Prandtl-K Macro, UDF, Prandtl Number — ANSYS Fluent CFD SimulationDescriptionThis project implements the Prandtl-K macro through a User-Defined Function (UDF) to customize turbulence modeling in ANSYS Fluent, demonstrated on turbulent flow through a channel with obstacles. UDFs let you extend Fluent's built-in turbulence models, and here the DEFINE_PRANDTL macro is used to supply custom Prandtl numbers to the turbulence kinetic energy and dissipation-rate equations — giving you direct control over parameters the standard interface fixes. As the opening project of the User-Defined Functions (UDF): Beginner CFD Training Package, it introduces the UDF workflow — writing, compiling, and hooking a macro into the solver — through a fundamental turbulence-modeling customization.MethodologyThe 2D geometry is modeled in Design Modeler and meshed in ANSYS Meshing with an unstructured grid of 97,972 cells, with obstacles placed in the channel to induce turbulence. The core of the project is a custom implementation of the Prandtl-number calculations for the turbulence kinetic energy (TKE) and turbulent dissipation rate (TDR), based on the RNG turbulence model, applied to the standard k-epsilon model through the DEFINE_PRANDTL macro. The UDF process is covered step by step: writing the custom Prandtl-number functions for TKE and TDR, implementing them in the DEFINE_PRANDTL macro, compiling and loading the UDF into ANSYS Fluent, and setting up the turbulence model to use the custom functions.AnalysisAfter running the simulations, a comparative study evaluates the effect of the custom UDF on the turbulence modeling, using comparative bar graphs of key parameters with and without the UDF, turbulence-variable contours, and velocity and pressure distribution comparisons. From these results you can see how the customized Prandtl numbers change the predicted turbulent flow around the obstacles, and assess the improvement in fidelity the UDF provides. This kind of customization enhances accuracy for complex turbulent flows and lets turbulence models be adapted to specific flow conditions, with applications from aerospace to industrial fluid dynamics. By the end of this project, you'll be able to write custom Prandtl-number functions, implement the DEFINE_PRANDTL macro, compile and load a UDF into ANSYS Fluent, and interpret a comparative study that quantifies how a UDF improves turbulence modeling.

Property Macro: Temperature-Dependent Viscosity UDF in ANSYS FluentDescriptionWelcome to the tenth chapter of our comprehensive User-Defined Function (UDF) Training Course. This module focuses on using the Property Macro to model temperature-dependent fluid viscosity in CFD simulations with ANSYS Fluent.In this simulation, water flow through a pipe is modeled under varying temperature conditions that affect the fluid's viscosity. The project demonstrates the power of User-Defined Functions for creating realistic material property models that enhance flow simulations. The main components of the simulation include the 3D modeling of a pipe using Design Modeler, structured meshing with 134,400 cells using ANSYS Meshing, and a CFD simulation in ANSYS Fluent with a custom UDF implementation that defines the viscosity variation.MethodologyThis approach takes advantage of ANSYS Fluent's UDF capabilities to define a temperature-dependent viscosity model. The core of the simulation lies in the custom implementation of the viscosity variation through a User-Defined Function. The viscosity is defined as a custom function based on temperature ranges, implemented through the DEFINE_PROPERTY macro for advanced material property definition, and integrated into the flow simulation as a piecewise viscosity model.The User-Defined Function plays a crucial role in accurately representing the fluid's behavior under varying temperature conditions. The implementation follows a clear sequence: first, the custom temperature-dependent viscosity function is written; next, the DEFINE_PROPERTY macro is implemented; the UDF is then compiled and loaded into ANSYS Fluent; and finally, the fluid properties are set up to use the custom viscosity model.ResultsAfter running the simulation, a thorough analysis is carried out to evaluate how effectively the custom UDF models the temperature-dependent viscosity and influences the flow characteristics. The results include dynamic viscosity contours on various cross-sectional planes, temperature–viscosity correlation plots, and a comparison of the flow patterns with and without the temperature-dependent viscosity.This simulation highlights the importance of accurate material property models in CFD, with applications ranging from industrial process engineering to thermal management systems. The benefits of custom viscosity models include improved accuracy in predicting flow behavior under varying temperature conditions, greater simulation fidelity for heat transfer problems, and the ability to model complex non-Newtonian fluids and their temperature dependence.The techniques covered in this module open up many possibilities for advanced CFD research and industrial applications, such as integrating pressure-dependent viscosity models, developing multi-variable property functions for complex fluids, and applying these methods to multiphase flows with varying material properties. By mastering the Property Macro and UDF implementation in ANSYS Fluent, you will be equipped to tackle complex fluid dynamics problems with a high degree of control over material properties—knowledge that is invaluable for simulating and optimizing systems involving temperature-sensitive fluids across many engineering disciplines, from chemical processing to HVAC system design.

