Particle-Laden Flow in a Micro-Bearing (DPM) — ANSYS Fluent CFD Simulation TrainingDescriptionIn certain industries — such as Microelectromechanical Systems (MEMS) and microfiltration — micro-channels and micro-bearings with flow thicknesses on the micrometer scale can carry particles on the nanometer scale. Under these conditions, the effect of the carried particles on the frictional force acting on the channel or bearing wall becomes very important. This project uses ANSYS Fluent and the Discrete Phase Model (DPM) to investigate how this wall frictional force differs with and without the presence of particles. The particles are spherical anthracite grains 400 nm in diameter, and the two-way interaction between the particles and the fluid is taken into account.The geometry consists of two concentric cylinders with diameters of 100 and 70 micrometers, drawn in ANSYS SpaceClaim. The outer cylinder rotates while the inner cylinder remains stationary. The mesh was generated in ANSYS Meshing and comprises 51,840 hexahedral elements — a density that resolves the flow dynamics, turbulence effects, and particle distribution within the domain. The element size is kept larger than the particle size to ensure proper particle tracking.MethodologyA pressure-based, transient solver is used to track the particle injection over time and capture the interaction between the particles and the fluid. The fluid filling the gap between the cylinders is Polyalphaolefin (PAO) 68. The SST k-omega turbulence model is adopted for its effectiveness in capturing the complex flow that develops around the rotating cylinder, and a suitable wall function resolves the near-wall region — essential for accurately representing the fluid–particle interactions near the walls and the resulting frictional force. Appropriate particle-dynamics forces and models are also included to improve the accuracy of the DPM settings.Results & ConclusionThe simulation yields the following results:The frictional force on the inner cylinder wall increases over time, while the frictional force on the outer cylinder wall decreases over time.In both the with-particle and without-particle cases, the frictional force on the outer cylinder wall exceeds that on the inner cylinder wall. This is likely because the outer cylinder is the rotating one, and the frictional force is proportional to the velocity gradient.The wall frictional force is higher when particles are present, because the particles raise the effective viscosity of the medium and thereby increase the friction.The plots show that, for the inner cylinder, the problem reaches steady state after 27 ms without particles and after 34 ms with particles. For the outer cylinder, steady state is reached after 11 ms without particles and after 20 ms with particles. Overall, then, the case without particles reaches steady state after 27 ms, while the case with particles reaches steady state after 34 ms.

Nanofluid in a Heat Source Channel (Mixture Multiphase) — ANSYS Fluent CFD SimulationDescriptionThis project presents a CFD simulation of nanofluid cooling in a heated channel — a cutting-edge approach to thermal management in electronics, heat exchangers, and compact cooling systems. Nanofluids are engineered fluids in which nanoscale solid particles (here, aluminum oxide) are suspended in a base liquid (water) to dramatically improve thermal conductivity and heat transfer. In this project, you'll model flow through a square channel packed with ten obstacle assemblies (diagonal barriers plus a central cylinder) sitting on a solid aluminum block heated by a constant flux of 170,000 W/m². You'll run the simulation in two stages — pure water, then nanofluid — and compare the cooling performance directly. Within the Nanofluid: Beginner CFD Training Package, this project introduces the core nanofluid heat-transfer setup with the mixture multiphase model, establishing the foundation the later heat-exchanger and field-effect cases build on.MethodologyThe 3D obstacle-filled channel, mounted on a solid heated base, is designed in Design Modeler and meshed with a fine unstructured grid of roughly 2.16 million elements for the geometrically complex flow path. The Al₂O₃ nanoparticle material properties are defined — density, specific heat, thermal conductivity, viscosity, particle diameter, and molecular weight. The Mixture multiphase model is used, the correct choice when solid particles mix into a fluid without a sharp interface. Conjugate heat transfer between the solid aluminum block and the flowing fluid is modeled through a constant heat-flux boundary. The study is run as a two-step comparison: single-phase pure water versus two-phase nanofluid at a 0.01 nanoparticle volume fraction, so the cooling performance of the two can be compared directly.AnalysisPost-processing produces mixture pressure, temperature, and phase-velocity contours on the X-Z and Y-Z planes, revealing how the nanofluid moves through the obstacle-filled channel and removes heat from the aluminum block. Comparing the two stages quantifies the heat-transfer enhancement the nanoparticles provide over pure water. Nanofluid cooling is at the frontier of electronics thermal management, solar collectors, and high-performance heat exchangers, and the Mixture-model + conjugate-heat-transfer workflow built here is directly applicable to any advanced cooling design. By the end of this project, you'll be able to define nanoparticle material properties, set up the Mixture multiphase model, couple conjugate heat transfer through a heated solid, run a single-phase-versus-nanofluid comparison study, and interpret the results to quantify the heat-transfer enhancement nanofluids provide.

