Multiphase CFD Simulation of Nanofluid inside a Minichannel, ANSYS FluentDescriptionThis project simulates heat transfer inside a minichannel using Al₂O₃ nanoparticles suspended in water, employing the Mixture multiphase model to capture the two-phase behavior — with water acting as the carrier phase and Al₂O₃ nanoparticles as the dispersed secondary phase.The geometry consists of 9 parallel minichannels, each 30 mm long with a 3×1 mm cross-sectional area, designed in SpaceClaim and meshed in ANSYS Meshing using hexahedral elements, totaling 2,850,000 cells.MethodologyGiven the low Reynolds number characteristic of minichannel flow, the flow regime was treated as laminar. A heat flux of 53 kW/m² was applied at the bottom surface, with all other walls treated as adiabatic. The Mixture multiphase model captures the nanofluid behavior throughout, with the mixture entering the channels at 30°C, functioning as the coolant for the applied heat load.ConclusionPost-processed contours reveal the resulting thermal and flow behavior throughout the channel array, with the nanoparticle volume fraction set to 1%. The nanoparticles serve to enhance the working fluid's thermal conductivity, thereby improving overall heat transfer performance compared to the base fluid alone.The mixture exits the channels at 33.6°C, having entered at 30°C — corresponding to a heat gain of 82.2 J. From this, the convective heat transfer coefficient was calculated as:h = Q / [A × (Tw − Tb)] = 82.2 / [0.00216 × 4.94] = 7703 W/m²·KThis result confirms the nanofluid's effectiveness in enhancing convective heat transfer within the compact minichannel geometry, demonstrating the practical benefit of nanoparticle addition for high-heat-flux cooling applications.

Nanofluid Flow in a Wave Sine Channel, Heat Transfer Analysis, ANSYS Fluent CFD Simulation TrainingDescriptionThis project simulates the wave motion of a nanofluid within a sinusoidal channel using ANSYS Fluent, with the nanofluid defined as Al₂O₃-water containing nanoparticles at a 1% volume fraction. The thermophysical properties of this nanofluid mixture were derived from standard nanofluid property equations, using the base thermophysical properties of both water and the Al₂O₃ nanoparticles as inputs.The nanofluid enters the channel at 300 K. Due to the channel's wavy geometry, the horizontal velocity of the incoming flow varies as a function of vertical position, defined through a custom velocity profile implemented as a UDF. Thermally, the channel's lower wall was assigned a constant heat flux of 320 W/m², while the upper wall was held at a constant temperature of 320 K.The 2D geometry was designed in Design Modeler, representing a sinusoidal channel 4 m long and 1 m wide, with a wavelength of 2 m and peak-to-trough height of 0.4 m. The domain was meshed in ANSYS Meshing using a structured grid totaling 21,300 elements.MethodologySeveral assumptions were applied to the simulation: a pressure-based solver was used, the simulation was run under steady-state conditions, and gravitational effects were excluded.Key simulation settings included:Viscous model: Laminar, with the energy equation enabledBoundary conditions: Velocity inlet defined via the UDF-based velocity profile at 300 K; pressure outlet at 0 Pa gauge pressure; upper wall held at a constant 320 K; lower wall assigned a constant heat flux of 320 W/m², both stationarySolution methods: Coupled pressure-velocity coupling, second-order pressure discretization, and second-order upwind schemes for both momentum and energyInitialization: Standard method, with 0 Pa gauge pressure, 0.0015 m/s x-velocity, 0 m/s y-velocity, and 300 K temperatureConclusionResults include 2D contours of temperature, pressure, and velocity throughout the channel, along with plots tracking pressure and velocity variation along a hypothetical horizontal line through the channel's midline. These results characterize how the sinusoidal wall geometry, combined with the nanofluid's enhanced thermal properties, shapes the resulting flow acceleration and heat transfer pattern as the nanofluid moves through the wavy channel structure.

