Microchannel Heat Source CFD Simulation, ANSYS Fluent TutorialDescriptionAs electronic devices become smaller and more powerful, conventional air cooling can no longer keep pace with the heat generated by modern processors and power electronics. Microchannel cooling addresses this challenge by circulating a liquid coolant through channels of microscale dimensions machined directly above the heat source. The exceptionally high surface-area-to-volume ratio of these channels enables heat transfer rates far beyond what conventional heat sinks can achieve, making microchannel technology a cornerstone of thermal management in high-performance computing, power electronics, and compact electronic devices.This project simulates a microchannel heat source in ANSYS Fluent as a conjugate heat transfer problem: heat generated by the source conducts through the solid structure and is absorbed by the coolant flowing through the microchannels. The case builds directly on the heat sink projects earlier in this package, moving the same physics down to the microscale.MethodologyThe mesh is generated to properly resolve both the fluid flow inside the microchannels and the heat conduction through the surrounding solid — a critical requirement at this scale, where thermal gradients are steep and channel dimensions are small.The heat source is defined with a thermal load representing the operating electronic component, while the coolant inlet and outlet boundary conditions specify the flow rate, pressure, and temperature of the working fluid. The energy equation is enabled to solve the conjugate heat transfer between the solid and fluid domains. Given the small channel dimensions and low Reynolds numbers typical of microchannel flows, the flow regime is laminar — one of the distinguishing physical characteristics of microscale heat transfer.AnalysisAt the end of the solution process, temperature contours, velocity vectors, and streamlines are extracted for both the solid and fluid domains. The temperature distribution shows how heat spreads from the source into the solid structure and is progressively absorbed by the coolant as it travels along the channels, while the velocity field reveals the laminar flow behavior characteristic of microscale geometries.The cooling performance is evaluated through the heat transfer coefficient and Nusselt number, while the pressure drop across the channels quantifies the pumping power required — the fundamental trade-off in microchannel design, where narrower channels improve heat transfer but increase hydraulic resistance. By completing this project, you will learn to set up conjugate heat transfer in microscale geometries, apply appropriate boundary conditions for liquid cooling, and evaluate both the thermal and hydraulic performance of a microchannel cooling system.

DescriptionMicrochannel heat sinks address a core challenge in electrical and power engineering: dissipating high heat fluxes from compact electronic components and power devices where conventional air cooling falls short. This CFD study uses ANSYS Fluent to analyze a cylindrical microchannel heat sink built around 86 rectangular microchannels arranged circumferentially about a cylindrical core, with a hydraulic diameter of 560 micrometers, a 5 mm internal radius, and 10 mm wall thickness. The work examines how this microfluidic cooling geometry manages heat extraction under realistic operating conditions relevant to power electronics and high-density circuit packaging.MethodologyGiven the circumferential symmetry of the channel arrangement, the simulation models a single representative segment using periodic boundary conditions, reducing computational cost while preserving solution accuracy across the full structure. The domain is discretized with a structured mesh of 1.5 million elements to resolve thermal gradients and flow features within the narrow channels. The cylindrical core is treated as the heat-generating electronic component, assigned a constant heat flux boundary condition of 243,507 W/m², while water coolant enters the microchannels at 0.59 m/s and 297 K.Results AnalysisThe simulation produces three-dimensional pressure, temperature, and velocity fields throughout the microchannel passages, along with two-dimensional temperature contours at multiple cross-sections to illustrate the thermal coupling between the solid core and the coolant. Results show effective heat extraction from the core, confirming the design's cooling capability under the specified load. Rotational reconstruction of the segment results yields the complete thermal profile of the full heat sink, offering a comprehensive performance picture that supports design optimization for thermal management in power-dense electronic and electrical systems.

