💥💥💥 How to define Design of Experiment in Ansys Fluent ?

Design of Experiments (DOE) is a method of systematically varying the input parameters of a simulation to explore their effects on the output responses. DOE can help you optimize your design, reduce the number of simulations, and understand the interactions between different factors.

To define a DOE in Ansys Fluent, you need to follow these steps:

• Create a parameter set in Ansys Workbench that contains the input parameters and output responses you want to vary and monitor. You can use expressions or tables to define the parameters.
• Drag and drop a Response Surface Optimization component from the Design Exploration toolbox to the project schematic. Connect it to the parameter set.
• Double-click on the Design of Experiments component to open it. You can see your input and output parameters under the Design of Experiments step. You can specify the lower and upper bounds for each input parameter, as well as the sampling method and number of points.
• Click on Generate Points to create a set of sampling points for each input parameter. These are the values that will be used in the simulations. You can also edit or import points manually if you want.
• Click on Update to run the simulations for each sampling point. This may take some time depending on the complexity of your model and the number of points. You can monitor the progress and results in the Solution Information window.
• After the update is completed, you can view and analyze the results in the Response Surface Viewer. You can plot response surfaces, contours, histograms, scatter plots, and more to visualize the relationships between the input parameters and output responses.

For more details and examples, you can refer to these resources:

• ANSYS Fluent DOE Tutorial: A tutorial that shows how to setup DOE with variable design parameters for ANSYS Fluent simulations using Rescale platform.
• Design of Experiments — Lesson 5: A lesson from ANSYS Innovation Courses that explains how to perform DOE for a plate with a hole optimization problem using ANSYS Workbench.
• Parametric Design Changes Entirely Within Ansys Fluent: A blog post from ANSYS that introduces the new mesh morphing capability that enables geometrical changes within the Fluent interface without having to revisit CAD.

What is sampling method in Response Surface Optimization in Ansys Workbench?

Sampling method is the technique used to select the points in the design space that will be evaluated by the simulation. Different sampling methods have different advantages and disadvantages, depending on the complexity and dimensionality of the problem. Some of the common sampling methods in Response Surface Optimization in Ansys Workbench are:

• Full Factorial: This method evaluates all possible combinations of the input parameters at each level. It is the most accurate but also the most expensive method, as it requires a large number of simulations. It is suitable for problems with a small number of input parameters (less than 5) and discrete levels (2 or 3).
• Central Composite: This method evaluates the input parameters at two levels (low and high), plus the center point and some axial points. It is a type of Box-Behnken design, which is a subset of full factorial design that reduces the number of simulations while maintaining the accuracy. It is suitable for problems with a moderate number of input parameters (less than 10) and continuous levels.
• Latin Hypercube: This method randomly samples the input parameters from a uniform distribution, ensuring that each parameter has only one value in each level. It is a type of Monte Carlo method, which is based on random sampling and statistical analysis. It is suitable for problems with a large number of input parameters (more than 10) and continuous levels.

You can choose the sampling method that best suits your problem in the Design of Experiments component in Ansys Workbench. You can also specify the number of points, the lower and upper bounds, and the distribution type for each input parameter. For more details and examples, you can refer to these resources:

• ANSYS Fluent DOE Tutorial: A tutorial that shows how to setup DOE with variable design parameters for ANSYS Fluent simulations using Rescale platform.
• Design of Experiments — Lesson 5: A lesson from ANSYS Innovation Courses that explains how to perform DOE for a plate with a hole optimization problem using ANSYS Workbench.
• ANSYS Software DOE: Everything You Need to Know: A blog post from Sunglass that provides an overview of DOE concepts and methods in ANSYS software.

How to model Full Fractional sampling method in Ansys Workbench ?

Full Fractional sampling method is a type of Design of Experiments (DOE) that evaluates all possible combinations of the input parameters at each level. It is the most accurate but also the most expensive method, as it requires a large number of simulations. It is suitable for problems with a small number of input parameters (less than 5) and discrete levels (2 or 3)¹.

To model Full Fractional sampling method in Ansys Workbench, you need to follow these steps:

• Create a parameter set in Ansys Workbench that contains the input parameters and output responses you want to vary and monitor. You can use expressions or tables to define the parameters.
• Drag and drop a Response Surface Optimization component from the Design Exploration toolbox to the project schematic. Connect it to the parameter set.
• Double-click on the Design of Experiments component to open it. You can see your input and output parameters under the Design of Experiments step. You can specify the lower and upper bounds for each input parameter, as well as the distribution type.
• Under Sampling Method, select Full Factorial from the drop-down list. This will generate all possible combinations of the input parameters at each level. You can also specify the number of levels for each parameter, or use the default value of 2.
• Click on Generate Points to create a set of sampling points for each input parameter. These are the values that will be used in the simulations. You can also edit or import points manually if you want.
• Click on Update to run the simulations for each sampling point. This may take some time depending on the complexity of your model and the number of points. You can monitor the progress and results in the Solution Information window.
• After the update is completed, you can view and analyze the results in the Response Surface Viewer. You can plot response surfaces, contours, histograms, scatter plots, and more to visualize the relationships between the input parameters and output responses.

For more details and examples, you can refer to these resources:

• Design of Experiments — Lesson 5: A lesson from ANSYS Innovation Courses that explains how to perform DOE for a plate with a hole optimization problem using ANSYS Workbench.
• Ansys Workbench: A webpage from ANSYS that introduces the features and benefits of Ansys Workbench platform.
• Module 05: Multiphysics: A PDF file from ANSYS Innovation Courses that covers some concepts and methods of multiphysics simulation using ANSYS software.

How to choose the number of levels for each parameter in DOE?

