<p>This work investigates how non-intrusive local/global coupling strategies can be applied in the context of robust design. The objective is to propagate uncertainties from the local to the global scale using non-intrusive techniques, in order to estimate how local variabilities impact global quantities. Several uncertainty propagation methods, including perturbation techniques, polynomial chaos expansions, and Monte-Carlo simulations, are tested and compared on academic examples. Depending on the level of uncertainty, perturbation methods and non-intrusive polynomial chaos expansions appear particularly effective. A key issue in extending the approach to more complex and computationally intensive problems is the ability to exploit non-converged solutions to approach robust design configurations. A preliminary step in this direction is proposed at the end of the paper and provides a good basis for future work on more complex and realistic problems with associated challenges.</p>

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Local/global non-intrusive coupling strategy for robust design: a first attempt

  • Léa Karaouni,
  • Ludovic Chamoin,
  • Olivier Allix,
  • Stéphane Guinard

摘要

This work investigates how non-intrusive local/global coupling strategies can be applied in the context of robust design. The objective is to propagate uncertainties from the local to the global scale using non-intrusive techniques, in order to estimate how local variabilities impact global quantities. Several uncertainty propagation methods, including perturbation techniques, polynomial chaos expansions, and Monte-Carlo simulations, are tested and compared on academic examples. Depending on the level of uncertainty, perturbation methods and non-intrusive polynomial chaos expansions appear particularly effective. A key issue in extending the approach to more complex and computationally intensive problems is the ability to exploit non-converged solutions to approach robust design configurations. A preliminary step in this direction is proposed at the end of the paper and provides a good basis for future work on more complex and realistic problems with associated challenges.