DescriptionMixing tank design with rotating impellers is a core application within rotary equipment and turbomachinery engineering, where accurate modeling of rotational flow behavior directly affects mixing efficiency and process performance. This CFD simulation uses ANSYS Fluent to analyze a closed mixing tank through the Single Reference Frame (SRF) method, a widely used approach for capturing rotational effects in turbomachinery without the computational cost of fully transient rotor motion. The study examines fluid behavior driven by a rotating impeller, with relevance to chemical process engineering, mixing and blending technology, wastewater treatment, and food and beverage processing.MethodologyThe three-dimensional tank geometry is built in ANSYS Design Modeler, and the domain is discretized in ANSYS Meshing using an unstructured grid of 278,775 elements, refined for mesh quality to support accurate results. The SRF method is configured to represent rotational movement within ANSYS Fluent, paired with a steady-state k-ε turbulence model. Boundary conditions are defined for an impeller rotating at 500 rpm, establishing the rotational reference frame used throughout the simulation.Results AnalysisPost-processing extracts pressure, velocity, and turbulent intensity contours to characterize flow behavior within the rotating system. Pressure distribution analysis reveals variations from the tank center to the walls and their effect on mixing efficiency, while velocity profiles across the tank are correlated with mixing effectiveness. Turbulence intensity patterns are visualized throughout the tank to assess their impact on mixing performance, and vortex formation driven by impeller rotation is examined to understand its influence on overall fluid dynamics. Together, these results support the optimization of mixing tank designs across a range of industrial applications.

MRF Method — Mixing Tank CFD Simulation, ANSYS FluentDescriptionThis project simulates a stirred mixing tank in ANSYS Fluent using the Multiple Reference Frame (MRF) method — the first of three mixing-tank studies that model the same tank with three different rotating-frame approaches. A mixing tank uses a rotating impeller to blend fluid, and it's a workhorse of chemical, pharmaceutical, food, and wastewater processing. The MRF method represents the spinning impeller through a rotating reference frame while the tank stays stationary, giving a steady-state picture of the flow at a fraction of the cost of a fully transient simulation. Within the Rotary Equipment: Beginner CFD Training Package, this project opens the three-part mixing-tank method comparison, establishing the MRF approach as the baseline against which the SRF and mesh-motion methods that follow are compared.MethodologyThe 3D model is created in ANSYS Design Modeler and meshed in ANSYS Meshing with 229,177 unstructured elements. The domain is divided into multiple zones for the MRF method — a rotating zone around the impeller and a stationary zone for the rest of the tank. The case is set up as a steady-state analysis with the k-ε turbulence model, and the boundary conditions define a 500 rpm impeller rotation within the stationary tank. This arrangement captures the effect of the rotating impeller on the surrounding fluid without physically moving the mesh, keeping the analysis steady and efficient.AnalysisPost-processing extracts pressure, velocity, and turbulent-intensity contours along with flow vectors around the impeller. The pressure field shows the variations around the impeller and their effect on mixing across the different zones; the velocity profiles reveal the flow patterns, particularly behind the impeller, and how they correlate with mixing effectiveness; and the turbulence-intensity field shows where the mixing is most vigorous, especially near the impeller. The flow vectors reveal the vortex formation that governs mixing efficiency. By the end of this project, you'll be able to set up a multi-zone MRF simulation of a stirred tank, define the rotating and stationary zones and impeller speed, and interpret the pressure, velocity, and turbulence fields to assess mixing performance — the baseline for comparing the SRF and mesh-motion methods in the next two projects.

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.

DescriptionThis project simulates fluid mixing inside a bioreactor agitated by a Rushton turbine using ANSYS Fluent, a mixing configuration widely used in pharmaceutical, food, biochemical, and perfumery applications wherever biochemical reactions require thorough fluid homogenization. The bioreactor is cylindrical, 0.8 m tall and 0.4 m in diameter, with a vertical stirrer mounted along its central axis. That stirrer is a Rushton-type turbine, a radial-flow impeller consisting of two rows of flat discs, each carrying six blades, chosen because radial-flow impellers of this type are a standard choice for mixing applications across process engineering. The geometry is built in Design Modeler and meshed in ANSYS Meshing with 3,558,726 elements, and given the inherently time-evolving nature of the mixing process, a transient solver is used.MethodologyThe rotational motion of the fluid around the Rushton turbine is defined using the Mesh Motion technique, with a distinct cylindrical inner region assigned a rotational velocity of 143 rpm about the vertical (Y) axis to represent the turbine's action on the surrounding fluid. Three rows of baffles line the interior of the bioreactor's cylindrical wall, breaking up the vortices that would otherwise form and reducing unwanted bulk rotation of the whole fluid volume. Turbulence is resolved using the RNG k-epsilon model.AnalysisThe results include 3D contours of pressure gradient, velocity, and turbulent kinetic energy throughout the bioreactor, along with 2D contours of pressure, velocity, and turbulent kinetic energy taken on two planes perpendicular to the stirrer axis, each passing through one of the turbine's disc rows. These fields show velocity and rotational flow intensifying around the impeller blades, exactly where the turbine imparts momentum to the fluid. Velocity vectors, examined in both 2D and 3D, trace how the fluid circulates fully around the stirrer's rotation axis, confirming the Rushton turbine is generating the s

