💥💥💥 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.