Lime Kiln Combustion CFD Simulation by ANSYS Fluent, TutorialDescriptionThis project simulates the combustion process of methane gas within a vertical lime kiln using ANSYS Fluent. The 3D geometry was designed in Design Modeler as a semi-modeled vertical furnace, leveraging the structure's symmetry to reduce computational cost. The model includes four fuel inlets positioned around the middle of the kiln's side surface, a fifth fuel inlet at the furnace center, a primary air inlet at the center of the kiln, a secondary air inlet in the furnace's lower section, an outlet for reaction products in the lower section, and a dedicated outlet for gas discharge at the top of the furnace. The domain was meshed in ANSYS Meshing, totaling 2,219,550 elements.MethodologyA vertical lime kiln consists of two main functional zones: the combustion zone and the preheating zone. Fuel and air enter through the middle of the kiln and undergo a combustion reaction, releasing substantial heat and raising the internal temperature. Separately, calcium carbonate (limestone) is introduced from the upper section of the kiln. As it absorbs heat generated by the combustion process, it undergoes a distinct thermal decomposition reaction, releasing carbon dioxide and producing calcium oxide (quicklime). This project focuses specifically on modeling the combustion reaction itself within the kiln, rather than the limestone decomposition process.The combustion reaction models a chemical interaction between air and methane, represented using the Species Transport model with its volumetric reaction sub-model. Flow entering through the four side inlets and the central inlet consists of 0.9 methane (CH₄) and 0.1 nitrogen (N₂), entering at 1.357 m/s and 300 K, alongside a simultaneous airflow entering from the central region.The model includes two outlets: reaction products exit through the bottom outlet at atmospheric pressure, while excess gases are drawn out through a separate suction-fan-driven outlet. Porous media was incorporated within the kiln, modeled as aluminum with a porosity coefficient of 0.3, an inertial resistance of 907.4 1/m, and a viscous resistance of 1,100,000 1/m². The standard k-epsilon turbulence model and the energy equation were used to solve the turbulent flow field and capture temperature variation throughout the domain.ConclusionResults include 2D and 3D contours of pressure, temperature, velocity, and mass fractions for O₂, CH₄, H₂O, CO₂, N₂, and CaCO₃. The results confirm that reaction products — including carbon dioxide and water vapor — form as a result of the combustion reaction between methane fuel and air, releasing significant heat in the process. This generated heat is precisely what drives the subsequent dissociation of calcium carbonate into quicklime, linking the combustion process directly to the kiln's primary industrial function.

Premixed Combustion in a Porous Zone, CFD Simulation ANSYS Fluent TrainingDescriptionThis project simulates premixed combustion within a porous zone using ANSYS Fluent, examining how the presence of porous media affects combustion behavior. Porous media combustion, often implemented through matrix-stabilized burners, works by allowing the flame to propagate through the solid matrix of a porous material rather than in free space. The solid matrix absorbs and redistributes heat through conduction and radiation, which tends to broaden the reaction zone, lower peak flame temperatures, and improve flame stability compared to conventional open-flame combustion. This approach is widely used in industrial burners and heating applications where more uniform heat distribution, reduced thermal peaks, and extended operational stability are desired — making the comparison between porous and non-porous combustion a valuable way to quantify these effects. The 3D geometry was designed in Design Modeler, consisting of two sections: a lower preheating region and an upper stable-burn region. The domain was meshed in ANSYS Meshing using a structured grid totaling 8,700 cells.MethodologyThis simulation models a simple premixed combustion case within a porous zone, examining how the porous structure influences combustion temperature and helps stabilize the flame — configured as a matrix-stabilized burner. Results were compared against an equivalent premixed combustion case without porosity to isolate the effect of the porous medium. The Species Transport model was used to represent the combustion process, with the incoming mixture flow set at a methane mass fraction of 0.23 and an oxygen mass fraction of 0.77. Combustion was initiated using the ignition spark sub-model. Gravitational effects were included at -9.81 m/s² along the y-axis, and turbulence was resolved using the SST k-omega model.ConclusionResults include 3D velocity fields, air and water volume fraction contours, and simulation animation. The findings show that the porous zone reduces static temperature within the combustion region and promotes a more uniform, stable combustion process compared to an equivalent case without porosity — consistent with the general behavior expected of matrix-stabilized porous burners, where the solid matrix's heat redistribution moderates and stabilizes the reaction zone.

