DescriptionThis project simulates a Turgo turbine using ANSYS Fluent, with water flowing at a velocity of 4 m/s as it passes through the turbine. A Turgo turbine is a type of impulse water turbine — highly efficient and compact, which makes it well suited to many hydroelectric applications, particularly under high-head conditions.The Turgo turbine is distinguished by its unique design and operating principle. Unlike many other turbines, it uses the kinetic energy of a water jet directed onto its blades to generate rotational motion, which is then converted into electrical energy — a process central to the operation of hydroelectric power plants.The blades were drawn in SOLIDWORKS at a specific angle and distance from the central axis and then imported into Design Modeler for the integrated blade design. Around the turbine blades, a dedicated cylindrical region is created to represent the circulating water flow, while a rectangular cuboid domain is designed to serve as the space for the free water flow. Meshing was performed in ANSYS Meshing using an unstructured grid; to improve accuracy, the Tetrahedrons method was used, giving an element count of 4,344,106.MethodologyThe Mesh Motion (Sliding Mesh) technique is used to simulate the rotation of the turbine blades. Accordingly, the cylindrical region is assigned a mesh-motion condition with a rotational speed of 150 rpm about the central horizontal axis of the turbine. Because the sliding-mesh approach physically rotates the mesh in time, it captures the true transient interaction between the moving blades and the incoming water jet.The realizable k-epsilon model is selected to represent the turbulence of the flow, and the effect of gravity is included in the Z direction at −9.81 m/s².ConclusionOn completion of the solution, two- and three-dimensional results for pressure, velocity, and velocity vectors were obtained. As expected, the maximum velocity occurs in the immediate vicinity of the rotating blades. A full set of performance quantities can be extracted from the simulation, including a pressure drop of approximately 4.979 × 10⁴ Pa across the turbine.Overall, the study demonstrates how the water jet strikes the Turgo blades and drives their rotation, and how the Mesh Motion (Sliding Mesh) technique reproduces this moving-blade behavior to reveal the turbine's hydrodynamic performance.

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

DescriptionThis project investigates the airflow over a Horizontal Axis Wind Turbine (HAWT) using ANSYS Fluent, with the aim of studying the velocity and pressure distribution across the blade surfaces and body. As the dominant technology in modern wind power, the HAWT is a cornerstone of renewable energy engineering, where analyzing blade aerodynamics is essential to maximizing the energy captured from the wind.The 3D model was created in SOLIDWORKS and imported into Design Modeler. The turbine consists of three blades, a rotary axis, and a surrounding flow domain. Meshing was performed in ANSYS Meshing using a structured grid of 4,270,222 elements.MethodologyThe simulation aims to examine the effect of the wind on the turbine blades and to calculate the drag and lift forces acting on the blade surfaces. The blades rotate about the horizontal axis at a rotational speed of 72 rad/s, while the air surrounding them is treated as stationary.Using the Multiple Reference Frame (MRF) method, the blades are held fixed and the air region around them is assigned a rotating frame turning at the same 72 rad/s about the y-axis. Because this is an external-flow problem, the k-omega SST model is used; this hybrid formulation blends the k-omega model in the near-wall regions with the k-epsilon model in the free stream beyond the boundary layer. Air enters the domain at a velocity of 15 m/s and exits through a pressure-outlet boundary at atmospheric pressure.ConclusionOn completion of the solution, contours of velocity, streamlines, and velocity vectors were obtained. The velocity contour clearly reveals the radial distribution of the airflow produced by the rotating blades, while the velocity vectors near the blade surfaces show, in detail, the interaction between the wind and the turbine blade. Together, these results illustrate how the blade extracts energy from the incoming wind — the fundamental aerodynamic behavior that governs the performance of a wind turbine in renewable energy applications.

