Multifidelity surrogate modeling of experimental and computational aerodynamic data sets
Multifidelity surrogate modeling of experimental and computational aerodynamic data sets
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.
289-298
Kuya, Yuichi
bd9eb9b2-3922-444c-817d-ee671d462676
Takeda, Kenji
e699e097-4ba9-42bd-8298-a2199e71d061
Zhang, Xin
3056a795-80f7-4bbd-9c75-ecbc93085421
Forrester, Alexander
176bf191-3fc2-46b4-80e0-9d9a0cd7a572
February 2011
Kuya, Yuichi
bd9eb9b2-3922-444c-817d-ee671d462676
Takeda, Kenji
e699e097-4ba9-42bd-8298-a2199e71d061
Zhang, Xin
3056a795-80f7-4bbd-9c75-ecbc93085421
Forrester, Alexander
176bf191-3fc2-46b4-80e0-9d9a0cd7a572
Kuya, Yuichi, Takeda, Kenji, Zhang, Xin and Forrester, Alexander
(2011)
Multifidelity surrogate modeling of experimental and computational aerodynamic data sets.
AIAA Journal, 49 (2), .
(doi:10.2514/1.J050384).
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.
This record has no associated files available for download.
More information
Published date: February 2011
Organisations:
Aerodynamics & Flight Mechanics
Identifiers
Local EPrints ID: 177041
URI: http://eprints.soton.ac.uk/id/eprint/177041
ISSN: 0001-1452
PURE UUID: b69bad0f-7718-407c-9b53-78ebd639713e
Catalogue record
Date deposited: 14 Mar 2011 14:10
Last modified: 14 Mar 2024 02:42
Export record
Altmetrics
Contributors
Author:
Yuichi Kuya
Author:
Kenji Takeda
Author:
Xin Zhang
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics