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.

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.

Magnetic Field Effect on Nanofluid in a 2D ChannelDescriptionThis project simulates the effect of a magnetic field on a nanofluid in a two-dimensional channel using ANSYS Fluent software. The problem is carried out and investigated through CFD analysis.The present model is designed in two dimensions using Design Modeler software. Because of its symmetrical geometry, the model is drawn as a two-dimensional channel. It has a length of 0.49 m and a width of 0.01 m, with an inlet boundary on the left and an outlet boundary on the right. The lower boundary of the domain is defined as the central axis, and adjacent to the channel's outer wall, a boundary is defined as the interface between the fluid and solid regions.The meshing of the present model is performed using ANSYS Meshing software. The mesh type is structured, and the number of elements is equal to 9,282.Magnetic Field MethodologyWhen metal or alloy particles of very small dimensions, on the order of the nano-scale, are mixed into a base fluid, a nanofluid is produced. Such fluids have applications such as enhancing heat transfer thanks to the conductivity of the metals.In this simulation, the effect of a magnetic field on the nanofluid's behavior and heat transfer is investigated. For this purpose, the magnetohydrodynamic (MHD) model is used, and the magnetic field is defined using the magnetic induction method. With this method, an external magnetic field is generated to apply a specific magnetic flux in different directions of the Cartesian coordinate system.The nanofluid defined in the model is based on iron oxide (Fe₃O₄) and contains 2% nanoparticles. It has a density of 1081.158 kg/m³, a specific heat capacity of 3841 J/kg·K, a thermal conductivity of 0.640835 W/m·K, and a viscosity of 0.001055 kg/m·s. A constant magnetic field is applied, with a magnetic flux of 1 tesla defined only along the y-axis, corresponding to the radial direction of the channel.In terms of boundary conditions, an insulation condition is applied to the outer wall of the channel, meaning that no electric current passes through it. For the inner wall and the common boundary between the solid and fluid parts of the model, a coupling condition is used to transmit electric current in both directions. The nanofluid stream enters the channel with a velocity of 0.0837 m·s⁻¹ and a temperature of 300 K, and exits at a pressure equal to atmospheric pressure. The outer wall of the channel is held at a constant thermal condition with a temperature of 320 K.The laminar model and the energy equation are enabled to solve the fluid flow equations and to calculate the temperature distribution inside the domain, respectively.Magnetic Field ConclusionAt the end of the solution process, two-dimensional contours of pressure, velocity, temperature, and the magnetic field in the horizontal and vertical directions are obtained. In addition, a diagram of the perpendicular magnetic field variation along the longitudinal direction of the channel's central axis is produced. The present results show the effect of applying a magnetic field and a thermal boundary condition on the nanofluid flow and its heat transfer.

DescriptionThis project uses ANSYS Fluent to simulate the flow of a nanofluid through a solid aluminum channel under an applied magnetic field. The flow is steady and modeled as a single-phase flow, with the thermophysical properties of the nanofluid — density, viscosity, specific heat, and thermal conductivity — calculated as functions of the nanoparticle volume fraction. The core of the study is the magnetohydrodynamic (MHD) interaction: the applied magnetic field acts on the electrically conducting nanofluid, altering its flow and heat-transfer behavior through the Lorentz force and Joule heating. The surface-averaged temperature of the nanofluid rises from 293.2 K at the inlet to 304.175 K at the outlet.Geometry & MeshThe fluid domain was created in SpaceClaim, and the computational grid was generated in ANSYS Meshing. The mesh is unstructured, with 26,000 elements.MethodologySeveral assumptions underpin the simulation: a pressure-based solver is used, the formulation is steady, and gravitational effects are neglected.Models — the energy equation is enabled; turbulence uses the standard k-epsilon model with standard wall functions; and the MHD model is applied using the magnetic-induction method, solving the MHD equations with the Lorentz force and Joule heating both included.Magnetic field — an external field B₀ is imposed by patch, with a 1 T component applied in the relevant directions.Materials — the working fluid is a water-based nanofluid (density 1312 kg/m³, specific heat 3248 J/kg·K, thermal conductivity 1.09387 W/m·K, viscosity 0.0011 kg/m·s, electrical conductivity 1,000,000 S/m, magnetic permeability 1.257 × 10⁻⁶); the solid channel is modified aluminum (density 2719 kg/m³, specific heat 871 J/kg·K, thermal conductivity 202.4 W/m·K, electrical conductivity 3.541 × 10⁷ S/m). The solid also carries an energy source representing Joule/MHD heating of 1,000,000 W/m³ applied through a UDF.Boundary conditions — Inlet: velocity inlet at 1 m/s, 5% turbulence intensity, turbulent viscosity ratio 10, and 293.2 K; the outer solid wall is held at 320 K (insulating for the magnetic field), and the fluid-solid interface is a coupled wall.Methods — SIMPLE pressure-velocity coupling; least-squares cell-based gradients; second-order for pressure, momentum, and energy; and first-order upwind for the turbulence quantities and the magnetic field components. The solution is initialized with a 1 m/s velocity and a temperature of 293.2 K.ConclusionWithout a magnetic field, the nanofluid's average temperature rises from 293.2 K at the inlet to 304.175 K at the outlet. When the magnetic field is applied, the outlet temperature increases further to 305.14 K. Plots of temperature and velocity along the centerline of the domain are presented for both cases (with and without MHD).Comparing the outlet temperatures with and without the magnetic field reveals the effectiveness of the MHD effect in this problem: applying the field raises the outlet temperature by about 1 K. This demonstrates how a magnetic field, through the Lorentz force and Joule heating acting on a conductive nanofluid, can be used to enhance heat transfer — the central principle behind MHD-based thermal management.

