsilon model selected to capture the turbulent behavior of the flow.Results and ConclusionTwo-dimensional contours of pressure, velocity, and turbulent intensity were obtained to characterize the flow field within the tank. The pressure contours show that water pressure in front of the impeller is considerably higher than behind it, consistent with the impeller's driving action on the fluid. As expected, flow velocity behind the impeller exceeds that observed elsewhere in the domain, while the turbulent intensity contours reveal the extent of turbulence generated throughout the tank as a result of the impeller's rotational motion.

Axial Flow Fan Stage (Rotor–Stator, MRF) — ANSYS Fluent CFD SimulationAn axial fan stage is a rotor–stator assembly that produces steady, directed airflow for industrial uses such as cooling freshly painted body parts. The two components share the work: the spinning rotor blades add energy to the air and induce swirl, and the stationary stator blades then straighten that swirling flow so it leaves the stage roughly normal to the outlet. This project uses ANSYS Fluent to model both the rotating and stationary zones of such a fan stage and evaluate its aerodynamic performance — a classic, transferable introduction to turbomachinery CFD.The 3-D rotor–stator geometry is built in Design Modeler with separate rotating and stationary zones defined, then meshed in ANSYS Meshing with about 244,675 cells. To keep the computation efficient, a periodic boundary condition is used to model only a single slice of the fan rather than the full annulus — the standard way to cut the cost of a turbomachinery analysis without losing fidelity.The rotation is handled with the Moving Reference Frame (MRF) method, which simulates the rotor spinning at 1800 rpm while the stator stays fixed — a steady-state approach to rotating machinery that avoids the expense of a fully transient moving mesh. Turbulence is modeled with the standard k-ε model across the rotating flow field.At the end of the solution, you generate 2-D and 3-D contours of pressure and velocity, along with streamlines and velocity vectors that clearly reveal the swirl induced by the rotor and its correction by the stator. From the results you extract the key turbomachinery performance metrics: a rotor tip linear velocity of about 31 m/s, a Tip Speed Ratio (TSR) of 4, and an outlet airflow rate of 16.14 L/s. By the end of this project, you'll be able to build a rotor–stator geometry with distinct motion zones, apply periodic boundaries to model a representative slice, set up the MRF method for rotating machinery, and post-process the flow to compute the performance metrics that define fan and compressor behavior — a workflow that transfers directly to blowers, axial compressors, pumps, and ventilation fans.

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.

Three-Phase Flow Simulation in a Zigzag Channel Using ANSYS FluentIntroductionThis project simulates a three-phase flow mixture consisting of air, water, and kerosene within a square cross-section channel using ANSYS Fluent. The channel geometry includes a vertical section with two inlet openings at its top and bottom, connected to a zigzag horizontal section terminating in an outlet. In the initial state, only air occupies the channel; as the simulation proceeds, water enters through the upper inlet while kerosene enters through the lower inlet, allowing the three-phase interaction to develop over time.Geometry and MeshThe three-dimensional geometry was designed in Design Modeler, consisting of a vertical channel for fluid entry connected to a zigzag horizontal path formed by a series of perpendicular teeth-like segments. The channel cross-section is square with a side length of 0.0002 m, featuring two inlet sections at the top and bottom of the vertical portion and a single outlet at the end of the horizontal zigzag section. The domain was meshed using ANSYS Meshing with a structured mesh totaling 416,000 elements.MethodologyThe VOF multiphase model was used to capture the interaction between the three fluid phases. A porous zone with a porosity coefficient of 0.1 was defined within the channel to represent the flow resistance encountered along the path. Both inlet sections were assigned pressure-inlet boundary conditions with a relative pressure of 1000 Pa, while the single outlet was defined as a pressure outlet with a relative pressure of 0 Pa. The simulation was solved using a transient solver to track the volume fraction evolution of each phase over time, running for a total of 5 seconds with a time step of 0.1 seconds.Results and ConclusionTwo- and three-dimensional contours of pressure, velocity, and volume fraction for each of the water, air, and kerosene phases were obtained at the final second of the simulation. These results capture the progressive redistribution of the three phases as water and kerosene advance through the vertical and zigzag sections of the channel, illustrating how the porous zone and channel geometry jointly influence the multiphase flow development and phase distribution throughout the domain.

