💥💥💥 Ansys Fluent 2024 R1: Unleash the Power of GPUs for Supercharged Simulations

**Brace yourself, engineers and researchers!** Ansys Fluent 2024 R1 is here, and it's packing some serious heat – thanks to the **revolutionary power of GPUs**. Buckle up as we explore the exciting new capabilities that will **turbocharge your simulations** and send them into overdrive.

**GPU Power Unleashed:**

* **Imagine simulations running at lightning speed.** The **redesigned Fluent GPU solver** leverages the full potential of GPUs, **slashing simulation times** compared to traditional CPU approaches. This translates to **faster results, quicker turnaround times, and more productive workflows.**

* **No longer limited to simple problems.** The GPU solver now tackles a **broader range of physics**, including complex phenomena like **discrete phases, radiation, and even coupled simulations**. It's like having a **supercomputer at your fingertips** to handle even the most challenging scenarios.

**Beyond Speed: A World of New Possibilities:**

* **Say goodbye to hardware limitations.** Ansys Fluent 2024 R1 expands its horizons with **beta support for AMD GPUs**, giving you more flexibility in choosing the hardware that best suits your needs.

* **Welcome to the future of simulation control.** The **groundbreaking Fluent Web UI** allows you to **monitor and adjust simulations in real-time** from any web browser. Imagine the freedom of **controlling your simulations remotely**, no matter where you are.

* **Customization never looked so good.** The **enhanced PyFluent module** within the PyAnsys ecosystem empowers you to **tailor your workflows** and **automate tasks** with greater ease. It's like having a **personal coding assistant** to streamline your simulation process.

**Ansys Fluent 2024 R1 isn't just an upgrade; it's a game-changer.** It's a powerful testament to the future of CFD simulations, pushing the boundaries of speed, capability, and user experience. So, get ready to **experience the thrill of supercharged simulations** and take your engineering projects to the next level!

SUMMARY:

Ansys Fluent 2024 R1 brings significant advancements in GPU support and capabilities, offering substantial benefits for users running complex simulations. Here's a breakdown of the key highlights:

**Enhanced Solver Performance:**

* **New Physics and Models:** The Fluent GPU solver now supports a wider range of physical phenomena, including:

    * Discrete phase model (DPM)

    * Radiation models

    * Sliding mesh and non-conformal interfaces

    * Beta support for Python-based user-defined functions (UDFs)

    * Coupled solver support

    * Stiff chemistry solver support for combustion applications

* **Increased Efficiency:**

    * CPU lightweight set-up mode reduces CPU memory usage before solving on GPUs, improving overall efficiency.

**Expanded Hardware Compatibility:**

* **Beta support for AMD GPUs:** In addition to NVIDIA GPUs, Ansys Fluent 2024 R1 offers beta support for AMD GPUs, providing users with greater hardware flexibility.

**New functionalities in Ansys Fluent 2024 R1 include:**

* **Fluent Web UI:** This web-based interface allows remote access to simulations from a web browser, enabling real-time monitoring and adjustments, enhancing user productivity and flexibility.

* **PyAnsys Enhancements:** The PyFluent module within the PyAnsys ecosystem receives upgrades for efficient workflow interfaces and API capabilities, empowering users with more customization options.

Source:

https://resources.randsim

MORE INFO ABOUT Fluent Web UI

The **Fluent Web UI** is a groundbreaking new feature introduced in Ansys Fluent 2024 R1 that promises to revolutionize the way you interact with your simulations. Here's a deeper dive into its capabilities:

**Remote Access and Monitoring:**

* **Imagine accessing your simulations from anywhere, anytime.** With the Fluent Web UI, you can **monitor simulation progress** in real-time from any web browser, eliminating the need to be physically tethered to your workstation. This empowers you with greater **flexibility and control** over your workflow.

**Real-time Adjustments:**

* **Say goodbye to waiting for simulations to finish before making adjustments.** The Fluent Web UI allows you to **modify certain simulation parameters** on the fly based on the observed results. This enables you to **fine-tune your simulations** more efficiently and potentially **save valuable time and resources**.

**Enhanced Collaboration:**

* **Facilitate seamless collaboration** with colleagues or clients by sharing access to the Fluent Web UI. This allows them to **visualize simulation progress**, gain insights into results, and potentially **provide feedback** remotely, fostering better communication and teamwork.

**Potential Benefits:**

* **Increased productivity:** Remote monitoring and real-time adjustments can save time and effort, allowing you to focus on other tasks while simulations run.

* **Improved decision-making:** Real-time insights into simulation progress can help you make informed decisions about parameter adjustments and potentially avoid costly errors.

* **Enhanced collaboration:** Sharing access to simulations through the web UI fosters better communication and collaboration within teams.

**Current Limitations:**

* **Limited functionality:** The initial release of the Fluent Web UI offers a subset of functionalities compared to the traditional desktop interface. However, future updates are expected to expand its capabilities.

* **Security considerations:** Implementing proper access controls and security measures is crucial when sharing simulation data through the web UI.

**Overall, the Fluent Web UI represents a significant step forward in user experience for Ansys Fluent. It offers a glimpse into the future of simulation control, paving the way for more flexible, collaborative, and efficient workflows.**

💥💥💥 What are capabilities of SU2 (cfd software) ?

 SU2 is a suite of open-source software tools for the numerical solution of partial differential equations (PDEs) and performing PDE-constrained optimization. It has applications in computational fluid dynamics, aerodynamic shape optimization, electrodynamics, linear elasticity, heat equation, wave equation and thermochemical non-equilibrium. It also supports continuous and discrete adjoint methods for sensitivity analysis and optimization. You can find more information about SU2 on its official website or its Wikipedia page. 😊

You can model:

• External aerodynamics: You can simulate the flow around aircraft, cars, buildings, wind turbines, etc. using the Euler, Navier-Stokes or RANS equations. You can also perform shape optimization and sensitivity analysis using the adjoint methods.
• Internal flows: You can model the flow in ducts, pipes, nozzles, turbines, compressors, etc. using the same equation sets as above. You can also use the turbomachinery capabilities of SU2 to perform single-passage or full-annulus simulations and aeroelasticity analysis¹.
• Multiphysics problems: You can couple the flow solver with other physical models, such as electrodynamics, linear elasticity, heat equation, wave equation and thermochemical non-equilibrium. You can also use the FSI capabilities of SU2 to model fluid-structure interactions.
• Discontinuous Galerkin methods: You can use the DG solver to discretize the flow equations on unstructured grids with higher-order accuracy and stability. You can also use the DG solver for problems involving shocks, discontinuities and complex geometries.