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.

Electric Field Effect on Nanofluid Heat Transfer (EHD) — ANSYS Fluent CFD Simulation TrainingThis project investigates the flow of a nanofluid through a bumpy channel under the influence of an applied electric field, using ANSYS Fluent. The flow is treated as steady-state and modeled using a single-phase approach, with the nanofluid's thermophysical properties—density, viscosity, specific heat, thermal conductivity, and electrical conductivity—adjusted to reflect the presence of the nanoparticles. The applied electric field alters the fluid's flow behavior, which in turn enhances heat transfer. The surface-averaged temperature of the nanofluid is 300 K at the inlet and 301.926 K at the outlet.Geometry and MeshThe fluid domain geometry was created in Design Modeler, and the computational mesh was generated in ANSYS Meshing. The mesh is unstructured, with a total of 17,640 elements.Setup and AssumptionsThe simulation uses a pressure-based solver under steady-state conditions, with gravity effects neglected. The energy equation is active, and turbulence is modeled using the realizable k-epsilon model with standard wall functions.The fluid is defined as a modified water-based nanofluid with a density of 998.2 kg/m³, specific heat of 4182 J/kg·K, thermal conductivity of 0.6 W/m·K, viscosity of 0.001003 kg/m·s, constant UDS diffusivity, electrical conductivity of 1,000,000 S/m, and a magnetic permeability of 1.257×10⁻⁶.At the inlet, a velocity inlet condition is applied with a velocity magnitude of 1 m/s, turbulence intensity of 5%, turbulent viscosity ratio of 10, and a temperature of 300 K. The outer solid wall is held at a fixed temperature of 340 K.The SIMPLE scheme handles pressure-velocity coupling, with least-squares cell-based gradients. Pressure and energy are discretized using second-order schemes, momentum uses second-order upwind, and turbulent kinetic energy and dissipation rate use first-order upwind. Hybrid initialization is used to start the solution.Results and DiscussionWith the electric field applied, the average outlet temperature of the nanofluid reaches 301.926 K, compared to 300 K at the inlet, corresponding to a heat flux of 72,474.1 W. Without the electric field, the outlet temperature drops slightly to 301.92 K.Comparing the two cases highlights the effect of the electric field: its application raises the outlet temperature by approximately 0.04 K and increases the heat transfer rate to the nanofluid by about 54 W/m².

Electric Field Effect on Nanofluid Heat Transfer (EHD) — ANSYS Fluent CFD SimulationDescriptionThis project uses ANSYS Fluent to investigate the effect of an electric field on nanofluid heat transfer in an N-shaped cooling pipe, applying the EHD (Electrohydrodynamic) module coupled with the DPM (Discrete Phase Model). A potential difference is established between the pipe shell (positive) and a central wire (negative), driving charged aluminum nanoparticles through the coolant to enhance heat transfer from the hot pipe walls. Cool water enters the pipe and absorbs heat from walls held at 390 K, with the outlet temperature rise used to evaluate the effect of the particles and electric field on cooling performance. Within the Magnetohydrodynamics & Electrohydrodynamics (MHD & EHD): All Levels CFD Training Package, this project opens the Electrohydrodynamics block, introducing the electric field effect as the EHD counterpart to the magnetic-field nanofluid cases.MethodologyThe 3D geometry is built in SpaceClaim, with an inlet, outlet, hot wall zone, an inner wall representing the central wire, and an outer wall representing the pipe shell. The domain is meshed in ANSYS Meshing using an unstructured grid of 2,966,928 elements and 720,300 nodes. The EHD model is combined with DPM to simulate the current generated between the positive and negative poles and its effect on heat transfer from the walls. Aluminum nanoparticles are modeled as inert solid particles with a diameter of 0.00001 m, a charge density of 23, and a total flow rate of 1e-20 kg/s, using the DPM model with interaction with the continuous phase. The energy equation is enabled to resolve the temperature distribution, and the results are compared between a case with particles and electric field versus a baseline case without them.AnalysisTemperature contours show more uniform heat distribution in the case with particles and electric field, with the average domain temperature rising by 0.1 K (310.43 K vs. 310.31 K) and the average outlet temperature rising by 0.5 K (316.59 K vs. 316.16 K) compared to the baseline. Velocity contours also show a more uniform flow field in the particle-laden case, indicating that the electric field's influence on the charged nanoparticles measurably improves cooling performance and heat distribution uniformity. By the end of this project, you'll be able to couple the EHD module with the Discrete Phase Model, drive charged nanoparticles through a coolant with an applied electric field, run a comparative study against a baseline without the field, and interpret the temperature and velocity fields that reveal how electrohydrodynamic effects enhance nanofluid heat transfer.

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