Current state-of-the-art Computer-Aided Engineering (CAE) tools can only account for the uncertainties inherent to the processes in a very limited way, making the obtained global optima unreliable. The inclusion of an advanced and reliable uncertainty quantification in the CAE tools, coupled to an efficient methodology, would therefore be a major breakthrough for CAE, allowing industrial partners to design quicker and obtain better, cheaper and more robust (i.e. less uncertainty sensitive) products.
The final objective of this project is to develop an efficient methodology for the optimization of industrial processes under uncertainty. The methodology will enable to construct/achieve robust designs. The methodology will handle uncertainties in the model parameters, as well as uncertainties in the design variables. Hereby, the emphasis is on a large number of design variables and uncertainties. The inclusion of uncertainty quantification in the design cycle requires combining the exploration of the design space (optimization) with exploration of the stochastic space and the development and use of accurate and efficient surrogate models.
In order to achieve the project objectives, major new developments are needed to bridge the gap from the current scientific state-of-the-art to the required technological readiness level. Breakthroughs are required with respect to the handling of large numbers of design variables and simultaneous uncertainties and with respect to the computational efficiency of the optimization procedure leading to acceptable design cycle times for industrial applications.
The developed technology will offer the following major opportunities in nearly every sector where products and processes have to be designed:
resulting in better, more efficient, more performant products or processes
changing company know-how from “alchemy” to “science”
resulting in more environmentally friendly products
decreasing the company’s product responsibility risk
savings on R&D time and costs
better defined product marketing strategies and marketing road maps
reduced time-to-market path.