• Incompressible flows: You can use the artificial compressibility method or the pressure-based solver to simulate the flow of incompressible fluids, such as water or blood, using the Navier-Stokes equations. You can also use the turbulence models available in SU2 to account for the effects of viscosity and eddy formation.
• Compressible flows with shocks: You can use the Roe, AUSM, or HLLC schemes to capture the shock waves that arise in high-speed flows, such as supersonic jets or rockets. You can also use the shock-capturing techniques, such as the Jameson or Venkatakrishnan limiters, to prevent spurious oscillations near the shocks.
• Reacting flows: You can model the combustion of fuels and oxidizers using the species transport equations and the Arrhenius reaction model. You can also use the flamelet model to simplify the chemistry and reduce the computational cost. You can also simulate the flow of ionized gases using the MHD equations and the Saha equation of state.

Can I model Conjugate Heat Transfer Analysis in SU2?

Yes, you can model conjugate heat transfer (CHT) analysis in SU2. CHT is a type of multiphysics problem that involves the coupling of the energy equation in both fluid and solid domains. SU2 can handle both static and transient CHT problems with different types of boundary conditions and interface treatments. You can also use the discrete adjoint method to compute sensitivities and gradients for CHT problems, which can be useful for shape optimization and design¹².

To set up a CHT simulation in SU2, you need to define multiple physical zones in the configuration file, each with its own solver and boundary conditions. You also need to specify the CHT interfaces between the zones, which can be either matched or non-matched. SU2 will automatically exchange the heat flux and temperature values across the interfaces during the simulation. You can find some examples and tutorials on how to perform CHT analysis in SU2 on its official website or its Wikipedia page. 😊

Can I model conjugate heat transfer analysis with rotating fan (mixer/ impeller)
in SU2 ?

Yes, you can model conjugate heat transfer (CHT) analysis with rotating fan (mixer/ impeller) in SU2. This is a type of multiphysics problem that involves the coupling of the fluid and solid domains with a rotating boundary condition. You can use the sliding mesh technique or the multiple reference frame (MRF) method to account for the relative motion between the rotating and stationary zones. You can also use the harmonic balance method to reduce the computational cost of unsteady simulations.

There are some examples and tutorials on how to perform CHT analysis with rotating fan in SU2 on its official website or in the literature¹²³. You can also find some useful tips and tricks on the SU2 forum or the [SU2 wiki]. 😊

⁴: https://su2code.github.io/forum/ : https://github.com/su2code/SU2/wiki

💥💥💥 Which software is better to model centrifugal pump, Ansys Fluent or CFX?

According to the Ansys Learning Forum¹, both CFX and Fluent are good CFD solvers, but they have some differences in their approaches, capabilities, and applications. Some of the main differences are:

- Fluent can handle 2D meshes and polyhedral meshes, while CFX can only handle 3D meshes with tetra and hexa topologies.

- Fluent uses a cell-centered method, while CFX uses a vertex-centered method.

- Fluent needs UDFs for customization, while CFX uses CEL (CFX Expression Language) which is also compatible with CFD-Post.

- Fluent has more tutorials and updates, while CFX has limited resources and development.

- Fluent can use GPU acceleration, while CFX cannot.

For non-Newtonian fluids, boundary layer effects are important. The CFX immersed solid method does not consider boundary layer effects, so it may not be reliable. A Fluent or CFX remeshing method is recommended².

CFX has been proven to be effective for turbomachinery problems, such as centrifugal pumps. Fluent is preferred for high Mach number flows¹.

You can also watch a video tutorial on how to model a centrifugal pump using CFX³.

Source:

(1) What are the differences between CFX and Fluent? - Ansys Learning Forum. https://forum.ansys.com/forums/topic/what-are-the-differences-between-cfx-and-fluent/.

(2) Is CFX or Fluent better for modeling a gear pump that is handling a non .... https://ansyskm.ansys.com/forums/topic/is-cfx-or-fluent-better-for-modeling-a-gear-pump-that-is-handling-a-non-newtonian-fluid/.

(3) #ANSYS CFX - Centrifugal Pump - YouTube. https://www.youtube.com/watch?v=zEfcIT4dt7U.

(4) http://cfd.ninja/.

More info about turbomachinery capabilities in Ansys CFX

CFX is a CFD software that specializes in turbomachinery applications, such as gas turbines, pumps, fans, compressors, and wind turbines. CFX has several features that make it suitable for these types of problems, such as:

- Streamlined turbo setup and post-processing, which allows you to easily define the geometry, boundary conditions, and performance parameters of your turbomachinery model¹.

- Rotor-stator interaction models, which account for the effects of relative motion and fluid exchange between rotating and stationary components².

- Transient blade row methods, which enable you to simulate unsteady phenomena such as blade flutter, stall, and surge with reduced computational cost².

- Highly scalable high-performance computing, which helps you speed up simulations and handle large and complex models².

You can also read some best practice guidelines for turbomachinery CFD³ or watch a video tutorial on how to model a centrifugal pump using CFX.

Source:

(1) Fluid Simulation for Turbomachinery | Ansys CFX - SimuTech Group. https://simutechgroup.com/ansys-software/fluids/ansys-cfx/.

(2) Ansys CFX | Industry-Leading CFD Software. https://www.ansys.com/products/fluids/ansys-cfx.

(3) Best practice guidelines for turbomachinery CFD. https://www.cfd-online.com/Wiki/Best_practice_guidelines_for_turbomachinery_CFD.

Can you recommend some books or courses on turbomachinery modeling?

Some books that you may find useful are:

- Principles of Turbomachinery, 2nd Edition by Seppo A. Korpela¹. This book covers the theory and applications of turbomachinery, including steam turbines, gas turbines, hydraulic turbines, wind turbines, compressors, and pumps. It also provides examples and exercises for each chapter.

- Turbomachinery: Fundamentals, Selection and Preliminary Design by Marco Gambini and Michela Vellini². This book presents a selection of preliminary sizing procedures for turbomachinery, using geometric correlations and losses models. It also examines all types of turbomachines, such as axial and radial turbines, axial and centrifugal compressors, and centrifugal pumps.

- Fundamentals of Turbomachinery by William W. Peng³. This book provides a comprehensive introduction to turbomachines and their applications, with up-to-date coverage of all types of turbomachinery. It also includes numerous examples, problems, and case studies.

Some courses that you may find helpful are:

- Introduction to Turbomachinery by MIT OpenCourseWare. This course introduces the basic concepts and principles of turbomachinery, such as thermodynamics, fluid mechanics, performance analysis, and design. It also covers various types of turbomachines, such as axial and radial flow turbines and compressors, and pumps and fans.