Conical Solar Collector CFD Simulation in ANSYS FluentIntroductionSolar energy represents the largest available energy source in the world, offering a clean, inexpensive, and virtually inexhaustible resource. Solar water heaters operate by absorbing solar energy through collector plates, with heating efficiency varying depending on the collector type; the heated water is typically stored in a double-walled, thermally insulated reservoir capable of maintaining temperature for up to three days. This project simulates heat transfer within a conical solar collector containing water, using ANSYS Fluent to analyze how the collector absorbs sunlight and warms the water inside its tank. The computational domain consists of a cubic air region with a velocity-inlet (1 m/s) and a pressure outlet, along with the conical collector itself, which includes a water inlet (0.01 m/s) and a pressure outlet, and features a glass layer and a steel layer to minimize convective heat loss.Geometry and MeshThe geometry, comprising the fluid domain and the conical solar collector, was designed in SpaceClaim and meshed in ANSYS Meshing, resulting in an unstructured mesh totaling 2,948,101 elements.MethodologyThe energy equation and a radiation model, using the solar ray tracing method combined with the Discrete Ordinates (DO) model, were activated to capture solar heating effects within the collector. Fluid flow behavior was resolved using the k-epsilon turbulence model with standard wall functions.Results and ConclusionContours of velocity, temperature, and streamlines were obtained to characterize the thermal and flow behavior within the collector. The average temperature of the collector walls reaches 308.67 K, while water entering the collector at 298.15 K is progressively heated to an outlet temperature of 306.8 K. The total heat transfer through the collector wall was calculated as 773 W, confirming the effectiveness of the conical design in capturing solar radiation and transferring thermal energy to the circulating water.

PCM Melting Rate Enhancement via Internal Fin and Nanoparticles — CFD Simulation in ANSYS FluentIntroductionThis project simulates the melting behavior of a phase change material (PCM) inside a two-dimensional cavity enhanced with an internal fin and dispersed nanoparticles, based on the methodology presented in a reference study on enhancing PCM melting rate through internal fins and nanoparticles. The simulation investigates how the combined effect of a conductive fin and CuO nanoparticle dispersion within paraffin wax accelerates the melting process compared to a plain PCM cavity.Geometry and MeshThe cavity geometry, with a width of W = 20 mm and filled with paraffin wax, was created in Design Modeler. The domain was meshed in ANSYS Meshing, generating approximately 10,000 structured cells.MethodologyGravitational acceleration was included in the simulation to capture buoyancy-driven natural convection effects during melting. Since several thermophysical properties of the PCM in the reference study were originally defined through user-defined functions, these properties were instead extracted at key reference points and represented using a polynomial linear model. The material properties used correspond to a mixture of paraffin wax and CuO nanoparticles. The left wall and the internal fin were maintained at a constant temperature of 350 K, while the right wall was held at 300 K, with all remaining walls treated as insulated. The case examined corresponds to a fin-to-cavity width ratio (w/W) of 0.5.Results and ConclusionAfter 375 seconds of simulation time, results show that heat is progressively transferred from the left wall toward the right wall due to the imposed temperature gradient, with the PCM undergoing a phase change as local temperatures reach the melting point. By the end of the simulation, 35.65% of the PCM had melted, illustrating the combined influence of the internal fin and nanoparticle enhancement on accelerating the melting process within the cavity.