DescriptionThis project uses ANSYS Fluent to simulate heat transfer inside a radiator using nanofluid flow, a core problem in nanofluid heat transfer modeling. The radiator operates by passing hot nanofluid through internal pipes while cold air flows over them, absorbing heat and carrying it to the surrounding environment. In this simulation, an Al2O3-water nanofluid enters at 0.1 m/s and 343.15 K through three internal pipes, while cold air passes over the pipes at 3 m/s and 293.15 K, with the goal of evaluating heat transfer performance in the presence of the nanofluid.MethodologyThe nanofluid is defined with a density of 1086.287 kg/m³, specific heat capacity of 3804.691 J/kg·K, thermal conductivity of 0.6672643 W/m·K, and viscosity of 0.00108236 kg/m·s. The 3D geometry is built in SpaceClaim as a symmetric half-model of the radiator to reduce computational cost, with air inlet/outlet sections on both sides and three internal pipes for nanofluid flow. The domain is meshed in ANSYS Meshing using an unstructured grid of 1,033,305 elements.The simulation uses a steady, pressure-based solver with gravity neglected. Turbulence is modeled using the standard k-epsilon model with standard wall functions, and the energy equation is enabled. Boundary conditions specify velocity inlets for both air (3 m/s, 293.15 K) and nanofluid (0.1 m/s, 343.15 K), pressure outlets (0 Pa gauge) for both streams, coupled thermal walls between the internal pipe surfaces, and a zero-heat-flux condition at the bottom wall. The solution uses coupled pressure-velocity coupling with second-order discretization for pressure, momentum, energy, and turbulence quantities, initialized with standard methods at 293.15 K and 3 m/s x-velocity.ConclusionResults include 2D and 3D contours of pressure, velocity, and temperature, characterizing the heat transfer performance of the nanofluid-cooled radiator system.

Shell and Tube Heat Exchanger with Helical Fin and Nanofluid — ANSYS Fluent CFD SimulationDescriptionThis project simulates heat transfer in a shell-and-tube heat exchanger enhanced by two techniques at once: helical fins in the shell and an Al₂O₃–water nanofluid as the working fluid. Shell-and-tube exchangers are among the most widely used heat-transfer devices in industry — one stream flows through the tubes, the other through the shell. Adding helical fins forces the shell-side fluid along a longer, swirling path, increasing its contact time with the tube surfaces, while the nanofluid raises the fluid's effective thermal conductivity. Together they target the same goal: a higher heat-transfer rate without enlarging the device. Within the Nanofluid: Beginner CFD Training Package, this project applies nanofluid to a real industrial heat exchanger and introduces the efficient single-phase property-correlation approach to nanofluid modeling.MethodologyThe key modeling decision is how to represent the nanofluid. Two approaches exist: a full multiphase model (base fluid plus dispersed nanoparticles), which is physically detailed but computationally expensive; or the single-phase property approach, where the nanofluid's density, specific heat, thermal conductivity, and viscosity are computed from established mixture correlations using the base-fluid and nanoparticle properties. This project uses the second method — accurate for thermal performance and far more efficient, which is the standard industrial choice for this type of study. The geometry is built in Design Modeler and meshed in ANSYS Meshing as an unstructured mesh wrapping around the tube bundle and helical-fin geometry, with the Al₂O₃–water nanofluid properties assigned from the mixture correlations.AnalysisThe results provide contours of temperature, velocity, and pressure through the exchanger. The temperature field maps the heat transfer along the shell side clearly, and the results confirm the design intent — both the nanofluid and the helical fins enhance heat transfer compared with a plain fluid and a finless shell, by raising conductivity and lengthening the shell-side flow path respectively. By the end of this project, you'll be able to model a nanofluid efficiently via the single-phase property-correlation method, set up a finned shell-and-tube exchanger, and evaluate heat-transfer enhancement from the temperature, velocity, and pressure fields.

