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.

Grain Drying Device CFD Simulation Using Two-Way DPM Model, ANSYS Fluent TrainingDescriptionThis project studies a grain drying device using the two-way Discrete Phase Model (DPM) combined with the Species model in ANSYS Fluent. Hot air enters the drying device, and 120,000 rice grains carrying 10% moisture are injected randomly over a 6-second period, with evaporation continuing for an additional 9 seconds after injection completes. The device's hot surfaces are maintained through contact with hot exhaust smoke from an engine, providing the elevated temperature conditions needed for efficient drying.Freshly harvested rice typically carries 20-30% moisture — a level that can corrupt the grains quickly if left untreated. Drying the grain before storage and milling is therefore essential, and this rice drying device provides a mechanical means of exposing grains to ambient hot air to accelerate moisture evaporation.The 3D geometry was built in Design Modeler, representing a 3 m × 1 m channel box containing four triangular passages, each 20 cm long. The domain was meshed in ANSYS Meshing using an unstructured grid totaling 556,145 elements.MethodologySeveral assumptions were applied to this simulation: it was run as transient (unsteady) to capture the time-dependent behavior of both the fluid and the injected particles, a pressure-based solver was used given the working fluid's incompressibility, the two-way DPM tracked the injected rice grain particles under the desired conditions, and gravitational acceleration was included at -9.81 m/s² in the y-direction.Key simulation settings included:Models: Energy equation enabled; Realizable k-epsilon viscous model with standard wall functions; Species Transport model using a mixture-template; DPM with continuous-phase interaction and unsteady particle tracking both enabledInjection: Surface-type injection at the inlet, 10,000 streams, modeled as evaporating water-liquid droplets (10% volatile component fraction) with a uniform diameter distribution of 0.005 m, injected at 363.15 K with a total flow rate of 0.5 kg/s over a 0–6 second injection windowBoundary conditions: Velocity inlet at 1 m/s, 5% turbulent intensity, 363.15 K, with DPM set to escape; outlet wall with DPM set to reflect; hot walls held at 773.15 K with DPM set to reflectSolution methods: SIMPLE pressure-velocity coupling, least-squares cell-based gradient scheme, second-order discretization for pressure, momentum, H₂O, and energy, and first-order upwind for turbulent kinetic energy and dissipation rateRun settings: Time step size of 0.05 s, 300 total time steps, maximum 40 iterations per time stepConclusionThe simulation tracks 10,000 injected rice grains over the 6-second injection period, with evaporation beginning almost immediately (though simplified/ignored during this initial injection phase for modeling purposes). The resulting H₂O mass fraction graph shows moisture peaking at approximately 0.015 at the end of injection, then declining steadily to zero over the following 9 seconds as evaporation removes moisture from the grains — confirming that the device successfully dries the injected rice grains within the modeled timeframe.

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.

Greenhouse Thermal and Humidity Analysis Using ANSYS FluentDescriptionThis project presents a numerical simulation of greenhouse airflow, heat transfer, solar radiation, and moisture behavior using ANSYS Fluent. The goal was to investigate how environmental factors and boundary conditions influence internal temperature distribution, heat transfer from the floor through embedded pipes, air velocity, and moisture content — together characterizing the greenhouse's overall thermal performance. The simulation was run under steady-state conditions to capture the system's long-term behavior, accounting for solar radiation through ray tracing and the specific thermal properties of the structure's materials.The 3D geometry was built in SpaceClaim, consisting of a computational domain, the greenhouse room region, and 9 floor-embedded pipes representing the fluid domain, alongside a ground region modeled as a solid body. The computational domain measured 30 m wide, 10 m high, and 40 m long, with the greenhouse itself measuring 4 m wide, 3.8 m high, and 10 m long. The greenhouse floor, positioned 3.3 m high, contained 9 pipes each with a cross-sectional area of 0.017671 m² and a length of 10 m. The domain was meshed in ANSYS Meshing, generating approximately 4,800,000 cells to balance simulation accuracy with computational cost.MethodologyThe simulation used a pressure-based solver, appropriate given the steady-state nature of the problem, with gravitational acceleration set to -9.81 m/s² in the Y-direction. The energy equation was activated to capture heat transfer throughout the domain, with turbulence modeled using the Realizable k-epsilon model and standard wall functions for near-wall treatment.Radiation effects were captured using the Discrete Ordinates (DO) model with solar ray tracing enabled to account for solar heating. Humidity behavior was captured using the Species Transport model, with density defined as an incompressible ideal gas mixture of air and water vapor.Boundary conditions included a mass flow rate inlet of 0.032 kg/s, nine pressure outlet surfaces each at 0 Pa gauge pressure, and no-slip conditions applied to all walls. Pressure-velocity coupling used the Coupled algorithm to ensure strong convergence, with the solution initialized using Fluent's standard initialization method.ConclusionResults include detailed distributions of temperature, velocity, radiation heat flux, and water vapor mass fraction throughout the greenhouse, presented through contour plots and vector fields alongside quantitative data at key interior locations. This output enables evaluation of temperature uniformity, ventilation effectiveness, solar radiation absorption, and moisture distribution — together offering a comprehensive picture of the greenhouse's thermal and humidity performance under the simulated conditions.

