Multifidelity surrogate modeling of experimental and computational aerodynamic data sets

Yuichi Kuya, Kenji Takeda, Xin Zhang, Alexander I.J. Forrester

Research output: Contribution to journalArticlepeer-review

78 Citations (Scopus)

Abstract

This study presents a multifidelity surrogate modeling approach, combining experimental and computational aerodynamic data sets. A multifidelity cokriging regression surrogate model is used. This study highlights how lowfidelity data from computations contribute to improving surrogate models built with limited high-fidelity data from experiments. Various types of sampling design for low-fidelity data are also examined to study the impact of characteristics of the sampling design on the final surrogate models. Replication, blocking, and randomization techniques originally developed for design of experiments are used to minimize random and systematic errors. Surrogate models representing the performance of an inverted wing with counter-rotating vortex generators in ground effect are constructed, where design variables of the wing ride height and incidence and the response of sectional downforce are examined. A cokriging regression containing 12 experimental and 25 computational data points sampled with a Latin hypercube design shows the best performance here, capturing general characteristics of the target map well.

Original languageEnglish
Pages (from-to)289-298
Number of pages10
JournalAIAA journal
Volume49
Issue number2
DOIs
Publication statusPublished - 2011 Feb
Externally publishedYes

ASJC Scopus subject areas

  • Aerospace Engineering

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