ACSC Performance with Diffuser Orifice Plate, Paper Numerical Validation, ANSYS Fluent CFD Simulation TutorialDescriptionThis project simulates an air-cooled steam condenser (ACSC) system within a 600 MW power plant, based on the reference article "Effects of a diffuser orifice plate on the performance of air-cooled steam condenser," with results compared and validated against the paper's published data using ANSYS Fluent.ACSC systems are designed primarily to prevent energy waste in power plants — they condense hot steam exiting the turbine and transfer the resulting water from the distillation process back to the steam turbine's pump section. The power plant modeled in this study consists of seven rows of ACSC systems, with this simulation focusing specifically on the fourth row. Each row contains eight fans, with each fan positioned beneath two diagonally oriented porous plates. During operation, hot, low-pressure steam exiting the turbine passes through a series of pipes and is transferred into the interior space bounded by the diagonal plates installed on either side of each pipe.The 3D geometry was built using SolidWorks and Design Modeler, representing the cooling system's eight rows of hot steam pipes, the diagonal porous plates flanking each pipe, and the eight fans mounted on a single platform. The domain was meshed in ANSYS Meshing using a hybrid mesh totaling 2,668,772 elements.MethodologySince the core objective involves the distillation process, a constant steam saturation temperature of 319.75 K was defined, reflecting that condensation occurs at saturation temperature under constant pressure. Each of the eight fans was modeled using the fan boundary condition, which requires specifying a pressure jump to represent the pressure difference generated across the fan along its defined direction. This pressure jump was defined through a polynomial function relating it to the axial velocity passing through the fan surface, imported directly into the fan boundary condition setup.ConclusionResults include 3D contours of velocity, pressure, and temperature. The temperature contour confirms that ambient air absorbs heat from the steam pipes, driving the condensation process as intended.A graph of volumetric effectiveness versus fan number was also extracted and validated against Figure 5-c of the reference article. The study's central objective was to examine how ambient airflow velocity affects the system's fan performance in terms of volumetric transfer from the cooling airflow. To quantify this, a dimensionless parameter — volumetric effectiveness — was defined as the ratio between the volumetric flow rate delivered by the fans in the numerical solution and the ideal volumetric flow rate of 428 m³/s. Comparing this result against the reference paper's data showed acceptable solution accuracy with low error, confirming the simulation faithfully reproduces the reference system's performance behavior.

Multiphase Flow in Porous Medium, Filter Cake Formation, ANSYS Fluent CFD Simulation TrainingDescriptionThis project simulates multiphase flow through a porous medium using ANSYS Fluent. The model consists of two regions: an upper column section containing water-soluble particles suspended in water, and a lower section containing the porous medium itself. The initial mixture carries a particle volume fraction of 0.185. As water flows in from the top of the column, it applies pressure to the mixture and drives it through the pores of the porous medium below — separating the soluble particles from the water flow in the process.The 2D geometry was designed in Design Modeler as a vertical column measuring 0.08 m in height and 0.0125 m in width, divided into two regions with the porous medium occupying the lower section. Given the model's symmetrical structure, only half the geometry was modeled, with a symmetry boundary condition applied. The domain was meshed in ANSYS Meshing using a structured grid totaling 572,852 elements.MethodologyWater enters from the top of the vertical column at a relative pressure of 100,000 Pa and a temperature of 288.15 K, flowing downward into the porous medium at the column's base. This porous medium is modeled as aluminum, with a porosity coefficient of 0.6 (the ratio of void/fluid space to total volume).Since this problem involves two mixed phases, a multiphase model was required — specifically the Eulerian multiphase model, the most comprehensive multiphase approach available, capable of solving separate momentum and energy equations for each phase individually. This model is well-suited to a wide range of multiphase phenomena, including bubble flows, droplet flows, vertical risers, cyclones, fluidized beds, bubble columns, slurry flows, sedimentation, and particle suspension — the filtration process modeled here falls within this same category.The primary phase was defined as liquid water, with the secondary phase representing the water-soluble particles (sludge), defined with a density of 2400 kg/m³, specific heat capacity of 4180 J/kg·K, thermal conductivity of 0.0454 W/m·K, and a viscosity following a power-law model. The simulation was run as transient.ConclusionResults include 2D contours of mixture pressure, along with velocity, temperature, and volume fraction for both the primary (water) and secondary (particle) phases. As the water flow moves downward through the column toward the porous medium, the results show that the water-soluble particles are unable to pass through the pores, while the water itself passes through freely.This selective separation — water passing through while particles are retained — confirms that the porous medium successfully filters the soluble particles out of the flow, producing the characteristic filter cake buildup at the medium's surface as particles accumulate and are progressively separated from the water stream.