- Turbomachinery Aerodynamics by edX. This course covers the aerodynamics of turbomachinery, such as flow phenomena, losses, blade design, and optimization. It also includes applications to gas turbines, steam turbines, wind turbines, and propellers.

- Turbomachinery and Propulsion by Coursera. This course covers the fundamentals of turbomachinery and propulsion, such as thermodynamics, fluid dynamics, performance, and efficiency. It also includes topics such as combustion, turbojets, turbofans, turboprops, and rockets.

Source:

(1) Principles of Turbomachinery, 2nd Edition | Wiley. https://www.wiley.com/en-us/Principles+of+Turbomachinery%2C+2nd+Edition-p-9781119518099.

(2) Turbomachinery: Fundamentals, Selection and Preliminary Design .... https://link.springer.com/book/10.1007/978-3-030-51299-6.

(3) Fundamentals of Turbomachinery | Wiley. https://www.wiley.com/en-us/Fundamentals+of+Turbomachinery-p-9780470124222.

💥💥💥 How to draw and calculate centrifugal pump in Ansys Fluent?

A centrifugal pump is a device that uses the rotation of an impeller to increase the pressure and flow rate of a fluid. Ansys Fluent is a software that can simulate the fluid dynamics and performance of a centrifugal pump using various methods and models¹.

To draw and calculate a centrifugal pump in Ansys Fluent, you will need to follow these general steps:

• Create or import the geometry of the pump, including the inlet, outlet, and impeller regions. You can use Ansys DesignModeler, Ansys SpaceClaim, or any other CAD software to create the geometry²³.
• Mesh the geometry using Ansys Meshing or Ansys TurboGrid. You can choose different meshing methods and parameters depending on the complexity and accuracy of the simulation²³.
• Set up the physics and boundary conditions of the simulation in Ansys Fluent. You can choose different models and methods to simulate the fluid flow, such as laminar or turbulent, steady or transient, single-phase or multiphase, etc. You can also specify the operating pressure, inlet and outlet conditions, and the rotation speed of the impeller²³.
• Solve the simulation and monitor the convergence and results. You can use different solvers and algorithms to solve the governing equations of the fluid flow, such as pressure-based or density-based, segregated or coupled, etc. You can also use different techniques to accelerate the convergence, such as pseudo-transient, under-relaxation, or multigrid²³.
• Post-process the results and analyze the performance of the pump. You can use Ansys Fluent or Ansys CFD-Post to visualize and quantify the results, such as pressure, velocity, mass flow rate, head, efficiency, torque, etc. You can also compare the results with experimental data or other simulations²³.

For more details and examples, you can watch this video tutorial¹ or refer to this forum post² or this online discussion⁴. 

How to draw (general rulez) centrifugal pump?

A centrifugal pump consists of a casing, an impeller, and a shaft. The casing is the outer part that encloses the fluid and directs it to the outlet. The impeller is the rotating part that transfers energy to the fluid and increases its pressure and velocity. The shaft is the part that connects the impeller to the motor and transmits the torque.

To draw the geometry of a centrifugal pump, you need to follow these steps:

• Draw a circle with the diameter of the inlet of the pump. This will be the base of the casing.
• Draw a spiral curve that starts from the inlet circle and ends at the outlet of the pump. This will be the shape of the casing. The spiral curve should have a constant pitch and a variable radius that increases with the angle. The pitch and the radius depend on the design specifications of the pump.
• Draw another circle with the diameter of the outlet of the pump. This will be the top of the casing.
• Extrude the spiral curve between the inlet and outlet circles to create the casing volume. You can use the coil tool with the spiral option to do this easily¹.
• Draw a circle with the diameter of the impeller inside the casing. This will be the base of the impeller.
• Draw a series of blades on the impeller circle. The blades should have a curved shape that follows the direction of the fluid flow. The number, shape, and angle of the blades depend on the design specifications of the pump.
• Extrude the blades along the impeller circle to create the impeller volume. You can use the sweep tool to do this easily².
• Draw a cylinder with the diameter of the shaft inside the impeller. This will be the base of the shaft.
• Extrude the shaft cylinder to the desired length. You can use the extrude tool to do this easily².

You have now drawn the geometry of a centrifugal pump. You can use any CAD software to do this, such as Ansys DesignModeler, Ansys SpaceClaim, or Inventor³. 

What are some common errors when modeling a centrifugal pump in Ansys Fluent?

Some common errors when modeling a centrifugal pump in Ansys Fluent are:

• Non-converging simulations: This means that the solver cannot find a solution that satisfies the governing equations of the problem. Possible causes include incorrect boundary conditions, poor quality mesh, inaccurate physical models, or numerical instabilities¹².
• Diverging simulations: This means that the solution becomes unstable and grows without bounds. Possible causes include incorrect boundary conditions, poor quality mesh, or numerical instabilities².
• Unphysical results: This means that the results from the simulation seem unrealistic or unphysical. Possible causes include incorrect boundary conditions, poor quality mesh, or inaccurate physical models².

To troubleshoot these errors, you can check your boundary conditions, refine your mesh, use appropriate physical models, and try different solver settings¹². 

What are some best practices for modeling pumps in Ansys Fluent?

Some best practices for modeling pumps in Ansys Fluent are:

• Use the watertight geometry workflow to create a clean and robust geometry of the pump¹.
• Use the mosaic poly-hexcore meshing with parallel scalability to generate a high-quality and efficient mesh of the fluid domain¹.
• Use inflation layers to capture the boundary layer effects near the walls of the pump¹.
• Use the sliding mesh method to model the rotation of the impeller and the interaction with the stationary parts of the pump²³.
• Use appropriate physical models and boundary conditions to simulate the fluid flow, such as laminar or turbulent, steady or transient, single-phase or multiphase, etc²³.
• Use different solvers and algorithms to solve the governing equations of the fluid flow, such as pressure-based or density-based, segregated or coupled, etc²³.
• Use different techniques to accelerate the convergence, such as pseudo-transient, under-relaxation, or multigrid²³.
• Use Ansys Fluent or Ansys CFD-Post to visualize and quantify the results, such as pressure, velocity, mass flow rate, head, efficiency, torque, etc²³.
• Compare the results with experimental data or other simulations to validate the accuracy and reliability of the simulation²³.

For more details and examples, you can watch this video tutorial³ . 

What are common mistakes in centrifugal pump design?

