Heat Sink Cooling CFD Simulation, ANSYS Fluent TrainingDescriptionHeat sinks are the most widely used thermal management solution in electronic and mechanical systems, protecting components from overheating — the leading cause of electronic device failure. Their performance depends on fin geometry, material properties, and the surface area available for heat exchange with the surrounding air. This project, the opening episode of the Electrical & Power: Beginner CFD Training Package, provides a practical introduction to heat sink cooling simulation in ANSYS Fluent.The case is a classic conjugate heat transfer (CHT) problem: heat generated by an electronic component conducts through the solid structure of the heat sink and is then removed by convection into the airflow passing over the fins. Understanding this combined conduction–convection mechanism, along with the boundary layers that develop around the fin surfaces, is the essential first step for anyone entering the field of electronics cooling and thermal design.MethodologyThe heat sink geometry, including both the solid body and the surrounding air domain, is prepared and imported into ANSYS Fluent. The mesh is generated so that both the solid and fluid zones are properly resolved — a critical requirement for capturing the heat exchange at the solid–fluid interface accurately.The thermal load is applied as a realistic heat source representing the operating electronic component, while appropriate boundary conditions define the temperature, pressure, and velocity of the cooling air. The energy equation is enabled to solve the conjugate heat transfer between the solid heat sink and the airflow, and a suitable turbulence model is configured to capture the complex flow behavior around the fins.AnalysisAt the end of the solution process, temperature contours and velocity vectors are extracted to evaluate the cooling performance of the heat sink. The temperature distribution shows how heat conducts through the fins and dissipates into the air, while the velocity field reveals the airflow patterns between the fins and the development of thermal boundary layers along their surfaces.From these results, the effectiveness of the heat sink can be assessed: identifying hot spots where heat dissipation is inefficient, evaluating the thermal resistance of the design, and recognizing opportunities for geometric optimization. By completing this project, you will be able to set up a basic conjugate heat transfer simulation, define heat sources and cooling air conditions, and interpret thermal and flow results — the foundational skills for all the electronics cooling projects that follow in this package.

Heat Sink Cooling with a Porous Medium, ANSYS Fluent CFD TutorialDescriptionThis project simulates fluid flow inside a porous medium used for heat sink cooling with ANSYS Fluent. The study of fluid flow in porous media is one of the most widely applied fields in science and engineering: a porous medium consists of a perforated material containing pores and void spaces, which greatly increases the surface area available for heat exchange. In this case, a porous aluminum foam in contact with a heat source acts as the heat sink — the coolant flows through the foam's internal structure and absorbs heat far more effectively than flow over a plain solid surface.The model is designed in three dimensions using Design Modeler. The geometry consists of a hollow inlet section followed by the porous aluminum foam, which is in direct contact with the heat source. The mesh is generated in ANSYS Meshing as a structured grid with a total of 7,680 cells.MethodologyThe fluid flow and heat transfer inside the porous medium are simulated in ANSYS Fluent. The coolant enters through the inlet boundary with a velocity of 1.99 m/s at a temperature of 300 K, passes through the porous zone, and absorbs the heat transferred into the foam from the attached heat source.The energy equation is enabled to solve the heat transfer between the porous medium and the fluid, and turbulence is modeled using the RNG k-epsilon model with the standard wall function.AnalysisAt the end of the solution process, the temperature, velocity, and pressure fields are obtained, along with streamlines and velocity vectors. The temperature results show how the fluid heats up as it passes through the porous zone, carrying thermal energy away from the heat source.The pressure contour reveals a key characteristic of porous media flow: the pressure behind the porous medium differs significantly from that at the outlet boundary, due to the flow resistance the porous zone imposes on the system. This pressure drop is the trade-off for the enhanced heat transfer — an essential design consideration when using porous inserts in cooling applications. By completing this project, you will learn to define a porous zone in ANSYS Fluent, set up conjugate heat transfer with a heat source, and evaluate the balance between cooling performance and pressure loss.

