Savonius VAWT CFD Simulation with Mesh Motion in ANSYS FluentDescriptionThis study investigates the airflow behavior and aerodynamic performance of a Savonius vertical axis wind turbine (VAWT) using ANSYS Fluent, evaluating pressure and velocity distribution around the rotor blades and analyzing the flow patterns developing during turbine operation. The simulation runs under transient conditions to capture the dynamic behavior of the rotating blades, along with the unsteady flow separation and vortex formation characteristic of Savonius rotors. The computational domain includes both a rotating rotor region and a stationary surrounding flow domain to accurately capture the interaction with incoming airflow.The geometry, representing a three-bladed Savonius VAWT rotor enclosed within a domain providing sufficient clearance for airflow, was designed in Design Modeler. Meshing was performed in ANSYS Meshing using a non-conformal, unstructured mesh to accommodate the complex blade geometry and accurately resolve boundary layer effects, totaling approximately 5.5 million cells, with refinement concentrated near the rotor surfaces to better capture velocity gradients and flow separation.MethodologyThe simulation used a pressure-based transient solver suited to incompressible flow, with air modeled as a Newtonian, incompressible fluid. Turbulence was resolved using the RNG k-epsilon model, selected for its strong performance in swirling and recirculating flows — behavior common to Savonius VAWT operation.The domain was divided into two zones: a rotating rotor zone and a stationary flow zone, with mesh motion applied to the rotor zone at a rotational speed of 144 rpm to represent turbine rotation. Boundary conditions included a velocity inlet at 12 m/s, a pressure outlet downstream, and no-slip walls on the turbine surfaces. The SIMPLE algorithm handled pressure-velocity coupling, with second-order discretization applied to both momentum and turbulence equations for improved accuracy.ConclusionPressure contours reveal high-pressure regions on the windward side of the blades and low-pressure regions on the leeward side, confirming that torque generation arises from this differential pressure force across the rotor. Velocity contours and streamlines reveal complex vortex structures and recirculation zones forming behind the rotor, illustrating the unsteady wake dynamics characteristic of drag-based turbines like the Savonius design.Velocity vectors further show how airflow deflects and accelerates around the blades, while the streamlines visualize the rotational flow entrainment occurring near the rotor arms. The overall flow pattern indicates a consistent power extraction process, though localized flow separation and elevated turbulence intensity in certain regions may reduce overall efficiency — together validating the Savonius VAWT's expected aerodynamic behavior and confirming CFD's effectiveness as a tool for evaluating this turbine design's performance.

Gorlov Vertical Axis Wind Turbine CFD Simulation, ANSYS FluentDescriptionThis project simulates a Gorlov vertical axis wind turbine using ANSYS Fluent. The initial turbine geometry was sourced from GrabCAD and subsequently re-designed in SpaceClaim with CFD-specific preliminaries in mind. The computational domain is a cube, with the turbine positioned centrally, close to the inlet boundary. The domain was meshed in ANSYS Meshing, with increased mesh accuracy applied specifically around the blades to better resolve the local flow behavior.MethodologySince the angle of attack on a vertical axis wind turbine continuously changes throughout its rotation, this angular motion was captured using the sliding mesh (mesh motion) model. Incoming wind enters at 10 m/s from the inlet boundary, while the turbine rotates at 12 rad/s. The domain's side walls were set as symmetric, effectively representing an open surrounding area rather than confined boundaries.ConclusionResults include velocity, pressure, and streamline visualizations throughout the domain. As the incoming airflow strikes the turbine blades, it drives the rotor's rotation, generating the torque captured in the resulting plot. Since the angle of attack changes continuously as the turbine rotates, this torque generation follows a distinctly sinusoidal trend — reaching peak values at certain rotational angles and dropping off at others, consistent with the cyclic aerodynamic loading characteristic of vertical axis wind turbine operation.

