Multifidelity surrogate modeling of experimental and computational aerodynamic data sets

Kuya, Yuichi, Takeda, Kenji, Zhang, Xin and Forrester, Alexander (2011) Multifidelity surrogate modeling of experimental and computational aerodynamic data sets AIAA Journal, 49, (2), pp. 289-298. (doi:10.2514/1.J050384).


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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.

Item Type: Article
Digital Object Identifier (DOI): doi:10.2514/1.J050384
ISSNs: 0001-1452 (print)
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TL Motor vehicles. Aeronautics. Astronautics
Organisations: Aerodynamics & Flight Mechanics
ePrint ID: 177041
Date :
Date Event
February 2011Published
Date Deposited: 14 Mar 2011 14:10
Last Modified: 18 Apr 2017 02:41
Further Information:Google Scholar

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