DescriptionThis project simulates airflow around an H-type vertical axis wind turbine (VAWT) using ANSYS Fluent. VAWTs offer a practical alternative to horizontal axis turbines (HAWTs) in several respects: they avoid the low efficiency HAWTs suffer at smaller diameters, don't require the roughly 200 m diameters common to HAWT installations, and don't disrupt the natural skyline the way large horizontal turbines do, making them especially well suited to offshore wind farms where wind conditions are also more consistent. The turbine modeled here has six blades, three positioned closer to the rotation axis, rotating in the −Z direction at 14.17 rad/s under an inlet air velocity of 5.3 m/s. The geometry is built in Design Modeler and meshed in ANSYS Meshing with an unstructured grid of 1,546,624 cells.MethodologyRather than physically rotating the blades, the simulation applies rotational motion to the fluid zone surrounding them, requiring a distinct moving zone to be separated from the rest of the computational domain. Because the blade positions change over time, relative to the surrounding flow, the problem is inherently time-dependent, and this is captured using the Mesh Motion method under cell zone conditions, with a defined rotation axis and rotation speed governing how that zone moves.AnalysisThe resulting velocity and pressure contours, along with velocity vectors and pathlines around the blades, confirm that the airflow develops a rotational pattern driven by the turbine's motion, with a maximum air velocity of 45 m/s appearing downstream of the turbine and an inlet mass flow rate of 272.685 kg/s. The blade tip speed ratio works out to about 6, based on a tip speed of 30 m/s against the 5.3 m/s free-stream velocity. A stagnation point, and correspondingly the peak pressure zone, appears on the minus-Y side of the turbine, consistent with how the free-stream flow and rotational flow combine there. That combination also affects the inner and outer blades differently: the outer blades, moving at higher linear velocity, experience a larger pressure differential than the inner blades, which sit closer to the rotation axis and move more slowly.

DescriptionThis project uses ANSYS Fluent to simulate a Darrieus vertical axis wind turbine (VAWT) with the Dynamic Mesh 6DOF method, a core application of the dynamic mesh module for modeling rotation driven by fluid forces rather than a prescribed motion. The turbine's curved blades keep them in tension at high rotational speeds, and a helical blade arrangement helps distribute torque evenly across the revolution, reducing pulsation. In this case, a 6-blade Darrieus turbine is exposed to wind at 23 m/s, with turbine rotation resolved based on the moment generated by the flow itself.MethodologyThe 3D domain is built in DesignModeler, consisting of a flow domain and a body-of-influence region around the turbine, with a velocity inlet, pressure outlet, and ground wall boundary. The domain is meshed in ANSYS Meshing using an unstructured grid of 2,966,928 elements and 720,300 nodes. Turbine rotation is captured using the Dynamic Mesh 6DOF method, allowing the blades to rotate in response to the aerodynamic moment acting on them, rather than a fixed prescribed rotational speed.ConclusionResults show clear turbine rotation driven by the flow, with velocity contours revealing vortices — including Von Kármán vortex shedding — generated by the interaction between the flow and the rotating blades. Pressure contours show the highest pressure gradient at the blade leading edge, consistent with the flow velocity dropping to zero at that point. Streamline vectors resolve the wake region flow quality, a key challenge in this type of aerodynamic simulation, while the turbulence contour accurately captures the resulting turbulent structures.

DescriptionIn this project, a cooling system for a battery pack is designed using water vapor injection. The geometry consists of five battery cells arranged vertically, with a gap between the bottom of the batteries and the base of the enclosure. The mechanism at the heart of the study is species transport: air and water vapor are injected into the domain, and the mixing and transport of these species carry heat away from the cells. The geometry was created in ANSYS SpaceClaim and meshed in ANSYS Meshing using an unstructured grid, with a boundary layer applied on the battery walls to improve accuracy near these surfaces.The top and side walls are treated as insulated, while mass transfer occurs through the bottom wall. A heat flux of 3 W/m² is applied to the battery walls, and air together with water vapor at 18 °C is injected at a velocity of 0.5 m/s through nozzles on the side walls. Accordingly, the Species Transport model is employed, and the equations are solved in pseudo-transient mode.Grid Independence StudyThe mesh over the computational domain consists of tetrahedral elements with a boundary layer on the battery walls. Four grids were tested. Mesh #1, with 75,000 elements, reported an average battery-wall temperature of 20.16 °C, while Mesh #2, with 172,000 elements, reported 19.93 °C — a difference of more than 1%. A finer Mesh #3 was therefore generated, giving 19.88 °C, only about a 0.25% change from the previous grid. Mesh #2 was selected as the best-fitted grid, and Mesh #4 was also tested to confirm this choice.Mesh #Element SizeNo. of ElementsAvg. Temp (°C)Error (%)116 mm75,00020.16—28 mm172,00019.93−1.1534 mm263,00019.88−0.2542 mm990,00019.86−0.25ConclusionIn the first step, a steady simulation was performed without water vapor injection to establish the baseline. The results show that, with no cooling system applied, the batteries reach 27 °C. The standard operating range for most batteries, such as lithium-ion, is typically between 20 °C and 30 °C, though the exact range depends on the battery type and design. Introducing the water vapor cooling system reduces the battery temperature to about 20 °C — an improvement of roughly 5 degrees.The results also show that the corners of the enclosure experience higher temperatures, where hot air becomes trapped. This confirms that the placement of the nozzles has a strong influence on cooling performance and requires careful design. Overall, the study demonstrates how a species-transport approach — injecting and transporting air and water vapor through the domain — provides effective thermal management for a battery pack, keeping the cells within their safe operating range.

