Collaborative Multidisciplinary Design Optimization

Aim

The goal is to introduce an integrated modeling process to automatically search through the design space with appropriate optimization algorithms. After completing the course, participants will have a good understanding of how practical design automation and optimization can be implemented in a product development process. This should lead to finding optimal solutions and reduce lead time by applying a more efficient product development process. Upon completion of the course, students will:

• understand how to create appropriate geometric models in different CAD tools
• be able to use a module-driven modeling method to create automated design frameworks, which can be easily improved and modified.
• be able to use different tools to create integration between the models.
• Understand how to create numerically effective surrogate models to reduce the time of simulation and thus make optimization more convenient.
• understand how different optimization algorithms are to be used in various technical issues involving multiple disciplines.
• understand how a complex problem should formally be stated with multiple objectives and boundary conditions.
• be able to discuss the reasonableness of the results

Content

The course focuses on tools and methods for model-based design and optimization within machine engineering. The course is divided into two parts. In the first part of the course, students are introduced to modeling and simulation routines as well as surrogate modeling and optimization strategies. In the second part of the course, an exam will be presented to be solved in a group. The content of the course is as follows:

• Multidisciplinary Optimization in the design process using various tools, such as modelFRONTIER
• Formulating design problems to optimization setups
• Geometric modeling using various CAD tools, such as CATIA V5
• Surrogate modeling
• Model integration between different modeling tools