Profile Macro, UDF, Pressure Profile — ANSYS Fluent CFD SimulationDescriptionThis project implements the Profile macro through a User-Defined Function (UDF) to create a realistic pressure distribution in an urban CFD simulation using ANSYS Fluent. Rather than applying a single uniform inlet pressure, the DEFINE_PROFILE macro defines a height-dependent pressure profile that captures the natural variation of atmospheric pressure with altitude — a boundary condition the standard interface cannot express directly. The case is demonstrated on a simplified urban environment, where accurate inlet conditions matter for realistic airflow. Within the User-Defined Functions (UDF): Beginner CFD Training Package, this project introduces the DEFINE_PROFILE macro, teaching how to set a spatially varying boundary condition through code.MethodologyThe 3D urban geometry is modeled in Design Modeler and meshed in ANSYS Meshing with a structured grid of 118,400 cells. The core of the project is a custom implementation of atmospheric pressure variation: a pressure function based on height (the Y-coordinate) is written and applied at the inlet boundary through the DEFINE_PROFILE macro, integrating an atmospheric-pressure model into the urban flow simulation. The UDF process is covered step by step: writing the custom pressure-profile function, implementing the DEFINE_PROFILE macro, compiling and loading the UDF into ANSYS Fluent, and setting up the inlet boundary condition to use the custom profile.AnalysisAfter running the simulation, the results are analyzed to evaluate how well the UDF creates a realistic pressure distribution. Post-processing includes pressure contours at the inlet boundary and on cross-sectional planes, vertical pressure-profile plots at the inlet and central domain locations, and a comparison of the UDF-generated profiles against theoretical atmospheric models to confirm the profile is correct. From these you can see how the height-dependent inlet pressure shapes the urban airflow more realistically than a uniform value would. This kind of custom profile improves the fidelity of urban airflow and tall-building aerodynamics, with applications from city planning to pollution-dispersion studies. By the end of this project, you'll be able to write a custom height-dependent profile function, implement the DEFINE_PROFILE macro, compile and load the UDF into ANSYS Fluent, and verify a UDF-generated boundary profile against a theoretical model.

DescriptionThis module uses ANSYS Fluent with a custom User-Defined Function (UDF) to simulate water flow through a porous medium channel, applying the DEFINE_SOURCE macro to implement a custom momentum source term. Rather than relying on Fluent's built-in porous media options, this project demonstrates how a UDF can directly modify the z-direction momentum equation to capture porous medium effects, offering greater flexibility and control over the modeled behavior.MethodologyThe 3D channel geometry is built in DesignModeler and meshed in ANSYS Meshing using a structured grid of 256,000 cells. The custom source term is formulated as a function of velocity and position, implemented via the DEFINE_SOURCE macro, with a source term derivative included for solver stability. The UDF is compiled and loaded into ANSYS Fluent, then integrated into the flow model to replace standard porous media treatment.ConclusionResults include 2D and 3D pressure contours, pressure gradient visualizations, and longitudinal pressure change plots, demonstrating the custom UDF's ability to capture porous media effects on the flow. This approach extends to applications such as groundwater flow and industrial filtration, and provides a foundation for more advanced UDF-based work involving multiphase flow through heterogeneous porous media, coupled heat transfer and reactions, or adaptive source terms for dynamic porous media behavior.

