Source Macro, UDF, Momentum Source Term CFD SimulationDescriptionThis project simulates water flow through a channel containing a porous medium using a custom User-Defined Function (UDF) in ANSYS Fluent, demonstrating how UDFs can accurately capture porous media effects without relying on the software's built-in porous zone options. The 3D geometry was designed in Design Modeler and meshed in ANSYS Meshing using a structured grid totaling 256,000 cells.MethodologyThis simulation modifies the momentum equation directly to represent the presence of a porous medium along the flow path, implementing a custom source term specifically within the z-direction momentum equation using the DEFINE_SOURCE macro. The source term itself is formulated as a function of both velocity and position, with its derivative also computed and supplied to the solver to maintain numerical stability throughout the solution process.Building and applying this UDF involves writing the custom source term equation, implementing it through the DEFINE_SOURCE macro, compiling and loading the resulting UDF into ANSYS Fluent, and configuring the flow model to reference this custom source term function in place of a standard porous zone definition.ConclusionResults include 2D and 3D pressure contours, pressure gradient visualizations, and longitudinal pressure change plots along the channel. These results confirm that the custom source term successfully reproduces the expected pressure drop behavior characteristic of flow through a porous medium, validating this UDF-based approach as a flexible alternative to built-in porous modeling options.This technique offers particular value where standard porous zone settings fall short of representing more specialized or custom porous media behavior — relevant to applications ranging from groundwater flow to industrial filtration — and provides a foundation for extending into more advanced scenarios, such as multiphase flow through heterogeneous porous media, coupled heat transfer and chemical reactions within porous flow, or dynamically adaptive source terms representing time-varying porous media behavior.

Capillary Action (Wicking), Water Flows in Porous Media, ANSYS Fluent Simulation TrainingDescriptionThis project simulates water flow in porous media driven by capillary action using ANSYS Fluent. Capillary action is the process by which a liquid moves through a narrow space without external assistance — and sometimes even against opposing forces like gravity — a phenomenon relevant to plant vessel transport, wicking materials, and various porous flow applications.The 3D geometry was designed in Design Modeler, consisting of three sections: a lower region containing resident water, an upper region containing resident air, and a middle vertical pipe defined as a porous zone connecting the two. The domain was meshed in ANSYS Meshing, totaling 178,325 elements.MethodologyThis simulation models simple porous and capillary flow behavior through a vertical, plant-vessel-like pipe. The porous media model is broadly applicable across single-phase and multiphase problems, including flow through packed beds, filter papers, perforated plates, flow distributors, and tube banks — in each case, incorporating an empirically determined flow resistance within a designated "porous" cell zone, functioning essentially as an added momentum sink within the governing momentum equations.The Eulerian multiphase model was used to represent the air and water phases, applying the Brooks-Corey model to capture capillary pressure-saturation behavior within the porous zone. Gravitational effects were included at -9.81 m/s² along the y-axis.ConclusionResults include 3D velocity fields, air and water volume fraction contours, and simulation animation. The results confirm the expected capillary behavior: resident water begins rising upward through the vessel toward its top, driven purely by the capillary effect within the porous zone — despite acting against gravity, illustrating the core physical mechanism this simulation set out to capture.

