💥💥💥 How to define Thermal Contact Conductance in Transient Thermal ( Ansys Workbench)

 **Thermal contact conductance** is the study of heat conduction between solid or liquid bodies in thermal contact¹. It is a measure of the **thermal conductivity**, or ability to conduct heat, between two bodies in contact¹. The **thermal contact conductance coefficient** is a property indicating the thermal conductivity between two bodies in contact¹. The inverse of this property is termed **thermal contact resistance**¹. 

When two solid bodies come in contact, such as A and B in Figure 1, heat flows from the hotter body to the colder body. A temperature drop is observed at the interface between the two surfaces in contact. This phenomenon is said to be a result of a thermal contact resistance existing between the contacting surfaces¹. Thermal contact resistance is defined as the ratio between this temperature drop and the average heat flow across the interface¹.

Most experimentally determined values of the thermal contact resistance fall between **0.000005 and 0.0005 m²K/W** (the corresponding range of thermal contact conductance is **200,000 to 2000 W/m²K**). The significance of thermal contact resistance depends on the thermal resistances of the layers compared to typical values of thermal contact resistance. It is significant and may dominate for good heat conductors such as metals but can be neglected for poor heat conductors such as insulators¹.

Thermal contact conductance is an important factor in various applications, including electronics, electronic packaging, heat sinks, brackets, industry, nuclear reactor cooling, gas turbine cooling, internal combustion engines, heat exchangers, thermal insulation, press hardening of automotive steels, hypersonic flight vehicles, thermal supervision for space vehicles, and residential/building science¹.

Please note that thermal contact resistance arises due to improper contact between two bodies due to irregularities like scratches, ruptures, or dirt on the surface, which causes less contact area available for heat conduction².

Source:

(1) Thermal contact conductance - Wikipedia. https://en.wikipedia.org/wiki/Thermal_contact_conductance.

(2) Thermal contact resistance: Definition, Formula (Heat transfer). https://mechcontent.com/contact-resistance-heat-transfer/.

(3) Thermal Contact Resistance | Neutrium. https://neutrium.net/heat-transfer/thermal-contact-resistance/.

(4) What is Thermal Contact Resistance - Thermal Engineering. https://www.thermal-engineering.org/what-is-thermal-contact-resistance-thermal-contact-conductance-definition/.

How to define Thermal contact resistance in Transient Thermal Ansys Workbench ?

**Thermal contact resistance** is created by the air gap between the contacting surfaces². It depends on surface roughness, how much pressure is applied on the surfaces in contact, and the kind of thermal paste used to fill up the air gap².

In **Transient Thermal Ansys Workbench**, you can define thermal contact resistance by specifying the **thermal contact conductance coefficient**¹. The inverse of thermal contact conductance is thermal contact resistance¹. The thermal contact conductance coefficient quantifies a contact's ability to conduct heat¹. It is used to calculate the amount of heat transferred through conduction between two bodies in contact¹. 

To define thermal contact resistance in Transient Thermal Ansys Workbench, you can follow these steps:

1. Open your Transient Thermal Ansys Workbench project.

2. Select the surfaces that are in contact and for which you want to define thermal contact resistance.

3. Right-click on the selected surfaces and choose "Properties" or "Contact Properties".

4. In the properties window, look for the option to define thermal contact conductance or thermal contact resistance.

5. Enter the desired value for thermal contact conductance coefficient or thermal contact resistance.

6. Save the changes and run the simulation.

Please note that the specific steps may vary depending on the version of Ansys Workbench you are using. It is recommended to refer to the official documentation or user guide for detailed instructions specific to your version of Ansys Workbench.

I hope this helps! Let me know if you have any further questions.

Source:

(1) Thermal contact resistance - Ansys Learning Forum. https://forum.ansys.com/forums/topic/thermal-contact-resistance/.

(2) Thermal Contact Conductance - ANSYS Innovation Courses. https://courses.ansys.com/wp-content/uploads/2020/03/Lesson-3-Thermal-contact.pdf.

(3) Intro to Transient Thermal Analysis - ANSYS Innovation Courses. https://courses.ansys.com/wp-content/uploads/2020/05/Lesson-1-Introduction-to-transient-analysis.pdf.

(4)https://www.engineersedge.com/heat_transfer/thermal_contact_conductance_13849.htm.

How to calculate contact thermal conductance between two steel plates ?