Some common mistakes in centrifugal pump design are:

• Choosing the wrong type of pump for the application. Different types of centrifugal pumps, such as radial or axial, have different characteristics and performance curves. Choosing the wrong type of pump can lead to inefficiency, cavitation, or failure¹².
• Using incorrect or inaccurate design parameters. The design of a centrifugal pump depends on various parameters, such as flow rate, head, specific speed, impeller diameter, blade number, blade angle, etc. Using incorrect or inaccurate values for these parameters can result in poor performance, excessive power consumption, or instability¹².
• Ignoring the effects of fluid properties and operating conditions. The fluid properties, such as density, viscosity, compressibility, and multiphase behavior, can affect the flow and performance of a centrifugal pump. The operating conditions, such as temperature, pressure, and suction, can also influence the pump behavior. Ignoring these effects can lead to errors in the design and simulation of the pump¹².
• Neglecting the rotor-stator interaction and the secondary flow. The rotor-stator interaction is the phenomenon of fluid flow between the rotating impeller and the stationary casing or diffuser. The secondary flow is the phenomenon of fluid flow in the radial or tangential direction due to the centrifugal force and the pressure gradient. These phenomena can cause losses, noise, vibration, or instability in the pump¹³.
• Not validating the design and simulation results with experimental data or other simulations. The design and simulation of a centrifugal pump are based on assumptions, simplifications, and models that may not capture the reality of the pump. Therefore, it is important to validate the results with experimental data or other simulations to ensure the accuracy and reliability of the design¹².

💥💥💥 How to model pressure drop in Ansys Fluent?

Pressure drop is the difference in pressure between two points in a fluid flow. It is caused by friction, turbulence, bends, valves, fittings, or other obstacles in the flow path. Pressure drop can affect the performance, efficiency, and safety of fluid systems, such as pipes, ducts, pumps, compressors, turbines, heat exchangers, etc.

There are different ways to calculate pressure drop in Ansys Fluent, depending on the type of flow, the boundary conditions, and the model assumptions. Some of the common methods are:

• Using the Darcy-Weisbach equation, which relates the pressure drop to the friction factor, the density, the velocity, the length, and the diameter of the pipe. This equation is valid for laminar and turbulent flows in smooth and rough pipes. You can use the Moody chart or the Colebrook equation to find the friction factor for a given Reynolds number and relative roughness. You can also use the Swamee-Jain equation or the Haaland equation to estimate the friction factor more easily. For more details, please see this video.
• Using the Bernoulli equation, which relates the pressure drop to the change in kinetic and potential energy of the fluid. This equation is valid for inviscid, incompressible, and steady flows along a streamline. You can use the Euler equation to extend the Bernoulli equation to rotational flows. You can also use the energy equation to account for the heat transfer and viscous dissipation in the flow. For more details, please see this guide.
• Using the porous media model, which relates the pressure drop to the permeability and the inertial resistance of the porous medium. This model is valid for flows in porous materials, such as filters, catalysts, packed beds, etc. You can use the Ergun equation or the Forchheimer equation to find the permeability and the inertial resistance coefficients for a given porosity and particle diameter. You can also use the core porosity model to define the pressure drop parameters and the effectiveness of a heat exchanger zone. For more details, please see this guide.
• Using the user-defined function (UDF), which allows you to write your own code to specify the pressure drop function based on your own criteria. This method is useful for cases where the existing models are not suitable or accurate enough for your simulation. You can use the DEFINE_PRESSURE_DROP macro to define the pressure drop across a boundary or an interface. You can also use the DEFINE_PROFILE macro to define the pressure drop as a function of the spatial coordinates. For more details, please see this forum post.

How to define flow resistance (pressure drop) at outlet in Ansys Fluent?

One way to define flow resistance at an outlet in Ansys Fluent is to use the outlet vent boundary condition. This boundary condition allows you to specify a loss coefficient that represents the pressure drop across the vent. The pressure drop is calculated as follows¹:

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Another way to define flow resistance at an outlet in Ansys Fluent is to use a user-defined function (UDF). This allows you to write your own code to specify the outlet boundary condition based on your own criteria. For example, you can use a UDF to define a pressure outlet boundary condition with a variable pressure drop that depends on the flow rate or the Reynolds number. You can find some examples of UDFs for outlet boundary conditions in this forum post and this forum post.

Is there possibilty to define pressure drop on contact between two domains in Ansys Fluent ?

Yes, there is a possibility to define pressure drop on contact between two domains in Ansys Fluent. One way to do this is to use the interface boundary condition and specify the jump option. This option allows you to define a pressure jump or a loss coefficient across the interface between two domains. You can also use a user-defined function (UDF) to define a custom pressure drop function across the interface. For more details, please refer to this guide or this forum post.

How to calculate  a loss coefficient, where fluid density is 1.2 kg/m3 , pressure drop is 40000 Pa and velocity is 20 m/s

💥💥💥 How to define free inlet and outlet in Ansys Fluent?

 A free inlet is a type of boundary condition that allows the fluid to enter or exit the domain depending on the pressure difference. It is suitable for external or unconfined flows, such as wind tunnel simulations or open channel flows.

To define a free inlet in Ansys Fluent, you can use the pressure inlet boundary condition and set the total (stagnation) pressure to zero. This will allow the fluid to flow in or out of the domain depending on the local pressure gradient. You will also need to specify the total temperature, turbulence parameters, and other scalar properties of the flow at the inlet. For more details, please refer to this guide.

Alternatively, you can use the velocity inlet boundary condition and set the velocity magnitude to zero. This will also allow the fluid to flow in or out of the domain depending on the pressure difference. However, you will need to use a user-defined function (UDF) to update the velocity direction at each time step, based on the local flow direction. For more information, please see this forum post.

How to define free outlet in Ansys Fluent?

A free outlet is a type of boundary condition that allows the fluid to exit the domain freely without any restriction. It is suitable for external or unconfined flows, such as jet flows or exhaust flows.

To define a free outlet in Ansys Fluent, you can use the pressure outlet boundary condition and set the gauge pressure to zero. This will allow the fluid to flow out of the domain based on the local pressure gradient. You will also need to specify the backflow properties of the flow at the outlet, such as temperature, turbulence parameters, and other scalar quantities. For more details, please refer to this guide.

Alternatively, you can use the outflow boundary condition and set the mass flow rate to zero. This will also allow the fluid to flow out of the domain without any restriction. However, you will need to ensure that there is no reverse flow at the outlet, as the outflow boundary condition does not account for backflow. For more information, please see this forum post.

What is the difference between pressure outlet and outflow?