The number of levels for each parameter in DOE depends on the type of design, the number of parameters, and the objective of the experiment. Generally, there are three types of designs based on the number of levels:

• Two-level designs: These are the simplest and most common type of DOE, where each parameter has only two levels, such as high and low, or +1 and -1. These designs are suitable for screening experiments, where the main goal is to identify the most significant parameters that affect the response. They are also efficient for optimization experiments, where the response is linear or near-linear with respect to the parameters. Two-level designs can be full factorial, where all possible combinations of the levels are tested, or fractional factorial, where only a fraction of the combinations are tested to save time and resources. Two-level designs can handle a large number of parameters (up to 50) with few interactions¹.
• Three-level designs: These are more complex and less common type of DOE, where each parameter has three levels, such as low, medium, and high, or -1, 0, and +1. These designs are suitable for optimization experiments, where the response is nonlinear or quadratic with respect to the parameters. They can also detect interactions between parameters more effectively than two-level designs. Three-level designs can be full factorial, where all possible combinations of the levels are tested, or fractional factorial, where only a fraction of the combinations are tested to save time and resources. Three-level designs can handle a moderate number of parameters (up to 10) with some interactions².
• Multi-level designs: These are the most complex and least common type of DOE, where each parameter has more than three levels, such as four or five. These designs are suitable for optimization experiments, where the response is highly nonlinear or has higher-order effects with respect to the parameters. They can also capture more details and variations in the response than two-level or three-level designs. Multi-level designs are usually full factorial, where all possible combinations of the levels are tested, as fractional factorial designs may lose some information at higher levels. Multi-level designs can handle a small number of parameters (less than 5) with many interactions³.

To choose the number of levels for each parameter in DOE, you need to consider the following factors:

• The type of design that best suits your problem and objective.
• The number of parameters that you want to study and their expected effects on the response.
• The available resources and time for conducting the experiment.

For example, if you have a problem with 5 parameters that you want to optimize for a nonlinear response, you may choose a three-level full factorial design that can test all possible combinations of the levels and capture the quadratic effects and interactions. However, if you have limited resources and time, you may choose a three-level fractional factorial design that can test only a subset of the combinations and still estimate the main effects and some interactions.

For more details and examples on how to choose the number of levels for each parameter in DOE, you can refer to these resources:

• What Is Design of Experiments (DOE)?: A webpage from ASQ that provides an overview of DOE concepts and methods.
• Types of Design of Experiments: A webpage from JMP that introduces different types of DOE based on the number of levels.
• A DOE in a Manufacturing Environment (Part 1): A blog post from Minitab that shows how to set up a two-level DOE for a manufacturing problem.

💥💥💥 How to increase precision in Ansys Workbench calculations?

 One way to increase the precision of your calculations in Ansys Workbench is to use the double-precision solver option in Fluent. This option allows you to use more digits after the decimal point and reduce the numerical errors. You can find this option in the Fluent Launcher, under Options, as shown in this tutorial and this one. Alternatively, you can also increase the precision of values or number of decimal places in SpaceClaim by changing the Primary Precision option in File > SpaceClaim Options > Units. You can learn more about this option from this forum post. 

How do I know if my calculations are precise enough?

There are several factors that can affect the precision of your calculations in Ansys Workbench, such as the mesh quality, the solver settings, the convergence criteria, and the material model. You can check the precision of your calculations by comparing the results with analytical solutions, experimental data, or other reliable sources. You can also monitor the residuals and other quantities of interest during the solution process to ensure that they reach a steady or periodic state. Additionally, you can perform a sensitivity analysis or a mesh refinement study to evaluate how the results change with different input parameters or mesh sizes. You can find more information and examples on how to check the precision of your calculations from the following sources:

• How to know the calculation has converged
• Fundamental Calculations of equivalent stress and strain in ANSYS Workbench
• Steady Flow Past Cylinder Physics Setup
• Physics Setup — Lesson 5

Can you explain more about mesh quality and refinement study?

Sure, I can explain more about mesh quality and refinement study in Ansys Workbench. Mesh quality is a measure of how well the mesh elements represent the geometry and the physics of the problem. Mesh refinement is a process of increasing the number of mesh elements or improving their shape to achieve more accurate results.

Mesh quality depends on several factors, such as the element type, the element shape, the element size, and the element alignment. Different types of elements have different advantages and disadvantages for different problems. For example, tetrahedral elements are easy to generate and can fit complex geometries, but they may have lower accuracy and stability than hexahedral elements. The element shape affects how well the element can approximate the curvature of the geometry and the gradients of the solution. The element size determines how fine or coarse the mesh is, and how well it can capture the details of the problem. The element alignment refers to how well the mesh follows the direction of the flow or the stress.

Mesh refinement is a way to improve the mesh quality by increasing the number of elements or changing their shape. Mesh refinement can be done globally or locally. Global mesh refinement applies to the whole domain or a large region, and it increases the resolution of the mesh uniformly. Local mesh refinement applies to a specific area of interest, such as a boundary layer, a corner, or a crack, and it increases the resolution of the mesh selectively. Mesh refinement can also be done adaptively or manually. Adaptive mesh refinement is an automated process that adjusts the mesh based on some criteria, such as the error estimate, the solution gradient, or the user-defined function. Manual mesh refinement is a user-controlled process that modifies the mesh based on some parameters, such as the element size, the inflation factor, or the smoothing factor.

Mesh quality and refinement study is important for obtaining reliable and accurate results in Ansys Workbench. A good quality mesh can reduce the numerical errors and improve the convergence of the solution. A refined mesh can capture the features and phenomena of interest more accurately and resolve the variations of the solution more precisely. However, mesh quality and refinement also have some trade-offs, such as increased computational cost, memory requirement, and solution time. Therefore, it is necessary to balance between mesh quality and refinement and computational efficiency.

You can learn more about mesh quality and refinement study in Ansys Workbench from these sources:

• Generating Mesh in ANSYS Workbench | Part-1
• How to check the MESH Quality in Ansys Workbench | Mesh Metrics | Skewness | Orthogonal Quality
• Tutorial on Meshing in ANSYS Workbench I ANSYS Basic Tutorials Apply various Mesh on geometry
• Ansys sturcutal analysis tutorials 23: mesh convergence and refinement ; Stress history plot
• mesh refinment study - Ansys Learning Forum
• Using Adaptive Convergence with Ansys Mechanical — Lesson 5
• MESH QUALITY AND ADVENCED TOPICS ANSYS WORKBENCH 16.0 - Academia.edu

How do I choose the right element size for my mesh?