Archimedes Spiral Wind Turbine CFD Simulation (Moving Mesh), ANSYS FluentDescriptionThis project simulates an Archimedes Spiral Wind Turbine using ANSYS Fluent. The Archimedes Spiral Wind Turbine is one of several innovative horizontal axis wind turbine (HAWT) designs explored to increase wind energy adoption in urban environments — though the success and widespread adoption of such designs can vary depending on advancements in engineering, cost-effectiveness, and broader market conditions.The model consists of two zones: a rotating zone, a small cylinder enclosing the spiral blade, and a stationary zone, represented as a surrounding duct enclosure. The geometry was designed in SolidWorks and meshed in ANSYS Meshing using an unstructured grid totaling 3,207,809 cells.MethodologyBlade rotation was captured using Mesh Motion, well suited to this unsteady problem, with the rotating zone spinning about the z-axis at an angular velocity of 300 rpm. Turbulence was resolved using the SST k-omega model.ConclusionThe resulting contours and pathlines reveal how the airflow behaves around the spiral blade. Velocity contours and streamlines clearly illustrate the aerodynamic character of this spiral blade design, with higher velocities concentrated in the inner region of the rotor due to local flow acceleration around the blade's blunt-body corners. Pressure distribution also varies notably along the blade's length, with significant differences observed between the root and tip regions.Understanding these aerodynamic characteristics — the velocity acceleration pattern and the root-to-tip pressure variation — is essential for optimizing the performance of this spiral-bladed turbine design as it develops toward practical urban wind energy applications.

Serrated Airfoil and Plain Airfoil Comparison, Darrieus VAWT, ANSYS Fluent CFD Simulation TrainingDescriptionThis project compares airflow behavior over two H-type Darrieus wind turbines — one with plain airfoils and one with serrated airfoils — using ANSYS Fluent. The Darrieus wind turbine is a vertical axis wind turbine (VAWT) that generates electricity from wind energy using several curved airfoil blades mounted on a rotating vertical shaft. A key advantage of vertical axis turbines is that they require no adjustment to wind direction and can operate effectively at low altitudes.The 3D geometry was built in Design Modeler, comprising a rotating zone and a surrounding stationary zone, with the computational domain measuring 50 cm in length and width and 300 cm in height. The domain was meshed in ANSYS Meshing using a hybrid approach — structured mesh in the stationary zone and unstructured mesh in the rotating zone — totaling 1,186,185 elements. Given the use of mesh motion for the rotating blades, the simulation was run using a transient solver.MethodologyVAWT performance is substantially affected by the dynamic stall phenomenon, driven by continuous variation in blade angle of attack as the turbine rotates. Large, sudden torque fluctuations occur as dynamic stall vortices form near the blade leading edge and are carried downstream — a behavior that occurs periodically at relatively low Reynolds numbers (Re < 10⁵), producing a sharp drop in lift coefficient and reducing both rotor torque and power output.This project investigates whether applying sinusoidal serrations to the leading edge of the turbine blades can control and reduce this dynamic flow separation, comparing the resulting performance directly against a conventional plain-airfoil H-type VAWT. Airflow entered the domain at 7 m/s, with turbulence resolved using the RNG k-epsilon model. Blade rotation was captured using Mesh Motion, with the rotating domain set to 2.8285 rad/s.ConclusionResults include 2D contours of pressure, velocity, and streamlines. Pressure contours show continuous variation across the blades as their position and angle of attack change throughout rotation — a core source of the dynamic stall challenge inherent to VAWT operation, and a contributor to blade fatigue given the wide range of forces experienced during each rotation cycle.Comparing the two designs revealed a slight increase in drag on the serrated airfoils relative to the plain ones, attributable to their increased surface area. However, lift coefficient also showed a slight increase with the serrated design, resulting from a smoother pressure gradient distribution across each airfoil's two surfaces compared to the plain configuration. This smoother pressure distribution ultimately translated into a modest increase in generated power, with the serrated airfoils producing more consistent, elevated power output across each rotation cycle — confirming that leading-edge serrations offer a meaningful, if incremental, performance improvement for H-type Darrieus VAWT designs.