To calculate the contact thermal conductance between two steel plates, you need to know the following parameters:

• The thermal conductivity of the steel plates, denoted by k (W/m•K).
• The thickness of each steel plate, denoted by L (m).
• The contact area between the plates, denoted by A (m2).
• The contact pressure between the plates, denoted by P (MPa).
• The surface roughness of the plates, denoted by R (μm).
• The interface fluid between the plates, such as air or oil.

The contact thermal conductance, denoted by h (W/m2K), is a measure of how well heat is transferred across the interface between the plates. It depends on the factors mentioned above, as well as the temperature of the interface. There is no simple formula for contact thermal conductance, but there are some empirical correlations that can be used to estimate it. One such correlation is given by Yovanovich (1981) as:

This correlation is valid for steel plates with surface roughness between 0.1 and 10 μm, contact pressure between 0.1 and 10 MPa, and interface temperature between 20 and 200 °C. It assumes that the interface fluid is air with a thermal conductivity of 0.026 W/m•K.

To use this correlation, you need to plug in the values of k, L, P, R and h into the equation and solve for h. Alternatively, you can use an online calculator such as this one to compute h for you.

I hope this helps you understand how to calculate contact thermal conductance between two steel plates. If you have any further questions, please feel free to ask me. 😊

💥💥💥 What is effective medium theory (EMT) ? Is this model available in Ansys Workbench?

 Effective medium theory (EMT) for thermal conductivity is a method to estimate the effective thermal conductivity of a composite material that consists of two or more phases with different properties¹. EMT can be used to model the thermal conductivity of materials that have anisotropic or heterogeneous structures, such as coiled aluminum sheets².

The basic idea of EMT is to replace the composite material with a homogeneous medium that has the same macroscopic behavior as the original material. The effective thermal conductivity of the homogeneous medium is calculated by averaging the thermal conductivities and volume fractions of the constituent phases, taking into account the shape, size, orientation, and distribution of the phases. There are many different EMT models, each with different assumptions and approximations. Some of the most common EMT models are:

• Maxwell model: This model assumes that one phase is dispersed as spherical inclusions in another phase, and that there is no interfacial thermal resistance between the phases³.
• Bruggeman model: This model assumes that both phases are randomly distributed in a symmetric way, and that there is no interfacial thermal resistance between the phases⁴.
• Maxwell-Garnett model: This model assumes that one phase is dispersed as ellipsoidal inclusions in another phase, and that there is no interfacial thermal resistance between the phases⁵.
• Hasselman-Johnson model: This model modifies the Maxwell model by introducing an interfacial thermal resistance between the phases.

Which from the above models are available in Ansys Workbench ?

According to the web search results, Workbench supports the following EMT models for thermal conductivity:

• Maxwell model: This model is available in the Engineering Data Sources panel under the Effective Medium Theory option¹.
• Bruggeman model: This model is available in the Fluent module under the Materials panel as a User-Defined Function (UDF) option².
• Maxwell-Garnett model: This model is not directly available in Ansys Workbench, but it can be implemented using a UDF or an External Model component³⁴.
• Hasselman-Johnson model: This model is not directly available in Ansys Workbench, but it can be implemented using a UDF or an External Model component³⁴.

¹: Missing FE modeler in Ansys Tool box - Ansys Learning Forum ²: Effective medium theory for thermal conductivity of coiled aluminum sheet ³: Importing FE Model in ANSYS Workbench - Eng-Tips ⁴: Worksheet Option in Ansys Workbench - Skill-Lync

How to define Maxwell model in Ansys Workbench ?

The Maxwell model is a type of effective medium theory (EMT) that can be used to estimate the effective thermal conductivity of a composite material that consists of two phases with different properties¹. To define the Maxwell model in Ansys Workbench, you need to do the following steps:

• Go to START > Ansys Em Suite 202x Rx > Modify integration with Ansys (run as admin) and integrate it with workbench².
• Launch Workbench. You will see Maxwell and other Electronics solvers in the analysis system².
• Drag and drop Maxwell into the workspace and then import your Maxwell project into it².
• Select Thermal Conductivity as the property to define and choose Anisotropic for the type³.
• Enter the values of the thermal conductivity tensor components for the composite material. You can use the formula given in this document to calculate these values based on the properties of the phases and their volume fractions¹.

How to define Bruggeman model in Ansys Workbench ?