Choosing the right element size for your mesh depends on several factors, such as the geometry, the physics, the accuracy, and the computational cost of your problem. There is no universal rule for selecting the optimal element size, but there are some general guidelines and methods that can help you. Here are some of them:

• You can start with a coarse mesh and refine it gradually until you reach a desired level of accuracy or convergence. You can use global or local refinement, adaptive or manual refinement, or a combination of them. You can also use different types of elements, such as tetrahedral, hexahedral, or hybrid elements, depending on the complexity and curvature of your geometry.
• You can use mesh sizing controls to specify the element size for a selected body, face, edge, or vertex. You can also use the sphere of influence option to apply a local element size around a point of interest. You can access these options by right-clicking on the Mesh in the project tree and selecting Insert > Sizing.
• You can use mesh metrics to evaluate the quality of your mesh, such as the skewness, the aspect ratio, the orthogonal quality, or the smoothness. You can check these metrics by right-clicking on the Mesh in the project tree and selecting Mesh Metrics. You can also use mesh statistics to see the number and type of elements in your mesh. You can access these statistics by right-clicking on the Mesh in the project tree and selecting Statistics.
• You can compare your results with analytical solutions, experimental data, or other reliable sources to validate your mesh. You can also monitor the residuals and other quantities of interest during the solution process to ensure that they are within acceptable limits. You can also perform a sensitivity analysis or a mesh convergence study to evaluate how your results change with different input parameters or mesh sizes.

You can learn more about how to choose the right element size for your mesh in Ansys Workbench from these sources:

• Correct mesh size - a quick guide! - Enterfea
• Learn Everything about the Ansys Workbench | Mesh Sizing - Skill-Lync
• Sizing a Mesh | Ansys Innovation Courses
• Defining Element Size Of A Body In ANSYS® Mechanical(Illustrated Expression) - ML
• Changing The Element Size In ANSYS® Meshing(Iluustrated Expression) - ML

How do I know if my element size is too small or too large?

The element size is a key parameter that affects the accuracy and efficiency of your mesh and solution in Ansys Workbench. If your element size is too small, you may have a very fine mesh that can capture the details of your problem, but it will also increase the computational cost, memory requirement, and solution time. If your element size is too large, you may have a very coarse mesh that can reduce the computational cost, memory requirement, and solution time, but it will also introduce numerical errors and affect the convergence of your solution.

To know if your element size is too small or too large, you can use some methods and criteria to evaluate your mesh and solution quality. Here are some of them:

• You can compare your results with analytical solutions, experimental data, or other reliable sources to validate your mesh and solution. You can also monitor the residuals and other quantities of interest during the solution process to ensure that they are within acceptable limits.
• You can use mesh metrics to evaluate the quality of your mesh, such as the skewness, the aspect ratio, the orthogonal quality, or the smoothness. You can check these metrics by right-clicking on the Mesh in the project tree and selecting Mesh Metrics. You can also use mesh statistics to see the number and type of elements in your mesh. You can access these statistics by right-clicking on the Mesh in the project tree and selecting Statistics.
• You can perform a sensitivity analysis or a mesh convergence study to evaluate how your results change with different input parameters or mesh sizes. You can use global or local refinement, adaptive or manual refinement, or a combination of them. You can also use different types of elements, such as tetrahedral, hexahedral, or hybrid elements, depending on the complexity and curvature of your geometry.

You can learn more about how to know if your element size is too small or too large in Ansys Workbench from these sources:

• Sizing on Ansys Workbench 19.2
• Element size too small
• Meshing Element size

Can you explain more about the aspect ratio and orthogonal quality metrics?

Sure, I can explain more about the aspect ratio and orthogonal quality metrics in Ansys Workbench. These are two of the mesh metrics that you can use to evaluate the quality of your mesh elements.

The aspect ratio quantifies the shape of the elements, where 1 is a perfectly shaped element and the element shape is worse with a higher aspect ratio. The aspect ratio is defined as the ratio of the shortest length of the element to the longest length of the element¹. For example, a tetrahedral element has an aspect ratio of 1 if it is a regular tetrahedron, and a higher aspect ratio if it is elongated or flattened. A hexahedral element has an aspect ratio of 1 if it is a cube, and a higher aspect ratio if it is a rectangular prism or a parallelepiped. The aspect ratio affects the accuracy and stability of the solution, as well as the convergence rate. Generally, you should aim for an aspect ratio less than 20 for structural analysis and less than 1000 for fluid analysis².

The orthogonal quality quantifies the alignment of the elements with respect to the geometry and the physics of the problem. The orthogonal quality is defined as the minimum value of the dot product between the element face normal and the vector from the face centroid to the opposite node, for all faces of the element³. The orthogonal quality ranges from 0 to 1, where 1 is the best value and 0 is the worst value. For example, a tetrahedral element has an orthogonal quality of 1 if it is equilateral and orthogonal to the geometry, and a lower orthogonal quality if it is skewed or distorted. A hexahedral element has an orthogonal quality of 1 if it is aligned with the geometry and the flow or stress direction, and a lower orthogonal quality if it is misaligned or twisted. The orthogonal quality affects the accuracy and robustness of the solution, as well as the numerical diffusion and dispersion. Generally, you should aim for an orthogonal quality greater than 0.1 for most problems⁴.

You can check these metrics by right-clicking on the Mesh in the project tree and selecting Mesh Metrics. You can also use mesh statistics to see the number and type of elements in your mesh. You can access these statistics by right-clicking on the Mesh in the project tree and selecting Statistics.

You can learn more about how to improve these metrics by using different meshing methods, such as global or local refinement, adaptive or manual refinement, or different types of elements, such as tetrahedral, hexahedral, or hybrid elements. You can find some tutorials and examples from these sources:

• Explanations Of All Mesh Metric Options In ANSYS® Mechanical
• 网格划分（二）：网格质量评估（基于ANSYS2020）
• Is Your Mesh Good Enough?
• Ansys Workbench Mesh Quality || Skewness || Aspect Ratio || Orthogonal

💥💥💥 How to fix "overflow" error in Ansys CFD?

 The error “overflow” in Ansys CFD means that a number is being generated that is too large to store. It could be related to dividing a number by a very small number. Some possible ways to fix this error are¹:

• Review the boundary conditions to make sure they are reasonable and consistent with the physical problem.
• Run the simulation in double precision mode, which can handle larger numbers and improve the accuracy of the solution.
• Try using a smaller time step or a different time scale factor, which can help avoid numerical instability and convergence issues.
• Try using a different turbulence model, such as SST, which can better capture the flow physics and reduce numerical errors.
• Try using a different advection scheme, such as High Resolution or Upwind, which can reduce numerical diffusion and oscillations.