Forced Convection of a Non-Newtonian Nanofluid in a Tube, Paper Numerical Validation, ANSYS Fluent TrainingDescriptionThis project simulates the forced-convection heat transfer of a non-Newtonian nanofluid flowing through a horizontal tube under constant wall heat flux, using ANSYS Fluent. It reproduces and validates against the reference paper "Modeling of forced convective heat transfer of a non-Newtonian nanofluid in the horizontal tube under constant heat flux with computational fluid dynamics."The defining feature of this case is the non-Newtonian flow model. A Newtonian fluid has a single, constant viscosity, but many real fluids do not — their apparent viscosity changes with the local shear rate. Here the working fluid is water carrying Al₂O₃ nanoparticles together with xanthan: the aluminium-oxide particles make it a nanofluid, while the xanthan makes it non-Newtonian, so its viscosity is no longer constant and cannot be described by Newton's law. Capturing this shear-dependent viscosity is exactly what the non-Newtonian flow model does, and this tube flow is a clean setting to demonstrate it.Rather than treating the nanofluid as a multiphase mixture, it is defined as a single new material with effective thermophysical properties taken from the paper: density 1126.384 kg/m³, specific heat 3700.264 J/kg·K, and thermal conductivity 0.615 W/m·K. Its non-Newtonian viscosity is described with the Herschel-Bulkley model — a yield-stress fluid that only begins to flow once a threshold stress is exceeded, after which it follows a power law. The model parameters are a power-law index of 0.149, a yield stress of 2.92 Pa, and a critical shear rate of 58.4 s⁻¹, all from Table 1 of the paper, at a 4% nanofluid concentration.The 2-D geometry was built in Design Modeler as a horizontal tube 1.2 m long and 0.00475 m in diameter. Because it is symmetric about its centerline, it is modeled as axisymmetric. The domain was meshed in ANSYS Meshing using a structured grid of 40,000 elements.Simulation MethodologyThe simulation uses a pressure-based, steady solver with gravity neglected, a laminar viscous model, and the energy equation enabled. The flow is studied at two Reynolds numbers, 900 and 1600. Because the fluid is non-Newtonian, the inlet velocity for each case is computed from the generalized Reynolds-number definition given in the paper, giving 1.2698 m/s for Re = 900 and 1.7327 m/s for Re = 1600. The nanofluid enters at 295 K, and the tube wall carries a constant heat flux of 8846.4 W/m². Pressure-velocity coupling uses SIMPLE, with second-order discretization for pressure, momentum, and energy.Paper Validation & ResultsValidation follows Figure 3-a of the paper, which plots the convective heat transfer coefficient (h) against Reynolds number at a dimensionless station of x/D = 147 (with D = 0.00475 m). The heat transfer coefficient is evaluated from Equation 9 of the paper using the applied heat flux (8846.4 W/m²) together with the wall temperature (Tw) and the fluid bulk temperature (Tf), extracted at that station from the wall and from a line through the tube.The simulation matches the paper closely at both Reynolds numbers:CasePresent simulationPaperErrorh at Re = 9001676.1 W/m²·K1700 W/m²·K≈ 1.4%h at Re = 16001846.8 W/m²·K1750 W/m²·K≈ 5.5%The agreement is strong on both counts that matter: the error magnitude stays within about 5.5%, and the behavior is reproduced correctly — the heat transfer coefficient rises with Reynolds number, exactly as in the reference. Two-dimensional temperature and velocity contours are also obtained at both Reynolds numbers along the mid-section of the tube.

Al2O3-Water Nanofluid in a Tube with Twisted Tape Inserts, Paper Numerical Validation, CFD Simulation Tutorial by ANSYS FluentDescriptionThis project simulates Al2O3-water nanofluid flow inside a circular tube fitted with twisted tape inserts using ANSYS Fluent, with results compared and validated against the reference article "Study on heat transfer and friction factor characteristics of Al2O3-water through circular tube twisted tape inserts with different thicknesses." The Al2O3-water fluid enters the computational domain at an initial temperature of 300 K and a velocity of 0.716 m/s, corresponding to a Reynolds number of 500, flowing through a multi-staged twisted circular tube whose outer wall is exposed to a heat flux of 5000 W/m², progressively raising the fluid's temperature as it travels through the tube.The geometry was designed in Design Modeler and meshed in ANSYS Meshing using a structured grid totaling 2,146,882 elements.MethodologyNanofluids are compounds consisting of a base fluid infused with fine solid particles at the nanoscale. While a full multiphase model — explicitly resolving both the base fluid and the dispersed solid particles — can capture this behavior, doing so carries a substantial computational cost. This simulation instead uses an alternative, more efficient approach: defining a single new "nanofluid" material within the computational domain, whose effective properties (density, specific heat capacity, thermal conductivity, and viscosity) are calculated using established formulas that combine the base fluid and nanoparticle properties.ConclusionThe simulation results were compared directly against the reference paper using the Nusselt number trend across varying Reynolds numbers, with validation performed specifically at Re = 500. The comparison showed a low error rate, confirming that the current simulation was performed correctly.Pressure and velocity contours further reveal that nanofluid pressure decreases progressively along the tube as it passes through the twisted tape inserts, since these obstacles disrupt the flow and induce pressure loss. At the same time, the nanofluid's temperature rises more substantially than it would in a plain tube — the spiral twisted tape forces the fluid to travel a longer path length, increasing its contact time with the heated outer wall and enhancing overall heat transfer.