Reverse Osmosis (RO) CFD Simulation, ANSYS Fluent TutorialDescriptionThis project simulates reverse osmosis using ANSYS Fluent.Reverse osmosis is one of the most widely used technologies in clean-water engineering, where it drives desalination and water-purification systems that turn seawater, brackish water, and contaminated supplies into potable water. Understanding how salt and impurities separate across the membrane is central to designing and improving these systems.Osmosis is a natural phenomenon in which a fluid tends to move from a region of lower concentration to one of higher concentration until the concentration on both sides is balanced. Imagine a semi-permeable membrane placed between pure and impure water. By osmosis, water moves toward the impure side until a pressure difference builds up across the membrane. This difference is called the osmotic pressure. If a pressure equal to the osmotic pressure is applied to the impure side, the fluid movement stops. If the applied pressure exceeds the osmotic pressure, the natural direction of flow reverses.Reverse osmosis desalination systems work on exactly this principle: a pressure beyond the osmotic pressure is applied across the semi-permeable membrane, and as the water passes through, salt and impurities are separated from it.This project is simulated in two parts. The first part looks only at fluid behavior driven by osmotic pressure. A closed chamber is modeled and divided into two sections by a barrier that is removed instantaneously. The left side holds a saltwater solution and the right side holds pure water. The goal is to observe how fluid moves between the two sections of different concentration, which illustrates the concepts of osmosis and osmotic pressure.Building on the first part, the second part studies the reverse osmosis desalination system itself. Here a membrane, modeled as a porous medium, is placed in the middle of the chamber. The water-and-salt mixture enters from the inlet on the left and moves toward the membrane. When the solution reaches the membrane, pure water passes through while the salt (the higher-concentration water) is trapped behind it.The geometry was built in two dimensions in Design Modeler as a simple rectangular chamber with a membrane between its two sections. The domain was meshed in ANSYS Meshing using a structured grid of 44,800 cells.Simulation MethodologyBecause an impure solution is used instead of a single pure fluid, a two-phase flow must be defined, so a multiphase model is used. Of the available options (VOF, Mixture, and Eulerian), the Eulerian model, which is the most complex of the three, is used here. Water is the primary phase and salt is the secondary phase dissolved in it, with a salt concentration of 0.02. Concentration is tracked through the volume fraction, and the solver handles the transport equations for that volume fraction.The membrane between the two sections is modeled as a porous medium, where the porosity (the ratio of empty space to total volume) sets its permeability. The simulation runs in two steps: in the first model the fluid moves naturally with no external forces, while the second model applies a driving boundary condition. Since the aim is to study how the system behaves over time, the solution is transient (time-dependent).Results & ConclusionAfter solving, two-dimensional contours of pressure and of the water and salt volume fractions were obtained. Because the solution is transient, the results are compared at different times to capture the system's behavior, and an animation of the change in dissolved-salt volume fraction was produced. Results were obtained for both simulation cases.In the first case (a closed chamber with no external boundary conditions), the left side initially holds water and salt while the right holds pure water. Over time, fluid moves from the higher-concentration side to the lower-concentration side and continues until both sides reach equilibrium. This movement occurs naturally, without external forces, and correctly reproduces the osmotic behavior of the fluid.In the second case, a porous membrane sits in the middle of the system. The water-and-salt solution is driven toward the membrane at a set velocity and pressure beyond the osmotic pressure, opposite to the natural osmotic direction. The results show pure water passing through the membrane while the dissolved salt is trapped behind it. This continues until a fully concentrated solution builds up behind the membrane and pure water collects beyond it. The pressure results also show the pressure difference across the system increasing over time. Together, these results confirm that the reverse osmosis system works correctly and successfully purifies the water.

DescriptionThis project simulates air gap membrane distillation (AGMD) using ANSYS Fluent. Producing potable water from saline or impure sources is a central goal of clean water engineering, and AGMD is one of the membrane distillation technologies developed specifically for this purpose.Water desalination systems fall into two broad categories: thermal desalination and membrane desalination. In the thermal method, a phase change is used to produce fresh water, whereas in the membrane method, specialized membranes separate the water from its impurities. Membrane distillation (MD) systems combine the two approaches — they rely on both a phase change and a dedicated filter membrane. Several MD configurations exist; the one studied here is Air Gap Membrane Distillation (AGMD).The AGMD system consists of four zones: the feed channel, the membrane layer, the air gap, and the cooling channel. Hot water flows through the feed layer while cold water flows in the opposite direction through the cooling layer. The membrane layer sits next to the feed water, and the air gap is placed between the membrane and the cooling channel. In operation, the water first undergoes surface evaporation, and the resulting pure vapor then condenses on the cold surface. For simplicity, the hot feed water is assumed to have already been converted to steam, so saturated steam flows through the feed channel, ready to condense in the air gap.The geometry was modeled in 2D using Design Modeler, and the model was meshed in ANSYS Meshing using a structured grid of 150,000 cells.MethodologyBecause the system involves the steam turning into water through condensation, together with the presence of an air gap, three phases must be represented, so a multiphase model is required rather than a single fluid. The VOF (Volume of Fluid) model is used, since it cleanly separates the different phases and resolves a distinct interface between them — the best choice for capturing a sharp boundary between the water and vapor phases. Air is defined as the primary phase, with liquid water and water vapor as the secondary phases; the volume fraction of each secondary phase is solved through its transport equation.A phase change occurs between the water and vapor phases, so a mass transfer is defined between them based on the evaporation-condensation mechanism. This mechanism governs the phase change between liquid and vapor, with Lee's equations used to calculate the mass transfer rate; these equations depend on the saturation temperature and the frequency coefficients of evaporation and condensation. In addition, the membrane is represented as a porous medium, with a porosity parameter — the ratio of void volume to total volume — defining its permeability.ConclusionOn completion of the solution, contours of temperature, the phase change rate between water and vapor, and the volume fraction of each of the water and vapor phases were obtained.The results show the temperature dropping on the cold side of the air gap, with the temperature contours clearly revealing the thermal boundary layer. The highest condensation (phase change) rate occurs in the regions where the temperature falls, and the negative sign of the phase change rate indicates the transformation from vapor to liquid. Examining the volume fraction contour of the distilled water reveals a film of liquid forming on the cold plate of the air gap; this freshly produced fresh water then runs to the bottom of the air gap under gravity.Overall, the results confirm that the desalination system operates correctly and that the membrane distillation mechanism performs as intended — demonstrating how AGMD converts hot saline feed into clean, condensed fresh water, and how CFD can be used to evaluate and optimize such clean-water technologies.