DescriptionThis lesson covers the CG-MOTION macro for prescribing reciprocating motion on a rigid body within ANSYS Fluent's dynamic mesh framework. The test case is a cubic object undergoing reciprocating translation inside a computational domain, chosen to demonstrate how a custom user-defined function can drive complex, prescribed object motion rather than relying on Fluent's built-in motion definitions. The 3D geometry is built in Design Modeler and meshed in ANSYS Meshing with an unstructured grid of 143,423 cells.MethodologyThe simulation uses Fluent's Dynamic Mesh model to deform the mesh around the moving object, with the object itself defined as a rigid body. The reciprocating translation is implemented through a custom velocity function written into the DEFINE_CG_MOTION macro, which is then compiled and loaded into Fluent and linked to the Dynamic Mesh setup so the mesh motion follows the UDF-prescribed velocity at every time step rather than a fixed or externally-solved motion.AnalysisThe results include time-dependent geometry snapshots, the motion path traced by the object, and plots of velocity and position over time. Together these confirm the UDF is driving the reciprocating motion as intended, correctly reversing direction and velocity at each cycle. The approach generalizes directly to applications with prescribed or periodic motion, such as piston engines or industrial mixing equipment, and provides a foundation for extending into coupled fluid-structure interaction, multiple independently-moving objects, or motion patterns that adapt to local flow conditions.

Pulsatile Blood Flow in Arterial Bifurcation — ANSYS Fluent CFD SimulationDescriptionThis project presents an ANSYS Fluent simulation of time-dependent pulsatile blood flow through a simplified arterial bifurcation model. Blood flow through the arteries isn't steady — it pulses with the cardiac cycle — and reproducing that rhythmic behavior is exactly the kind of custom boundary condition a UDF makes possible. Here a User-Defined Function drives the pulsatile inlet, and the simulation reveals where an arterial bifurcation is most vulnerable to disease. Within the User-Defined Functions (UDF): Beginner CFD Training Package, this project applies the profile-macro skills to a biomedical case, using a UDF-defined transient inlet to capture the pulsating cardiac cycle.MethodologyThe fluid domain was created in Design Modeler, with mesh generation performed in ANSYS Meshing using an unstructured mesh of 168,367 elements. The blood mass flow rates are specified as 0.001570178 kg/s at the inlet and 0.00078576 kg/s at each outlet, with an inlet blood pressure of 250 Pa (approximately 1.87515 mmHg) — noting that physiological pressure in major arteries typically ranges between 80 and 120 mmHg. The pulsatile characteristics of the blood flow are captured through a User-Defined Function (UDF), which modulates the inlet velocity as a sinusoidal function of time, replicating the cardiac cycle's rhythmic nature. A transient solver provides the time-resolved flow data.AnalysisThe transient results are presented at t = 0.162 s, corresponding to peak systolic velocity, and yield clinically relevant insights into arterial pathology. The pressure-contour analysis reveals critical stress concentrations at the bifurcation apex, where the flow streams diverge: blood pressure reaches 125 Pa there — about half the inlet pressure — identifying this region as vulnerable to arterial wall rupture. The wall shear stress (WSS) distribution identifies areas susceptible to stenosis: consistent with medical literature establishing low WSS as a stenosis predictor, the bifurcation apex exhibits minimal shear-stress values, indicating heightened risk for atherosclerotic plaque development and subsequent arterial narrowing. By the end of this project, you'll be able to implement a UDF that drives a sinusoidal pulsatile inlet velocity, run a transient biomedical flow simulation, and interpret the time-resolved pressure and wall-shear-stress fields that reveal where an arterial bifurcation is prone to rupture and stenosis.

Sloshing of a Tanker Truck — ANSYS Fluent CFD SimulationThis project simulates the sloshing behavior of liquid inside a tanker truck during braking, using ANSYS Fluent. The two-phase flow field is modeled using the Volume of Fluid (VOF) method, with air as the primary phase and water as the secondary phase. The truck is traveling at 15 m/s and decelerates to a stop over 3 seconds, meaning the water inside the tank experiences both gravitational acceleration and braking deceleration during this period.Geometry and MeshThe geometry was created in SpaceClaim, with the tanker measuring 12,300 × 1,900.1867 mm. The model was meshed in ANSYS Meshing using a structured mesh throughout the domain, totaling 233,700 cells.Setup and AssumptionsGiven the incompressible nature of the flow, a pressure-based, transient solver is used. Gravity is set to -9.81 m/s² along the Y-axis, while braking deceleration is applied along the X-axis as 5 m/s² for the first 3 seconds and zero afterward, defined through a time-dependent expression.The multiphase model is set to VOF with two phases—air (primary) and water (secondary)—using sharp interface modeling, explicit formulation, and a constant surface tension coefficient of 0.072 N/m. Turbulence is modeled using the realizable k-epsilon model with scalable wall functions.Air is defined with a density of 1.225 kg/m³ and viscosity of 1.7894×10⁻⁵ kg/m·s, while water-liquid has a density of 998.2 kg/m³ and viscosity of 0.001003 kg/m·s.The SIMPLE scheme is used for pressure-velocity coupling, with PRESTO! for pressure and second-order upwind discretization for momentum, turbulent kinetic energy, and turbulent dissipation rate. The volume fraction is solved using a compressive scheme. The domain is initialized using the standard method, with the water region patched to a volume fraction of 1.The simulation runs with a time step size of 0.002 s, a maximum of 20 iterations per time step, and a total of 5,000 time steps.ResultsUpon completion, contours of velocity, pressure, water volume fraction, eddy viscosity, streamlines, and turbulence intensity are extracted. The results show that under the combined effects of gravity and braking deceleration, the water inside the tanker shifts and impacts the front wall of the tank. After the 3-second braking period ends, the truck comes to rest and gravity becomes the only force acting on the water.