Nanofluid Heat Transfer in Double Pipe Heat Exchanger, Paper Numerical Validation, ANSYS Fluent TrainingDescriptionThis project examines heat transfer inside a double pipe heat exchanger fitted with louvered strip inserts, based on the reference article "Heat transfer enhancement of nanofluids in a double pipe heat exchanger with louvered strip inserts." Results are validated against the paper's published data using ANSYS Fluent.A louvered strip runs the length of the inner pipe, angled at 30 degrees with 60 mm spacing between successive strips. A nanofluid flows through the pipe at a Reynolds number of 30,000, which — using the outer tube's diameter as the characteristic length — corresponds to an inlet velocity of 1.537279 m/s, entering at 293 K.The heat exchanger wall breaks into three functional zones: a central section where the outer tube carries a constant heat flux of 200,000 W/m², flanked by insulated entry and exit sections at either end. The louvered strip attached to the inner tube stays thermally insulated throughout. The goal is to determine the Nusselt number along the outer tube wall specifically within this heated central zone.Geometry & MeshBuilt as a 2D model in Design Modeler, the double-pipe geometry sets the inner tube diameter at 0.001 m and the outer tube at 0.0196 m, with an outer tube length of 1.5 m and an inner tube length of 0.5 m. The louvered strips, angled at 30 degrees and spaced 0.06 m apart, run along the inner tube's body. ANSYS Meshing produced a structured grid of 296,880 elements.MethodologyThe simulation runs as steady-state with a pressure-based solver, gravity excluded. Turbulence uses the RNG k-epsilon model with standard wall functions, energy equation on. The inlet is a velocity inlet at 1.537972 m/s and 293 K; the outlet holds 0 Pa gauge pressure. The heated wall carries a fixed 200,000 W/m² flux, while the louvered strip walls stay at zero heat flux. SIMPLE handles pressure-velocity coupling, with second-order (upwind, where applicable) discretization across pressure, momentum, turbulence, and energy. Initialization follows the standard method, matching the inlet conditions.ConclusionThe resulting Nusselt number along the heated outer wall — calculated using the outer tube diameter as the characteristic length and the 293 K bulk fluid temperature as reference — was checked against Figure 6-A of the reference article at Re = 30,000:ReNusselt Number (Paper)Nusselt Number (Present Work)30,00011021041.359The close agreement between the two values confirms the simulation reproduces the paper's reported heat transfer enhancement. Additional 2D contours of pressure, temperature, and velocity, along with 2D pathlines, round out the results.

Heat Sink Cooling Performance Using Water and Nanofluids (TiO₂, SiO₂, Fe₃O₄) in ANSYS FluentDescriptionThis project presents a numerical analysis of heat transfer and fluid flow through a heat sink using ANSYS Fluent, investigating how different working fluids affect overall cooling performance. Four cases were examined: water alone as a single-phase baseline (Case 1), followed by three nanofluid cases using water combined with TiO₂ (Case 2), SiO₂ (Case 3), and Fe₃O₄ (Case 4) nanoparticles. For the nanofluid cases, the Mixture multiphase model was applied, with water defined as the primary phase and each respective nanoparticle type as the secondary phase.The computational domain represents a heat sink structure comprising one fluid zone and two solid zones — the base plate and the fins. The geometry was built in Design Modeler to capture both fluid and solid regions in sufficient detail to resolve the heat transfer process accurately, and the domain was meshed in ANSYS Meshing, producing a high-quality mesh of approximately 1.6 million elements that captures fine geometric detail while keeping computational cost reasonable.MethodologyAll four cases were solved under steady-state conditions using a pressure-based solver, with the SIMPLE algorithm handling pressure-velocity coupling and discretization schemes chosen carefully to minimize numerical diffusion. Case 1 used a single-phase flow model, while Cases 2 through 4 applied the Mixture multiphase model to capture the interaction between the water carrier fluid and each nanoparticle type.Boundary conditions were consistent across all cases: a velocity inlet at 1.96 m/s, a pressure outlet, and no-slip wall conditions with appropriate heat flux or temperature assignments throughout the domain.ConclusionThe resulting temperature and velocity contours reveal clear differences in cooling behavior across the four cases. Case 1 (pure water) established the baseline thermal and flow behavior. Case 2 (water + TiO₂) showed improved heat dissipation relative to pure water, while Case 3 (water + SiO₂) achieved even better cooling performance thanks to SiO₂'s enhanced thermal conductivity. Case 4 (water + Fe₃O₄) showed the most substantial improvement, with considerably lower temperatures near both the fins and the base plate.These trends are reflected clearly in the summarized zone temperatures:ZoneCase 1 (Water)Case 2 (TiO₂)Case 3 (SiO₂)Case 4 (Fe₃O₄)Flow305.92 K306.45 K307.51 K305.08 KSolid352.36 K361.52 K355.15 K319.75 KFin314.73 K326.43 K323.09 K303.27 KBase Plate341.38 K351.30 K345.65 K314.72 KAcross the fin and base plate zones — the regions most critical to effective heat sink performance — the addition of nanoparticles consistently reduced temperatures compared to water alone, with the heat distribution also becoming more uniform. Among the three nanofluids tested, Fe₃O₄-water delivered the strongest cooling performance, followed by SiO₂-water and then TiO₂-water — confirming the potential of magnetite-based nanofluids as particularly effective advanced coolants for electronics and industrial thermal management applications.

