Radiation Heat Transfer in Combustion Chamber, ANSYS Fluent TrainingDescriptionThis project investigates the steady combustion of methane and air within a simple extended cubical combustion chamber using ANSYS Fluent, with particular attention to radiation heat transfer — a critical consideration given the extremely high temperatures involved in combustion chambers. The 3D geometry was designed in Design Modeler and meshed in ANSYS Meshing, totaling 384,112 elements.MethodologyThe simulation captures a mixture static temperature reaching a maximum of 3500 K within the chamber. Methane and air enter the domain through separate inlets — methane through a single inlet, while airflow enters through two inlets to promote a more uniform fuel-air mixture. Air and fuel enter at mass flow rates of 0.00468 kg/s and 0.000205 kg/s, respectively.The chemical reaction between methane and air produces CO₂ and H₂O; since the combustion is air-rich, oxygen and nitrogen remain unconsumed at the end of the reaction. The Species Transport model was activated to simulate combustion, with volumetric reactions enabled.Given the high temperatures generated within the chamber, radiation heat transfer required explicit modeling, so the Discrete Ordinates (DO) model was also enabled. Turbulence was resolved using the RNG k-epsilon model, chosen for its ability to more accurately capture intense heat flux generation within the domain compared to other k-epsilon variants.ConclusionResults include 2D contours of temperature, velocity, species mass fraction, streamlines, and velocity vectors throughout the combustion chamber, with an outlet mixture mass flow rate of 0.004885042 kg/s. The primary combustion process occurs within the chamber itself, clearly visible in the temperature and reaction heat contours, which show the maximum temperature gradient and peak reaction heat concentrated in this region. The combustion process is similarly evident in the species mass fraction contours — the CO₂ mass fraction, for instance, shows a sharp increase corresponding directly to the combustion reaction taking place.

Bluff-Body Mild Burner CFD Simulation, ANSYS Fluent TrainingDescriptionThis project simulates combustion within a bluff-body mild burner using ANSYS Fluent. A burner is a device that combines a controlled amount of air with fuel within a safe enclosed space, converting fuel energy into heat energy while producing combustion gases as a byproduct. Since the resulting flame transfers heat into the chamber interior through both convection and radiation, the Discrete Ordinates (DO) radiation model is applied, alongside the Species Transport model to capture the combustion process occurring within the chamber.The burner operates by spraying fuel through a dedicated jet inlet into the chamber, while air enters symmetrically from four directions, combining with the fuel to sustain the flame. The chamber's internal flow path is cyclic — part of the gas exits through the exhaust section, while the remainder recirculates back into the enclosure along the same circular path.Several assumptions were applied to the simulation: it was run under steady-state conditions using a pressure-based solver, with gravitational effects excluded.Geometry & MeshThe 3D geometry was designed in Design Modeler. Given the model's symmetrical structure, only a 90-degree section was modeled, with the two lateral surfaces defined as symmetry boundaries. The geometry consists of three small-diameter inlet ducts (two air inlets and one fuel inlet) and one small-diameter exhaust outlet pipe.The domain was meshed in ANSYS Meshing using an unstructured grid totaling 1,107,286 elements, with boundary layer mesh applied at the inlet and outlet sections to improve the accuracy of near-wall flow behavior.MethodologyKey simulation settings included:Viscous model: Realizable k-epsilon with enhanced wall treatmentSpecies model: Non-premixed combustionRadiation model: Discrete Ordinates (DO), with the energy equation enabledBoundary conditions: Velocity inlets for air (2 m/s, 300 K) and fuel (1 m/s, 300 K), each with internal emissivity of 1, zero NO pollutant mass fraction, and mixture fraction settings appropriate to each stream (fuel set to a mean mixture fraction of 1); pressure outlet at the exhaust (0 Pa gauge, internal emissivity of 1); outer walls set to zero heat flux with opaque boundary type and internal emissivity of 1Solution methods: Coupled pressure-velocity coupling, PRESTO! for pressure discretization, and second-order upwind schemes applied across momentum, energy, turbulent kinetic energy, turbulent dissipation rate, pollutant NO, discrete ordinates, mean mixture fraction, and mixture fraction varianceInitialization: Hybrid methodConclusionThe simulation captures the combustion behavior within the bluff-body mild burner, characterizing how the cyclic recirculating flow pattern sustains flame stability while combining radiation and convective heat transfer mechanisms to distribute thermal energy throughout the chamber. The resulting flow, temperature, and species distribution fields reflect the coupled effects of the non-premixed combustion process and the recirculating exhaust pathway central to this burner's mild combustion design.

