Today it is possible, through Computer-Aided Engineering (CAE) to perform optimizations on all kind of structures. Computer-Aided Engineering has grown strongly over the last few decades, and in the future it will put its mark even stronger on the process of product design. However, during the CAE design process only an average value of many input parameters is used while in real conditions the actual input parameters will differ from this average value. Also the models upon which the optimization process is based are in most cases a "best guess"-method and geometrical tolerances can influence the result significantly. Understanding these uncertainties will enhance the reliability of the models and make them a powerful tool within the optimization process.

For uncertainty management and optimization of engineering products, it is necessary to include all sources of uncertainty in the simulations. This in turn can dramatically increase the computational cost and memory requirement (“the curse of dimensionality”); i.e. with increasing number of uncertain parameters the number of simulation calls increases exponentially. From an industrial point of view, developing efficient reduced order models to reduce the computational cost is therefor of great interest.

The EUFORIA methodology includes a number of mathematical approaches to propagate uncertainties- both on input and design- and to converge as efficient as possible to a global optimum with a minimized number of samplings. This will result in a Pareto front of global optimal designs that serves a certain productstrategy. To achieve this goal, a combination of different mathematical algorithmes have been studied. These are called “building blocks”. Each building block is a piece of software code – a script -  with a specific function, that wraps around an existing code and improves its efficiency and cost.

The final objective of this project is to develop an efficient methodology for the optimization of industrial processes under a large number of variables and uncertainties. It offers major opportunities in nearly every sector where products and processes have to be designed:

  1. resulting in better, more efficient, more performant products or processes
  2. changing company know-how from “alchemy” to “science”
  3. resulting in more environmentally friendly products
  4. decreasing the company’s product responsibility risk
  5. savings on R&D time and costs
  6. better defined product marketing strategies and marketing road maps
  7. reduced time-to-market path.