DescriptionThis project simulates a Humidification Dehumidification (HDH) desalination system using ANSYS Fluent, a core clean water engineering method for producing fresh water from saline sources. The system operates in two coupled stages: a humidifier (evaporator), where hot salt water is sprayed and evaporates into dry air to produce salt-free humid air, and a dehumidifier (condenser), where that humid air contacts cold tube surfaces and condenses into fresh water. This two-step cycle mirrors real-world HDH desalination units, making it directly relevant to sustainable, low-energy freshwater production.MethodologyThe system is modeled in two separate simulations reflecting its two stages. The 3D geometry is built in DesignModeler and meshed in ANSYS Meshing — a hybrid structured/unstructured mesh of 206,928 cells for the humidification chamber, and an unstructured mesh of 553,086 cells for the dehumidification chamber. Cooling tubes in the dehumidifier are simplified using a constant-temperature thermal wall boundary rather than explicit pipe modeling.In the humidification simulation, salt water is introduced as a discrete phase using the Discrete Phase Model, with droplets (1e-6 m diameter) surface-injected over 10 seconds and evaporating into the dry air stream. The Species Transport model tracks the resulting water vapor concentration without chemical reaction, and the internal membrane packing is represented as a porous medium defined by porosity, viscous resistance, and inertial resistance.In the dehumidification simulation, the VOF multiphase model captures the distinct liquid water and water vapor phases, with evaporation-condensation mass transfer defined via Lee's equations based on saturation temperature and phase-change frequency.ConclusionThe humidification stage results, evaluated at the final simulation second, show water vapor mass fraction and discrete particle concentration contours, along with animations tracking droplet spray behavior and vapor mass fraction buildup over time — confirming steady production of humid, salt-free air. The dehumidification stage results show velocity, temperature, mass transfer rate, and phase volume fraction contours, confirming condensation occurring in regions where vapor temperature drops below saturation near the cold tube surfaces, with the highest freshwater yield concentrated around those cooled surfaces. Together, these results validate the HDH system's core function: converting saline water into clean, potable water through a fully coupled evaporation-condensation cycle.

Solar Indirect Dryer CFD Simulation, ANSYS FluentDescriptionA solar indirect dryer is a passive ventilation system driven purely by solar energy, consisting of two main components: a collector that absorbs solar radiant heat, and a drying chamber where food or fruit is arranged on trays, with air flowing through them to remove moisture. As the collector walls absorb solar radiation, their temperature rises, warming the air inside — this heated air becomes less dense and begins to rise naturally due to buoyancy, driving airflow through the system without any mechanical assistance.This project simulates an indirect solar dryer located in Egypt, modeled at 12:00 PM on July 1st. The collector has a surface area of 8 m², with the drying chamber sized to accommodate four trays. The geometry was designed in SpaceClaim and meshed in ANSYS Meshing, totaling 1,330,000 elements.MethodologySince capturing the temperature-driven density difference responsible for buoyancy is central to this problem, the material's density model was set to incompressible ideal gas, with an operating density of 1.225 kg/m³. The energy equation was activated alongside a radiation model — the Discrete Ordinates (DO) model was selected specifically because air participates directly in radiative heat exchange within this domain — with solar ray tracing enabled to account for incoming solar radiation.To capture the flow resistance and pressure drop introduced by the trays and food items without explicitly modeling their intricate geometry, a porous medium was used to represent their combined effect on the surrounding airflow.ConclusionResults include velocity and temperature contours throughout the dryer. Temperature and density contours show that air near the collector wall absorbs heat and correspondingly decreases in density: air entering at 314 K rises to 322 K after passing through the collector, with density dropping from 1.225 kg/m³ at the inlet to 1.095851 kg/m³ at the collector exit — this density reduction is precisely what drives the air's upward buoyant motion through the system.The pressure contour further shows a clear pressure drop as air passes through the trays, consistent with the porous resistance applied there. Altogether, heat transfer from the collector walls to the air establishes a natural, self-sustaining airflow through the dryer, reaching a mass flow rate of 0.0908 kg/s — confirming that the passive, buoyancy-driven design successfully generates sufficient airflow for effective drying without any external power input.

