Applications of multi-fidelity multi-output Kriging to engineering design optimization
Applications of multi-fidelity multi-output Kriging to engineering design optimization
Surrogate modelling is a popular approach for reducing the number of high fidelity simulations required within an engineering design optimization. Multi-fidelity surrogate modelling can further reduce this effort by exploiting low fidelity simulation data. Multi-output surrogate modelling techniques offer a way for categorical variables e.g. the choice of material, to be included within such models. While multi-fidelity multi-output surrogate modelling strategies have been proposed, to date only their predictive performance rather than optimization performance has been assessed. This paper considers three different multi-fidelity multi-output Kriging based surrogate modelling approaches and compares them to ordinary Kriging and multi-fidelity Kriging. The first approach modifies multi-fidelity Kriging to include multiple outputs whereas the second and third approaches model the different levels of simulation fidelity as different outputs within a multi-output Kriging model. Each of these techniques is assessed using three engineering design problems including the optimization of a gas turbine combustor in the presence of a topological variation, the optimization of a vibrating truss where the material can vary and finally, the parallel optimization of a family of airfoils.
multi-output, multi-fidelity, kriging
Toal, David
dc67543d-69d2-4f27-a469-42195fa31a68
Toal, David
dc67543d-69d2-4f27-a469-42195fa31a68
Toal, David
(2023)
Applications of multi-fidelity multi-output Kriging to engineering design optimization.
Structural and Multidisciplinary Optimization, 66 (6), [125].
(doi:10.1007/s00158-023-03567-z).
Abstract
Surrogate modelling is a popular approach for reducing the number of high fidelity simulations required within an engineering design optimization. Multi-fidelity surrogate modelling can further reduce this effort by exploiting low fidelity simulation data. Multi-output surrogate modelling techniques offer a way for categorical variables e.g. the choice of material, to be included within such models. While multi-fidelity multi-output surrogate modelling strategies have been proposed, to date only their predictive performance rather than optimization performance has been assessed. This paper considers three different multi-fidelity multi-output Kriging based surrogate modelling approaches and compares them to ordinary Kriging and multi-fidelity Kriging. The first approach modifies multi-fidelity Kriging to include multiple outputs whereas the second and third approaches model the different levels of simulation fidelity as different outputs within a multi-output Kriging model. Each of these techniques is assessed using three engineering design problems including the optimization of a gas turbine combustor in the presence of a topological variation, the optimization of a vibrating truss where the material can vary and finally, the parallel optimization of a family of airfoils.
Text
Authors_Accepted_Manuscript
- Accepted Manuscript
Text
s00158-023-03567-z
- Version of Record
More information
Accepted/In Press date: 4 April 2023
e-pub ahead of print date: 15 May 2023
Additional Information:
Funding Information:
This was partially funded via the COLIBRI (Collaboration Across Business Boundaries) research program (Grant no. 113296), a project sponsored by the Aerospace Technology Institute and Innovate UK.
Keywords:
multi-output, multi-fidelity, kriging
Identifiers
Local EPrints ID: 477629
URI: http://eprints.soton.ac.uk/id/eprint/477629
ISSN: 1615-147X
PURE UUID: 5ac7a1ec-1474-45fa-af6e-1e7be879e0e4
Catalogue record
Date deposited: 09 Jun 2023 16:57
Last modified: 17 Mar 2024 03:10
Export record
Altmetrics
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
