Scientific Programme

Welcome to BOQUSE 2013!

Optimization and design in the presence of uncertain operating conditions, material properties and manufacturing tolerances poses a tremendous challenge to the scientific computing community. In many industry-relevant situations the performance metrics depend in a complex, non-linear fashion on those factors and the construction of an accurate representation of this relationship is difficult. In addition to that, the numerical simulation in Fluid Mechanics is far to be predictive because of the presence of numerous sources of uncertainty, in particular in shock-dominated turbulent flows. In this case, the problem is to find an efficient representation of the stochastic solution, when the flow presents some discontinuities, thus producing a shock evolving in the coupled physical/stochastic space. As a consequence, developing efficient numerical techniques for handling uncertainties in fluid-flow problems is very challenging.

This workshop is intended to be an exchange forum for scientists working on innovative and efficient techniques for uncertainty quantification and robust design in Fluid Mechanics. More precisely, we hope that this could be source of inspiration for improving and developing new ideas. Three thematic days will be organised around the following themes :

  • Computational methods for uncertainty propagation
  • Uncertainty analysis in fluid dynamics simulations
  • Sensitivity and Optimization
Each day, three invited lectures are planned in the morning and in the early afternoon. Then, a session with contributed talks (based on abstract selection) will follow.


THEMES OF THE CONFERENCE

Uncertainty propagation, sensitivity analysis, robust optimization and robust design with uncertain computational model, treatment of discontinuities in the stochastic space, large number of uncertain inputs, inverse problem, application to complex flows (turbulent, unsteady, discontinuous, with real-gas effects, ...), structural-form and model uncertainty, modelling of experimental errors.