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.

Transformer Room Ventilation CFD Simulation, ANSYS Fluent TrainingDescriptionThis project simulates air conditioning within a transformer room using ANSYS Fluent. Transformers transfer electrical energy between two or more windings through electromagnetic induction, and for safety reasons, they're typically housed in dedicated rooms requiring an optimized air conditioning system to manage the substantial heat these units generate as they operate.The 3D geometry was designed in Design Modeler, representing a two-part room separated by a thin membrane wall. The room features 10 air inlet ducts positioned along the top and 4 outlet ducts along the side walls, with 3 transformers modeled as internal heat sources. A porous medium representing a louver window was applied at each airflow outlet. The domain was meshed in ANSYS Meshing, totaling 592,411 elements.MethodologyThe room's central divider was modeled as a wooden wall with a thermal conductivity of 0.173 W/m·K. The three transformers were modeled as aluminum components (thermal conductivity of 202.4 W/m·K), each generating a constant heat source of 6060.606 W/m³.Airflow enters the room through ducts positioned atop one of the transformer room walls, at velocities of 1.531 m/s and 2.04 m/s, angled at 45 degrees, and entering at 303.15 K; it exits through outlets held at atmospheric pressure. To improve the distribution of hot exhaust air leaving the room, louver windows were incorporated at the exhaust ducts, modeled as a porous zone with a porosity coefficient of 0.6 and a viscous resistance (inverse permeability) of 211,100,000 1/m².Convective heat transfer was also applied around the transformer walls, assuming a fluid bulk temperature of 300 K and a heat transfer coefficient of 24 W/m²·K. Turbulence was resolved using the standard k-epsilon model, with the energy equation enabled to capture temperature variation throughout the domain.ConclusionResults include 2D contours of pressure, temperature, and velocity throughout the transformer room. The temperature contour confirms that forced convection and the resulting airflow motion within the room effectively lower the overall temperature — demonstrating that the combined ventilation and louver-assisted exhaust design successfully manages the heat generated by the transformers, keeping the room within a safer operating temperature range.

Before I rewrite this one — this source text doesn't mention porous media anywhere; it's entirely about the Monte Carlo radiation model applied to photon transport in a CT scan environment. Given your instruction to treat all 10 as porous-media-relevant, want me to:Write it up straight, matching your standard format, without forcing a porous media connection (since none appears in the source), orFlag this to you as likely miscategorized before I write it, in case you want to swap it out?Assuming you'd rather I just proceed and write it accurately from the source content:Monte Carlo Radiation, CT Scan CFD SimulationDescriptionThis project simulates radiation patterns and absorption within a Computerized Tomography (CT) scan environment using the Monte Carlo (MC) radiation model in ANSYS Fluent, focusing on patient safety and image quality optimization. The simulation captures how radiation interacts with the human body and surrounding medical equipment — knowledge directly relevant to medical physicists and radiologists working to balance diagnostic image quality against radiation exposure.The 3D geometry represents a full CT scan room, including the CT machine, patient bed, and patient body, meshed using a high-fidelity unstructured grid totaling 4,390,045 cells.MethodologyThe Monte Carlo radiation model was used to accurately track individual photons from their source through to either absorption within the body or exit from the domain, solving the Radiative Transfer Equation (RTE) to capture photon-environment interaction throughout the scan room. This approach establishes a direct correlation between radiation intensity and photon angular flux, with radiant heat flux calculated based on the local photon incidence rate.Simulation setup involved configuring the Monte Carlo radiation model parameters, defining the CT scanner's radiation source characteristics, assigning material properties for both the patient's body and the surrounding medical equipment, and specifying boundary conditions governing radiation absorption and reflection throughout the domain.ConclusionResults include volumetric absorbed radiation dose within the patient's body, incident radiation patterns across various surfaces, radiation intensity distribution throughout the CT scan environment, and temperature changes resulting from radiation absorption.Two key regions were examined in detail: the radiation path and intensity distribution in the air between the CT scanner and the patient before body contact, and the penetration depth and absorption pattern of radiation once inside the patient, across different body regions. Together, these results characterize how radiation dose is distributed and absorbed throughout the scanning process — information directly applicable to optimizing CT scan protocols for reduced patient radiation exposure while maintaining diagnostic image quality.