Server Room Cooling with 6 Cabinets — ANSYS Fluent CFD SimulationDescriptionServer rooms generate large amounts of heat, and keeping that heat within a safe band is critical: manufacturers typically specify an operating range of about 10–32 °C, and drifting below or above that range creates unstable conditions that threaten the equipment. Cooling is therefore one of the core challenges in data-center design, alongside airflow planning, power redundancy, and fire suppression. This project uses ANSYS Fluent to model the airflow and temperature distribution inside a six-cabinet server room and determine whether the cooling keeps every rack within the safe thermal range. Within the HVAC Engineering: Beginner CFD Training Package, it introduces forced-convection thermal management with multiple heat sources in a realistic data-center layout — a step up from the passive and natural-ventilation cases earlier in the package.MethodologyThe room is modeled in three dimensions in Design Modeler, measuring 7 × 4 × 2 m, with six server cabinets each measuring 1 × 0.6 × 1.8 m arranged as heat sources. The domain is meshed in ANSYS Meshing with a structured grid of 448,000 elements.The simulation treats the room as a forced-convection problem. Cool air enters at 15 °C, and each of the six cabinet racks is modeled as a 400 W heat source. Because forced convection dominates over natural convection here, air density is taken as constant. The key variable studied is the inlet air speed, run at two values — 0.5 m/s and 1 m/s — to see how supply velocity affects how well the racks are cooled. The goal is to find conditions that hold the entire room within the safe sub-32 °C range.AnalysisAt the end of the solution, you generate 2D and 3D contours of temperature and streamlines, along with plots of the maximum and average fluid temperature. The results tell a clear engineering story: at the lower inlet speed of 0.5 m/s, the maximum-temperature condition is not satisfied — parts of the room exceed the safe 32 °C limit. Raising the inlet speed to 1 m/s brings the maximum temperature down to around 30 °C, back inside the safe band. In other words, increasing the supply airflow directly improves rack cooling and resolves the overheating. By the end of this project, you'll be able to set up a 3D forced-convection cooling simulation with multiple heat sources, run a comparative study across inlet conditions, and use temperature contours and bulk-temperature plots to verify that a cooling design meets a required thermal limit.

DescriptionThis project investigates steady airflow (ventilation) within a storage container room containing two internal walls, simulated using ANSYS Fluent and studied through CFD analysis. Proper ventilation of this kind is a core HVAC concern, since maintaining a consistent airflow is essential for effective cooling and air distribution in storage environments.The three-dimensional geometry was created in Design Modeler, and meshing was performed in ANSYS Meshing. A structured mesh was used, comprising 115,635 elements.MethodologyHere, ANSYS Fluent is used to examine steady airflow through a storage container room fitted with two walls. Such container rooms are commonly used to store perishable industrial goods, which must be kept under continuous, steady airflow to ensure adequate cooling and ventilation.In this study, the airflow is simulated within a 0.5 × 0.5 × 1 m chamber containing two walls positioned across the flow path, representing the storage enclosure. Air enters the domain at a velocity of 5 m/s and accelerates to a maximum of roughly 20 m/s after passing over the second wall, as a result of the constricting geometry.The standard k-epsilon turbulence model, together with the energy equation, was enabled to resolve the turbulent flow field and compute the temperature distribution throughout the domain.ConclusionOnce the solution converged, two-dimensional contours of pressure, velocity, and streamlines were obtained. As shown, the inlet air velocity of 5 m/s rises to a peak of about 20 m/s owing to the geometry of the enclosure.The normal force exerted on the domain walls is 15.8526 N. Intense turbulence is observed in the region between the two walls, where the turbulent kinetic energy reaches values as high as 2 J/kg.

