Browsers supply a great deal of view-related functionality by listing the parts of a model in a tabular and/or tree-based
format, and providing controls inside the table that allow you to alter the display of model parts.
FE geometry is topology on top of mesh, meaning CAD and mesh exist as a single entity. The purpose of FE geometry
is to add vertices, edges, surfaces, and solids on FE models which have no CAD geometry.
An exploration is a multi-run simulation. Each exploration includes input design variables, and output responses.
Explorations may also include goals, consisting of an objective and constraints.
An input design variable is a system parameter that influences the system performance in the chosen output response.
Typical design variables may be a part's thickness, shape, or material property. Ranges, with lower and upper bounds,
are specified and the variable's value will vary within the exploration. The terms input, input design variable, and
design variable are used interchangeably.
Constraints need to be satisfied for an optimization to be acceptable. Constraints may also be associated with a DOE.
While not used in the evaluation of the DOE, constraints can be useful while visualizing DOE results. Limits on displacement
or stress are common examples.
In this tutorial, you will learn how to use some of the Artificial Intelligence tools exposed in Design Explorer by
creating a design of experiments (DOE) and training an ML model using the Design Explorer workflow.
Tools and workflows that are dedicated to rapidly creating new parts for specific use cases, or amending existing
parts. The current capabilities are focused on stiffening parts.
DOE Using OptiStruct
In this tutorial, you will learn how to perform a design of experiments (DOE) using the Design Explorer workflow.
Optimization Using Radioss
In this tutorial, you will learn how to perform an optimization using the Design Explorer workflow.
Field Prediction for Real Time Results Evaluation
In this tutorial, you will learn how to use some of the Artificial Intelligence tools exposed in Design Explorer by creating a design of experiments (DOE) and training an ML model using the Design Explorer workflow.