Twisted Tape Inserts and Vortex Generators in Heat Exchanger — ANSYS Fluent CFD SimulationDescriptionThis project investigates heat-transfer enhancement in a tubular heat exchanger using CFD, with nanofluid flow as the central modeling theme. The working medium in the inner tube is a hot alumina (Al₂O₃) nanofluid — a base liquid carrying suspended nanoparticles that raise its effective thermal conductivity and alter its flow and heat-transfer behavior relative to a conventional fluid. Treating this medium correctly is the core of the study, and it is combined with two passive enhancement devices — twisted-tape inserts and vortex generators — to examine how geometry and nanofluid properties together govern thermal performance. Enhancing heat transfer in tubular exchangers matters across many industrial processes, where higher thermal efficiency translates directly into energy and cost savings. Within the Nanofluid: Beginner CFD Training Package, this project combines nanofluid with passive turbulence-promoting geometry, building on the finned heat-exchanger case toward more complex enhancement techniques.MethodologyThe configuration has two sections: an inner passage carrying the hot alumina nanofluid and an outer passage carrying ambient air. As the nanofluid flows through the inner tube while the cooler air passes through the outer section, heat is transferred from the nanofluid to the air, and the simulation captures this cooling process and its effect on overall efficiency — with the specific aim of assessing how the twisted-tape inserts and vortex generators reshape the flow patterns, heat-transfer characteristics, and pressure drop. The geometry was created in ANSYS Design Modeler and meshed in ANSYS Meshing with 4,427,809 elements. The simulation uses a pressure-based solver, appropriate for the incompressible flow typical of heat-exchanger applications, with a steady-state approach representing continuous operation under constant flow conditions. The RNG k-ε turbulence model is applied to capture the complex swirling and recirculating flow created by the inserts, and the energy equation is enabled to resolve the temperature field and heat transfer throughout the system.AnalysisThe results give a detailed picture of the coupled flow and thermal behavior. The pressure field shows high pressure near the vortex generators and low pressure in the core flow, ranging from about −544.64 Pa to 1960.45 Pa, with an area-weighted average static pressure of 1953.92 Pa at the gas inlet and 206.98 Pa at the nanofluid inlet and both outlets at atmospheric pressure. The temperature field clearly shows the cooling of the nanofluid as it traverses the tube, falling from 353.15 K at the inlet to 352.50 K at the outlet, while the air rises from 298.15 K to 323.31 K as it absorbs the transferred heat. The velocity pathlines and contours reveal the complex flow induced by the geometry: the flow accelerates through the twisted-tape and vortex-generator regions, reaching velocities up to 0.5 m/s, and the twisted tape imposes a swirling motion that intensifies mixing and heat transfer. The turbulent kinetic energy peaks near the vortex generators and in their wakes, reaching up to 72.69 m²/s², driving the enhanced mixing in those regions. Taken together, the results demonstrate the strong interplay between fluid flow and heat transfer: the inserts and vortex generators create regions of high velocity and turbulence that directly enhance the cooling of the nanofluid. By the end of this project, you'll be able to represent a nanofluid working medium combined with passive turbulence-promoting geometry, apply the RNG k-ε model to capture insert-induced swirl, and evaluate thermal performance from the temperature, velocity, pressure, and turbulence fields.