HAWT and VAWT Comparison, (Mesh Motion and MRF)DescriptionThis project provides a detailed comparison of modeling techniques for two distinct wind turbine designs — Horizontal Axis Wind Turbines (HAWT) and Vertical Axis Wind Turbines (VAWT) — using ANSYS Fluent. Each turbine type is simulated using the CFD approach best suited to its rotational behavior: VAWT is modeled using Mesh Motion, while HAWT is modeled using the Moving Reference Frame (MRF) method, offering a direct, practical comparison of both techniques applied to two genuinely different turbine geometries.MethodologyThe VAWT simulation uses dynamic mesh motion, physically rotating the mesh to capture the turbine's motion through the domain, run as a transient analysis to resolve the time-dependent flow phenomena characteristic of vertical axis turbine operation, with an interface boundary defined between the rotating and stationary mesh zones.The HAWT simulation instead applies the MRF technique, treating the surrounding fluid as rotating relative to the blades rather than physically moving the mesh, configured as a steady-state simulation for greater computational efficiency — reflecting the inherent trade-off between MRF's computational speed and Mesh Motion's higher temporal accuracy for capturing unsteady rotational effects.Key performance metrics — lift, drag, and moment coefficients — were extracted for both turbine types, along with power output and overall efficiency, enabling a direct aerodynamic comparison between the two designs.ConclusionThe results reveal the distinct flow patterns characteristic of each turbine design, along with the relative strengths and limitations of each configuration. This comparison also clarifies when each simulation method is most appropriate: Mesh Motion suits cases like VAWT, where blade angle of attack continuously changes throughout rotation and unsteady effects are central to the physics, while MRF suits cases like HAWT, where the flow field around the rotor is more consistently steady, allowing faster computation without significant loss of accuracy.Together, these results offer practical guidance for selecting the appropriate simulation method based on turbine type and required accuracy, as well as insight into blade design optimization and how varying wind conditions affect performance across both HAWT and VAWT configurations.

Water Wheel (Pelton Wheel), ANSYS Fluent CFD Simulation TrainingDescriptionThis project simulates the performance of a water wheel — a classic example of a Pelton turbine — using ANSYS Fluent.Most water wheels are mounted vertically on a horizontal axis, though horizontal mounting on a vertical shaft is also possible. The fluid flow equations are solved using the averaged form of the Navier-Stokes equations within ANSYS Fluent.The turbine has a diameter of 0.7 m, with the free surface boundary positioned 0.2 m below the wheel's center. Water velocity ranges between 3 and 5 m/s, depending on average river conditions, from which the turbine's rotational speed is determined to avoid drag or disruption in the flow — in this simulation, the turbine rotates at 60 rpm.MethodologyThe wheel's blades are positioned perpendicular to specific turbine sections to reduce friction and increase nozzle thrust, while a portion of the turbine remains outside the water. As a result, the wheel operates across two distinct phases — water and air — as it rotates about its axis, modeled using the VOF (Volume of Fluid) multiphase model.The turbine geometry was designed in SOLIDWORKS and divided into smaller sections to improve both geometric detail and mesh quality. The model was split into two rotating (Rotor) regions and one stationary (Stator) region: the rotor comprises the turbine itself along with a surrounding cylinder, while the static region encloses this rotating cylinder.Meshing was performed in ICEM CFD. The rotor section was meshed using an unstructured grid, with finer mesh density applied at the turbine's leading edge to capture the complex flow behavior and high gradients present in that region. The stationary zone used a structured mesh, which reduces overall element count while maintaining high mesh quality. The two separately meshed regions were then coupled together to form the complete domain.Impeller rotation was applied incrementally, with 3 degrees of rotation per time step — a value that should be reduced further for higher simulation accuracy. This motion was handled using the Mesh Motion approach, with the static and rotating mesh regions sliding relative to one another across a shared interface boundary.ConclusionThe results clearly capture the wheel's continuous rotational motion as it interacts with the incoming water stream, with velocity and pressure contours highlighting how the flow strikes each bucket in sequence to sustain the wheel's rotation. The VOF-based volume fraction contours trace the air-water interface as it deforms around the submerged buckets, showing the free surface dipping and recovering as each bucket enters and exits the water.Pathlines around the turbine illustrate how incoming flow is redirected by the curved bucket geometry, transferring momentum to the wheel and producing the torque that drives its rotation. Together, these results confirm that the coupled rotor-stator mesh motion setup successfully reproduces the expected physical behavior of a Pelton-type water wheel operating at the free surface, providing a validated basis for evaluating design changes such as bucket geometry, submersion depth, or rotational speed in further studies.