Seed Drying Via Hydraulic Mechanism, ANSYS FluentDescriptionThis project simulates seed drying through a hydraulic mechanism process using ANSYS Fluent. Drying refers to the removal of moisture from grain, a process critical for reducing seed moisture content to a safe level that preserves viability and stability during storage — without adequate drying, seeds risk rapid spoilage from mold growth, self-heating, and increased microbial activity.The geometry consists of a simple semi-cylindrical chamber, with a set of spherical shapes positioned inside representing wet seeds. Hot airflow enters from the bottom of the chamber at 303.15 K and 0.15 m/s, moving upward and exiting through the top. As this hot air stream moves through the chamber, it carries moisture away from the seeds — importantly, this occurs through moisture transmission from the seed region to the surroundings, not evaporation, which is what distinguishes this as a hydraulic drying mechanism rather than an evaporative one. For comparison, evaporation-based drying (using the Discrete Phase Model to track individual grain particles) is covered separately in the related "Grain Drying Device" and "Rice Dryer" projects.The 3D geometry was designed in Design Modeler, representing the semi-cylindrical chamber interior with the spherical seed particles positioned at its center. The domain was meshed in ANSYS Meshing using an unstructured grid totaling 6,286,496 elements.MethodologySince the computational domain contains a combination of air and H₂O, the Species Transport model was used to capture this mixture's behavior. The spherical seeds themselves were modeled as porous media, with moisture assumed to have penetrated the internal cavities of each seed, defined with a porosity coefficient of 0.418. The seed zone was initialized as wet, carrying the initial moisture content that the simulation tracks as it dries.ConclusionResults include 2D and 3D contours of temperature, pressure, and velocity throughout the chamber. The results show that moisture (H₂O) content within the seeds progressively decreases as the hot air stream moves upward through the chamber — confirming that the hydraulic drying mechanism successfully transports moisture away from the porous seed particles and out of the domain via the rising airflow.

Evaporation-Condensation Process in a Porous Heat ExchangerDescriptionThis study investigates the performance of a porous heat exchanger using ANSYS Fluent, focusing on the evaporation-condensation process. The study aims to analyze the complex multiphase flow, heat transfer, and phase change phenomena occurring within the heat exchanger, offering valuable insight into its operation and efficiency.The heat exchanger geometry, consisting of a series of circular pipes within a rectangular domain, was built in Design Modeler and meshed in ANSYS Meshing, producing a high-resolution grid of 8,777,268 elements to accurately capture the flow and heat transfer phenomena involved.MethodologyThe simulation used a pressure-based solver under transient conditions, enabling time-dependent analysis of the flow and heat transfer processes. The Volume of Fluid (VOF) multiphase model was employed with implicit formulation to capture the interaction between water and vapor phases, with the evaporation-condensation mechanism enabled to simulate the resulting phase change.Turbulence was resolved using the standard k-epsilon model with standard wall functions, providing a robust approach for capturing the complex flow patterns forming around the pipes, with the energy equation solved to account for heat transfer throughout the domain. The domain itself was modeled as a porous medium, representing the heat exchanger's complex internal structure and enabling a more realistic representation of flow resistance and heat transfer behavior within it.ConclusionThe mass transfer rate contour reveals significant activity concentrated around the pipes, marking the regions where phase change occurs — red areas indicate evaporation, while blue areas indicate condensation, consistent with the expected behavior of a heat exchanger where the pipes serve as condensing surfaces for incoming vapor.The static temperature contour shows a clear gradient across the exchanger, with incoming fluid cooling as it flows across the pipes and reaching its lowest temperatures near the pipe surfaces — the temperature distribution that drives condensation and demonstrates the exchanger's effectiveness at transferring thermal energy. Vapor volume fraction plots further reveal high vapor concentration in the bulk flow region and lower concentration near the pipes, directly marking where condensation occurs, while the corresponding water volume fraction contour shows liquid accumulating near the pipe surfaces — particularly below them, consistent with gravity-driven condensate collection.Velocity magnitude contours show higher flow velocities in the spaces between pipes and lower velocities near the pipe surfaces and in wake regions, a pattern that shapes both heat transfer intensity and condensate accumulation throughout the exchanger. Together, these results confirm the CFD simulation successfully captures the intricate interplay between fluid flow, heat transfer, and phase change within the exchanger, offering valuable insight for optimizing heat transfer efficiency and condensate management in future porous heat exchanger designs.