IGBT Heat Sink Cooling CFD Simulation, ANSYS Fluent TrainingDescriptionThis project simulates the cooling of an IGBT heat sink using ANSYS Fluent. An insulated-gate bipolar transistor (IGBT) is a three-terminal power semiconductor device, widely used as an electronic switch in inverters, motor drives, and power converters. During operation, IGBTs generate considerable thermal energy, and excessive heat directly degrades their performance and lifespan. Applying an effective cooling strategy — such as an air- or liquid-cooled heat sink — dissipates this surplus heat, enabling higher power densities, better performance, and more compact module designs.In this simulation, the heat sink is in contact with a heat source applying a flux of 14,583 W/m² on one surface, while air flows across the opposite surface at a mass flow rate of 0.25 kg/s, serving as the primary cooling mechanism for the assembly.MethodologyThe simulation geometry includes both the heat source and the heat sink. The model is designed and meshed in Gambit® software, using an unstructured grid with a total of 11,872,367 elements to resolve the details of the flow and thermal fields.The energy equation is activated to model the heat transfer between the heat source, the heat sink body, and the cooling airflow. Given the flow conditions in this configuration, the laminar viscous model is applied to resolve the airflow characteristics throughout the system.AnalysisAt the end of the solution process, temperature distributions, velocity profiles, surface heat flux patterns, and Nusselt number contours are extracted and examined. The results clearly demonstrate how the cooler airflow reduces the temperature of the heat sink as it passes over its surface.The thermal exchange between the cold air stream and the heat source successfully lowers the overall system temperature, confirming that the cooling mechanism achieves the project's objective. The simulation validates the effectiveness of the selected air-cooling approach for IGBT thermal management — a directly applicable result for power electronics design, where reliable heat dissipation determines both the power rating and the physical size of the module. By completing this project, you will learn to apply a surface heat flux boundary condition, model laminar convective cooling, and evaluate heat sink performance through temperature, heat flux, and Nusselt number analysis.

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.

Battery CFD Simulation Concepts in ANSYS Fluent: A Comprehensive OverviewWelcome to the 2nd chapter of our Battery Training Course. In this training video, we describe the Battery Model Concepts in ANSYS Fluent software. We provide you with a detailed and comprehensive tutorial; so that you will master all concepts of the battery model without any problems.Introduction to BatteryIn the first step, we present a general introduction to the battery. This introduction provides a basis for using the battery model in ANSYS Fluent.Battery MechanismBattery Geometry DefinitionSingle Battery and Battery PackFundamental Battery ConceptsIn the next step, we discuss battery concepts in ANSYS Fluent software. We provide the different solution methods of the battery model and corresponding formulations; so that you could set up the battery model settings with advanced knowledge.Battery Solution MethodsFor example, we introduce different solution methods for coupling thermal and electrochemical behaviors. We describe these solution methods comprehensively and study the related governing equations.CHT Coupling MethodFMU-CHT Coupling MethodCircuit Network Solution MethodMSMD Solution Method (Multi-Scale Multi-Domain)Battery electrochemistry modelsIn the solution methods, potential and energy equations are solved in ANSYS Fluent. We introduce different electrochemical models for computing the source terms in equations such as the current transfer and heat generation rate.NTGK ModelEquivalent Circuit Model (ECM)Newman P2D ModelIn different electrochemical models, we explain all relations and the corresponding coefficients. Then, we refer to the model parameters and their dependence on DoD (depth of discharge) and SoC (State of Charge).Battery Pack definitionAfter an introduction to electrochemical models, we focus on the computational domain of the model. So, we define battery cell, battery module, and battery pack. We mention the comparison between parallel and series connections, and the nPmS pattern arrangement.Then, we introduce the different types of connections in battery packs.Real ConnectionsVirtual Connections (Tab Surface Based and Active Zone Volume Based)Battery Advanced OptionsIn addition, we mention a series of optional capabilities and tools in battery modeling.Thermal Abuse ModelBattery Life Model (Cycle Life Loss and Calender Life Loss)Pack Builder ModelBattery Model Settings in ANSYS FluentIn the final step, we discuss the battery model settings in ANSYS Fluent. We review all the steps necessary for a battery simulation process.so, we explain all settings tabs of the battery model in ANSYS Fluent.ّModel OptionsConductive ZonesElectric ContactsModel ParametersAdvanced OptionsIn battery simulation, we specify the operating conditions during the battery charging/discharging. Hence, we can use different electrical parameters.C-rateCurrentVoltagePowerResistanceProfile (Time-Schedules and Event-Scheduled)Why This Episode Is Crucial for Your Battery CFD JourneyThis foundational episode equips you with:A comprehensive understanding of battery principlesInsight into ANSYS Fluent’s capabilities for battery simulationPractical knowledge of setting up various electrochemistry modelsBy mastering these concepts, you’ll be well-prepared to tackle more advanced battery simulations in subsequent chapters of the course.Target AudienceThis episode is ideal for:Beginners in battery CFD simulationExperienced CFD users new to battery modelingResearchers and engineers looking to refresh their battery simulation fundamentalsLearning OutcomesAfter completing this episode, you will:Understand the core principles of battery cell and battery packBe familiar with ANSYS Fluent’s battery modeling capabilitiesKnow how to set up different solution methods and electrochemistry models in ANSYS FluentBe prepared for more advanced battery simulations in future episodesEmbark on your electrolysis CFD simulation journey with this comprehensive introduction, setting a strong foundation for the exciting chapters ahead!