What is numerical instability?

Numerical instability is a concept that refers to the tendency of some numerical algorithms or procedures to produce inaccurate or unreliable results due to errors in the computation. These errors may be caused by factors such as round-off errors, truncation errors, or ill-conditioning of the problem. Numerical instability can lead to large deviations from the expected or exact solution, and sometimes even to divergence or failure of the algorithm. Numerical stability is a desirable property of numerical algorithms, which means that the errors do not significantly affect the final result. Numerical stability depends on both the algorithm and the problem being solved. Some algorithms are more robust than others, and some problems are more sensitive than others. Numerical stability can be assessed by various criteria, such as forward error, backward error, condition number, or error propagation. For more information and examples of numerical instability and stability, you can refer to the web search results¹²³. 

How can I improve numerical stability in my simulation?

There are several ways to improve numerical stability in your simulation, depending on the cause and nature of the instability. Some general tips are:

• Review your mesh quality and resolution, and make sure they are adequate for capturing the flow features and gradients. A poor mesh can introduce numerical errors and instability. You can use mesh adaptation techniques to refine the mesh in regions of interest¹.
• Choose appropriate boundary conditions and initial conditions that are consistent with the physical problem and the expected solution. Inappropriate or unrealistic boundary conditions can cause spurious oscillations or divergence in the solution¹.
• Use relaxation factors to control the rate of change of the solution variables during the iteration process. Relaxation factors are coefficients that dampen the effect of the new computed values on the old values. A smaller relaxation factor can improve stability, but it can also slow down convergence. A larger relaxation factor can speed up convergence, but it can also cause instability. The optimal relaxation factor depends on the problem and the solver, but a typical range is between 0.15 and 0.7².
• Use a suitable time step size and time integration scheme for transient simulations. A too large time step size can cause numerical instability and loss of accuracy. A too small time step size can increase the computational cost and time. A good practice is to use a time step size that satisfies the Courant-Friedrichs-Lewy (CFL) condition, which relates the time step size to the spatial discretization and the flow velocity. The CFL number is defined as CFL = uδt/δx, where u is the flow velocity, δt is the time step size, and δx is the spatial discretization. A CFL number less than 1 is usually recommended for stability³. The time integration scheme can also affect stability and accuracy. For example, an explicit scheme can be more unstable than an implicit scheme, but it can also be more accurate for some problems³.
• Use a suitable spatial discretization and advection scheme for convective terms. The spatial discretization determines how the solution variables are interpolated or extrapolated at the cell faces from the cell centers. The advection scheme determines how the convective fluxes are calculated at the cell faces from the solution variables. Different schemes have different levels of accuracy, stability, and computational cost. For example, a first-order upwind scheme can be more stable than a second-order central difference scheme, but it can also introduce more numerical diffusion and smearing of sharp gradients³.

These are some of the common ways to improve numerical stability in your simulation. 

Can you explain more about relaxation factors?

Sure, I can explain more about relaxation factors. Relaxation factors are coefficients that control the amount of change in the solution variables during the iterative process of solving a system of equations. They are used to improve the stability and convergence of the solution, especially for nonlinear and complex problems. Relaxation factors can be either under-relaxation or over-relaxation, depending on whether they are less than or greater than one. Under-relaxation means that only a fraction of the predicted change in the solution variable is applied, which can slow down the convergence but increase the stability. Over-relaxation means that more than the predicted change in the solution variable is applied, which can speed up the convergence but decrease the stability. The optimal value of the relaxation factor depends on the problem and the algorithm, and it is usually determined by trial and error. Some common ranges of relaxation factors are between 0.15 and 0.7 for flow variables, and between 0.5 and 1 for turbulence variables¹. Relaxation factors are often used in CFD simulations, where they can affect the accuracy and efficiency of the solution. For example, relaxation factors can help avoid numerical instability and divergence in steady-state simulations, or reduce numerical diffusion and oscillations in transient simulations². Relaxation factors are also related to the time step size and the spatial discretization schemes, which can also influence the stability and accuracy of the solution³. 

How do I choose the optimal relaxation factor?

Choosing the optimal relaxation factor in Ansys CFD depends on the problem and the algorithm you are using, and it is usually determined by trial and error. There is no universal rule for selecting the best value, but some general guidelines are¹²³:

• A relaxation factor that is too small (< 0.15) can slow down the convergence rate and increase the computational cost.
• A relaxation factor that is too large (> 0.7) can cause numerical instability and divergence of the solution.
• A relaxation factor that is close to 1 (no relaxation) can sometimes improve the accuracy of the solution, but it can also introduce numerical errors and oscillations.
• A relaxation factor that is close to 0 (full relaxation) can sometimes improve the stability of the solution, but it can also introduce numerical diffusion and smearing of sharp gradients.

Therefore, a good practice is to start with a moderate value of the relaxation factor (between 0.3 and 0.7) and then adjust it according to the behavior of the solution. You can monitor the residuals, the convergence history, and the physical quantities of interest to see if the solution is stable, accurate, and realistic. You can also use the auto-relaxation feature in Ansys CFD, which can dynamically change the relaxation factor for the flow variables to speed up convergence while trying to keep the solution stable². However, this feature may not work for all problems, and you may need to try different values manually to find the optimal one.

Can you explain more about auto-relaxation feature?

Sure, I can explain more about the auto-relaxation feature in Ansys CFD. Auto-relaxation is a technique that automatically adjusts the relaxation factors for the flow variables during the iterative process of solving the system of equations. Relaxation factors are coefficients that control the rate of change of the solution variables from one iteration to the next. They are used to improve the stability and convergence of the solution, especially for nonlinear and complex problems. A smaller relaxation factor can increase stability, but it can also slow down convergence. A larger relaxation factor can speed up convergence, but it can also cause instability. The optimal relaxation factor depends on the problem and the algorithm, and it is usually determined by trial and error.