Mastering Blast Wave Simulation: TNT Explosion CFD Modeling in ANSYS FluentThis lesson walks through a CFD simulation of a TNT explosion, a problem central to engineering safety, defense applications, structural protection, and blast planning. An explosion is an extremely fast exothermic reaction that suddenly generates large volumes of hot gaseous products, producing a sharp spike in pressure and temperature and launching compression waves that propagate outward through the surrounding air. This project models the rapid decomposition of TNT, in which 2 moles of TNT generate 22 moles of gaseous products, and tracks the resulting spherical pressure wave as it propagates and dissipates across the domain.What You'll LearnThis module covers the underlying physics of an explosion, including the fast exothermic reaction, the sudden pressure rise, and the resulting sequence of compression and expansion waves. You'll set up a half-sphere computational domain with a 5 m radius containing a central TNT charge modeled as a 5 cm radius half-sphere in SpaceClaim, taking advantage of symmetry to reduce computational cost, and generate a large structured mesh of approximately 2.67 million elements capable of resolving a traveling pressure wave. You'll learn why this problem demands a transient solver to correctly capture the moving wave front, and how to configure the Species Transport model with a defined species mixture and volume reaction to represent the TNT decomposition process. The lesson also covers setting up finite-rate turbulence-chemistry interaction with the direct source chemistry solver, applying the Realizable k-epsilon turbulence model with the energy equation activated, and a critical modeling decision: defining the mixture density using the ideal gas law so the simulation can correctly capture wave propagation. Finally, you'll learn how to post-process temperature and pressure contours over time, generate an animation of the propagating compression wave, and quantify the resulting wave speed, found to be approximately 420 m/s.Why It MattersBlast modeling plays a critical role in protecting buildings, vehicles, and people from explosive events. The reacting-flow, ideal-gas, and transient simulation workflow developed in this lesson transfers directly to detonations, deflagrations, gas explosions, and pressure-vessel safety analysis across the defense, oil and gas, and process industries.

Cylinder Piston Motion Simulation Using UDF-Driven Dynamic Mesh in ANSYS FluentDive into advanced CFD simulation with this episode on cylinder-piston motion in ANSYS Fluent, built around a user-defined function (UDF) that drives the piston's dynamic mesh motion. This hands-on tutorial forms the second chapter of the Dynamic Mesh Training Course, guiding you through the complete workflow for simulating the motion of a four-stroke engine's cylinder-piston system, from geometry creation through result analysis.Four-Stroke Engine FundamentalsYou'll first understand the four stages of piston motion that the simulation must capture: the intake stroke, where the piston descends as the intake valve opens; the compression stroke, where the piston ascends and compresses the in-cylinder flow; the power stroke, where the piston reaches top dead center at the point of explosion; and the exhaust stroke, where the piston descends again as the exhaust valve opens.Model Setup and MeshingThe geometry was created in Design Modeler and meshed using ANSYS Meshing, establishing the computational domain representing the cylinder-piston assembly and its associated valve regions.UDF-Driven Dynamic Mesh ImplementationThe piston's reciprocating motion is defined through a compiled UDF applied via the In-Cylinder dynamic mesh option, with key parameters — crank radius, connecting rod length, and piston stroke cutoff — specified to govern the piston's kinematic behavior. The UDF implements the full-piston motion function, driving the boundary movement of the piston surface throughout the four strokes. Building on this, rigid body motion is applied to the piston surface and valves, with profiles used to describe the time-varying valve lift, while deforming and stationary mesh zones are configured to accommodate the moving boundaries without degrading mesh quality.Simulation MethodologyThe dynamic mesh model is configured to work in conjunction with the UDF-defined reciprocating motion, and the simulation is solved using a time-dependent, transient approach with solver settings selected to maintain stability and accuracy as the mesh deforms and moves throughout each stroke.Results and AnalysisPressure and velocity contours are analyzed throughout the piston cycle, and animations of the mesh deformation and resulting flow behavior are generated to verify the correct operation of the UDF-driven cylinder-piston system across all four strokes.Why This Episode Is EssentialThis episode provides practical experience applying UDF-driven Dynamic Mesh techniques to a real-world internal combustion engine problem, strengthening your understanding of engine dynamics and building transferable skills for a wide range of moving-boundary CFD simulations.Who Should Watch This Episode?This episode is ideal for mechanical and automotive engineers, CFD specialists expanding their skill set, researchers in fluid dynamics and engine design, and students pursuing advanced studies in computational engineering.Take Your CFD Skills to the Next LevelBy completing this episode, you'll be equipped to simulate complex moving-boundary problems using UDF-driven Dynamic Mesh techniques, apply this workflow to a variety of engineering scenarios, and analyze and optimize internal combustion engine designs using ANSYS Fluent.

