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In the process of geometry-based optimization, the problem of having to model a resulting geometry without knowing the exact values of characteristic parameters can arise. In order to open and further edit the optimized geometry inside the CAESES environment, the parameter set belonging to the new shape needs to be determined. The use of neural networks offers a great opportunity to solve this kind of problem since they are a powerful tool for constructing a predictive model based on data.

Ansys CFD tools like Fluent or CFX are a popular choice for engineers when it comes to evaluating the fluid-dynamic behavior of their designs. When used in an automated design process, they require a suitable CAD tool that can reliably produce the different geometry variants to be analyzed. In our experience, a crucial bottleneck that we set out to solve with CAESES.

When our colleague Heinrich is not supporting our customers or working on R&D projects, he likes to spend his free time making things. So when he encounters a challenge that might benefit from a convenient parametric approach, CAESES is an obvious choice. Just like this time, when he set out to create a bunch of wooden drawers.

This study describes a simulation-driven optimization process to investigate the influence of tire tread patterns on water splashing.

This project was carried out in cooperation with GEMÜ Gebr. Müller Apparatebau and SimScale with the aims of the exploration of the potential and possible avenues for improvement of a valve, and integration of SimScale as a cloud-based CFD solver in the process.