Cylinder Piston Motion (UDF-Driven Dynamic Mesh) — ANSYS Fluent CFD SimulationDescriptionThis project simulates the motion of a four-stroke engine's cylinder-piston system in ANSYS Fluent, built around a User-Defined Function (UDF) that drives the piston's dynamic mesh motion. Inside an internal combustion engine, the piston moves through four stages — intake, compression, power, and exhaust — while the valves open and close in sequence, and here that reciprocating motion is defined through a compiled UDF rather than a preset option. The simulation captures the complete cycle: the piston descending as the intake valve opens, the flow compressing as the piston ascends, the piston reaching top dead center at the power stroke, and the exhaust valve opening as the piston descends again. Within the User-Defined Functions (UDF): Beginner CFD Training Package, this project combines a UDF with dynamic mesh, showing how custom code drives a moving boundary in a real engine problem.MethodologyThe geometry is created in Design Modeler and meshed in ANSYS Meshing, establishing the computational domain of the cylinder-piston assembly and its valve regions. The piston's reciprocating motion is defined through a compiled UDF applied via the In-Cylinder dynamic mesh option, with the key parameters — crank radius, connecting-rod length, and piston stroke cutoff — specified to govern the piston's kinematic behavior. The UDF implements the full-piston motion function, driving the boundary movement of the piston surface throughout the four strokes. Building on this, rigid body motion is applied to the piston surface and valves, with profiles describing the time-varying valve lift, while deforming and stationary mesh zones are configured to accommodate the moving boundaries without degrading mesh quality. The dynamic mesh model works in conjunction with the UDF-defined reciprocating motion, and the case is solved with a transient approach and solver settings chosen to maintain stability as the mesh deforms and moves through each stroke.AnalysisPressure and velocity contours are analyzed throughout the piston cycle, and animations of the mesh deformation and resulting flow behavior are generated to verify the correct operation of the UDF-driven cylinder-piston system across all four strokes. From these results you can follow how the flow is drawn in, compressed, and expelled through the cycle, and confirm the UDF drives the piston and valve motion correctly. By the end of this project, you'll be able to write and compile a UDF that drives piston motion through the In-Cylinder dynamic mesh option, define crank parameters and valve-lift profiles, configure deforming and rigid-body mesh zones, and interpret the pressure and velocity fields of a UDF-driven four-stroke engine cycle.