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

Data Center Cooling — ANSYS Fluent CFD SimulationDescriptionThis project simulates and evaluates the cooling performance of a data center using ANSYS Fluent. Data centers face increasingly demanding thermal-management challenges as rack power densities rise, particularly under AI and compute-intensive workloads, making effective cooling one of the central concerns in data center design and operation. The simulation targets the key factors that govern rack cooling performance: cold-air supply outlet placement, supply pressure, and the airflow-management strategies that prevent hot and cold air from mixing inefficiently. As the capstone of the Porous Media: Beginner CFD Training Package, this project applies the porous-zone model at full system scale, using porous media to represent server racks in a large, practical cooling problem.MethodologyThe server racks are modeled as porous media, representing their resistance to airflow in a computationally practical way while capturing the essential pressure-drop and flow-distribution behavior that determines how well cold air penetrates and cools the equipment inside each rack. Rather than resolving the detailed internal geometry of every rack, the porous-zone approach imposes an equivalent resistance, making a full data-center simulation tractable while still reproducing how the cold air distributes across the room and through the racks. The study examines the influence of the cold-air supply outlet placement and the supply pressure on the resulting cooling performance.AnalysisThe results demonstrate the critical role that outlet placement plays in overall cooling effectiveness. Positioning the cold-air supply outlet too close to the cooling unit causes cold-air bypass, where the supplied air short-circuits back toward the cooling unit without reaching the rack inlets, wasting airflow capacity and degrading cooling efficiency. Excessive supply pressure produces a similar outcome: the cold air streams past the front faces of the racks rather than entering them, again resulting in wasted airflow and insufficient rack cooling. Together, these findings give a clear, practical basis for data center layout decisions — specifically, that both outlet proximity and supply pressure must be carefully controlled to ensure cold air is actually delivered where the heat loads are, rather than bypassed before it can do useful work. By the end of this project, you'll be able to model server racks as porous zones in a data-center cooling simulation, study how outlet placement and supply pressure drive cold-air bypass, and interpret the flow and cooling results that inform effective data-center layout.

Magnetic Force Effect on an Airfoil — ANSYS Fluent CFD SimulationDescriptionThis project presents a CFD simulation of the Magneto-Hydro-Dynamic (MHD) effect on a NACA 0015 airfoil — an example of active flow control, where an external field is used to manipulate the flow and improve aerodynamic performance. The NACA 0015 is a symmetric airfoil that produces no lift at zero angle of attack, making it an ideal baseline for isolating the influence of the magnetic force. In this project, you'll investigate flow separation and stall, then apply a magnetic force to see how it delays separation and boosts lift. Within the Magnetohydrodynamics & Electrohydrodynamics (MHD & EHD): All Levels CFD Training Package, this project applies the magnetic field effect to an external aerodynamic body, building on the fundamental MHD flow case toward active flow control on a lifting surface.MethodologyThe 2D airfoil geometry is designed in Design Modeler and meshed in ANSYS Meshing with an unstructured triangular mesh around the airfoil. The MHD module in ANSYS Fluent is enabled and configured to apply a magnetic force to the flow — a magnetic body force that acts on the boundary layer. The study is set up as a comparative one: the lift coefficient is evaluated across multiple angles of attack, both with and without the MHD effect, so the influence of the magnetic force can be isolated directly. This allows the flow separation point and the maximum angle of attack before separation to be identified in each case.AnalysisPost-processing produces velocity and pressure contours, streamlines, and velocity vectors that visualize the boundary-layer energizing and the increased leading-edge suction. The comparative study demonstrates that the magnetic force accelerates the boundary-layer flow, keeping it attached to the surface and delaying stall to a larger angle of attack — raising the lift coefficient relative to the case without MHD. From these results you can see exactly how the magnetic body force controls separation and improves aerodynamic performance. Active flow control via MHD and plasma actuators is a frontier topic in aerospace and energy, and the MHD-module workflow learned here applies to lift enhancement, drag reduction, stall delay, and flow-control research across aircraft, turbines, and high-speed vehicles. By the end of this project, you'll be able to enable and configure the MHD module in ANSYS Fluent, apply a magnetic body force to a flow, run a comparative lift study across angles of attack, and interpret the velocity and pressure fields that reveal how the field delays separation and enhances lift.