Archimedes Screw Turbine (AST), CFD Simulation, ANSYS Fluent TutorialDescriptionThis project simulates an Archimedes Screw Turbine (AST) — also known as an Archimedes screw generator or screw turbine — consisting of 3 blades, using ANSYS Fluent. This hydraulic machine applies the principle of the Archimedean screw to convert the potential energy of upstream flow into kinetic energy.The turbine is modeled as installed within a river, with water flowing through it via a fixed-level inlet positioned in an upper box of the domain. The turbine is set at a 30-degree angle relative to the ground, with water entering from the upper level. The outlet is defined with zero gauge pressure, while all remaining surfaces are treated as stationary walls.The 3D geometry was designed in Design Modeler, featuring two rectangular boxes at the turbine's inlet and outlet ends that guide the flow into and out of the turbine. The turbine's interior and exterior radii measure 0.5 m and 1 m, respectively. The domain was meshed in ANSYS Meshing, generating over 2 million elements.MethodologyThe turbine's rotational motion is modeled using an unsteady Mesh Motion approach, with the rotating zone containing the screw turbine rotating independently within the domain.The VOF model defines the water and air phases, with the domain's upper level maintaining a fixed water level via the Open Channel Flow boundary condition. Turbulence is resolved using the standard two-equation k-epsilon model.ConclusionResults include 2D and 3D contours and vector fields for water pressure and velocity. Water flow enters through the upper box, passes over the turbine blades, and exits through the outlet face.Turbine output power was calculated by multiplying the moment obtained from ANSYS Fluent by the turbine's angular velocity (1.05 rad/s, corresponding to 10 rpm), yielding a maximum output of approximately 5 kW at this operating speed. The moment parameter's variation over the course of the solution is captured to illustrate this calculation.Static pressure contours across the domain further show a clear pressure decrease as the flow passes through the channel, consistent with the expected energy extraction occurring as water moves through the turbine.

Centrifugal Pump with Fluid-Structure Interaction (FSI) — ANSYS Fluent CFD Simulation TrainingThis project simulates the fluid flow and structural dynamics within a centrifugal pump using ANSYS Fluent, with the full case analyzed through CFD post-processing.The 3D geometry was created in Design Modeler and represents a centrifugal pump, with several blades arranged in the central zone. The fluid enters from the outside of the pump and, after rotating around the blades, exits axially from the center. The model was meshed in ANSYS Meshing, for a total of more than 3,649,835 cells.MethodologyPumps are industrial machines that move fluid from one place to another through mechanical action. They fall into two main categories — dynamic and positive-displacement pumps — and centrifugal pumps are among the most common dynamic types. A centrifugal pump raises the fluid pressure from inlet to outlet, driving the flow; the force that creates this pressure comes from an electric motor that rotates the impeller. The fluid enters at the center of the impeller and exits at the edge of the blades, so the centrifugal force increases its velocity and kinetic energy.The model consists of three main parts: the central blades, defined as a solid body, and the casing around the pump, defined as the fluid passage, with a distinct central zone defined for the fluid so that the rotational motion of the blades can be applied to it. This central fluid zone uses the moving-reference-frame (frame motion) method at a rotational speed of 1500 rpm, meaning the fluid rotates around the blades at this speed. Water enters radially through the inlet port at 140 m/s and exits axially at the center of the pump at a relative pressure of 0 Pa.The SST k-omega model solves the turbulent flow equations, chosen for its accuracy in predicting flow patterns both near and far from the walls. To capture the interaction between the fluid flow and the structural response of the pump components, a Fluid-Structure Interaction (FSI) model is employed. This allows the deformation of the pump blades under fluid loading to be captured, giving a realistic depiction of the fluid–structure coupling.ResultsThe contours show that the pressure increases radially, rising from the central part of the pump toward the periphery. Because of the rotational motion in the central region, the maximum velocity appears there, along with the largest pressure difference (pressure gradient). Integrating the FSI model into the simulation provides critical insight into the structural dynamics of the impeller blades, contributing to a more robust understanding of the pump's operational efficiency and its potential failure points under dynamic loading.