Flow Between 2 Concentric Cylinders (Eulerian) — ANSYS Fluent CFD SimulationDescriptionThis project presents a CFD simulation of two-phase non-Newtonian flow between two concentric cylinders — a benchmark geometry used across drilling engineering, polymer processing, biomedical devices, and food technology. Unlike Newtonian fluids such as water or air, non-Newtonian fluids change their viscosity in response to applied shear, and capturing that behavior correctly is critical for accurate predictions. In this project, you'll model a Power-Law non-Newtonian base fluid (k = 0.021, n = 0.75) flowing through an annular channel with a rotating inner cylinder, while a denser soluble secondary phase travels through it using the Eulerian multiphase model. As the opening project of the Non-Newtonian Flow: Beginner CFD Training Package, it introduces the core idea of shear-dependent viscosity in the simplest, most fundamental geometry — the concentric annulus.MethodologyThe 3D annular geometry (1 m length, 0.0225 m inner diameter, 0.03125 m outer diameter) is designed in Design Modeler and meshed in ANSYS Meshing with a structured grid of roughly 1.4 million elements, appropriate for annular and rotating-flow problems. The Power-Law viscosity model is configured in Fluent by setting the consistency index k, the flow behavior index n, and clamping the minimum and maximum viscosity bounds. The Eulerian multiphase model is set up with two implicit phases, including phase-specific densities, viscosities, and inlet volume fractions. A rotating wall boundary condition (100 rpm on the inner cylinder) is applied — essential for any Taylor–Couette-type analysis — and Coupled pressure–velocity coupling with PRESTO! pressure discretization is chosen for the rotating multiphase flow.AnalysisPost-processing produces 2D and 3D contours of pressure, velocity, and volume fraction for both phases, revealing how the Power-Law fluid responds to the shear imposed by the rotating inner cylinder and how the denser secondary phase distributes through the annulus. From these fields you can study how the apparent viscosity varies with shear rate and how the two phases interact in the rotating annular flow. The same workflow underpins drilling mud analysis, polymer extrusion, blood flow in narrow vessels, paint coating, and food processing — anywhere viscosity isn't constant. By the end of this project, you'll be able to configure the Power-Law non-Newtonian viscosity model, set up an Eulerian two-phase flow with a rotating wall, and interpret the pressure, velocity, and volume-fraction fields that characterize non-Newtonian flow in a concentric-cylinder geometry.

Description: Distillation column trays are one of the most important pieces of equipment in clean water treatment, chemical separation, and process engineering, working by bringing rising vapor into direct contact with falling liquid on a perforated tray so volatile components evaporate while heavier components condense and drain. This project focuses on the hydrodynamic two-phase behavior of air and water at the tray location, examining how the two phases interact, mix, and separate, without yet introducing heat transfer or evaporation effects.Methodology: A symmetrical 3D tray column geometry is built in Design Modeler, modeling only half the chamber to reduce computational cost, and meshed with an unstructured grid of roughly 866,000 elements suited to two-phase tray flow. The VOF multiphase model is configured with air as the primary phase and water as the secondary phase, using implicit formulation and sharp interface modeling, alongside mixed boundary conditions of a velocity inlet for gas at 23.35 m/s, a mass flow inlet for liquid at 4 kg/s, and pressure outlets for both phases. Turbulence is captured with the RNG k-ε model and standard wall functions to handle the swirling, separating flow, with PRESTO! pressure discretization and the Modified HRIC scheme for volume fraction, and the domain is initialized with a patched water region to start from a realistic phase distribution.Analysis: Post-processing includes pressure contours, velocity contours, phase volume fractions, and velocity vectors across both 2D cross-sections and 3D views, capturing how the vapor and liquid phases distribute and interact across the tray. Since distillation columns underpin desalination plants, wastewater treatment, and petrochemical refineries alike, this hydrodynamic groundwork provides a portable, industry-relevant foundation for tackling multiphase separation problems in real process equipment.