The difference between pressure outlet and outflow boundary conditions in Ansys Fluent is that:

• Pressure outlet allows you to specify the static (gauge) pressure at the outlet, and the software will calculate the mass flow rate based on the pressure gradient. You also need to define the backflow properties in case of reverse flow at the outlet. This boundary condition is suitable for cases where the outlet pressure is known or important for the simulation results.
• Outflow assumes a zero diffusion flux for all flow variables except the pressure, and the software will calculate the pressure at the outlet based on the mass conservation. You do not need to define the backflow properties, but you need to ensure that there is no reverse flow at the outlet. This boundary condition is suitable for cases where the flow is fully developed and the outlet pressure is not known or important for the simulation results.

For more information, please refer to this guide or this forum post.

What is the difference between pressure inlet and velocity inlet?

The difference between pressure inlet and velocity inlet boundary conditions in Ansys Fluent is that:

• Pressure inlet allows you to specify the total (stagnation) pressure at the inlet, and the software will calculate the mass flow rate based on the pressure gradient. You also need to define the backflow properties in case of reverse flow at the inlet. This boundary condition is suitable for cases where the inlet pressure is known or important for the simulation results¹.
• Velocity inlet allows you to specify the velocity magnitude and direction at the inlet, and the software will calculate the total pressure based on the mass conservation. You do not need to define the backflow properties, but you need to ensure that there is no reverse flow at the inlet. This boundary condition is suitable for cases where the inlet velocity is known or important for the simulation results².

For more information, please refer to this guide or this forum post.

💥💥💥 What is k-epsilon model in Ansys Fluent and what is used for ?

 The k-epsilon model is a type of turbulence model that is used in computational fluid dynamics (CFD) simulations. It is based on two transport equations for the turbulence kinetic energy (k) and its dissipation rate (epsilon). The k-epsilon model can capture the effects of mean velocity gradients, buoyancy, compressibility, and heat and mass transfer on the turbulent flow. It is one of the most widely used turbulence models in engineering applications because of its robustness, economy, and reasonable accuracy for a wide range of flows¹².

Ansys Fluent is a software package that allows users to perform CFD simulations using various turbulence models, including the k-epsilon model. Ansys Fluent provides three variants of the k-epsilon model: the standard, the RNG, and the realizable k-epsilon models. Each variant has different methods of calculating the turbulent viscosity, the turbulent Prandtl numbers, and the generation and destruction terms in the epsilon equation³. The k-epsilon model in Ansys Fluent can be used for modeling internal and external flows, separated flows, jets, flows with high-pressure gradients, and flows through curved geometries⁴⁵.

Source:

(1) ANSYS FLUENT 12.0 User's Guide - 33.3.3 Viscous Model Dialog Box - ENEA. https://www.afs.enea.it/project/neptunius/docs/fluent/html/ug/node1022.htm.

(2) 12.4.1 Standard k Model Overview - University of Washington. https://courses.washington.edu/mengr544/handouts-10/Fluent-k-epsilon.pdf.

(3) ANSYS FLUENT 12.0 User's Guide - 12.4 Steps in Using a Turbulence Model. https://www.afs.enea.it/project/neptunius/docs/fluent/html/ug/node411.htm.

(4) Difference between K-epsilon and K-omega Turbulence Model - Ansys .... https://forum.ansys.com/forums/topic/difference-between-k-epsilon-and-k-omega-turbulence-model/.

(5) k-epsilon and Laminar differrence - Ansys Learning Forum. https://forum.ansys.com/forums/topic/k-epsilon-and-laminar-differrence/.

What are the limitations of k-epsilon model?

The k-epsilon model is a popular and widely used turbulence model, but it also has some limitations that affect its accuracy and applicability. Some of the limitations are:

- It assumes that the turbulence is isotropic, which means that the ratio between Reynolds stress and mean rate of deformation is the same in all directions. This is not true for flows with strong anisotropy, such as swirling, rotating, or curved flows²³.

- It requires a high Reynolds number, which means that the inertial forces are much larger than the viscous forces. This is not true for flows near the wall, where the viscous effects are important. Therefore, the k-epsilon model needs a near-wall treatment, such as wall functions or low-Reynolds number corrections, to account for the wall effects¹².

- It does not capture the effects of history and rapid distortion on the turbulence. This means that the model cannot handle flows with large adverse pressure gradients, boundary layer separation, or shock waves. Therefore, the k-epsilon model may under-predict the extent of recirculation zones or over-predict the turbulent kinetic energy at stagnation points¹⁴⁵.

These limitations can be overcome by using more advanced turbulence models, such as Reynolds stress models, large eddy simulation, or detached eddy simulation, which can account for the effects of anisotropy, compressibility, history, and distortion on the turbulence. However, these models are also more complex, computationally expensive, and sensitive to initial and boundary conditions. Therefore, the choice of the turbulence model depends on the trade-off between accuracy and efficiency for a given flow problem.

Source: 

(1) The k-ε Turbulence Model - COMSOL Multiphysics. https://doc.comsol.com/5.5/doc/com.comsol.help.cfd/cfd_ug_fluidflow_single.06.088.html.

(2) OpenFOAM: User Guide: k-epsilon. https://www.openfoam.com/documentation/guides/latest/doc/guide-turbulence-ras-k-epsilon.html.

(3) K-epsilon turbulence model - Wikipedia. https://en.wikipedia.org/wiki/K-epsilon_turbulence_model.

(4) Validation and analysis of turbulence modeling in pipe elbow under .... https://link.springer.com/article/10.1007/s40430-022-03899-9.

(5) Limitations and empirical extensions of the k-epsilon model as applied .... https://ntrs.nasa.gov/search.jsp?R=19840035309.

Is k-epsilon model good for low quality meshes ?

The k-epsilon model is not very suitable for low quality meshes, especially near the wall regions. This is because the k-epsilon model requires a high Reynolds number, which means that the inertial forces are much larger than the viscous forces. This is not true for flows near the wall, where the viscous effects are important. Therefore, the k-epsilon model needs a near-wall treatment, such as wall functions or low-Reynolds number corrections, to account for the wall effects¹². However, these treatments are sensitive to the mesh quality and resolution, and may not work well for coarse or skewed meshes³⁴.

A better alternative for low quality meshes is the k-omega model, which is more robust and accurate for near-wall flows. The k-omega model does not require a high Reynolds number, and can handle flows with large adverse pressure gradients, boundary layer separation, or shock waves. The k-omega model is also good in resolving internal flows, separated flows and jets and flows with high-pressure gradient and also internal flows through curved geometries³. However, the k-omega model may be more computationally expensive and sensitive to initial and boundary conditions than the k-epsilon model². Therefore, the choice of the turbulence model depends on the trade-off between accuracy and efficiency for a given flow problem.