DescriptionThis project simulates the airflow over the impeller of an electric motor using ANSYS Fluent, investigated through CFD analysis. In an electric motor, this impeller acts as a cooling fan, driving air over the machine to carry away the heat generated by electrical losses in the windings and core — making its aerodynamic performance an important consideration in electrical and power machine design, since a motor's temperature limits its continuous rating, efficiency, and insulation life.Turbomachines, also known as fluid machines, are widely used across industry, so understanding their behavior in the surrounding fluid is essential. They fall into two broad categories: the first transfers energy to the fluid, while the second extracts energy from the fluid and delivers it to the system in various forms. Fans and compressors belong to the first group, while wind and water turbines belong to the second. An electric-motor impeller is itself a turbomachine of the first type, and studying the motion of its blades within the surrounding flow helps analyze its behavior and ultimately improve the design and material selection of the blades. Here, the airflow over the impeller is examined. Air enters the computational domain at 80 m/s, and the impeller rotates at 1000 rpm. The geometry was created in Design Modeler and meshed in ANSYS Meshing using an unstructured grid of 1,786,708 cells.MethodologyThe rotation of the impeller is modeled using the Multiple Reference Frame (MRF), or Frame Motion, approach. In this method, the fluid around the impeller blades is treated as rotating while the blades themselves are held stationary, with the rotational velocity of the fluid set equal to that of the impeller. This is applied through the MRF tool in the Cell Zone Conditions — an efficient way to capture the steady rotating-blade behavior without physically moving the mesh.ConclusionOn completion of the solution, contours of pressure, velocity, and temperature were obtained, along with pathlines and velocity vectors around the blades. The pathlines clearly reveal the rotational motion of the flow around the impeller. The pressure contour shows higher pressure on the front face of the impeller, where it meets the incoming airflow, and a large pressure drop behind it. The velocity contour shows the velocity increasing radially, reaching its maximum around the blade tips — a clear signature of the impeller's rotation. Together, these results characterize how the impeller moves air through the motor, providing the kind of insight into cooling airflow and blade loading that supports the thermal management and design of electrical machines.

Server Room Cooling CFD Simulation with 6 CabinetsDescriptionServer 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 outside 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 system keeps every rack within the safe thermal range. The case demonstrates how CFD serves as a practical design-verification tool: instead of guessing whether a cooling configuration is adequate, the simulation shows exactly where hot regions form and how changing the supply airflow resolves them.MethodologyThe room is modeled in three dimensions in Design Modeler, measuring 7 × 4 × 2 m, with six server cabinets — each 1 × 0.6 × 1.8 m — arranged inside 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. Since forced convection dominates over natural convection in this configuration, the air density is taken as constant. The key design variable is the inlet air velocity, which is studied at two values — 0.5 m/s and 1 m/s — to evaluate how the supply airflow rate affects rack cooling. The objective is to find the conditions that hold the entire room below the safe 32 °C limit.AnalysisAt the end of the solution process, 2-D and 3-D contours of temperature and streamlines are generated, along with plots of the maximum and average air temperature. The results tell a clear engineering story: at the lower inlet velocity of 0.5 m/s, the thermal requirement is not satisfied — parts of the room exceed the safe 32 °C limit. Raising the inlet velocity 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 — a quantitative demonstration of the relationship between air supply and thermal safety that underpins real data-center design. By completing this project, you will be able to set up a 3-D 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.

