💥💥💥 Which turbulence model will be appropriate for cfd analyzes with the MRF or Sliding Mesh model (Ansys Fluent)?

  Turbulence modeling is an important aspect of computational fluid dynamics (CFD) simulations, as it affects the accuracy and efficiency of the results. There are different types of turbulence models available in Ansys Fluent, each with its own advantages and limitations. The choice of the best model depends on several factors, such as the flow characteristics, the computational resources, and the desired level of detail.

One of the most widely used turbulence models in Ansys Fluent is the Spalart-Allmaras model, which is based on a two-equation approach that solves for both the momentum and energy transfer rates in turbulent flows. This model can capture a wide range of turbulent structures and scales, but it also requires a large amount of computational time and memory. The Spalart-Allmaras model can be further divided into several submodels, such as k-ε RNG, k-ε Realizable k-ε Standard k-ω SST, Reynolds Stress Model Detached Eddy Simulation Large Eddy Simulation¹.

Another popular turbulence model in Ansys Fluent is the k-ω SST model, which is based on a one-equation approach that solves for only the energy transfer rate in turbulent flows. This model can reduce the computational cost significantly compared to the Spalart-Allmaras model, but it also has some limitations. For example, it cannot handle shocks or compressible flows very well. The k-ω SST model can be further divided into several submodels, such as k-ω SST 2D/3D².

A more advanced turbulence model in Ansys Fluent is the Large Eddy Simulation (LES) method, which is based on a spatially averaged approach that resolves only the large eddies in turbulent flows. This method can provide more accurate results than RANS models for complex industrial flows that exhibit high levels of turbulence and nonlinearity. However, LES also requires more computational resources and efforts than RANS models³.

For your specific case of using an MRF (rotating fan) model in Ansys Fluent, you may want to consider using either the Spalart-Allmaras or the LES method depending on your objectives and constraints. If you want to achieve a high level of detail and accuracy for your fan flow simulation, you may opt for using LES with appropriate mesh resolution and boundary conditions. If you want to achieve a faster and more economical simulation for your fan flow analysis, you may opt for using Spalart-Allmaras with appropriate submodel selection and parameterization.

What is the difference between RANS and LES?

The difference between RANS and LES is mainly in how they handle the turbulence in the fluid flow. RANS stands for Reynolds-Averaged Navier-Stokes, which is a method that solves the Navier-Stokes equations for the fluid motion and then averages them over a time period to obtain the mean values of the velocity and pressure fields. LES stands for Large Eddy Simulation, which is a method that solves only the large eddies in the turbulent flow and then uses them to model the small-scale structures. Both methods are based on solving the Navier-Stokes equations, but they have different advantages and disadvantages.

RANS is simpler and faster than LES, as it does not require a detailed model of the small-scale structures. However, RANS also has some limitations, such as:

• It cannot capture the transient effects of turbulence, such as shocks or recirculation.
• It cannot resolve all scales of turbulence, especially near walls or boundaries.
• It requires a large amount of computational resources and time to achieve a high level of accuracy.

LES is more accurate and realistic than RANS, as it can resolve all scales of turbulence and capture the transient effects. However, LES also has some challenges, such as:

• It requires a very fine mesh resolution to resolve all scales of turbulence.
• It requires a lot of computational resources and time to solve all scales of turbulence.
• It may introduce numerical errors or instabilities due to numerical diffusion or dissipation.

Therefore, choosing between RANS and LES depends on several factors, such as:

• The type and complexity of the flow
• The desired level of detail and accuracy
• The available computational resources and time
• The trade-off between speed and quality

What is the difference between k-ε and k-ω models?

The k-ε and k-ω models are two different types of turbulence models that are used in Ansys Fluent to simulate turbulent flows. They have different assumptions and methods for solving the energy transfer rate in the flow, which affects the accuracy and efficiency of the results. Here are some of the main differences between them:

• The k-ε model is based on a two-equation approach that solves for both the momentum and energy transfer rates in turbulent flows. This model can capture a wide range of turbulent structures and scales, but it also requires a large amount of computational time and memory¹.
• The k-ω model is based on a one-equation approach that solves for only the energy transfer rate in turbulent flows. This model can reduce the computational cost significantly compared to the k-ε model, but it also has some limitations. For example, it cannot handle shocks or compressible flows very well¹.
• The k-ε model can be further divided into several submodels, such as k-ε RNG, k-ε Realizable k-ε Standard k-ω SST, Reynolds Stress Model Detached Eddy Simulation Large Eddy Simulation¹. Each submodel has its own advantages and disadvantages depending on the flow characteristics.
• The k-ω model can be further divided into several submodels, such as k-ω SST 2D/3D². Each submodel has its own advantages and disadvantages depending on the flow characteristics.
• The k-ε model poorly resolves the viscous layer unlike the k-ω model. Furthermore, the k-ω model is good in resolving internal flows, separated flows and jets and flows with high-pressure gradient and also internal flows through curved geometries³.

Therefore, choosing between the k-ε and k-ω models depends on several factors, such as:

• The type and complexity of the flow
• The desired level of detail and accuracy
• The available computational resources and time
• The trade-off between speed and quality