Source: 

(1) K-epsilon Turbulence Model | Global Settings | SimScale. https://www.simscale.com/docs/simulation-setup/global-settings/k-epsilon/.

(2) K-epsilon turbulence model - Wikipedia. https://en.wikipedia.org/wiki/K-epsilon_turbulence_model.

(3) Difference between K-epsilon and K-omega Turbulence Model. https://forum.ansys.com/forums/topic/difference-between-k-epsilon-and-k-omega-turbulence-model/.

(4) k-epsilon model: which criteria do I have to meet for the near wall mesh?. https://forum.ansys.com/forums/topic/k-epsilon-model-which-criteria-do-i-have-to-meet-for-the-near-wall-mesh/.

What is the difference between k-epsilon and k-omega models?

The k-epsilon and k-omega models are both types of turbulence models that are used in computational fluid dynamics (CFD) simulations. They are based on two transport equations for the turbulence kinetic energy (k) and either its dissipation rate (epsilon) or its specific dissipation rate (omega). The main difference between them is that the k-epsilon model is more suitable for flow away from the wall, while the k-omega model is more accurate for near-wall flow regions¹².

The k-epsilon model assumes that the turbulence is isotropic, which means that the ratio between Reynolds stress and mean rate of deformation is the same in all directions. This is not true for flows with strong anisotropy, such as swirling, rotating, or curved flows. The k-epsilon model also requires a high Reynolds number, which means that the inertial forces are much larger than the viscous forces. This is not true for flows near the wall, where the viscous effects are important. Therefore, the k-epsilon model needs a near-wall treatment, such as wall functions or low-Reynolds number corrections, to account for the wall effects. However, these treatments are sensitive to the mesh quality and resolution, and may not work well for coarse or skewed meshes¹².

The k-omega model does not assume isotropy, and can handle flows with large adverse pressure gradients, boundary layer separation, or shock waves. The k-omega model does not require a high Reynolds number, and can resolve the viscous layer without wall functions. The k-omega model is also good in resolving internal flows, separated flows, jets, flows with high-pressure gradients, and flows through curved geometries¹³. However, the k-omega model may be more computationally expensive and sensitive to initial and boundary conditions than the k-epsilon model².

A common variant of the k-omega model is the SST (shear stress transport) model, which blends the k-epsilon and k-omega models to achieve the best of both worlds. The SST model uses the k-omega formulation in the near-wall region and switches to the k-epsilon formulation in the free-stream region. The SST model can capture the effects of mean velocity gradients, buoyancy, compressibility, and heat and mass transfer on the turbulent flow. The SST model is one of the most widely used turbulence models in engineering applications because of its robustness, economy, and reasonable accuracy for a wide range of flows¹⁴.

Source: 

(1) Which Turbulence Model Should I Choose for My CFD Application?. https://www.comsol.com/blogs/which-turbulence-model-should-choose-cfd-application/.

(2) Use of k-epsilon and k-omega Models - CFD Online. https://www.cfd-online.com/Forums/main/75554-use-k-epsilon-k-omega-models.html.

(3) Difference between K-epsilon and K-omega Turbulence Model. https://forum.ansys.com/forums/topic/difference-between-k-epsilon-and-k-omega-turbulence-model/.

(4) What is the difference between k-epsilon and k-omega model .... https://rampfest-hudson.com/what-is-the-difference-between-k-epsilon-and-k-omega-model/.

(5) What is the difference between k-epsilon and K-Omega?. https://vidque.com/what-is-the-difference-between-k-epsilon-and-k-omega/.

💥💥💥 Which turbulence model for the rotating fan in Ansys Fluent?

There are different turbulence models available in Ansys Fluent, such as Spalart-Allmaras, k-epsilon, k-omega, SST, and GEKO. Each model has its own advantages and disadvantages, depending on the flow conditions and the accuracy required. Some factors that may influence the choice of turbulence model are:

- The Reynolds number of the flow

- The near-wall treatment

- The inlet boundary conditions

- The mesh resolution and quality

- The computational cost and time

For a rotating fan with radial inlet and outlet, you may want to consider using a sliding mesh model with a turbulent boundary layer approach. This can capture the effects of blade tip gap variation, flow recirculation inside blade passage, and other phenomena that depend on the turbulence modeling. You may also want to use a low-Re model such as k-omega or SST for better resolution of boundary layers.

One possible turbulence model that you can try is the GEKO (Generalized K-Omega) model, which offers a flexible and robust approach to RANS turbulence modeling. It has several tunable model constants that can be adjusted to match specific flow conditions. You can watch two videos that introduce this model in Ansys Fluent [here](^3^).

Another possible turbulence model that you can try is the SST-kw (Steady-State Turbulence-kw) model, which is recommended for highly accurate resolution of boundary layers. It has a wall function mesh that simplifies the computation and reduces the computational time. You can read more about this model in [this document](^4^).

Source:

(1) Introducing the GEKO Turbulence Model in Ansys Fluent. https://ansyskm.ansys.com/announcement/introducing-the-geko-turbulence-model-in-ansys-fluent/.

(2) ANSYS FLUENT 12.0 User's Guide - 12. Modeling Turbulence - ENEA. https://www.afs.enea.it/project/neptunius/docs/fluent/html/ug/node405.htm.

(3) Modeling Turbulent Flows Introductory FLUENT Training. https://www.southampton.ac.uk/~nwb/lectures/GoodPracticeCFD/Articles/Turbulence_Notes_Fluent-v6.3.06.pdf.

(4) Fan Modelling: turbulence model -- CFD Online Discussion Forums. https://www.cfd-online.com/Forums/fluent/45907-fan-modelling-turbulence-model.html.

(5) Lecture 7: Turbulence Modeling Introduction to ANSYS Fluent - Academia.edu. https://www.academia.edu/36090206/Lecture_7_Turbulence_Modeling_Introduction_to_ANSYS_Fluent.

Which turbulence model will be the best for centrifugal fan ?

There is no definitive answer to which turbulence model is the best for a centrifugal fan, as different models may have different advantages and disadvantages depending on the flow conditions and the accuracy required. However, based on the web search results, I can provide you with some information and suggestions that may help you choose a suitable model for your simulation.

One of the factors that may influence the choice of turbulence model is the Reynolds number of the flow, which is a dimensionless parameter that characterizes the ratio of inertial forces to viscous forces in a fluid. The Reynolds number can be calculated from the density, velocity, and viscosity of the fluid, as well as from the geometry and size of the fan components. Generally, higher Reynolds numbers indicate more turbulent flows, which may require more complex models to capture the effects of turbulence.

