What is your approach to analyzing complex geometries in structural optimization?

1 Define the design space

The first step in any structural optimization is to define the design space, or the region of the structure that can be modified by the optimizer. This can be done by using parametric models, such as CAD or FEA software, or by using non-parametric models, such as mesh morphing or level-set methods. The choice of the model depends on the level of detail and flexibility required for the optimization. Parametric models are easier to control and modify, but they may have limitations in representing complex geometries. Non-parametric models can handle more complex shapes, but they may require more computational resources and fine-tuning.

2 Choose the objective and constraints

The next step is to choose the objective and constraints for the optimization, or the criteria that the optimized structure should satisfy. These can include structural performance, such as stiffness, strength, or fatigue life, as well as other aspects, such as weight, cost, or manufacturability. The objective and constraints should be clearly defined and quantifiable, and they should be compatible with the design space and the optimization method. For example, if the design space is discrete, the objective and constraints should also be discrete, or vice versa.

3 Select the optimization method

The third step is to select the optimization method, or the algorithm that will search for the optimal solution within the design space. There are many types of optimization methods, such as gradient-based, evolutionary, or meta-heuristic methods, and each one has its own advantages and disadvantages. The choice of the method depends on the complexity of the problem, the size of the design space, and the availability of computational resources. For complex geometries, it is important to choose a method that can handle nonlinearities, multiple objectives, and local optima, as well as a method that can provide a good balance between exploration and exploitation of the design space.

4 Perform the optimization

The fourth step is to perform the optimization, or to run the optimization method on the design space with the objective and constraints. This can be done by using dedicated software tools, such as MATLAB, ANSYS, or Optistruct, or by using custom codes or scripts. The optimization process may require several iterations, adjustments, and validations, depending on the complexity of the problem and the quality of the results. For complex geometries, it is important to monitor the convergence and robustness of the optimization, as well as to check the feasibility and reliability of the optimized solutions.

5 Analyze the results

The final step is to analyze the results, or to evaluate and compare the optimized solutions with respect to the objective and constraints. This can be done by using graphical tools, such as plots, charts, or contour maps, or by using numerical tools, such as statistics, sensitivity analysis, or trade-off analysis. The analysis should provide insights into the performance and behavior of the optimized structure, as well as the trade-offs and limitations involved in the optimization. For complex geometries, it is important to verify the accuracy and validity of the results, as well as to identify the key factors and parameters that influence the optimization.

Structural optimization is a valuable tool for aerospace engineering, but it can be challenging when dealing with complex geometries. By following these steps, you can improve your approach to analyzing complex geometries in structural optimization, and achieve better and more efficient designs.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?