Rosseland Radiation Model, Combustion of Train in TunnelDescriptionThis project simulates the combustion of a train within a tunnel environment using ANSYS Fluent, focusing on the resulting radiation heat transfer captured through the Rosseland radiation model — a method specifically suited to optically thick media such as the dense combustion products generated in this confined-space fire scenario.The 3D geometry represents the tunnel interior with the train positioned inside, meshed using an unstructured grid totaling 372,705 cells.MethodologyCombustion was modeled using the Species Transport model with a volume-based reaction definition representing diesel-air combustion. Radiation heat transfer was captured using the Rosseland approximation, a simplified form derived from the P-1 radiation model that becomes appropriate once the optical thickness of the medium exceeds approximately 3 — a condition well-suited to the soot- and combustion-product-laden atmosphere generated by a train fire within an enclosed tunnel.Boundary conditions were configured to represent fuel leakage and its interaction with the surrounding air, coupling the combustion source with the broader tunnel airflow.ConclusionResults include detailed contours of temperature distribution, velocity fields, radiative heat flux, and mass fractions of fuel, carbon dioxide, oxygen, and water vapor. Together, these results characterize how combustion and radiation heat transfer interact within the confined tunnel geometry, illustrating how the Rosseland approximation captures radiative heat exchange through the optically thick combustion products generated by the fire.These results are directly relevant to tunnel and railway fire safety assessments, offering insight into thermal and radiative conditions during a train fire event that can inform tunnel safety design, ventilation strategy, and emergency response planning.

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.

PCM Solar Collector CFD Simulation by ANSYS Fluent TutorialDescriptionThis project simulates heat transfer within a PCM-based solar collector using ANSYS Fluent. The system centers on a U-shaped tube carrying water flow, surrounded by a cylindrical space filled with phase change material (PCM). This PCM region is itself enclosed by three concentric layers: an aluminum layer that absorbs incoming solar radiation, an air gap layer, and an outer glass layer.The collector operates through a straightforward thermal pathway: sunlight passes through the glass layer, heating the enclosed air gap; this heat then transfers to the aluminum absorber layer, which in turn transfers heat inward to the PCM. During the day, as the absorber captures solar heat, the PCM absorbs part of this energy to drive its melting process. At night, as ambient conditions cool, the PCM releases its stored latent heat by solidifying, transferring that heat into the water flowing through the U-shaped tube — effectively storing daytime solar heat for use during colder nighttime hours.Geometry & MeshThe 2D geometry was designed in Design Modeler, consisting of two parallel pipes forming the U-shaped tube, surrounded by the cylindrical PCM layer. Around this, an incomplete cylindrical aluminum absorber layer was placed, followed by an incomplete cylindrical air gap layer, and finally an incomplete cylindrical glass layer as the outermost boundary.The domain was meshed in ANSYS Meshing using a structured grid totaling 969,866 elements.MethodologyThe Solidification and Melting model was used to represent the PCM's phase-change behavior, with the material defined by a density of 910 kg/m³, specific heat capacity of 2100 J/kg·K, thermal conductivity of 0.5 W/m·K, and viscosity of 0.0273 kg/m·s. Its solidus temperature was set to 302 K, liquidus temperature to 310 K, and latent heat of fusion to 178,000 J/kg.Radiative heat transfer and incoming solar radiation were captured using the Discrete Ordinates (DO) radiation model, which solves the radiative transfer equations across a discrete set of finite solid angles — well suited to this system's transparent glass layer, reflective surfaces, and wavelength-dependent transmission behavior. Solar ray tracing was activated to apply the solar load directly, requiring inputs such as the site's longitude and latitude, the date and time of the simulated radiation, solar direction, and both direct and diffuse radiation intensities. The laminar model and energy equation were enabled to solve the fluid flow and capture temperature variation throughout the domain.ConclusionResults include 2D and 3D contours of pressure, velocity, temperature, and the liquid mass fraction produced within the PCM. The results confirm that the PCM within the central cylindrical region undergoes a clear phase change, generating liquid within that zone as it absorbs solar heat. Comparing the U-shaped tube's inlet and outlet temperatures further confirms that heat is successfully transferred into the water flow — validating the collector's core function of capturing, storing, and later releasing solar thermal energy through the PCM's melting-solidification cycle.