The auto-relaxation feature is designed to dynamically change the relaxation factor for each flow variable based on its residual behavior. The residual is a measure of how well the solution satisfies the equation for each variable. A lower residual means a more accurate solution. The auto-relaxation feature tries to find the optimal balance between stability and convergence by increasing or decreasing the relaxation factor according to the residual trend. For example, if the residual is decreasing steadily, the auto-relaxation feature will increase the relaxation factor to speed up convergence. If the residual is oscillating or increasing, the auto-relaxation feature will decrease the relaxation factor to improve stability.

The auto-relaxation feature can be enabled or disabled in Ansys CFD by selecting or deselecting the Auto Relaxation option in the Numerics panel¹. The auto-relaxation feature can sometimes improve the convergence rate and efficiency of the solution, but it may not work for all problems. In some cases, if the auto-relaxation feature causes divergence or instability, it is recommended to try with manual relaxation factors that are fixed over the iterations². You can also monitor and adjust the relaxation factors manually by using the Relaxation Factors option in the Numerics panel¹.

💥💥💥 What is Design Xplorer in Ansys Workbench and what is used for ?

 Design Xplorer is a tool that helps you optimize your design by exploring different parameters and their effects on your objectives. You can use Design Xplorer to perform design of experiments, sensitivity analysis, response surface fitting, optimization, and robust design. Design Xplorer works with Ansys Workbench, which is a platform for simulation and engineering applications. You can use Design Xplorer to improve the performance, reliability, and efficiency of your design (1,2). 

Source: 

(1) ANSYS 2020 Workbench Tutorial | Introduction to Static Structural | 2020 R2. https://www.youtube.com/watch?v=qbsx1c-R_qU.

(2) Using Ansys DesignXplorer in Ansys Mechanical - YouTube. https://www.youtube.com/watch?v=EMUbHd-DGbc.

How does Design Xplorer work?

To use Design Xplorer, you need to follow these steps:

- Define your input parameters and output responses in Ansys Workbench

- Set up your design exploration method, such as what-if study, parameter correlation, design of experiments, optimization, or six sigma analysis

- Run the design exploration and view the results in charts, tables, or graphs

- Select the best design point based on your objectives and constraints

- Update your design model with the optimal parameters

You can learn more about Design Xplorer from the following resources:

- Introduction to Ansys DesignXplorer (CFD) | Ansys Training¹

- Using Ansys DesignXplorer in Ansys Mechanical | Ansys Virtual Academy - YouTube²

- DesignXplorer vs optiSLang for Workbench CFD Optimization³

I hope this helps you understand how Design Xplorer works. If you have any questions, feel free to ask me. 😊

Source:

(1)https://ketiv.com/ava.

(2)https://twitter.com/KetivTech.

(3) Introduction to Ansys DesignXplorer (CFD) | Ansys Training. https://www.ansys.com/training-center/course-catalog/fluids/introduction-to-ansys-designxplorer-cfd.

(4) Using Ansys DesignXplorer in Ansys Mechanical | Ansys Virtual ... - YouTube. https://www.youtube.com/watch?v=EMUbHd-DGbc.

(5) DesignXplorer vs optiSLang for Workbench CFD Optimization. https://forum.ansys.com/forums/topic/designxplorer-vs-optislang-for-workbench-cfd-optimization/.

(6)  https://facebook.com/KETIVTech/.

(7)  https://www.linkedin.com/company/keti.

(8) https://ketiv.com/blog/subscribe/.

(9) https://ketiv.com/avaIntroduction.

What is the difference between Design Xplorer and optiSLang?

Design Xplorer and optiSLang are both tools that can help you perform design exploration and optimization studies using Ansys Workbench. However, they have some differences in their features and capabilities. Here are some of the main differences:

- Design Xplorer is an integrated tool within Ansys Workbench, whereas optiSLang is a separate product that can be coupled with Ansys Workbench or other software tools¹².

- Design Xplorer includes a subset of the algorithms and methods that are available in optiSLang, such as design of experiments, response surface fitting, optimization, and six sigma analysis². OptiSLang offers more advanced and flexible algorithms and methods, such as meta-models of optimal prognosis, robustness evaluation, reliability analysis, and multidisciplinary optimization¹.

- Design Xplorer may be more suitable for simpler optimization problems or when you want to work within Ansys Workbench environment². OptiSLang may be more suitable for complex optimization problems or when you need to integrate different tools or workflows into your process¹.

You can learn more about the differences between Design Xplorer and optiSLang from the following resources:

Source:

(1) DesignXplorer vs optiSLang for Workbench CFD Optimization. https://forum.ansys.com/forums/topic/designxplorer-vs-optislang-for-workbench-cfd-optimization/.

(2) Optislang and designXplorer - Ansys Learning Forum. https://forum.ansys.com/forums/topic/optislang-and-designxplorer/.

(3)https://www.ansys.com/content/dam/amp/2023/january/quick-request/academic-product-bundle-reference-table-4-11-23.pdf.

Can I use Design Xplorer for structural simulations?

Yes, you can use Design Xplorer for structural simulations. Design Xplorer can work with Ansys Mechanical, which is a tool for finite element analysis (FEA) of structural and coupled-field problems¹. You can use Design Xplorer to study, quantify, and graph various structural responses on parts and assemblies, such as stress, strain, displacement, vibration, fatigue, etc². You can also use Design Xplorer to optimize your design by finding the best combination of input parameters that satisfy your objectives and constraints².

To use Design Xplorer for structural simulations, you need to have Ansys Workbench and Ansys Mechanical installed on your computer. You also need to define your input parameters and output responses in Ansys Workbench, and set up your design exploration method in Design Xplorer. 

Source:

(1) Introduction to Ansys DesignXplorer (FEA) | Ansys Training. https://www.ansys.com/training-center/course-catalog/structures/introduction-to-ansys-designxplorer-fea.

(2) Structural Analysis & Simulation Software | Ansys. https://www.ansys.com/products/structures.

(3) A Scalable Simulation Portfolio for Structural Designers. https://www.3ds.com/cloud/scalable-simulation-portfolio-structural-designers.

Can I use Design Xplorer for fluid dynamics simulations?