DescriptionThis project uses ANSYS Fluent to simulate a microfluidic droplet generator, applying the Volume of Fluid (VOF) multiphase model to a core biomedical engineering problem. Microfluidic droplet generators are widely used in biomedical and bioengineering research to isolate biological entities and create controlled microenvironments for in-vitro analysis. The simulation models the interaction between two immiscible phases (water and oil, or their biological equivalents) as droplets form within a microchannel.MethodologyThe 3D device geometry is built in DesignModeler and meshed in ANSYS Meshing using an unstructured grid, refined locally to resolve droplet interface behavior accurately. The VOF model is configured to capture surface tension effects and wall adhesion, both critical to realistic droplet formation, with a patching approach used to improve computational efficiency. Droplet characteristics are controlled by varying inlet velocities of each phase, surface tension parameters, and channel geometry.ConclusionResults characterize droplet formation and breakup behavior under the given flow and geometric conditions, providing a basis for validating against experimental data. The findings translate directly to biomedical device design — informing how channel geometry and fluid properties (e.g., PBS, blood) affect droplet generation for applications such as biological entity separation and diagnostic sample analysis.

Carbonate Cake Filtration Simulation Using the Eulerian Multiphase Model — ANSYS Fluent TutorialDescriptionFiltration is one of the most widely used physical separation processes in industry, essential for removing solid particles from liquids in applications such as water treatment and purification, chemical processing, and environmental remediation. As filtration proceeds, the separated solids accumulate on the filter surface and form a layer known as the filter cake. While the cake itself can improve capture efficiency, its continuous growth increases flow resistance and gradually reduces the performance of the filtration unit — making accurate prediction of cake formation a key concern in filter design and operation.This tutorial, part of the Clean Water: Beginner CFD Training Package, presents a complete simulation of carbonate cake filtration in ANSYS Fluent. The model captures the interaction between three phases — water as the carrier fluid, suspended carbonate particles, and the carbon filter medium — allowing you to study how carbonate particles are captured by the filter and how the cake layer develops over time. Whether you are a process engineer, a CFD specialist, or a chemical engineering student, this project provides a practical foundation for simulating industrial separation processes.MethodologyThe filtration unit geometry is created in ANSYS Design Modeler, and a high-quality structured mesh is generated in ANSYS Meshing to ensure accurate resolution of the multiphase flow field.The simulation is built on the Eulerian multiphase model, the most rigorous approach for modeling interpenetrating phases with strong momentum coupling. Key elements of the setup include:Activating the Granular and Packed Bed options to represent the solid carbonate phase and the stationary filter mediumDefining granular phase property models for the granular temperature calculationApplying interphase momentum exchange mechanisms, including drag, lift, and virtual mass forces between each phase pairThe energy equation is enabled to compute the temperature distribution within the domain, and the Ranz-Marshall correlation is used to model heat transfer between the water and the filter phase. Turbulence is modeled with the standard k-epsilon model, providing a robust balance of accuracy and computational cost for this class of flow.AnalysisAt the end of the solution process, contours of phase volume fraction, temperature, and velocity are extracted and interpreted. The results show how the carbonate concentration changes across the filter as particles are progressively captured by the carbon medium, confirming the physical separation of the solid phase from the water stream. The temperature profile of the feed water through the unit is also examined, illustrating the thermal interaction between the flow and the filter.Most importantly, the simulation reveals the dynamics of cake layer formation: carbonate particles accumulate on the filter surface, the cake thickness grows over time, and the added flow resistance begins to affect the filtration performance. These insights demonstrate how CFD can be used to evaluate filter efficiency, optimize filtration unit design, and support decisions on filter maintenance and scale-up for industrial clean-water applications.

