Data-driven surrogate modeling of aerodynamic forces on the superstructure of container vessels

Publikationstyp
Journal Article
Date Issued
2022-03-07
Sprache
English
Author(s)
Pache, Rupert 
Rung, Thomas  orcid-logo
Institut
Fluiddynamik und Schiffstheorie M-8  
TORE-DOI
10.15480/882.4223
TORE-URI
http://hdl.handle.net/11420/11850
Journal
Engineering applications of computational fluid mechanics  
Volume
16
Issue
1
Start Page
746
End Page
763
Citation
Engineering Applications of Computational Fluid Mechanics 16 (1): 746-763 (2022)
Publisher DOI
10.1080/19942060.2022.2044383
Scopus ID
2-s2.0-85126205820
Publisher
Taylor & Francis Group
Peer Reviewed
true
The operation of fluid engineering systems is usually governed by a wide range of different parameters. Investigations of the entire parameter spectrum using classical, first-principle based CFD methods are costly with regards to CPU and wall-clock time. Therefore, a near real-time assessment of complex flows using CFD to support the operation is deemed unfeasible. The paper is concerned with methods for data-based surrogate models to predict the forces exerted by the aerodynamic pressure field on the superstructure of a full-scale container ship for different container loading conditions and wind directions. The strategy aims to assist a fuel-efficient operation and is based on a two-step approach. During an initial step, a reduced representation of pressure fields obtained from 3D Navier–Stokes simulations is compiled. To this extent, a classical proper orthogonal decomposition is compared with convolutional neural network autoencoders. A subsequent parameterization employs a feedforward neural network to link the reduced model with the operational parameters, i.e. the angle of attack and container loading condition, and to enable a rapid on board assessment. Both methods provide a similar agreement for the pressure fields, as well as the resulting forces, with the CNN-based surrogate model being significantly more compact.
Subjects
Surrogate modeling
ship aerodynamics
machine learning
Autoencoder
ship operation
MLE@TUHH
DDC Class
530: Physik
600: Technik
620: Ingenieurwissenschaften
Publication version
publishedVersion
Lizenz
https://creativecommons.org/licenses/by/4.0/
Publisher‘s Creditline
The Version of Record of this manuscript has been published and is freely available in Engineering Applications of Computational Fluid Mechanics 7 March 2022 https://www.tandfonline.com/10.1080/19942060.2022.2044383
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