DescriptionThis project uses ANSYS Fluent to simulate a wind tower system paired with a qanat, a traditional passive cooling arrangement rooted in the architecture of hot, arid regions. Passive ventilation techniques rely entirely on natural driving forces rather than mechanical equipment, and they generally fall into two categories: wind-driven systems, where airflow is generated by pressure differences, and buoyancy-driven systems, where temperature-induced density differences create natural convection. The wind tower and qanat combination sits in a hybrid category, drawing on both mechanisms at once.In this setup, a tall tower rises above the building and works together with an underground channel — the qanat — which acts as a natural cooling reservoir. When wind strikes the tall structure, it creates a pressure imbalance: high pressure builds on the windward face while a low-pressure zone forms behind it, driving suction that pulls air through the system. Meanwhile, inside the underground channel, incoming hot air passes over a body of cool water, picking up moisture and losing heat before rising into the building through the floor to condition the interior air.To keep the model manageable, the water surface inside the channel wasn't explicitly represented; instead, a fixed-temperature boundary condition of 278 K was applied to the channel walls to represent its cooling effect. The incoming hot air enters the channel at 0.2 m/s and 300 K. Window surfaces exposed to sunlight and outdoor heat were assigned a constant temperature of 298 K. The geometry, built in Design Modeler, consists of three connected components: the room, the tower, and the underground channel. Meshing was carried out in ANSYS Meshing using an unstructured approach, producing 402,198 cells.MethodologyThe simulation was run as a steady-state, time-independent case using a pressure-based solver in ANSYS Fluent. Because natural convection plays a central role here, buoyancy effects were captured by allowing air density to vary with temperature rather than treating it as constant — warmer, lighter air rises, which is what drives hot air out through the tower. This density variation was modeled using the incompressible ideal gas law, where density depends on temperature and operating pressure rather than on local pressure fluctuations, consistent with the assumption of constant pressure throughout the domain.ConclusionThe simulation produced temperature, pressure, and velocity contours in both 2D and 3D, along with velocity vector fields. The temperature results clearly show the cooling pathway: cool air drawn in through the underground channel and hot air escaping through the tower opening. The pressure field confirms the mechanism driving this exchange, with high pressure outside and lower pressure inside the room and tower pulling hot air upward and out. The velocity vectors trace this circulation clearly, showing cool air entering rapidly at floor level, circulating through the room, and exiting through the tower once conditioning is complete. Overall, the results confirm that this passive wind tower and qanat system successfully performs natural air conditioning without any mechanical input.

DescriptionThis project uses ANSYS Fluent to simulate natural convection in a solar chimney, a passive solar system central to renewable energy and sustainable building engineering. Solar chimneys use buoyancy-driven airflow, generated by solar heating of an absorber surface, to drive natural ventilation and passive cooling in buildings without mechanical energy input. This simulation examines how heat absorbed at the chimney surface drives air movement through the vertical channel.MethodologyThe solar chimney geometry is built in DesignModeler and meshed with a structured grid of 510,000 cells, refined in critical flow regions for accuracy. The Fluent setup activates the energy equation to capture temperature changes and includes gravity effects to model buoyancy-driven flow, with a heat flux of 55 W/m² applied at the absorber surface to represent solar heating.ConclusionResults include temperature, velocity, and pressure contours along with streamlines showing air circulation patterns within the chimney. The mass flow rate at 0.5 m height is reported at 0.128 kg/m³, characterizing the strength of the buoyancy-driven flow. These findings support the design and evaluation of solar chimneys for passive building ventilation, offering insight relevant to sustainable architecture, HVAC optimization, and renewable energy integration in energy-efficient buildings.