Heat Exchanger with Baffle Cut and Mixture Nanofluid — ANSYS Fluent CFD SimulationDescriptionThis project presents a CFD investigation of the combined effects of baffle configuration and nanofluid application on shell-and-tube heat exchanger performance. The simulation examines a shell-and-tube exchanger incorporating two heat-transfer-enhancement techniques at once: strategic baffle placement and an Al₂O₃–water nanofluid as the working medium. The nanofluid improves thermal performance by raising the effective thermal conductivity without a significant viscosity penalty, while the baffles create beneficial flow patterns and extend the shell-side flow path — together achieving superior heat transfer while maintaining acceptable hydraulic performance. Within the Nanofluid: Beginner CFD Training Package, this project combines the mixture nanofluid model with baffle-cut geometry, building on the earlier heat-exchanger cases toward more advanced enhancement strategies.MethodologyThe heat exchanger has a shell of 1 m diameter and 4.5 m length, carrying the Al₂O₃–water nanofluid as the cold shell-side stream, with water as the hot tube-side stream through 0.15 m diameter tubes of 3 m active length. Four baffles of 0.7 m length are arranged on the shell side, with 0.15 m shell-side and 0.3 m tube-side connection nozzles. The domain — shell-side flow path with baffles, tube-side flow path, and solid tube walls — is meshed in ANSYS Meshing with 450,980 elements, with fluid–solid interfaces defined for conjugate heat transfer. The nanofluid is modeled with the Mixture multiphase model, with water as the continuous phase and Al₂O₃ particles as the dispersed phase, capturing interphase drag, particle distribution, and thermal effects. The Al₂O₃ nanoparticles have a thermal conductivity of 40 W/m·K and a density of 3970 kg/m³, with effective properties calculated from mixture theory. A pressure-based coupled solver is used with second-order discretization, the k-ε turbulence model with standard wall functions, and a steady-state solution.AnalysisThe results are visualized through temperature contours that reveal the thermal gradients and quantify the heat-transfer enhancement over conventional fluids, isolating the contribution of the nanofluid's raised thermal conductivity. Streamline analysis shows the complex flow patterns induced by the baffles, identifying recirculation zones that promote mixing and the flow acceleration in the baffle-restricted areas. Together these clarify how the baffles and nanofluid enhance heat transfer synergistically — beyond what either could achieve alone. From these results you can draw design guidance on optimal baffle placement with nanofluids and on balancing thermal enhancement against pumping power. By the end of this project, you'll be able to set up a shell-and-tube exchanger with baffles, model a nanofluid with the Mixture multiphase model and conjugate heat transfer, and evaluate the combined heat-transfer enhancement from the temperature and flow fields.

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

Nanofluid Porous Mixer for Enhanced Heat Transfer — ANSYS Fluent CFD SimulationDescriptionThis project investigates the mixing of hot (303 K) and cold (293 K) nanofluid streams, comparing two configurations: one using 28 mixers and another using 54 mixers, each modeled as a porous medium. The aim is to assess how the mixer arrangement influences the blending of the two streams and the resulting heat transfer.The geometries were drawn in SpaceClaim and meshed in ANSYS Meshing. Two geometries are considered: the first has 2 rows of mixers, and the second has 4 rows. Both meshes are unstructured, built with the triangular method, comprising 87,501 cells for case 1 and 83,180 cells for case 2. The domain has two inlets, both with a velocity of 0.1 m/s; the upper inlet is at 293 K and the lower inlet at 303 K.MethodologyA coupled algorithm was used for pressure-velocity coupling, and the Realizable k-epsilon model with standard wall functions was selected as the turbulence model. The nanofluid is treated as a single-phase fluid with modified thermophysical properties — density, viscosity, specific heat, and thermal conductivity — calculated as functions of the nanoparticle volume fraction using the standard nanofluid property correlations. The mixers are represented as aluminum porous media with a permeability of 1.The porosity is defined as the ratio of the void volume (Vv) to the total volume (Vt). Based on the dimensions of the problem, the porosity is 0.937 for the 2-row case and 0.875 for the 4-row case.ConclusionContours and vectors of velocity, static pressure, and temperature were obtained. As the figures show, the velocity contour is more uniform in the 2-row case. This can be attributed to the smaller number of mixer blocks and, consequently, the smaller variation in velocity gradient caused by the flow striking their sharp edges. The maximum and average velocities are higher in the 4-row case, indicating that although the 4-row case has more separation zones, the separations are more substantial in the 2-row case.The pressure readings show more negative values in the 2-row case, which is consistent with Bernoulli's principle. Pressure is more positive at the top of the domain, where the temperature is lower than elsewhere. The temperature contours indicate that the maximum and average temperatures are essentially the same for both cases; however, in the 4-row case the geometry produces a wider range of temperature variation with more gradual changes.Finally, the temperature is plotted along the centerline of each geometry. The diagram shows that in the 4-row case the temperature is higher at the center of the domain, a result of the greater number of separation zones enhancing the local mixing.

