💥💥💥 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.