Resampling strategies to improve surrogate model‐based uncertainty quantification: Application to LES of LS89

Abstract

Summary Uncertainty quantification (UQ) is receiving more and more attention for engineering applications particularly from robust optimization. Indeed, running a computer experiment only provides a limited knowledge in terms of uncertainty and variability of the input parameters. These experiments are often computationally expensive, and surrogate models can be constructed to address this issue. The outcome of an uncertainty quantification study is, in this case, directly correlated to the surrogate's quality. Thus, attention must be devoted to the design of experiments to retrieve as much information as possible. This work presents 2 new strategies for parameter space resampling to improve a Gaussian process surrogate model. These techniques indeed show an improvement of the predictive quality of the model with high‐dimensional analytical input functions. Finally, the methods are successfully applied to a turbine blade large‐eddy simulation application: the aerothermal flow around the LS89 blade cascade.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 15 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.