Another factor that may influence the choice of turbulence model is the near-wall treatment, which is a method to account for the effects of boundary layer separation and recirculation near the wall. The near-wall treatment can be done by using different approaches, such as using a wall function mesh or using a modified k-epsilon model. The wall function mesh simplifies the computation by replacing some terms in the k-epsilon model with wall functions that depend on local velocity gradients. However, this approach may introduce some errors or inaccuracies in certain regions or flow regimes.

A third factor that may influence the choice of turbulence model is the inlet boundary conditions, which are given by various parameters such as inlet pressure, inlet velocity distribution function (VDF), inlet blade angle, and inlet clearance gap. These parameters affect how the flow enters and exits the fan impeller and volute. For example, an inlet VDF can determine whether there is a uniform or non-uniform flow distribution along the impeller passage. An inlet blade angle can affect how much lift or drag force is generated by each blade section. An inlet clearance gap can affect how much turbulence is generated by mixing between air and fluid.

A fourth factor that may influence the choice of turbulence model is the mesh resolution and quality, which are determined by various parameters such as grid size, grid spacing, grid type (sliding or fixed), grid refinement level (global or local), etc. The mesh resolution and quality affect how accurately and efficiently each grid element represents a physical quantity such as velocity or pressure. Higher resolution and quality grids can capture more details and features of complex flows, but they also require more computational resources and time.

A fifth factor that may influence the choice of turbulence model is also computational cost and time, which are affected by various parameters such as number of iterations per time step (NIT), time step size (GST), solver type (finite volume or finite difference), etc. Computational cost and time are important considerations when performing large-scale simulations with many fans or other components. Lower cost and time simulations can reduce operational costs and increase efficiency.

Based on these factors, you may want to consider using one or more models that have been tested and validated for similar flow conditions as your simulation case. Some examples of models that have been used for centrifugal fans are:

- The k-epsilon model (an industry standard for many years), which has two main advantages: it has simple mathematical form; it has good accuracy for low-Reynolds-number flows.

- The SST-kw (Steady-State Turbulence-kw) model (recommended for highly accurate resolution of boundary layers), which has two main advantages: it has a wall function mesh that simplifies computation; it has good accuracy for high-Reynolds-number flows.

- The GEKO (Generalized K-Omega) model (a flexible and robust approach to RANS turbulence modeling), which has several tunable model constants that can be adjusted to match specific flow conditions.

- The SST-reattachment modification (RM) model (a proposed modification to improve SST performance), which has two main advantages: it reduces local inflow in confined areas; it improves agreement with experimental data.

- The RSM-SSG (Speziale et al., Sarkar & Gatski) model (a modified k-epsilon model with improved near-wall treatment), which has two main advantages: it reduces numerical diffusion; it improves agreement with experimental data.

You can find more information about these models in [this document](^4^), [this article](^2^), [this paper](^3^), [this paper](^5^), [this paper](^6^).

Source:

(1) Tonal noise of voluteless centrifugal fan generated by turbulence .... https://pubs.aip.org/aip/pof/article/33/7/075110/1077103/Tonal-noise-of-voluteless-centrifugal-fan.

(2) A Comparative Study on Numerical Flow Simulations of a Centrifugal .... https://www.mdpi.com/1996-1073/16/23/7864/html.

(3) Assessment of Turbulence Model Predictions for an Aero-Engine .... https://asmedigitalcollection.asme.org/turbomachinery/article/133/1/011025/421599/Assessment-of-Turbulence-Model-Predictions-for-an.

(4) Computational turbulent flow characteristics in a centrifugal pump. https://pubs.aip.org/aip/adv/article/12/7/075025/2818977/Computational-turbulent-flow-characteristics-in-a.

(5) http://dx.doi.org/10.5293/IJFMS.2020.13.3.623.

(6) Effect of Blade Profile on the Performance of a Centrifugal Fan with .... https://www.jstage.jst.go.jp/article/ijfms/13/3/13_623/_pdf/-char/en.

(7) https://doi.org/10.3390/en16237864.

💥💥💥 Which turbulence model is the best for conjugate heat transfer (CHT) analysis in Ansys Fluent?

 Conjugate heat transfer (CHT) is a phenomenon that occurs when two fluids with different temperatures are in contact, causing heat transfer between them. CHT analysis in Ansys Fluent requires a turbulence model that can capture the effects of turbulence on the heat transfer coefficient (HTC) and the temperature distribution in the fluid. There are several turbulence models available in Ansys Fluent, each with its own advantages and limitations. Some of the most common ones are:

• Standard k-ε model: This is one of the simplest and most widely used RANS turbulence models. It assumes isotropic turbulence and employs two transport equations: one for turbulent kinetic energy (k) and the other for its dissipation rate (ε). The model performs well for a wide range of turbulent flows but may struggle in complex flow situations.
• Realizable k-ε model: This improves upon the standard k-ε model by addressing its deficiencies in certain flow situations. It introduces additional equations to account for non-isotropic effects, providing better accuracy in flows with strong streamline curvature and swirling motion.
• Reynolds Stress Model (RSM): This is a more advanced RANS turbulence model that solves additional equations for the Reynolds stresses, which capture the anisotropic behavior of turbulence. The RSM can handle complex flow geometries and boundary conditions, but it requires more computational resources than other RANS models.
• Large Eddy Simulation (LES): This is a hybrid turbulence model that combines RANS and LES approaches to simulate large-scale turbulent structures. LES can provide higher accuracy than RANS models in capturing the effects of turbulence on heat transfer, but it also requires more computational time and memory.
• Detached Eddy Simulation (DES): This is a variant of LES that focuses on simulating detached eddies, which are large-scale vortices that move away from their source regions. DES can improve the accuracy of CHT analysis by reducing the effects of wall roughness and boundary layer separation.
• Scale-Adaptive Simulation (SAS): This is another variant of LES that adapts to different scales of turbulence by using different numerical schemes for different regions of the flow domain. SAS can achieve high accuracy and efficiency by reducing numerical errors and computational costs.
• Wall-Adapting Local Eddy-viscosity (WALE) Model: This is a hybrid turbulence model that combines LES and WALE approaches to simulate local eddy-viscosity effects near walls. WALE can improve the accuracy of CHT analysis by capturing the effects of wall roughness, boundary layer separation, and viscous dissipation.
• Spalart-Allmaras (SA) Model: This is a hybrid turbulence model that combines LES and SA approaches to simulate large-scale turbulent structures near walls. SA can improve the accuracy of CHT analysis by capturing the effects of wall roughness, boundary layer separation, viscous dissipation, and pressure fluctuations.
• Shear Stress Transport (SST) Model: This is a hybrid turbulence model that combines LES and SST approaches to simulate shear stress transport effects near walls. SST can improve the accuracy of CHT analysis by capturing the effects of wall roughness, boundary layer separation, viscous dissipation, pressure fluctuations, and shear stress gradients.
• Reynolds Stress Transport (RST) Model: This is another hybrid turbulence model that combines LES and RST approaches to simulate Reynolds stress transport effects near walls. RST can improve the accuracy of CHT analysis by capturing the effects of wall roughness, boundary layer separation, viscous dissipation, pressure fluctuations, shear stress gradients, and Reynolds stresses.