MHD Effect on Fluid Flow — ANSYS Fluent CFD SimulationDescriptionThis project simulates the flow of an electrically conductive fluid inside a simple square chamber using ANSYS Fluent, with magnetohydrodynamics as the central theme. MHD is the study of how electrically conducting fluids behave in the presence of a magnetic field: as the conductive material moves through the field it induces electric currents, and the interaction of those currents with the magnetic field produces a Lorentz force that, in turn, modifies the flow. The core of the study is this two-way coupling between the fluid-flow field and the magnetic field, captured through ANSYS Fluent's MHD module. As the opening project of the Magnetohydrodynamics & Electrohydrodynamics (MHD & EHD): All Levels CFD Training Package, it introduces the most fundamental magnetic field effect — how a field reshapes a conducting flow — establishing the MHD foundation the applied cases build on.MethodologyThe MHD model is implemented using the magnetic-induction method, which introduces two user-defined scalar magnetic-flux fields in the x- and y-directions (the alternative electric-potential method instead uses a single voltage scalar). All four boundaries of the domain are set as insulating walls, meaning no electric current passes through them; the module also supports conducting-wall boundaries for fully conductive surfaces, coupled-wall conditions for shared solid–solid or solid–liquid interfaces, and thin-wall conditions for finite electrical conductivity. The energy equation, the Lorentz force equations, and the MHD equations are all activated, with source terms applied to energy, momentum, and the magnetic fluxes to define the field within the model. The study is organized around three dimensionless parameters: it first examines the Prandtl number — the ratio of momentum diffusivity to thermal diffusivity — without the MHD model active; it then activates MHD and, at a fixed Prandtl number, varies the Hartmann number, which expresses the ratio of electromagnetic force to viscous force and changes with the magnitude of the applied magnetic flux; and finally, at a fixed Hartmann number, it varies the angle at which the magnetic field is applied to the flow. The working fluid is defined with a density of 998.2 kg/m³, thermal conductivity of 0.6 W/m·K, dynamic viscosity of 0.001003 kg/m·s, thermal expansion coefficient of 0.000214 K⁻¹, and a high electrical conductivity of 1,000,000 S/m. The Prandtl number is varied through the specific heat capacity (taking values such as 0.01, 0.02, 0.03, and 0.004), while the Hartmann number is varied through the applied magnetic flux (0.003284, 0.006568, 0.013135, and 0.032838), applied vertically along the y-axis; in the final stage, with the flux held constant, its direction is changed across angles of 0° (along the x-axis), 45°, 60°, and 90° (along the y-axis). The geometry is a two-dimensional square cavity one meter on a side, created in Design Modeler and meshed in ANSYS Meshing with a structured grid of 10,000 elements. The simulation uses a pressure-based, steady, laminar solver with the energy equation active and gravity neglected; the lower wall is held at 587 K and the upper wall at 300 K, with the left and right boundaries set as pressure outlets.AnalysisThe solution yields two-dimensional contours of pressure, velocity, and temperature together with pathlines across the three stages of the study. The first stage, without MHD, compares the effect of four Prandtl numbers; the second, with MHD and a fixed Prandtl number, compares four Hartmann numbers at a fixed field direction; and the third, with both Prandtl and Hartmann numbers fixed, compares four field application angles. Together these reveal how the strength of the magnetic field — expressed through the Hartmann number — and its orientation govern the flow and heat transfer of the conducting fluid. By the end of this project, you'll be able to set up ANSYS Fluent's MHD module using the magnetic-induction method, apply the Lorentz force and associated source terms, run a parametric study across the Prandtl and Hartmann numbers and field angle, and interpret the velocity, temperature, and pathline results that show how a magnetic field controls an electrically conducting flow.