Non-Equilibrium Porous Aluminum Foam Heat Sink, Paper Numerical Validation, CFD Simulation by ANSYS FluentDescriptionThis project simulates inlet airflow into a vertical cylindrical chamber containing an aluminum fin connected to a heat source, with the domain modeled as a porous material — a non-equilibrium porous aluminum foam heat sink, simulated using ANSYS Fluent. Results are compared and validated against the reference article "Heat transfer characteristics of aluminum-foam heat sinks with a solid aluminum core."The 2D geometry, modeled axisymmetrically due to its symmetrical structure, was designed in Design Modeler, consisting of three main regions: the incoming open airflow space, the porous aluminum zone, and a solid aluminum fin. The domain was meshed in ANSYS Meshing using a structured grid totaling 87,000 elements.MethodologyAirflow enters the chamber vertically downward from the upper section and exits horizontally through the side sections at the bottom, assumed to be fully developed given the cylinder's relatively tall geometry. Inlet air enters at 300 K, with velocity varied across the study since the Reynolds number itself is a variable parameter, ranging between 29 and 89 — placing the flow within the laminar regime throughout.A small cylindrical aluminum fin sits at the bottom of the cylinder, surrounded by a porous aluminum foam medium occupying the space between the inner and outer cylinders. This porous zone was defined with a viscous resistance (inverse permeability) of 58,888,270 1/m², an inertial resistance of 1000.794 1/m, and a porosity coefficient of 0.87 — the ratio of fluid space to total volume — all derived from the reference paper's formulas.Since the porous zone is not in thermal equilibrium, the non-equilibrium thermal option was enabled, requiring a heat source term defined via the relation Hsf·Asf·(Ts−Tf). Based on the article's formulas, the interfacial area density was set to 2864.27 1/m and the interfacial heat transfer coefficient to 54.78671 W/m²·K. A copper heat source at the chamber's base, which experimentally produces surface temperatures between 320 K and 360 K, was modeled here at a constant surface temperature of 330 K.Three distinct interface boundaries were defined: between the porous medium and the aluminum fin, between the air and the aluminum fin, and between the porous medium and the open air. In the first two cases, since aluminum solid borders one side, only heat transfer occurs across the interface with no mass transfer; in the third case, both heat and mass transfer occur simultaneously.ConclusionThis study aims to evaluate the Nusselt number — representing the ratio of convective to conductive heat transfer — within the porous region of the chamber across varying Reynolds numbers. The Nusselt number was calculated at the contact surface between the solid fin and the porous material at the chamber's base, across the full range of tested Reynolds numbers, and compared directly against Figure 3 of the reference article to confirm the simulation's validity and accuracy relative to the published results.

Microchannel Heat Sink Optimization: DOE Applying LHSD MethodDescriptionThis project presents an optimization process for improving the thermal performance of a microchannel heat sink using Design of Experiment (DOE) techniques in ANSYS. Microchannel heat sinks are well-suited for dissipating the substantial heat generated by high-power electronic devices, valued for their high heat transfer coefficients and large specific surface area.The heat sink modeled here consists of a solid zone body containing a cooling fluid channel filled with a porous medium. While microchannel heat sinks typically contain several parallel channel rows, this project models only a single representative section for construction simplicity.MethodologyThe 3D microchannel heat sink geometry was designed in Design Modeler and meshed in ANSYS Meshing. Three input parameters were defined for optimization: two geometric factors — the length and height of the cooling channel's rectangular cross-section — and one operating factor, the porosity of the channel's porous medium. Maximum microchannel surface temperature was defined as the target output parameter.The Design Exploration tool drove the optimization process. Design points were first generated using Latin Hypercube Sampling Design (LHSD), producing 10 design points based on the defined minimum and maximum ranges for all three input parameters. The Response Surface Methodology (RSM), using Genetic Aggregation, was then applied to estimate output parameter values across the full design space.ConclusionThe resulting 2D and 3D response surface plots reveal the combined effect of all three input parameters on maximum temperature. Increasing channel length and height both reduce maximum surface temperature, since larger cross-sectional dimensions increase the incoming fluid's flow rate, enhancing heat transfer and improving overall cooling performance. Increasing the porous medium's porosity similarly reduces maximum temperature, since a more porous medium enhances the heat transfer process — though this porosity effect proved smaller in magnitude than the dimensional (length and height) parameters.Local sensitivity plots further quantify how strongly each input parameter influences the output, while goodness-of-fit plots confirm the accuracy of the RSM-estimated results relative to the actual values obtained at the sampled design points — together validating this DOE-based approach as an effective method for optimizing microchannel heat sink thermal performance.