The Bruggeman model is a type of effective medium theory (EMT) that can be used to estimate the effective thermal conductivity of a composite material that consists of two or more phases with different properties¹. To define the Bruggeman model in Ansys Workbench, you need to do the following steps:

• Go to the Fluent module in the Analysis System and double-click Setup².
• In the Fluent Launcher panel, select Double Precision and User-Defined Functions (UDF) options and press OK².
• In the Fluent Text User Interface (TUI), type the following command to load the UDF library that contains the Bruggeman model: /define/user-defined/user-defined-functions/load [library-name] [library-path] where [library-name] is the name of the UDF library and [library-path] is the location of the UDF library on your computer².
• In the Fluent GUI, go to Materials and select the composite material that you want to define. Under Thermal Conductivity, select User-Defined and choose the Bruggeman model from the drop-down list².
• Enter the values of the thermal conductivity and volume fraction for each phase of the composite material. You can use the formula given in this document to calculate these values based on the properties of the phases¹.

¹: Effective medium approximations - Wikipedia ²: Parameter model in Fluent and Workbench - Ansys Learning Forum ³: Effective medium theory for thermal conductivity of coiled aluminum sheet

💥💥💥 What is OptiSLang and what is use for ? 🧐

 OptiSLang is a software for process integration and design optimization, which enables the automation and streamlining of CAE-based simulations. OptiSLang connects with various Ansys tools for simulating different physics domains, such as fluid mechanics, structural mechanics, and optics. OptiSLang uses advanced artificial intelligence and machine learning algorithms to build optimal predictive models, which efficiently explore the design space and identify the best design configurations. OptiSLang allows engineers to make better decisions faster, leading to more innovative designs and shorter time to market .

What are the benefits of using OptiSLang?

Some of the benefits of using OptiSLang are:

- It can automate and streamline the simulation process by integrating multiple CAx tools and different physics domains into a holistic, multi-disciplinary approach to optimization¹.

- It can accelerate the search for the best and most robust design configuration by using state-of-the-art algorithms for design exploration, optimization, robustness and reliability analysis¹.

- It can leverage the latest artificial intelligence and machine learning technologies to build optimal predictive models, which efficiently explore the design space and identify the best design configurations¹².

- It can enable experts to easily create web applications that can be deployed to Ansys Minerva, allowing non-experts to run the application and carry out design studies as needed².

- It can reduce the time and cost of running thousands of designs by using neural networks and smart layout to automatically find the best configuration².

- It can extract the relation from design variables to results as behavior models, which can be implemented in system simulation as table or C-code³.

Source: 

(1) Ansys optiSLang | Process Integration & Design Optimization Software. https://www.ansys.com/products/connect/ansys-optislang.

(2) Top 3 New Features in Ansys optiSLang. https://www.ansys.com/en-gb/blog/top-3-new-features-in-ansys-optislang.

(3) Understand your Design - PRACE. https://materials.prace-ri.eu/340/1/robustDesignOptimization.pdf.

What are some use cases of OptiSLang?

Some use cases of OptiSLang are:

- Process integration and design optimization: OptiSLang can automate and streamline the simulation process by integrating multiple CAx tools and different physics into a holistic, multi-disciplinary approach to optimization. OptiSLang can accelerate the search for the best and most robust design configuration by using state-of-the-art algorithms for design exploration, optimization, robustness and reliability analysis¹.

- Reduced-order modeling: OptiSLang can leverage the latest artificial intelligence and machine learning technologies to build optimal predictive models, which efficiently explore the design space and identify the best design configurations. OptiSLang can reduce the time and cost of running thousands of designs by using neural networks and smart layout to automatically find the best configuration¹².

- Model calibration: OptiSLang can extract the relation from design variables to results as behavior models, which can be implemented in system simulation as table or C-code³. OptiSLang can also calibrate these models by comparing them with experimental data and adjusting the parameters accordingly².

- Ansys Minerva integration: OptiSLang can enable experts to easily create web applications that can be deployed to Ansys Minerva, allowing non-experts to run the application and carry out design studies as needed. Ansys Minerva is a platform that enables collaboration, data management, and process automation across the entire product lifecycle².

- Advanced reliability methods: OptiSLang can help engineers make a safety statement for complex systems such as Level 3 autonomous driving assistance systems (ADAS) using scenario-based simulation. OptiSLang can perform uncertainty quantification and reliability analysis based on advanced methods such as Subset Simulation, Importance Sampling, or Line Sampling, which are more efficient and robust than Monte Carlo Sampling.

Source: 

(1) Ansys optiSLang | Process Integration & Design Optimization Software. https://www.ansys.com/products/connect/ansys-optislang.