DescriptionThis project simulates the HVAC performance of a room fitted with a solar chimney using ANSYS Fluent. The model consists of two main parts: the interior of the room and a sloping solar chimney mounted on the room's ceiling. As a passive, sun-driven ventilation device integrated into the building envelope, the solar chimney is a natural subject for architectural engineering, where the goal is to improve indoor comfort and air quality through the building's own design rather than mechanical systems.The solar chimney has glass plates on its side surfaces that are in contact with the outdoor environment. As a transparent medium, the glass admits solar energy, while a plate at the back of the chimney acts as a heat-absorbing surface. The absorbing surface behind the chimney is assumed to be held at a constant temperature of 335.15 K. The glass surface exposed to the outdoor environment, in contrast, exchanges heat with its surroundings by convection: the ambient air temperature is 308.15 K, and the convective heat transfer coefficient is 8 W/m²K. In addition, the solar energy absorbed inside the chimney is represented as a constant volumetric heat source of 15,000 W/m³.MethodologyThe 2D geometry was created in Design Modeler and comprises two parts: a room measuring 2 m × 3 m, and a solar chimney 2 m long, inclined at 45 degrees to the room's ceiling, with a width of 0.15 m. Meshing was performed in ANSYS Meshing using a structured grid of 42,846 elements.The airflow enters through the inlet at the bottom of the room under a pressure-inlet boundary condition. Air at 308.15 K is drawn into the room by the heat generated within the solar chimney and then carried out to the external environment, establishing a continuous, buoyancy-driven ventilation path through the space.ConclusionOn completion of the solution, two-dimensional contours of pressure, temperature, and velocity were obtained, along with pathlines and velocity vectors.In addition, the temperature distribution across the chimney was plotted at a point midway along its length (1 m from the inlet and outlet), together with the velocity variation across the chimney outlet section. The transverse temperature profile at a point 1 m from the chimney inlet, over the 0.15 m thickness, was extracted and compared against the corresponding temperature profile reported in a reference paper, providing validation of the simulation.As the results demonstrate, the heat of the solar chimney successfully induces natural airflow that ventilates the room passively — drawing fresh air in at the base and expelling it through the inclined chimney — illustrating how a solar chimney can be integrated into an architectural design to enhance indoor ventilation with minimal energy input.

Phase Change Material (PCM) in a Finned Tube — ANSYS Fluent CFD SimulationDescriptionThis project presents a CFD analysis of thermal energy storage using Erythritol phase change material (PCM) in a three-layer tube heat exchanger with copper fins. PCMs store and release thermal energy through latent heat — absorbing heat as they melt from solid to liquid and releasing it as they solidify — which makes them well suited to applications such as capturing solar energy during the day and releasing it at night. This simulation investigates the phase-transition dynamics and heat-transfer mechanisms of the PCM over an extended charging period. Within the Solidification & Melting: Beginner CFD Training Package, this project applies the phase-change model to a finned tube, showing how enhanced surfaces overcome the low thermal conductivity that limits PCMs.MethodologyThe system is a three-layer tube heat exchanger with enhanced surfaces: copper tubes and fins for superior thermal conductivity, Erythritol as the PCM in the storage layer, and liquid silicone as the heat-transfer fluid circulating through the inner tube. The domain — inner-tube flow path, copper tube walls, fins, and PCM region — is meshed with a hybrid structured/unstructured grid of 107,718 elements, with coupled wall conditions between the different materials. The Solidification and Melting module handles the phase transition, run as an extended 12,000-second transient analysis to capture the complete phase dynamics. The Erythritol PCM is defined through its solidus and liquidus temperatures, latent heat of fusion, density, specific heat, and thermal conductivity, while the copper tubes and fins carry high thermal conductivity. The silicone heat-transfer fluid enters at 343.15 K and 1 m/s, the outer walls are adiabatic (zero heat flux), and the inner walls are automatically coupled thermal interfaces.AnalysisPost-processing visualizes the temperature distribution through the PCM, tracks the progression of the solid-liquid phase front over time, and quantifies the liquid-fraction development as the PCM melts, along with the heat-transfer pathways — conduction through the fins and convection in the liquid regions. From these results you can evaluate the energy-storage capacity absorbed as latent heat, the transient response during charging, the effectiveness of the fins in enhancing heat transfer, and how heat penetrates from the tube surface into the PCM volume — yielding practical design guidance on fin geometry and spacing, flow rate, and inlet temperature. The results highlight how copper fins accelerate the charging process and overcome the inherent thermal-conductivity limitation of PCMs. By the end of this project, you'll be able to set up an extended transient Solidification and Melting simulation with coupled solid–fluid–PCM zones, define PCM and fin material properties, and interpret the phase-front, liquid-fraction, and temperature results that characterize a finned PCM heat exchanger.Note: since your list now has both #1 (finned tube, described here as a three-layer tube exchanger) and #7 (shell-and-tube finned heat exchanger), just keep an eye that those two stay clearly differentiated when you write #7 — different geometry framing so they don't read as duplicates.