Master Boat Propeller CFD Simulation with ANSYS CFX Dive into the dynamic world of marine propulsion with our comprehensive tutorial on “Boat Propeller, Steady State” using ANSYS CFX. This pivotal episode in our “ANSYS CFX: All Levels” course offers an in-depth exploration of rotational fluid dynamics, essential for marine engineers, naval architects, and propulsion system designers. Unlock Advanced CFD Techniques for Marine Propulsion Analysis Learn to harness the power of ANSYS CFX to simulate and analyze complex flow patterns around rotating boat propellers. This tutorial provides a detailed approach to modeling steady-state rotational flow, crucial for optimizing propeller designs and enhancing vessel performance. Key Learning Objectives: - Master the setup of 3D propeller models in SpaceClaim - Develop proficiency in tetrahedral mesh generation with inflation layers for rotating geometries - Understand the application of Shear Stress Transport (SST) turbulence model in propeller simulations - Analyze thrust generation and vortex formation in steady-state rotational flow Comprehensive Simulation Setup and Methodology Gain hands-on experience in configuring and executing a professional-grade CFD simulation for boat propellers, covering all aspects from geometry creation to advanced flow visualization. 1. Precise 3D Geometry and Advanced Mesh Generation - Create optimized 3D models of boat propellers using SpaceClaim - Implement tetrahedral meshing with 5-layer inflation for enhanced boundary layer resolution - Optimize mesh quality for accurate flow simulations (over 4 million elements across stationary and rotating zones) 2. ANSYS CFX Configuration for Rotational Flow Simulation - Set up steady-state simulation with rotating domain for the propeller zone - Configure Shear Stress Transport (SST) turbulence model for accurate flow prediction - Implement Frozen Rotor frame change model for interface handling 3. Advanced Data Analysis and Visualization Techniques - Extract and interpret velocity, pressure, and turbulence kinetic energy distributions - Analyze vortex formation and thrust generation using 2D contours, vectors, and streamlines - Evaluate propeller performance through pressure distribution visualization Real-World Applications and Industry Relevance This tutorial is crucial for professionals and researchers in: Marine engineering and naval architecture Offshore industry propulsion system design Recreational boat manufacturing Underwater vehicle propulsion optimization Key Simulation Outcomes and Propulsion Insights 1. Thrust Generation Analysis - Interpret the complex pressure distribution on propeller blades - Understand the mechanism of thrust production in rotating propellers 2. Flow Dynamics Evaluation - Analyze velocity patterns and vortex formation behind the propeller - Assess the impact of blade design on water displacement and thrust efficiency 3. Performance Optimization - Evaluate the effectiveness of the propeller design in generating thrust - Understand the relationship between rotational speed and flow characteristics Elevate Your CFD Skills in Marine Propulsion Simulation By completing this specialized tutorial, you’ll gain: Cutting-edge skills in applying CFD to complex rotational flow problems Proficiency in setting up and analyzing steady-state propeller simulations in ANSYS CFX Deep understanding of thrust generation mechanisms in marine propellers Insights into optimizing propeller designs for improved efficiency and performance Who Should Take This Advanced Tutorial Marine engineers specializing in propulsion systems Naval architects working on vessel design optimization Mechanical engineers focusing on rotating machinery Graduate students in marine engineering or fluid dynamics Don’t miss this opportunity to significantly advance your CFD simulation skills in marine propulsion analysis. Enroll now in our “ANSYS CFX: All Levels” course and master the art of simulating boat propellers with ANSYS CFX!