(2) Mastering Ansys optiSLang: 5 Useful Methods for Reusing Existing ... - PADT. https://www.padtinc.com/2022/09/27/ansys-optislang-reusing-results/.

(3) Ansys + Daimler. https://www.ansys.com/content/dam/amp/2021/december/quick-request/optislang-case-study/Ansys-Daimler-Case-Study.pdf.

How can I get a license for OptiSLang?

To get a license for OptiSLang, you need to contact Ansys or one of its authorized partners and request a trial or purchase a subscription. You can find more information about the pricing and packaging of OptiSLang on the Ansys website¹. According to the website, there are two license options for OptiSLang: premium and enterprise. The premium license option allows you to run up to four design point variations concurrently, while the enterprise license option allows you to run up to eight design point variations for a design of experiments (DoE) study³. You also need to have a compatible Ansys product license, such as Ansys Fluent, Ansys Mechanical, or Ansys SPEOS, to use OptiSLang with those tools³.

Source: 

(1) Ansys optiSLang | Process Integration & Design Optimization Software. https://www.ansys.com/products/connect/ansys-optislang.

(2) Top 3 New Features in Ansys optiSLang. https://www.ansys.com/blog/top-3-new-features-in-ansys-optislang.

(3) optislang licensing - Ansys Learning Forum. https://forum.ansys.com/forums/topic/optislang-licensing-2/.

(4) Optislang Licensing - Ansys Learning Forum. https://forum.ansys.com/forums/topic/optislang-licensing/.

(5) Download NSYS optiSLang 2022 R1 Win64 full license forever. http://clickdown.org/download-nsys-optislang-2022-r1-win64-full-license-forever/.

💥💥💥 What is ROM Builder in Ansys Workbench and what is it for?

 ROM Builder is a tool that allows you to create reduced order models (ROMs) from your computational fluid dynamics (CFD) simulations in Ansys Workbench. ROMs are simplified representations of complex systems that can capture the essential behavior of the system with much less computational cost. ROMs can be used for various purposes, such as design optimization, parameter studies, system simulation, digital twins, and real-time control¹.

To build a ROM, you need to run a number of design points through a solver. The results from these runs are then combined into a ROM using Ansys DesignXplorer’s 3D ROM builder. These ROMs can then be combined into a system simulation, or digital twin, using Ansys Twin Builder¹.

ROM Builder is available for Fluent systems in Ansys Workbench. You can set up and build a ROM by defining the input parameters, output variables, and design points in the ROM Builder component. You can also export the ROM in standard formats, such as FMU or ROMZ, that can be imported into other software tools².

If you want to learn more about how to use ROM Builder in Ansys Workbench, you can watch some video tutorials here, here, or here. You can also read some articles here, here, or here. I hope this helps you understand what ROM Builder is and what it is for. 😊

How to define ROM Builder for Ansys Fluent?

To define ROM Builder for Ansys Fluent, you need to follow these steps:

• Drag and drop a 3D ROM system from the Design Exploration toolbox in Workbench to your Fluent project¹.
• Define the input parameters that you want to vary in your ROM, such as boundary conditions, material properties, or operating conditions. You can do this in the Parameters tab of the ROM Builder component².
• Define the output variables that you want to capture in your ROM, such as forces, moments, heat transfer coefficients, or pressure drops. You can do this in the Variables tab of the ROM Builder component².
• Define the design points that you want to run for your ROM, using either a predefined or a custom design of experiments (DOE) method. You can do this in the Design Points tab of the ROM Builder component².
• Run the design points and generate the solution snapshots for your ROM. You can do this by right-clicking on the ROM Builder component and selecting Update².
• Build and evaluate the ROM using DesignXplorer’s 3D ROM builder. You can do this by right-clicking on the 3D ROM builder component and selecting Update².
• Export the ROM in a standard format, such as FMU or ROMZ, that can be imported into other software tools, such as Twin Builder².

Is ROM Builder only for Steady State Cases, what module do I need to use for Transient Simulations in Ansys Workbench ?

ROM Builder is only for steady state cases in Ansys Workbench. You cannot build ROMs based on transient analysis using ROM Builder¹. If you want to create ROMs for non-linear transient systems, you need to use Ansys Twin Builder. These types of ROMs are called Dynamic ROMs; Twin Builder has a Dynamic ROM builder to make the process very simple². You can find more information about Twin Builder and Dynamic ROMs in the Ansys Learning Hub - Systems - Twin Builder³. You can also watch a video on how Reduced-Order Models can be used in real-time here. 