Yes, you can use Design Xplorer for fluid dynamics simulations. Design Xplorer can work with Ansys Fluent and Ansys CFX, which are tools for computational fluid dynamics (CFD) simulations². You can use Design Xplorer to study, quantify, and graph various fluid dynamics responses, such as pressure, velocity, temperature, turbulence, etc³. You can also use Design Xplorer to optimize your design by finding the best combination of input parameters that satisfy your objectives and constraints³.

To use Design Xplorer for fluid dynamics simulations, you need to have Ansys Workbench and Ansys Fluent or Ansys CFX installed on your computer. You also need to define your input parameters and output responses in Ansys Workbench, and set up your design exploration method in Design Xplorer. 

Source:

(1) CFD Software: Fluid Dynamics Simulation Software - Ansys. https://www.ansys.com/products/fluids.

(2) Insights from Sir James Dyson on accelerating product development .... https://www.computationalfluiddynamics.com.au/dyson-simulation-driven-product-development-with-design-exploration/.

(3) Fluids | Special Issue : Deep Learning for Fluid Simulation - MDPI. https://www.mdpi.com/journal/fluids/special_issues/deep_learning_fluid.

(4) Fluid Dynamics Simulation - Weber State University. https://physics.weber.edu/schroeder/fluids/.

(5) Marine Computational Fluid Dynamics (CFD): Simulation for ship design. https://resources.sw.siemens.com/en-US/e-book-marine-cfd-software.

What is the difference between Ansys Fluent and Design Xplorer?

Ansys Fluent and Design Xplorer are both tools that can help you perform fluid dynamics simulations using Ansys Workbench. However, they have different purposes and features. Here are some of the main differences:

- Ansys Fluent is a tool for computational fluid dynamics (CFD) simulations. It can solve complex fluid flow problems involving heat transfer, turbulence, multiphase flows, etc⁵. You can use Ansys Fluent to model and analyze the behavior of fluids in various engineering applications, such as aerodynamics, combustion, HVAC, etc⁵.

- Design Xplorer is a tool for design exploration and optimization. It can help you study the effects of different input parameters on your output responses, such as pressure, velocity, temperature, etc⁴. You can use Design Xplorer to find the optimal design that meets your objectives and constraints, such as minimizing drag, maximizing efficiency, etc⁴.

- Ansys Fluent is an integrated tool within Ansys Workbench, which means you can easily set up and run your CFD simulations using the Workbench interface. You can also use other Workbench tools, such as Ansys Meshing, Ansys CFD-Post, etc., to prepare and post-process your CFD models⁵.

- Design Xplorer is a separate tool that can be coupled with Ansys Workbench or other software tools. You need to define your input parameters and output responses in Ansys Workbench first, and then export them to Design Xplorer. You can also import your design points from Design Xplorer back to Ansys Workbench to update your CFD models².

Source: 

(1) Compare Ansys CFX vs. Ansys Fluent | G2. https://www.g2.com/compare/ansys-cfx-vs-ansys-fluent.

(2) Introduction to Ansys DesignXplorer (CFD) | Ansys Training. https://www.ansys.com/training-center/course-catalog/fluids/introduction-to-ansys-designxplorer-cfd.

(3) Connection between Design Explorer and stand alone Fluent sessions .... https://forum.ansys.com/forums/topic/connection-between-design-explorer-and-stand-alone-fluent-sessions/.

(4) Introduction to Ansys DesignXplorer (FEA) | Ansys Training. https://www.ansys.com/training-center/course-catalog/structures/introduction-to-ansys-designxplorer-fea.

(5) ANSYS DesignXplorer: Using Response Surfaces - Part 1 - Ansys Knowledge. https://ansyskm.ansys.com/forums/topic/ansys-designxplorer-using-response-surfaces-part-1/.

💥💥💥 How to model in OptiSLang in Ansys?

 OptiSLang is a software tool that enables you to perform sensitivity analysis, optimization, and robustness evaluation of your simulation models in Ansys Workbench. To model in OptiSLang, you need to follow these steps:

• Install OptiSLang as an add-on to Ansys Workbench. You can download it from the official website.
• Launch Ansys Workbench and create a new project. You will see a new tab called “optiSLang” in the toolbox. Drag and drop it to the project schematic.
• Connect the optiSLang component to the other components of your simulation workflow, such as geometry, mesh, setup, and solution. You can also use parameters and results from other components as inputs and outputs for optiSLang.
• Double-click on the optiSLang component to open its graphical user interface. Here you can define your optimization problem, such as objectives, constraints, variables, and algorithms.
• Run the optiSLang component to start the optimization process. You can monitor the progress and results in the optiSLang interface or in Ansys Workbench.
• Analyze the results of the optimization, such as sensitivity plots, Pareto fronts, response surfaces, and robustness measures. You can also export the results to other formats or tools for further processing.

For more details and examples, you can watch some video tutorials on YouTube¹²³ or read some documentation on Rescale⁴ or Ansys⁵. 

What is sensitivity analysis?

Sensitivity analysis is a technique that helps you understand how changes in one or more input variables affect the output of a mathematical model or system. For example, you can use sensitivity analysis to see how different interest rates affect the monthly payments of a loan, or how different weather conditions affect the growth of a crop. Sensitivity analysis can also help you identify the most important input variables that have the greatest impact on the output, and test the robustness of your model or system under different scenarios¹²³

Sensitivity analysis can be performed in different ways, such as varying one input variable at a time while keeping the others fixed, or varying multiple input variables simultaneously using a range of values or distributions. Some common methods of sensitivity analysis include:

• One-way sensitivity analysis: This method involves changing one input variable at a time and observing the effect on the output. This can help you determine the individual contribution of each input variable to the output.
• Multi-way sensitivity analysis: This method involves changing two or more input variables at a time and observing the effect on the output. This can help you capture the interactions and nonlinearities among the input variables.
• Monte Carlo simulation: This method involves randomly sampling values from predefined distributions for each input variable and running the model or system multiple times to generate a distribution of possible outputs. This can help you account for the uncertainty and variability in the input variables and the output.

Sensitivity analysis is a useful tool for financial modeling, engineering design, scientific research, policy making, and many other fields. It can help you improve your understanding of your model or system, validate your assumptions, optimize your decisions, and communicate your results.