PCM-Enhanced PV Panel, ANSYS Fluent CFD SimulationDescriptionThermal management of photovoltaic (PV) systems presents a significant challenge in solar energy technology, since PV modules can experience temperature increases of up to 35°C above ambient conditions during peak sunlight exposure — a rise that substantially impacts performance, with power output declining by approximately -0.65% for every 1°C increase in temperature.This project compares two configurations: a conventional PV panel without any cooling enhancement, and an enhanced system incorporating PCM-based passive cooling. The PV system was modeled as a multi-layer structure comprising a glass cover layer, an Ethylene-Vinyl-Acetate (EVA) layer, silicon solar cells, a second EVA layer, and a Tedlar back sheet, with the PCM-enhanced configuration additionally incorporating a layer of RT42 paraffin PCM.The full 3D geometry, including both the conventional and PCM-enhanced configurations, was built in SpaceClaim, capturing all PV panel layers along with the PCM containment structure. The domain was meshed in ANSYS Meshing using a structured grid of approximately 1,000,000 elements, sized to adequately resolve thermal gradients while maintaining computational efficiency.MethodologyThe Solidification and Melting model was activated to capture the PCM's phase-change dynamics, while the Discrete Ordinates (DO) radiation model captured radiative heat transfer throughout the system. Solar ray tracing was enabled for precise solar load calculations, using location-specific parameters set to longitude -84.63°, latitude 13.65°, and UTC-5 time zone — eliminating the need for simplified radiation source terms and enabling more realistic solar irradiation modeling. Momentum equations were deliberately disabled, focusing computational resources specifically on the system's thermal behavior.The simulation ran over a period of 19,500 seconds (approximately 5.4 hours) to evaluate thermal performance across both configurations.ConclusionThe PCM-enhanced system achieved an average PV temperature of 313.25 K (40.1°C), compared to 315.06 K (41.91°C) for the conventional system — a difference of 1.81 K. The PCM-enhanced configuration also showed a notably more uniform temperature distribution across the panel, with temperature contours revealing effective heat absorption by the PCM layer and lower peak temperatures thanks to its thermal buffering effect; by contrast, the conventional system exhibited higher thermal gradients and less uniform distribution overall.Liquid fraction contours reveal the PCM's phase transition progressing gradually from top to bottom, with liquid fraction values ranging from 0.122 to 0.267 — confirming a progressive, effective thermal energy storage process rather than an abrupt phase change. The PCM case also showed a broader overall temperature range (312–317 K) compared to the conventional case (312–315 K), reflecting the PCM's role in redistributing and moderating heat across the panel.This 1.81 K temperature reduction translates to an estimated 1.18% improvement in electrical efficiency, based on the standard -0.65%/°C temperature coefficient for PV performance. Beyond the direct efficiency gain, the more stable operating temperature also suggests reduced thermal stress on PV components and improved long-term reliability — confirming that PCM-based passive cooling offers a measurable, meaningful improvement in PV thermal management under realistic solar loading conditions.

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.