💥💥💥 What is porous jump boundary conditions and how to define it in Ansys Fluent ?

 Porous jump boundary conditions are used to model a thin membrane that has known velocity (pressure-drop) characteristics. It is a 1D simplification of the porous media model available for cell zones in Ansys Fluent¹. Examples of uses for the porous jump condition include modeling pressure drops through screens and filters, and modeling radiators when you are not concerned with heat transfer¹.

To define a porous jump boundary condition in Ansys Fluent, you need to follow these steps¹:

1. Identify the porous-jump zone. This is a type of internal face zone that represents the interface between cells, rather than a cell zone. You can use the Boundary Conditions task page to change the appropriate face zone to a porous-jump zone.
2. Set the Face Permeability of the medium. This is the coefficient that determines the pressure drop across the porous jump due to viscous effects. You can use different methods to calculate or estimate the face permeability, depending on the available data and assumptions.
3. Set the Porous Medium Thickness. This is the thickness of the porous medium that is used to calculate the pressure drop across the porous jump due to inertial effects. You can use a small value that is representative of your problem, such as 1e-5 m.
4. Set the Pressure-Jump Coefficient. This is the coefficient that determines the pressure drop across the porous jump due to inertial effects. You can use different methods to calculate or estimate the pressure-jump coefficient, depending on the available data and assumptions.
5. Define the discrete phase boundary condition for the porous jump (for discrete phase calculations). This is the option that specifies what happens to the particles that encounter the porous jump. You can choose to trap, escape, reflect, or rebound them.

How to fdefine porous jump in console  - Ansys Fluent ?

To enter these inputs in the console, you can use the following commands:

/define/boundary-conditions/porous-jump
zone-name <name-of-the-zone>
face-permeability <value-of-face-permeability>
porous-medium-thickness <value-of-porous-medium-thickness>
pressure-jump-coefficient <value-of-pressure-jump-coefficient>
discrete-phase-boundary-condition <trap|escape|reflect|rebound>

For example, if you want to add a porous jump boundary condition for a zone named “filter” with a face permeability of 1e-10 m^2, a porous medium thickness of 1e-5 m, a pressure-jump coefficient of 0.5 kg/m^3, and a discrete phase boundary condition of trap, you can type:

/define/boundary-conditions/porous-jump
zone-name filter
face-permeability 1e-10
porous-medium-thickness 1e-5
pressure-jump-coefficient 0.5
discrete-phase-boundary-condition trap

You can find more information about the porous jump model and its inputs in this web search result. I hope this answer is helpful for you.

💥💥💥 What are mesh quality and mesh errors in Ansys Fluent ?

 Mesh quality and mesh errors in Ansys Fluent are related to how well the mesh represents the geometry and the physics of the problem. A good mesh should have the following characteristics:

• It should conform to the shape of the domain and capture the important features of the geometry.
• It should have a smooth distribution of nodes and elements, without sudden changes in size or shape.
• It should have a low skewness and aspect ratio, which measure how far the elements deviate from an ideal shape (such as a square or an equilateral triangle).
• It should have enough resolution in regions where the flow is complex, such as boundary layers, shear layers, shock waves, etc.
• It should be compatible with the solver settings and the turbulence model used.

Mesh errors can occur when the mesh does not meet these criteria, and they can affect the accuracy and stability of the numerical solution. Some common mesh errors are:

• High skewness: This means that some elements are distorted or twisted, which can cause interpolation errors and convergence problems¹.
• Incorrect boundary layer meshing: This means that the mesh near the walls is not fine enough to resolve the viscous effects, which can lead to inaccurate predictions of wall shear stress and heat transfer¹.
• Coarse meshing: This means that the mesh is too coarse to capture the flow features, which can lead to numerical diffusion and loss of accuracy¹.
• A large aspect ratio leading to interpolation error: This means that some elements are stretched or compressed, which can cause errors in calculating the gradients and fluxes².

To check and improve the mesh quality in Ansys Fluent, there are several tools and methods available, such as:

• The Report Quality button in the General task page, which prints a message to the console with information about the mesh quality criteria¹.
• The Mesh Adaptation feature, which allows you to refine or coarsen the mesh based on various parameters, such as gradients, residuals, curvature, etc¹.
• The TUI commands or the Ansys Meshing tool, which allow you to modify or repair the mesh manually or automatically³.

How to check mesh errors and mesh quality in Ansys Fluent ?