The best turbulence model for CHT analysis in Ansys Fluent depends on several factors, such as:

• The type and geometry of the flow domain
• The size range and distribution of turbulent structures
• The presence or absence of wall roughness or boundary layer separation
• The desired level of accuracy or efficiency
• The available computational resources

Therefore, there is no definitive answer to this question. However, some general guidelines are:

• For low-Reynolds number flows with smooth boundaries or simple geometries, standard k-ε or realizable k-ε models may suffice.
• For high-Reynolds number flows with complex geometries or strong turbulent structures near walls or interfaces, RSM or SAS models may be preferred.
• For very high-Reynolds number flows with large-scale turbulent structures or strong anisotropy effects near walls or interfaces, DES or WALE models may be beneficial.
• For very high-Reynolds number flows with large-scale turbulent structures or strong anisotropy effects near walls or interfaces combined with wall roughness or boundary layer separation effects, SST, RST, or GEKO models may be advantageous.

Can you explain more about GEKO model?

GEKO stands for Generalized K-Omega, and it is a new and flexible turbulence model that combines the k-epsilon and k-w models to simulate large-scale turbulent structures near walls. GEKO can improve the accuracy of conjugate heat transfer (CHT) analysis by capturing the effects of wall roughness, boundary layer separation, viscous dissipation, pressure fluctuations, shear stress gradients, and Reynolds stresses¹.

GEKO is a two-equation model, based on the k-omega model formulation, but with the flexibility to tune the model over a wide range of flow scenarios. The key to such a strategy is the provision of free parameters which the user can adjust for specific types of applications without negative impact on the basic calibration of the model². In other words, instead of providing users flexibility through a multitude of different models, the current approach aims at providing one framework, using different coefficients to cover different application sectors.

The generic idea behind the model is as follows²:

• The basic equation for turbulent kinetic energy (k) is derived from the standard k-epsilon model.
• The basic equation for dissipation rate (ε) is derived from the realizable k-epsilon model.
• The basic equations are combined into one equation by using a blending function that depends on several free parameters.
• The free parameters are adjusted to match specific flow conditions by using optimization methods or trial-and-error.

The GEKO model has several advantages over other turbulence models, such as:

• It can handle complex flow geometries and boundary conditions with high accuracy and efficiency.
• It can reduce numerical errors and computational costs by using different numerical schemes for different regions of the flow domain.
• It can adapt to different scales of turbulence by using different coefficients for different regions of the flow domain.
• It can capture both isotropic and anisotropic effects in turbulence by using additional equations for Reynolds stresses.

The GEKO model also has some limitations and challenges, such as:

• It requires more computational resources than other turbulence models due to its higher order equations and coefficients.
• It requires more user input and tuning than other turbulence models due to its free parameters and optimization methods.
• It may not be suitable for some flow situations that require very high-Reynolds number flows or very low-viscosity flows.

If you want to learn more about the GEKO model, you can check out some of these resources:

• Best Practice: Generalized k-(GEKO) Two-Equation Turbulence Modeling in Ansys CFD: This is a technical paper that provides background information on the model and a description of its tunable parameters. It also provides some examples and guidelines for optimal usage.
• Generalized k-(GEKO) Two-Equation Turbulence Model in Ansys CFD: This is another technical paper that provides more details on the model formulation and implementation. It also compares the performance of GEKO with other turbulence models on various test cases.
• Webinar: ANSYS Tips & Tricks - ANSYS Fluent - Generalized K-Omega (GEKO) Model: This is a webinar that introduces GEKO as a new turbulence model family in Ansys Fluent. It also demonstrates how to use GEKO for CHT analysis on some sample problems.
• Introducing the GEKO Turbulence Model in Ansys Fluent: This is a video series that provides an overview of GEKO as a new turbulence model family in Ansys Fluent. It also explains how to use GEKO for CHT analysis on some sample problems.
• ANSYS Fluent: Introduction to the GEKO Turbulence Model Part I: This is an online forum thread that discusses how to use GEKO as a new turbulence model family in Ansys Fluent. It also provides some tips and tricks for tuning GEKO parameters. 😊

How do I use GEKO in Ansys Fluent?

To use GEKO in Ansys Fluent, you need to follow these steps:

• First, you need to select the GEKO turbulence model from the Model menu in the Model Setup dialog box. You can also use the Model tab in the Model Setup dialog box to access the GEKO options.
• Next, you need to adjust the free GEKO parameters that control the behavior of the model. These parameters are: CSEP, CNW, CMIX, CJET, CCORNER, and CCURV. Each parameter has a different effect on the model output and can be tuned to match your specific flow conditions. You can use optimization methods or trial-and-error to find the optimal values for these parameters.
• Then, you need to define your own turbulent flow equation for CFD modeling. This is an optional step that allows you to customize the model further by adding additional equations or terms that are not included in the standard GEKO formulation. You can use user-defined functions (UDFs) or macros to create your own turbulent flow equation and assign it to a new equation in the Model tab of the Model Setup dialog box.
• Finally, you need to run your simulation and analyze your results. You can use various tools and features in Ansys Fluent to visualize and interpret your results, such as contours, streamlines, vorticity contours, velocity vectors, etc.

If you want more details on how to use GEKO in Ansys Fluent, you can check out some of these resources:

• Best Practice: Generalized k-(GEKO) Two-Equation Turbulence Modeling in Ansys CFD: This is a technical paper that provides background information on the model and a description of its tunable parameters. It also provides some examples and guidelines for optimal usage.
• ANSYS Fluent: Introduction to the GEKO Turbulence Model Part I: This is an online forum thread that discusses how to use GEKO as a new turbulence model family in Ansys Fluent. It also provides some tips and tricks for tuning GEKO parameters.
• How Engineers Customize GEKO so the CFD Model Matches the Turbulent Flows: This is a blog post that explains how engineers can use six free coefficients to tweak GEKO to match their experimental flows.