DPM-Drag Macro, UDF, Drag between Particles and Fluid CFD SimulationDescriptionThis project simulates a chamber designed for spraying discrete particles into a continuous fluid medium, using ANSYS Fluent, demonstrating how User-Defined Functions can accurately capture the drag forces acting between particles and the surrounding fluid.The 3D geometry was designed in Design Modeler and meshed in ANSYS Meshing using an unstructured grid totaling 127,100 cells.MethodologyThis simulation uses the Discrete Phase Model (DPM) to represent particle spraying and its interaction with the surrounding fluid, with a custom implementation of the drag force calculation at the core of the setup. Standard drag laws available in Fluent — spherical, Stokes-Cunningham, non-spherical, and high-Mach-number — provide a baseline, but this project instead implements a custom drag law using the DEFINE_DPM_DRAG macro, defining Reynolds-number-dependent drag force relations directly.Building and applying this UDF involves writing the custom drag force equation, implementing it through the DEFINE_DPM_DRAG macro, compiling and loading the resulting UDF into ANSYS Fluent, and configuring the DPM model to reference this custom drag law in place of a standard built-in option.ConclusionResults include a comparison of penetration length across the different drag models, particle tracking visualization for the UDF-based simulation, and a time-dependent analysis of particle trajectories over a 4-second window. This comparison confirms that the custom DPM-drag UDF produces meaningfully different — and more tailored — particle dispersion behavior relative to the standard built-in drag laws, validating this approach as a flexible way to improve prediction accuracy in spray-based particle-fluid systems.This technique offers particular value in applications where standard drag laws fall short of capturing specific particle or flow characteristics — relevant to industrial coating processes and pharmaceutical aerosol delivery systems — and provides a foundation for extending into more advanced scenarios, such as multiphase flows with custom particle interactions, UDF-enhanced spray nozzle optimization, or the integration of thermal effects into particle-fluid drag calculations.

Hyperthermia Therapy of a Cancer Tissue, ANSYS Fluent CFD Simulation TrainingDescriptionCommon cancer treatment methods include surgery, radiotherapy, and chemotherapy, each carrying disadvantages such as aggressiveness, irreversibility, and significant side effects. Hyperthermia therapy offers an alternative approach: applying warming to prevent oxygen and nutrients from reaching unhealthy tissue, causing the proteins within that tissue to change nature in a way that surrounds cancer cells and makes them identifiable to the immune system.This project examines blood flow through capillaries passing through tissue containing cancerous tumors, simulating hyperthermia therapy using ANSYS Fluent. The model represents a spherical region of healthy body tissue through which blood flows slowly, containing several veins arranged uniformly along the x-axis for simplification (real vein structures within body tissue more closely resemble a branching, bush-like pattern). Four spherical cancerous tumors of varying diameters are positioned at the center of this tissue, on the surface of the modeled veins. While healthy and cancerous tissue actually differ in their thermophysical properties, this simulation simplifies the problem by treating both with the same properties.Each cancerous tumor acts as a heat source, releasing thermal energy per unit volume to drive heat transfer and substantially increase local blood flow — the central mechanism this study investigates in terms of how blood vessels and surrounding tissue respond to hyperthermia treatment.Incoming blood flow through the capillary was set to 0.08 m/s at 310.15 K, while blood flow surrounding the capillary was set to 0.000035 m/s, also at 310.15 K. The geometry was designed in Design Modeler and meshed in ANSYS Meshing using an unstructured grid totaling 717,087 cells.MethodologyThe energy equation was activated to capture the heat transfer effects central to hyperthermia therapy. The tissue itself was modeled as a porous medium, since blood flows through the capillaries via the empty cavities within the tissue structure, with a porosity coefficient of 0.05 defined as the ratio of void space to total tissue volume.Each of the four spherical cancer tissues was heated via simulated ultrasonic waves over a 10-second period, implemented using the Source Term option. A custom UDF defined the heat generation rate per unit volume within each sphere: approximately 10,000,000 W/m³ at each sphere's center, decreasing progressively at greater distances from that center point. The simulation was run as unsteady (transient) to capture this time-dependent heating process.ConclusionResults include volume rendering (3D contours) alongside standard contours of velocity, pressure, and temperature throughout the domain. The temperature distribution surrounding the cancerous tumors — the central focus and primary challenge of this simulation — is clearly captured in the resulting figures, illustrating how the applied heat source elevates local tissue temperature and drives the increased blood flow response central to the hyperthermia treatment mechanism.