Powder Distribution in Mixer, Transient CFD SimulationDescriptionThis project simulates powder distribution within a stationary fluid inside a mixer tank. Powder initially settles at the bottom of the tank, after which the mixer propeller begins rotating to disperse it throughout the surrounding fluid. Given the high computational cost of this simulation, it was run only for a limited time window.The mixing tank geometry was designed in SpaceClaim and meshed in ANSYS Meshing, generating approximately 3,000,000 cells.MethodologyThe working fluids — powder and liquid water — were modeled using the Eulerian multiphase model, with the powder treated as the secondary phase using the granular option to capture its particulate, granular nature. Propeller rotation was captured using mesh motion, with the propeller rotating at 30 rpm.ConclusionResults reveal a clear pattern of powder distribution within the fluid after a set period of mixing. The powder appears distinctly layered, with the highest concentration settled at the bottom of the container, transitioning through a thin, powder-rich boundary layer just above this region — indicating the powder is distributed in a gradient rather than uniformly.This pattern confirms that, within the limited mixing time simulated, the powder has not yet been evenly dispersed throughout the fluid — an expected outcome given the mixer's short operating duration. The results also show that the central shaft and impeller visibly influence local flow patterns and powder distribution in their immediate vicinity, highlighting their role in shaping the overall mixing behavior even at this early stage.

Side Entry Mixing Tank in 3 Different Rotational Speeds, ANSYS Fluent CFD Simulation TrainingDescriptionThis project simulates a side entry mixing tank at multiple rotational speeds using ANSYS Fluent. Mixing is a critical process across many industries — in oil and gas, for instance, water settling within storage tanks causes significant damage through corrosion, leakage, and eventual tank perforation. Side-entry mixing blades are commonly used to prevent this, since space limitations often prevent a blade from entering the tank from above. This simulation examines mixing performance at three rotational speeds — 400, 900, and 1400 rpm — using the mesh motion method.The 3D geometry was designed in SpaceClaim, with a computational domain measuring 400 cm long, 400 cm wide, and 375 cm high. The domain was meshed in ANSYS Meshing using a relatively fine grid totaling 933,102 elements.MethodologySeveral assumptions were applied to the simulation: a pressure-based solver was used, the simulation was run as unsteady (transient), and gravity was included at -9.81 m/s² in the z-direction.Key simulation settings included:Viscous model: Standard k-epsilon with standard wall functionsMultiphase model: VOF, with three phases — air, oil, and waterBoundary conditions: Stationary tank walls, with frame motion enabled on the fluid at rotational velocities of 400, 900, and 1400 rpm across the three separate casesSolution methods: SIMPLE pressure-velocity coupling, PRESTO! for pressure discretization, second-order upwind for momentum, and first-order upwind for turbulent kinetic energy and dissipation rateInitialization: Standard methodConclusionThe mixing tank contains both water and oil, with water settling toward the tank bottom due to its higher density prior to mixing. The side mixer was used to blend the tank's contents across all three rotational speeds, with mixing completeness tracked using three sensors positioned at different locations within the tank, monitoring the oil and water volume fractions at each point over time.These results showed clear differences in mixing time across the three speeds: 100 seconds at 400 rpm, 14 seconds at 900 rpm, and 8 seconds at 1400 rpm. This trend reveals that increasing rotational speed up to a certain point (around 900 rpm) delivers substantial mixing time reduction, but pushing speed higher still yields comparatively little further benefit — a finding directly relevant to selecting an appropriately sized mixer motor without over-specifying rotational speed and unnecessarily increasing equipment cost.