Agricultural Drone Sprayer CFD Simulation Training by ANSYS FluentDescriptionThis project simulates an agricultural drone sprayer using ANSYS Fluent. The 3D geometry was designed in Design Modeler, with the plane's water storage defined as a water inlet and a planned flight angle of 0 degrees. The front side of the plane serves as the air entrance, all remaining sides are defined as pressure outlets, and the plane's body itself is treated as a wall. The domain was meshed in ANSYS Meshing, totaling 464,852 elements.MethodologyThe system involves two distinct fluids: air as the primary phase and a pesticide (referred to as toxin) as the secondary phase, defined with a density of 0.9512 kg/m³. The Eulerian multiphase model was used to capture the interaction between these two phases.The pesticide enters the domain at 5 m/s, with gravity included at -9.81 m/s² along the y-axis, while the surrounding air moves at 15 m/s. Turbulence was resolved using the SST k-omega model.ConclusionResults include 2D velocity fields, air and water volume fraction contours, and simulation animation. The results show the pesticide dropping from the drone's cargo section, progressively interacting with the surrounding airflow as it falls, before ultimately reaching the ground and spreading across its surface — confirming the simulation's ability to capture the full spray dispersal process from release to ground coverage.

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.

Broad-Crested Weir CFD Simulation by ANSYS FluentDescriptionThis project simulates a broad-crested weir, investigating flow rate, drag force, and water level across different channel sections. The inlet has a combined height of 98.5 + 30.4 mm.The geometry was built in SpaceClaim as a 0.075×0.25×5 m domain, featuring two separate inlets — one for water and one for air — with the weir positioned 1.5 m from the inlet. The domain was meshed in ANSYS Meshing using a structured grid, chosen for better visualization and simulation accuracy around the water surface region, totaling 1,222,021 elements.MethodologyA pressure-based solver was used given the incompressibility of the fluids, with gravitational acceleration included throughout. The VOF multiphase model was applied, with air as the primary phase and water as the secondary phase. Turbulence was resolved using the RNG k-epsilon model with standard wall functions for near-wall treatment.The water inlet was set to a velocity of 0.058491 m/s, while the air inlet used a pressure inlet boundary condition. The Coupled algorithm handled pressure-velocity coupling throughout the simulation.ConclusionResults were extracted as 2D and 3D contours under steady-state conditions, with particular focus on drag force, flow rate, and water level. ANSYS Fluent reported a drag force of 4.55 N acting on the weir and an outlet mass flow rate of 0.557 kg/s.Water level was examined along five lines positioned at increasing distances from the inlet — 0.5 m, 1.67 m (weir midpoint), 2.5 m, 3.5 m, and 4.5 m — tracking flow depth via the height-volume fraction of water at each location. The resulting flow depths were:Distance from Inlet (m)Flow Depth (mm)0.51311.671302.5223.2 (hydraulic jump)383.5384.538The maximum flow depth occurs just behind the weir, reaching approximately 130 mm, before dropping sharply to 22 mm immediately downstream. At 3.2 m from the inlet, a hydraulic jump occurs, with flow depth rising back to 38 mm and remaining steady through the rest of the channel — a classic transition from supercritical to subcritical flow downstream of the weir.The outlet volumetric flow rate was reported as 0.0005570402 m³/s, with a pressure differential of 139.48 Pa between the inlet and outlet — together confirming the weir's expected effect on channel flow behavior, water level distribution, and downstream hydraulic jump formation.