IntroductionA building's façade — the side of the structure most often in direct contact with the surrounding environment — has become a key area of focus in architectural and energy engineering, particularly regarding its effect on passive ventilation performance.Beyond giving a building its distinctive visual identity, the façade plays a critical role in overall energy performance. Its shape and configuration can vary widely, directly influencing thermal comfort and energy consumption. In fact, façade design offers engineers an opportunity to meet part of a building's energy demand through passive ventilation strategies alone.Project DescriptionThis project simulates a three-floor apartment building located in Sydney, the capital of New South Wales, Australia, examining different façade configurations to identify the design that best achieves natural ventilation, optimal thermal comfort, and air changes per hour (ACH).The analysis begins by establishing the site's geographical and thermal conditions. Sydney sits at approximately 151.20° longitude and -33.865° latitude, and experiences its coldest conditions during July and August, with an average temperature of around 283 K. The modeled building spans three floors, each with an 80 m² cross-sectional area and a floor height of 2.8 m.Per the client's requirement, glass was specified as the façade material. With the dual design goals of contributing to the building's energy needs while maintaining effective natural ventilation (measured via ACH), three distinct façade configurations were developed and evaluated:Case 1: A façade extending across the full front of the building, featuring one separate inlet and outlet vent.Case 2: The façade divided into three independent sections — one per floor — each isolated from the others, with inlet and outlet vents installed on this segmented façade.Case 3: A full-extension façade similar to Case 1, but fitted with only a single inlet and a single outlet.AnalysisThe simulation results show that temperature distribution remains fairly uniform across floors in Cases 1 and 2, while Case 3 exhibits a noticeable temperature difference between floors — clearly visible in its temperature volume rendering.In terms of average building temperature, the results were 303.4 K, 305.8 K, and 293.4 K for Cases 1, 2, and 3, respectively.The façade's geometric layout and vent arrangement were also found to significantly affect ventilation behavior. Streamline data showed that reducing the number of vents helped trap warm air within the façade zone, allowing it to function as an effective insulating layer against cold ambient air.Overall, Case 2 delivered the best combination of thermal performance and ventilation efficiency, successfully meeting both design targets compared to the alternative configurations.It's worth noting that passive ventilation systems are intended to supplement — not fully replace — a building's living-condition requirements; the less favorable thermal performance observed on the first floor is therefore considered an expected trade-off rather than a design failure.

Computer Room Air Conditioning CFD Simulation, DPMDescriptionThis project simulates air conditioning within an office housing several computers and simulators, using ANSYS Fluent. The air conditioning system combines floor heating with ceiling cooling — a configuration where the buoyancy effect drives free heat transfer throughout the space: hot air rises from the floor toward the ceiling due to its lower density, while cooler air sinks in the opposite direction, establishing a circulating flow that sustains the office's air conditioning process.Airflow enters the office through circular sections at 0.6125 m/s and 291.1 K, exiting through rectangular sections on the ceiling at atmospheric pressure. The 3D geometry was designed in Design Modeler, representing an office space measuring 6.35 m by 5.4 m at floor level and 2.7 m high, containing 4 computer cases and 4 simulators positioned as internal heat sources, alongside the floor and ceiling air inlet/outlet sections. The domain was meshed in ANSYS Meshing using an unstructured grid totaling 3,089,035 elements.MethodologyGiven the radiant heat transfer occurring between surfaces within the office, a radiation model was included in the simulation. Particles were also defined using the Discrete Phase Model (DPM) at the inlet sections, enabling study of airflow behavior and the resulting circulation pattern throughout the office interior.The 4 computer cases and 4 simulators each generate heat as part of their normal operation, transferring it into the surrounding space. The computer cases were assigned a heat flux boundary condition of 152.5 W/m², while the simulators were assigned 90.56 W/m².ConclusionResults include 2D and 3D contours of pressure, velocity, and temperature, along with 3D velocity vectors throughout the office. Examining the tracked particles' behavior and displacement patterns provides direct insight into the resulting airflow circulation — confirming how the combined floor-heating, ceiling-cooling configuration, radiant heat exchange between surfaces, and internal equipment heat loads together shape the office's overall thermal and airflow environment.