There are several ways to check mesh errors and mesh quality in Ansys Fluent. Here are some of the resources that might help you:

• You can watch this YouTube video Ansys Fluent Tutorial - Check mesh quality errors and define mesh adaptation that shows how to use the Fluent interface to check and improve the mesh quality.
• You can also read this forum post When I open fluent and do mesh quality check, I’m that discusses a common warning message and how to fix it using the TUI commands or the Ansys Meshing tool.
• You can refer to this user’s guide ANSYS FLUENT 12.0 User’s Guide - 6.2.2 Mesh Quality that explains how to use the Report Quality button in the General task page and what the output means.

💥💥💥 How to speed up convergence for steady state analysis in Ansys Fluent - 5 tips

There are several factors that can affect the convergence speed for steady state analysis in ANSYS Fluent, such as the mesh quality, the solver settings, the initial conditions, the boundary conditions, and the physical models. Here are some general tips that may help you to speed up your convergence¹²³:

• Check your mesh quality and refine it if necessary. A good mesh should have a high element quality, a low aspect ratio, and a smooth transition between different sizes. You can use the mesh adaption feature to refine the mesh in regions of high gradients or curvature.
• Choose the appropriate solver settings for your problem. For example, if you are simulating natural or mixed convection, you may need to use a lower convergence criteria for the energy equation (e.g., 1e-12) and turn on gravity and radiation models. You may also need to use a small velocity in the direction opposite to gravity as an initial condition for steady state problems. You can also adjust the under-relaxation factors (URF) for different equations to improve stability and convergence. A common choice is to use 0.7 for pressure and 0.3 for momentum.
• Use a good initial guess for your solution. You can use the patching feature to specify different values for different regions of your domain. You can also use a previous solution from a similar case or a coarser mesh as an initial guess. A good initial guess can reduce the number of iterations needed to reach convergence.
• Check your boundary conditions and make sure they are consistent and realistic. For example, if you are using a pressure outlet boundary condition, you may need to specify a backflow temperature or use an outflow boundary condition instead. You can also use a mass flow inlet boundary condition instead of a velocity inlet boundary condition if you have compressible flow or variable density flow.
• Choose the appropriate physical models for your problem. For example, if you are simulating turbulent flow, you may need to use a turbulence model that can capture the effects of buoyancy and wall functions. You can also use a coupled pressure-velocity solver instead of a segregated solver if you have high-speed flow or high-pressure gradients.

I hope these tips can help you to speed up your convergence for steady state analysis in ANSYS Fluent. 😊

What are the mechanics of how relaxation factors work in Ansys Fluent?

The relaxation factors in Ansys Fluent are parameters that control the update of the computed variables at each iteration. They are used to improve the stability and convergence of the solution process. The relaxation factors are based on the following formula:

xk+1 = w.xcal + (1-w).xk

where xk is the value of the variable at iteration k, xcal is the value calculated from the equation, and w is the relaxation factor. The relaxation factor can range from 0 to 2, but usually it is between 0 and 1. A relaxation factor of 1 means that the variable is fully updated with the calculated value, while a relaxation factor of 0 means that the variable is not updated at all. A relaxation factor between 0 and 1 means that the variable is partially updated with a weighted average of the previous and calculated values.

The relaxation factors can affect the speed and accuracy of the solution. A higher relaxation factor can increase the convergence rate, but it can also cause instability or divergence if it is too high. A lower relaxation factor can increase the stability, but it can also slow down the convergence or cause oscillations if it is too low. Therefore, choosing appropriate relaxation factors for different equations and problems is important for obtaining a good solution.

Ansys Fluent provides default values for the relaxation factors that are suitable for most cases. However, some problems may require adjusting the relaxation factors to achieve better convergence or stability. For example, some turbulent flows or high-Rayleigh-number natural-convection problems may need lower relaxation factors for pressure, momentum, energy, and turbulence equations. Conversely, some flows with constant density or weak coupling between temperature and momentum may allow higher relaxation factors for temperature equation.

You can set or change the relaxation factors for each equation in the Solution Controls task page under Under-Relaxation Factors. You can also click the Default button to restore the default values. For more details about how to specify solution controls in Ansys Fluent, you can refer to this course or this user’s guide. You can also watch some video tutorials (https://www.youtube.com/watch?v=gZc7eS1xcFU) (https://www.youtube.com/watch?v=PrSpOf-TXiE) on how to model different types of flows in Ansys Fluent. I hope this helps you understand how relaxation factors work in Ansys Fluent. 😊