Offshore Pipeline Considering Hydrodynamic Force, ANSYS Fluent CFD Simulation TrainingDescriptionThis project simulates seawater flow around an offshore pipeline using ANSYS Fluent.Offshore pipelines are a core part of marine engineering, carrying oil, gas, and other resources across the seabed between platforms and shore. As seawater waves pass over these pipelines, they generate drag and lift forces on the pipe. To keep the line safe and stable, it must be positioned so that it experiences the lowest possible hydrodynamic loading, which makes this kind of analysis important for offshore pipeline design.The 2-D model was built in ICEM and consists of a rectangular seawater domain with a circular cross-section representing the pipe. Two key geometric parameters govern the study: the pipe diameter (D) and the gap between the bottom of the pipe and the seafloor (e), expressed through the e/D ratio. The pipe diameter is fixed at 0.4 m, and two cases are considered, e = 0.2 m and e = 0.1 m, giving e/D = 0.5 and e/D = 0.25. The seawater domain is 12 m long and 3.24 m high.The model was meshed in ICEM using a structured grid of 135,417 elements. To capture the flow accurately, the mesh is refined near the pipe: the circumference of the circular section is split into five segments, and the cells closest to the pipe are smaller and of higher quality.Simulation MethodologyThe main geometric variable in this study is the pipe-to-seafloor gap ratio (e/D). Because the seawater motion is wavy rather than steady, the inlet velocity is defined as a wave-flow equation through a UDF. Likewise, the pressure inside the seawater is measured relative to atmospheric pressure and varies with the wave motion, so the wave (ambient) pressure is also imposed through a UDF. In total, the inlet horizontal velocity, the relative wave pressure, the turbulent kinetic energy, and the turbulence dissipation rate are all defined as UDFs.The goal is to compare the hydrodynamic forces on the pipeline over one full wave period and identify the optimal configuration. The seawater wavelength (the distance between two wave peaks) is 163.20 m, with a corresponding period of 10.3 s, giving a wave angular frequency of 2π/Tw = 2π/10.3 ≈ 0.61 rad/s. The maximum velocity at a wave peak is 2.729 m/s, and k_m and ε_m denote the maximum turbulent kinetic energy and the maximum turbulence dissipation rate, respectively.In the wave-pressure equation, H is the wave height and d is the seawater depth. The term −z is the height of the water column at the point where the dynamic pressure is evaluated, and d − (−z) is the distance from that point down to the seabed.Results & ConclusionAfter solving, we obtained two-dimensional contours of velocity and pressure, along with two-dimensional velocity vectors, for both cases (e/D = 0.5 and e/D = 0.25). These results are taken at the final instant of the simulation (10.3 s), i.e., at the end of one complete wave period.We also obtained time-history graphs of the drag and lift hydrodynamic forces and of the drag and lift coefficients, again for both e/D cases. Comparing the two configurations shows how the pipe's distance from the seabed affects the hydrodynamic loading, which is what determines the optimal placement of the line.

DescriptionThis project simulates the use of Dielectric Barrier Discharge (DBD) plasma actuators to prevent boundary layer separation on a NACA 0012 airfoil using ANSYS Fluent. Flow separation on the suction side of an airfoil reduces lift, increases drag, and in severe cases causes stall, all driven by the boundary layer losing enough energy to detach from the surface. DBD actuators address this without moving parts: a high voltage difference between cathode and anode ionizes the surrounding air into plasma, generating an electrical current that in turn produces a magnetic field, which acts on the ions in the boundary layer and adds momentum back into the flow, suppressing separation. The airfoil has a chord length of 0.1 m and carries two DBD actuators, one near the point of maximum thickness and one near the trailing edge. Two flow cases are studied: 50 m/s at 15° angle of attack, and 20 m/s at 12° angle of attack. The geometry is built in SpaceClaim and meshed in ANSYS Meshing with a structured grid of 463,701 elements.MethodologyTurbulence is resolved with the k-epsilon model, and the energy equation is active. The electromagnetic physics — voltage distribution, and the x and y components of the magnetic vector potential — are introduced through three User-Defined Scalars (UDS), with their steady-state transport equations solved alongside the flow equations. A source term in the magnetic field transport equations is added via a UDF, using an electrical diffusion coefficient of 0.5 for ionized air and a magnetic permeability of 1.2566×10⁻⁴. Current density components are computed from the potential field, and the resulting electromagnetic body forces are fed back into the flow through three additional source terms: one each in the energy equation, x-momentum equation, and y-momentum equation, all implemented via UDFs. User-Defined Memory (UDM) is used to store and expose the current density vector for post-processing. Air density follows the ideal gas law throughout.AnalysisVelocity and pressure contours around the airfoil, along with velocity vectors, are obtained for both flow cases, confirming that the plasma-induced body force effectively suppresses boundary layer separation that would otherwise occur at these angles of attack. Lift coefficient results and surface pressure distributions along the airfoil are reported for each case, quantifying how much the DBD actuation improves aerodynamic performance relative to an uncontrolled baseline, and demonstrating that the UDF-based electromagnetic coupling is delivering the intended momentum addition to the boundary layer.