Heller Dry Cooling Tower CFD SimulationDescriptionThis project simulates the airflow and heat transfer inside a Heller-type dry cooling tower — the indirect cooling system used in thermal power plants to reject heat from the working fluid to the ambient air without evaporative water loss. After leaving the condensers, the hot water is pumped through a ring of air-cooled heat exchangers at the base of the tower; cooling air is drawn through them by natural draft, created by the density difference between the warm air inside the tower and the cooler air outside.The core physics governing tower performance is the temperature driving force: the larger the gap between the working fluid and the ambient air, the stronger the natural draft and the more effective the cooling. This is also why dry cooling towers lose efficiency in summer — as the ambient temperature rises, the driving force shrinks, forcing the plant to cut power output and increase water consumption to maintain cooling. This simulation makes that buoyancy-driven mechanism directly visible and quantifiable.MethodologyThe model includes the cooling tower, the heat-exchanger (radiator) ring, the surrounding flow domain, and the air inlet. The geometry is built and meshed in GAMBIT software with an unstructured mesh of 1,343,988 cells.The case is solved as a steady-state problem using a pressure-based solver. Gravity is enabled at −9.81 m/s² in the Y direction — an essential setting, since the natural draft is entirely buoyancy-driven. The energy equation is activated, and turbulence is modeled with the standard k-ε model with standard wall functions.The boundary conditions reproduce the natural-draft configuration: the inlet is defined as a pressure inlet at 0 Pa gauge total pressure (normal to boundary), and the outlet as a pressure outlet at 0 Pa gauge with a backflow temperature of 303 K. The heat-exchanger ring is modeled as a heated radiator surface at 318 K with a heat generation rate of 14,861.52 W/m³, with its shadow face at 313 K. The tower walls are stationary with zero heat flux.The solution uses SIMPLE pressure–velocity coupling with first-order upwind discretization for the momentum, energy, and turbulence equations (standard scheme for pressure and density), initialized at 303 K.AnalysisAt the end of the solution process, contours of velocity, pressure, and temperature are extracted, along with flow streamlines through the tower. Together, these results reveal the complete natural-draft mechanism: ambient air is drawn in through the heat exchangers, heats up as it crosses the radiator ring, loses density, and rises through the tower — sustaining the draft without any fan or external power.The temperature field across the radiator ring shows the cooling capacity available to the plant, directly linking the simulation results to power plant performance. By completing this project, you will learn to set up a buoyancy-driven natural-draft flow, represent a heat-exchanger ring using a heated radiator surface with a volumetric heat generation rate, configure a steady pressure-based solver with the energy equation, and interpret cooling-tower performance from temperature and streamline fields.

DescriptionThis project simulates the water flow through a Francis hydraulic turbine using ANSYS Fluent. As a cornerstone of hydroelectric power generation, a water turbine is a turbomachine that converts the kinetic energy of flowing water — or the potential energy stored in a head (height) difference — into mechanical rotational motion, which is subsequently transformed into electrical power by a coupled generator. The Francis turbine is one of the most widely deployed turbine types in power plants because the arrangement of its blades allows it to harness kinetic and potential energy simultaneously, making it highly effective across a broad range of head and flow conditions.In operation, water first enters the volute (spiral casing), whose circular geometry imparts a rotational (swirling) component to the incoming flow. This swirl ensures the fluid strikes the blades at the correct angle, maximizing operational efficiency. The flow is then delivered at a controlled rate to the runner blades, where the momentum of the water drives the runner and produces useful mechanical work. Finally, the water exits the runner in an axial direction. In the present case, water enters the turbine's inner chamber at a mass flow rate of 1.996 kg/s, with the runner blades rotating at 158 rpm.MethodologyThe rotation of the blades is modeled using the Multiple Reference Frame (MRF) approach, also known as frame motion. In this method, the fluid region surrounding the blades is assigned a rotational motion, while the blades themselves are held stationary relative to that rotating frame — effectively reproducing the rotational flow field around the runner without physically moving the mesh. The geometry was built in Design Modeler and consists of two main components: fixed walls carrying stationary vanes at fixed angles, and moving walls carrying the rotating vanes. Meshing was performed in ANSYS Meshing using an unstructured grid of 4,653,160 elements, with local refinement applied near the blades to better capture the flow behavior in these critical regions.ConclusionOn completion of the solution, two- and three-dimensional contours of pressure, velocity, path lines, and velocity vectors were extracted. As expected, the peak velocity occurs in the immediate vicinity of the rotating blades. A full set of performance results can be derived from the simulation, including a pressure drop of approximately 2.3 × 10³ Pa across the turbine.