PCM Melting Rate Enhancement via Internal Fin and Nanoparticles — CFD Simulation in ANSYS FluentIntroductionThis project simulates the melting behavior of a phase change material (PCM) inside a two-dimensional cavity enhanced with an internal fin and dispersed nanoparticles, based on the methodology presented in a reference study on enhancing PCM melting rate through internal fins and nanoparticles. The simulation investigates how the combined effect of a conductive fin and CuO nanoparticle dispersion within paraffin wax accelerates the melting process compared to a plain PCM cavity.Geometry and MeshThe cavity geometry, with a width of W = 20 mm and filled with paraffin wax, was created in Design Modeler. The domain was meshed in ANSYS Meshing, generating approximately 10,000 structured cells.MethodologyGravitational acceleration was included in the simulation to capture buoyancy-driven natural convection effects during melting. Since several thermophysical properties of the PCM in the reference study were originally defined through user-defined functions, these properties were instead extracted at key reference points and represented using a polynomial linear model. The material properties used correspond to a mixture of paraffin wax and CuO nanoparticles. The left wall and the internal fin were maintained at a constant temperature of 350 K, while the right wall was held at 300 K, with all remaining walls treated as insulated. The case examined corresponds to a fin-to-cavity width ratio (w/W) of 0.5.Results and ConclusionAfter 375 seconds of simulation time, results show that heat is progressively transferred from the left wall toward the right wall due to the imposed temperature gradient, with the PCM undergoing a phase change as local temperatures reach the melting point. By the end of the simulation, 35.65% of the PCM had melted, illustrating the combined influence of the internal fin and nanoparticle enhancement on accelerating the melting process within the cavity.

Parabolic Solar Collector with Nano Fluid, Paper Numerical Validation, ANSYS Fluent TutorialDescriptionThis project examines heat transfer within the tube of a parabolic solar collector carrying water, based on the reference paper "Thermal performance analysis of solar parabolic trough collector using nanofluid as working fluid: A CFD modeling study." Results are validated against the paper's published data using ANSYS Fluent.In a parabolic trough collector, a tube runs along the focal line of a curved reflector, which concentrates incoming solar radiation onto the tube to heat the fluid inside. Here, only the water-carrying pipe itself is modeled — an aluminum tube split into upper and lower wall sections, reflecting the uneven way sunlight strikes each side. Water enters at a Reynolds number of 30,000 and 320 K, which works out to an inlet velocity of 0.5024043 m/s once the fluid's thermophysical properties are factored in.The 3D geometry, built in Design Modeler, takes advantage of the tube's symmetry to model only half a semi-cylindrical section: a thin outer solid wall wrapping a fluid conduit 0.06 m in diameter, 2 m long, with a 0.002 m wall thickness. ANSYS Meshing produced a structured grid of 1,475,000 elements.MethodologyFollowing the reference paper's relationships, the tube wall carries two distinct constant heat fluxes — 750 W/m² on top and 19,500 W/m² on the bottom — capturing the asymmetric solar loading characteristic of a trough collector, where the reflector concentrates far more energy onto the underside of the tube than reaches the top directly.ConclusionThe simulation centers on the Nusselt number, calculated at the fluid-wall interface using the Report command and validated against the paper's reported values. Consistent with how the reference defines its own results, this comparison focuses on the fully developed flow region near the pipe's outlet.Checking the Nusselt number at several distances from the outlet against the paper's Figure 4 (at Re = 30,000) shows the two data sets converging as the flow approaches full development — the closer to the pipe's end, the tighter the match, confirming the simulation's validity in that regime.Additional 2D and 3D contours of pressure, velocity, and temperature round out the results, with the 2D views taken across the model's symmetry plane.

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.

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.