Storage Tank Containing PCM — ANSYS Fluent CFD SimulationDescriptionThis project simulates the thermal performance of phase change materials (PCMs) within a storage tank using ANSYS Fluent, with solid–liquid phase change as the central theme. PCMs store and release thermal energy by melting and solidifying, and capturing that transition is the core of the study. Here the PCMs take the form of spheres arranged inside a vertical cylindrical storage tank. Hot water enters through an inlet pipe at the top of the tank at 0.1 m/s and 343 K, flows through the interior space around the spheres, and exits from the upper part of the tank. As the warm water transfers heat to the spheres, the PCM melts — and because this behavior is governed by the phase change between solid and liquid states, the Solidification and Melting model is used. As the opening project of the Solidification & Melting: Beginner CFD Training Package, it introduces the phase-change model on a baseline thermal-storage tank, establishing the foundation the enhancement and applied cases build on.MethodologyThe process is inherently time-dependent, so a transient solver is used over a total simulation time of 100 s with a time-step size of 1 s. The study is carried out across several configurations to isolate the factors that govern melting: two PCM materials (paraffin and SAT-G), two sphere radii (4 cm and 5 cm), and two melting temperatures (333.15 K and 332 K), tracking how the liquid mass fraction evolves with the spheres' size, the melting temperature, and the material itself. The geometry is three-dimensional and was created in Design Modeler, then meshed in ANSYS Meshing with an unstructured grid of 757,886 elements. The heart of the methodology is the Solidification and Melting model, which is specifically formulated for the phase change between solid and liquid states; it tracks the advancing melt front through the liquid fraction in each cell rather than meshing a moving interface explicitly. The two materials, paraffin and SAT-G, are defined in Fluent through their respective thermophysical properties so that each melts according to its own characteristics.AnalysisAfter solving, the simulation yields two- and three-dimensional contours of pressure, temperature, velocity, and the liquid and solid mass fractions at the final instant of the process, together with a graph of the PCM liquid mass fraction over time. The results show that the longer the heating continues, the greater the fraction of PCM that melts; as the material absorbs heat and melts, the tank temperature rises, and the regions of higher temperature correspond to regions of lower pressure. Comparing the configurations reveals how material choice, sphere size, and melting point together determine how quickly and completely the storage medium charges with heat. By the end of this project, you'll be able to set up a transient Solidification and Melting simulation, define multiple PCM materials through their thermophysical properties, run a comparative study across geometry and melting-point variations, and interpret the liquid-fraction and temperature results that quantify PCM melting in a thermal storage tank.

DescriptionOne of the drawbacks of gasoline fuel is that its temperature drops during cold seasons or in cold locations. As the gasoline cools, sediments and gummy deposits form first; then, as the temperature continues to fall, the heavier hydrocarbon molecules begin to freeze and solidify. To prevent gasoline from freezing, the fuel temperature in the storage tank must be raised. One effective way to do this is to circulate a hot fluid through pipes inside the tank. Helical (spiral) tubes are particularly attractive in space-constrained situations, since they provide greater heat transfer within a given volume.The 3D geometry was created in Design Modeler. The model consists of two main parts: the fuel tank and an internal spiral tube carrying the hot water flow. Meshing was performed in ANSYS Meshing using an unstructured grid, with a total of 511,821 cells.MethodologyThis project simulates a gasoline fuel tank containing a single-pass spiral tube that runs through the tank. The inner tube carries water at a temperature higher than that of the gasoline, transferring heat to the fuel and thereby raising its temperature to prevent freezing inside the tank. To capture the phase change of the gasoline, the Solidification and Melting module is used to model the phase change material (PCM). The simulation is transient.ConclusionThe greatest degree of melting occurs in the region immediately surrounding the helical tube, where the hot water delivers the most heat. As the heating process continues, the liquid mass fraction steadily increases throughout the tank. The study was conducted over a limited time period, and the results presented here correspond to the end of the simulation.