What is the difference between sensitivity analysis and optimization?

Sensitivity analysis and optimization are two related but distinct techniques that can help you understand and improve your mathematical models or systems. The main difference between them is that sensitivity analysis focuses on how the output of a model or system changes with respect to changes in the input variables, while optimization focuses on finding the optimal values of the input variables that maximize or minimize the output of a model or system.

Sensitivity analysis can help you answer questions such as:

• How sensitive is the output to changes in one or more input variables?
• Which input variables have the most or least impact on the output?
• How does uncertainty or variability in the input variables affect the output?

Optimization can help you answer questions such as:

• What are the best values of the input variables that achieve the desired output?
• What are the trade-offs or constraints involved in optimizing the output?
• How does the optimal solution change with respect to changes in the input variables?

Both sensitivity analysis and optimization can be performed using various methods, such as analytical, numerical, graphical, or simulation-based approaches. Some examples of applications of sensitivity analysis and optimization are:

• Financial modeling: You can use sensitivity analysis to see how different interest rates, inflation rates, exchange rates, or market conditions affect the profitability or risk of an investment portfolio. You can use optimization to find the optimal allocation of assets that maximizes your expected return or minimizes your risk.
• Engineering design: You can use sensitivity analysis to see how different design parameters, such as dimensions, materials, loads, or operating conditions affect the performance or reliability of a product or system. You can use optimization to find the optimal design that maximizes your performance or minimizes your cost.
• Scientific research: You can use sensitivity analysis to see how different experimental factors, such as temperature, pressure, concentration, or time affect the outcome or response of a process or phenomenon. You can use optimization to find the optimal experimental conditions that maximize your response or minimize your error.

Examples of using Ansys OptiSLang  in machine learning 

Ansys OptiSLang is a software tool that can help you perform sensitivity analysis, optimization, and robustness evaluation of your simulation models in Ansys Workbench. It can also use artificial intelligence and machine learning techniques to generate metamodels, such as neural networks, that can approximate the behavior of complex systems with high accuracy and efficiency. Some examples of using Ansys OptiSLang in machine learning are:

• Top 3 New Features in Ansys optiSLang¹: This blog post introduces the new Deep Feedforward Network (DFN) metamodel that leverages the latest AI/ML technology using neural networks to significantly reduce the time it takes to run thousands of designs.
• Ansys optiSLang Getting Started²: This training course teaches you how to use optiSLang to conduct sensitivity analysis and optimization using various methods, such as response surfaces, direct optimization, and AI/ML-based metamodels.
• Ansys Motor-CAD and optiSLang: Optimizing E-Machine Designs³: This training course shows you how to carry out optimization of an electric machine design by coupling Motor-CAD and optiSLang. You will learn how to use the integrated optiSLang export tool in Motor-CAD to set up an optimization project and use AI/ML-based metamodels to speed up the optimization process.
• Machine Tool Optimization with Ansys optiSLang⁴: This webinar demonstrates how to apply optiSLang within Ansys Workbench to optimize the design of a machine tool. You will see how to use AI/ML-based metamodels to reduce the computational cost and time of the optimization.
• Ansys optiSLang | Process Integration & Design Optimization Software⁵: This webpage provides an overview of the features and benefits of optiSLang, including its ability to use AI/ML-based metamodels for fast and accurate simulation of complex systems.

How does Ansys OptiSLang compare to other optimization tools?

Ansys OptiSLang is a powerful and versatile tool for process integration and design optimization (PIDO) that can help you automate and streamline your simulation workflows, explore the design space, and find the optimal solutions for your engineering problems. It can also handle uncertainties and variations in your input parameters and output responses, and provide robustness and reliability analysis for your designs. Ansys OptiSLang can be compared to other optimization tools based on several criteria, such as:

• Integration: Ansys OptiSLang can be easily integrated with Ansys Workbench and Ansys Electronics Desktop, as well as other CAx tools and physics solvers, through its graphical user interface (GUI) or its application programming interface (API). It can also use custom code and third-party applications, such as Python, MATLAB, Excel, or Abaqus, in its automated workflows. Ansys OptiSLang supports various data formats and file types, and can extract and transform data from different sources. It can also run on local machines or on cloud platforms, such as Rescale or AWS.
• Optimization: Ansys OptiSLang offers a wide range of optimization methods, such as gradient-based, evolutionary, or surrogate-based algorithms. It can handle single- or multi-objective optimization problems, with linear or nonlinear constraints. It can also perform global or local optimization, depending on the user’s preference. Ansys OptiSLang can automatically select the best optimization method for a given problem, or let the user choose from a list of available methods.
• Robustness: Ansys OptiSLang can account for uncertainties and variations in the input parameters and output responses of a simulation model or system. It can perform sensitivity analysis to identify the most influential parameters and rank them according to their impact on the output. It can also perform robustness evaluation to measure the variation of the output due to the variation of the input. It can also perform reliability analysis to estimate the probability of failure or success of a design under different scenarios.
• Metamodeling: Ansys OptiSLang can use artificial intelligence and machine learning techniques to create metamodels, such as neural networks, that can approximate the behavior of complex systems with high accuracy and efficiency. Metamodels can be used to reduce the computational cost and time of the optimization process, as well as to explore the design space and generate response surfaces. Ansys OptiSLang can automatically generate metamodels from simulation data, or let the user customize them.

These are some of the features that make Ansys OptiSLang a unique and powerful tool for PIDO. Of course, there may be other optimization tools that have similar or different capabilities, depending on the specific application domain and user requirements. You can learn more about Ansys OptiSLang and how it compares to other optimization tools by checking out some of the web search results I found for you¹²³. 

What are some best practices for using Ansys OptiSLang?