Fluidized Bed Bio-Reactor, ANSYS Fluent TrainingDescriptionThis project simulates a fluidized bed bio-reactor (FBR) using ANSYS Fluent, applying the Eulerian multiphase model to capture the interaction between air and silicon particles within the reactor.Fluidized bed bio-reactors are versatile devices used across a range of industries, including biomedical research, food processing, and chemical and pharmaceutical manufacturing. Their defining principle — fluidization, where an upward gas flow suspends solid particles in a fluid-like state — offers advantages in mixing, heat transfer, and reaction efficiency that make FBRs a widely adopted technology across these sectors.The bio-reactor geometry was designed in Design Modeler and meshed in ANSYS Meshing using a structured mesh, with mesh quality specifically optimized to accurately capture the complex multiphase behavior characteristic of fluidized systems.MethodologyThe Eulerian multiphase model was configured with the Granular sub-model activated to represent the particle phase, including phase property models for calculating granular temperature and drag and virtual mass forces defined between the air and particle phases.Heat transfer between the air and particles was captured using the Ranz-Marshall model, with the energy equation enabled to resolve temperature distribution throughout the reactor and the standard k-epsilon model applied for turbulence. Particle-particle interactions were captured through defined restitution coefficients, with gravitational effects included to properly represent particle motion, and the simulation run to capture the fluidized bed's inherently transient behavior.ConclusionResults include particle distribution and motion patterns throughout the reactor, along with the resulting temperature changes driven by air-particle heat transfer — together illustrating how fluidization influences the reaction kinetics within the bed.These results offer practical insight into bio-reactor design optimization, informing process efficiency improvements and providing a foundation for scaling fluidized bed bio-reactors up to industrial production levels — directly relevant to bioprocess engineering across biomedical, food, and pharmaceutical manufacturing applications.

Packed Bed Reactor with Particles, CFD Simulation Training with ANSYS FluentDescriptionReactor models range from pseudo-homogeneous to heterogeneous, from one-dimensional to three-dimensional, and from assumed flow patterns to fully computed flow and transport fields. Among these, packed bed reactors (also known as fixed-bed reactors) are widely used for catalytic processes, particularly favored for heterogeneous reactions where solid-fluid contact strongly influences reaction rate. A packed bed reactor consists of a cylindrical vessel filled with solid reactant material, with a second reactant entering through the inlet face and flowing through the packed solids.Compared to fluidized bed reactors — which typically achieve near-isothermal conditions and more uniform product output — packed bed reactors generally experience a temperature drop near the reactor inlet regardless of the wall temperature profile, along with comparatively poorer mixing and more heterogeneous behavior.This project simulates a packed bed reactor using ANSYS Fluent. The 2D geometry was designed in Design Modeler as a rectangle measuring 120 mm long and 40 mm wide, meshed in ANSYS Meshing using a structured quad mesh totaling 5,000 elements.MethodologySeveral assumptions were applied to the simulation: a pressure-based solver was used, only fluid behavior was examined (no heat transfer was modeled), the simulation was run as unsteady, and gravitational effects were included at -9.81 m/s² along the y-axis.Key simulation settings included:Viscous model: SST k-omega, with a mixture turbulence multiphase modelMultiphase model: Eulerian, with implicit formulation across two phases — gas (flow) and solid particlesBoundary conditions: Velocity inlet with the flow phase at 0.05 m/s (volume fraction 1) and particles at 0 m/s (volume fraction 0); pressure outlet at 0 Pa gauge pressure; stationary upper and lower wallsSolution methods: Phase Coupled SIMPLE for pressure-velocity coupling, PRESTO! for pressure discretization, and first-order upwind schemes for momentum, turbulent dissipation rate, turbulent kinetic energy, and volume fractionInitialization: Standard method with a patch applied to define the packed particle region, setting particle volume fraction to 0.65 within that patched zoneConclusionResults include 2D contours of pressure, velocity, and volume fraction for both the water flow and the alumina particles. Water enters the reactor and passes through the particles — packed and fixed within the lower half of the reactor — ultimately exiting at the same velocity as the inlet flow.The static pressure contour further shows a clear pressure drop as flow passes through the packed particle region, after which pressure remains constant at zero through to the outlet face — confirming that the packed bed of particles introduces the expected flow resistance while the reactor otherwise maintains steady, consistent flow behavior downstream.

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.