DescriptionThis project investigates the flow of a nanofluid through a bumpy channel under the influence of an applied electric field, using ANSYS Fluent. The study centers on the nanofluid itself: it is treated as steady-state and modeled with a single-phase approach, in which the fluid's thermophysical properties — density, viscosity, specific heat, thermal conductivity, and electrical conductivity — are adjusted to reflect the presence of the suspended nanoparticles. This modified-property treatment is the defining feature of nanofluid modeling, allowing the enhanced heat-transfer behavior of the particle-laden fluid to be captured without simulating each particle individually. The applied electric field alters the fluid's flow behavior, which in turn enhances the heat transfer. The surface-averaged temperature of the nanofluid rises from 300 K at the inlet to 301.926 K at the outlet.Geometry & MeshThe fluid domain was created in Design Modeler, and the mesh was generated in ANSYS Meshing. The mesh is unstructured, with a total of 17,640 elements.MethodologyThe simulation uses a pressure-based solver under steady-state conditions, with gravitational effects neglected. The energy equation is active, and turbulence is modeled using the realizable k-epsilon model with standard wall functions.The working 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, a turbulence intensity of 5%, a 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 with second-order schemes, momentum with second-order upwind, and the turbulent kinetic energy and dissipation rate with first-order upwind. Hybrid initialization is used to start the solution.ConclusionWith the electric field applied, the average outlet temperature of the nanofluid reaches 301.926 K, compared with 300 K at the inlet, corresponding to a heat flux of 72,474.1 W. Without the electric field, the outlet temperature is slightly lower at 301.92 K.Comparing the two cases highlights the influence 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. The result demonstrates how coupling an electric field with a modified-property nanofluid model can be used to enhance convective heat transfer — a promising strategy for thermal-management applications where nanofluids are employed as high-performance working fluids.

DescriptionNanofluids sit at the center of nano-fluid flow modeling, where suspending nanoscale metal or alloy particles in a base fluid enhances thermal conductivity and heat transfer performance. This CFD study uses ANSYS Fluent to simulate the effect of a magnetic field on an iron oxide (Fe₃O₄) nanofluid flowing through a two-dimensional channel, examining how magnetohydrodynamic (MHD) effects influence flow behavior and heat transfer in nanofluid systems.MethodologyThe two-dimensional channel geometry, exploiting the symmetry of the problem, is built in Design Modeler with a length of 0.49 m and a width of 0.01 m, featuring an inlet on the left, an outlet on the right, a central axis as the lower boundary, and a fluid-solid interface adjacent to the outer wall. The domain is discretized in ANSYS Meshing using a structured grid of 9,282 elements. The nanofluid is modeled with 2% Fe₃O₄ nanoparticles by volume, assigned a density of 1081.158 kg/m³, specific heat capacity of 3841 J/kg·K, thermal conductivity of 0.640835 W/m·K, and viscosity of 0.001055 kg/m·s. The magnetic field is introduced through the magnetic induction method, applying a constant magnetic flux of 1 tesla along the y-axis, corresponding to the channel's radial direction. An insulation condition is set on the outer wall to prevent electric current flow, while a coupling condition governs current transmission across the fluid-solid interface at the inner wall. The nanofluid enters at 0.0837 m/s and 300 K, exits at atmospheric pressure, and the outer wall is held at a constant 320 K. The laminar flow model and energy equation are enabled to resolve the velocity field and temperature distribution.Results AnalysisPost-processing yields two-dimensional contours of pressure, velocity, temperature, and magnetic field components in both horizontal and vertical directions, along with a profile of the perpendicular magnetic field variation along the channel's central axis. The results demonstrate how the applied magnetic field, combined with the thermal boundary condition, influences nanofluid flow behavior and heat transfer performance within the channel.