Some best practices for using Ansys OptiSLang are:

• Define your optimization problem clearly and precisely, such as the objectives, constraints, variables, and responses. Use meaningful names and units for your parameters and results.
• Use the graphical user interface (GUI) and the wizards to set up and automate your simulation workflows. You can also use the application programming interface (API) or the Pythonic interface to customize your workflows and integrate external tools.
• Choose the appropriate optimization method for your problem, such as gradient-based, evolutionary, or surrogate-based algorithms. You can also use the automatic optimization method selection (AOMS) feature to let optiSLang choose the best method for you.
• Perform sensitivity analysis to identify the most influential parameters and rank them according to their impact on the output. You can use various methods, such as variance-based, correlation-based, or metamodel-based sensitivity analysis.
• Perform robustness evaluation to measure the variation of the output due to the variation of the input. You can use methods such as Monte Carlo simulation, Latin hypercube sampling, or design of experiments (DOE) to generate random or deterministic input samples.
• Perform reliability analysis to estimate the probability of failure or success of a design under different scenarios. You can use methods such as first-order reliability method (FORM), second-order reliability method (SORM), or advanced mean value (AMV) method to calculate the reliability index or failure probability.
• Use artificial intelligence and machine learning techniques to create metamodels, such as neural networks, that can approximate the behavior of complex systems with high accuracy and efficiency. You can use metamodels to reduce the computational cost and time of the optimization process, as well as to explore the design space and generate response surfaces.
• Reuse existing results from previous simulations or experiments to improve your optimization process. You can use methods such as stacking a new optiSLang system with a wizard, tuning an optiSLang system, using the reevaluation wizard, or using an external data set in optiSLang.
• Analyze and visualize the results of the optimization, such as sensitivity plots, Pareto fronts, response surfaces, and robustness measures. You can also export the results to other formats or tools for further processing.

These are some of the best practices for using Ansys OptiSLang that I found from some of the web search results¹²³⁴. I hope they help you to use optiSLang more effectively and efficiently. 

What are some common mistakes to avoid when using Ansys OptiSLang?

Some common mistakes to avoid when using Ansys OptiSLang are:

• Not defining your optimization problem clearly and precisely, such as the objectives, constraints, variables, and responses. This can lead to suboptimal or invalid solutions, or errors and warnings during the optimization process.
• Not using the graphical user interface (GUI) and the wizards to set up and automate your simulation workflows. This can make your workflows more complicated and prone to errors, or miss some important features or options that optiSLang offers.
• Not choosing the appropriate optimization method for your problem, such as gradient-based, evolutionary, or surrogate-based algorithms. This can affect the efficiency and accuracy of the optimization process, or cause convergence or divergence issues.
• Not performing sensitivity analysis to identify the most influential parameters and rank them according to their impact on the output. This can result in wasting computational resources on irrelevant or redundant parameters, or overlooking important interactions or nonlinearities among the parameters.
• Not performing robustness evaluation to measure the variation of the output due to the variation of the input. This can lead to overfitting or underfitting your design to a specific scenario, or ignoring the uncertainty or variability in your input parameters and output responses.
• Not using artificial intelligence and machine learning techniques to create metamodels, such as neural networks, that can approximate the behavior of complex systems with high accuracy and efficiency. This can increase the computational cost and time of the optimization process, or limit your ability to explore the design space and generate response surfaces.
• Not reusing existing results from previous simulations or experiments to improve your optimization process. This can result in repeating unnecessary or costly simulations, or missing some valuable information or insights from your data.
• Not analyzing and visualizing the results of the optimization, such as sensitivity plots, Pareto fronts, response surfaces, and robustness measures. This can prevent you from understanding and communicating your design performance, trade-offs, constraints, and reliability.

These are some of the common mistakes that I found from some of the web search results¹²³⁴. 

How do I choose the right optimization algorithm in Ansys OptiSLang?

Choosing the right optimization algorithm in Ansys OptiSLang depends on several factors, such as the type, size, and complexity of your optimization problem, the availability and accuracy of derivatives, the computational cost and time of each simulation, and the desired quality and robustness of the optimal solution. There is no single algorithm that works best for every problem, so you may need to experiment with different methods and compare their performance and results.

Ansys OptiSLang offers a wide range of optimization methods, such as gradient-based, evolutionary, or surrogate-based algorithms. Each method has its own advantages and disadvantages, and some general guidelines for choosing them are:

• Gradient-based methods: These methods use the information of the derivatives of the objective function and the constraints to find the optimal solution. They are usually fast and efficient for smooth and convex problems, but they may require analytical or numerical derivatives, which can be difficult or expensive to obtain. They may also get stuck in local optima or fail to converge for noisy or discontinuous problems. Examples of gradient-based methods in optiSLang are NLPQLP¹, SLSQP¹, and IPOPT¹.
• Evolutionary methods: These methods use a population of candidate solutions that evolve over generations according to some rules of selection, crossover, and mutation. They are usually robust and flexible for complex and nonlinear problems, but they may require a large number of simulations, which can be computationally intensive. They may also converge slowly or prematurely to suboptimal solutions. Examples of evolutionary methods in optiSLang are Genetic Algorithm¹, Differential Evolution¹, and Particle Swarm Optimization¹.
• Surrogate-based methods: These methods use a simplified model (metamodel) that approximates the behavior of the original simulation model. They are usually effective and accurate for high-dimensional and expensive problems, but they may require a sufficient amount of training data, which can be challenging to obtain. They may also introduce errors or biases due to the metamodel assumptions or limitations. Examples of surrogate-based methods in optiSLang are Adaptive Response Surface Method¹, Trust Region Method¹, and Efficient Global Optimization¹.

In addition to these methods, optiSLang also provides an Automatic Optimization Method Selection (AOMS) feature that can help you choose the best method for your problem based on some criteria, such as the number of variables, objectives, constraints, evaluations, and metamodels. You can also use a combination of different methods in a hybrid or sequential approach to exploit their strengths and overcome their weaknesses.

For more details and examples on how to choose and use different optimization methods in optiSLang, you can check out some of the web search results²³⁴ or video results⁵⁶⁷ that I found for you. 

Can I use Ansys OptiSLang for topology optimization?

Yes, you can use Ansys optiSLang for topology optimization. Topology optimization is a method of finding the optimal shape and material distribution for a given design problem, such as minimizing weight or maximizing stiffness. Ansys optiSLang can help you automate the topology optimization process by connecting different CAx tools and physics, such as Ansys Mechanical, Ansys Fluent, or Ansys Discovery. You can also use optiSLang to perform design exploration, sensitivity analysis, robustness evaluation, and reliability assessment of your optimized designs. For more information, you can visit the Ansys optiSLang webpage or read the blog post about optimizing design optimization efforts.