DescriptionThis project presents a CFD investigation of thermal energy storage using a phase change material (PCM) within a finned shell-and-tube heat exchanger. The study captures the complex transient behavior of the PCM melting process, demonstrating the effectiveness of latent-heat storage for thermal-management applications.The appeal of a PCM lies in its phase transition: it stores energy by absorbing latent heat as it melts from solid to liquid, and releases that energy as it solidifies from liquid back to solid. This makes PCMs valuable for thermal regulation in both heating and cooling systems — for example, absorbing heat during the day and releasing it at night over a diurnal cycle.The heat exchanger consists of a cylindrical shell (tank) filled with uniformly distributed PCM, through which a copper tube follows a winding path. Cross-shaped copper fins are placed along the tube to enhance heat transfer, with copper chosen throughout for its high thermal conductivity and the tube wall set to a thickness of 0.001 m. Meshing was performed using an unstructured grid of 2,448,380 elements, resolving the PCM volume, the copper tube, the copper fins, and the fluid flow path, along with the interfaces between the different materials and phases.MethodologyThe phase transition is captured using the Solidification and Melting model, which is the heart of the simulation. The PCM has a solidus temperature of 314.15 K, a liquidus temperature of 317.15 K, and a latent heat of fusion of 255,000 J/kg.The PCM is paraffin, with a density of 750 kg/m³, a specific heat capacity of 2000 J/kg·K, a thermal conductivity of 0.2 W/m·K, and a viscosity of 0.008 kg/m·s. The heat transfer fluid is water, entering at 325.15 K with a mass flow rate of 1.4973 kg/s, while the copper tube and fins provide the high-conductivity structural path for the heat. The analysis is transient, run over a duration of 1200 seconds, with the time step and convergence tolerances chosen to accurately capture the phase-change process.ConclusionThe results track the thermal evolution and phase transition throughout the storage medium: the temperature distribution across the PCM, the progression of the melting front as the solid-liquid interface advances over time, the development of the liquid fraction as the PCM melts, and the enhanced heat transfer near the tube and fin surfaces.From these, the system's performance can be evaluated — the total thermal energy stored in the PCM, the charging rate in response to the heat input, the temperature gradients that develop, and the contribution of the fins to the overall heat transfer. Together, they offer practical engineering insight into fin placement and tube routing, the influence of flow rate and inlet temperature, and the transient response during the charging cycle.Overall, the simulation shows how a finned shell-and-tube configuration helps overcome the inherently low thermal conductivity of PCMs, using the Solidification and Melting model to reveal the melting behavior at the core of latent-heat storage. Such systems are well suited to applications that depend on efficient thermal storage and release, including building climate control, solar thermal systems, and waste heat recovery.

Decomposition of MgO with Argon Gas for Magnesium Particle Production — ANSYS Fluent SimulationIntroductionThermal decomposition, or thermolysis, is a chemical breakdown driven by heat. The decomposition temperature of a substance is the temperature at which it chemically breaks apart. Such reactions are typically endothermic, since energy is required to sever the chemical bonds within the compound. In line with the equation below, the decomposition of magnesium oxide is an endothermic reaction, and here the process is driven by preheating the system with argon gas:MgO(s) → Mg(s) + O₂(g)This project presents a Computational Fluid Dynamics (CFD) simulation of a magnesium–oxygen (Mg–O) thermal reaction using ANSYS Fluent. The aim is to investigate the coupled interactions between fluid flow, heat transfer, and chemical reaction within a specialized reactor geometry. A clear understanding of these processes is essential for optimizing the design and operation of Mg–O-based energy systems, which hold promise for clean energy production and storage.The geometry was created in ANSYS Design Modeler and meshed in ANSYS Meshing, producing a structured grid of 53,760 elements. This level of refinement provides a good balance between computational accuracy and efficiency.MethodologyA steady-state, pressure-based solver was used together with the SST k-omega turbulence model. Reaction modeling was handled with the Species Transport model coupled to the Eddy-Dissipation turbulence-chemistry interaction. The Discrete Phase Model (DPM) was activated to capture particle behavior, with droplet-type particles evaporating from the MgO-particle phase into the MgO-fluid phase.For the boundary conditions, argon gas together with MgO particles is injected from the right inlet, while argon gas alone enters from the left inlet.ConclusionThe CFD simulation of the Mg–O thermal reaction offers valuable insight into the coupled processes occurring inside the reactor. The key findings are as follows:Static Pressure — The pressure field ranges from −1.893 to 2.994 Pa, with higher values near the walls and lower values in the central region, a distribution that promotes reactant mixing.Temperature — Temperatures span 300–700 K, peaking in the lower chamber and at the outlet, which marks the primary reaction zone.Velocity — Velocity magnitudes range from 0 to 2.199 m/s, with complex flow patterns and recirculation zones that enhance mixing and boost reaction rates.Species Distribution — The Mg mass fraction (0–0.06) is highest in the lower chamber, coinciding with the high-temperature regions. The MgO-fluid mass fraction (0–0.1) peaks in the central chamber, illustrating product formation and transport. The O₂ mass fraction (0–0.039) is inversely correlated with the Mg concentration, confirming the progress of the reaction.Together, these results demonstrate the interplay between fluid dynamics, heat transfer, and chemical reaction. The reaction is most intense in the lower chamber, where significant recirculation strengthens mixing, and the formation and distribution of the MgO-fluid product are clearly observed.