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Physics-based learning models for ship hydrodynamics

Physics-based learning models for ship hydrodynamics
Physics-based learning models for ship hydrodynamics
We present the concepts of physics-based learning models (PBLM) and their relevance and application to the field of ship hydrodynamics. The utility of physics-based learning is motivated by contrasting generic learning models for regression predictions, which do not presume any knowledge of the system other than the training data provided with methods such as semi-empirical models, which incorporate physical insights along with data-fitting. PBLM provides a framework wherein intermediate models, which capture (some) physical aspects of the problem, are incorporated into modern generic learning tools to substantially improve the predictions of the latter, minimizing the reliance on costly experimental measurements or high-resolution highfidelity numerical solutions. To illustrate the versatility and efficacy of PBLM, we present three wave-ship interaction problems: 1) at speed waterline profiles; 2) ship motions in head seas; and 3) three-dimensional breaking bow waves. PBLM is shown to be robust and produce error rates at or below the uncertainty in the generated data at a small fraction of the expense of high-resolution numerical predictions
computers in design, hull form hydrodynamics, seakeeping, machine learning, CFD
0022-4502
1-12
Weymouth, G.D.
b0c85fda-dfed-44da-8cc4-9e0cc88e2ca0
Yue, D.K.P.
57718539-e2de-4784-a042-9f43f0901bcf
Weymouth, G.D.
b0c85fda-dfed-44da-8cc4-9e0cc88e2ca0
Yue, D.K.P.
57718539-e2de-4784-a042-9f43f0901bcf

Weymouth, G.D. and Yue, D.K.P. (2013) Physics-based learning models for ship hydrodynamics. Journal of Ship Research, 57 (1), 1-12. (doi:10.5957/JOSR.56.4.120005).

Record type: Article

Abstract

We present the concepts of physics-based learning models (PBLM) and their relevance and application to the field of ship hydrodynamics. The utility of physics-based learning is motivated by contrasting generic learning models for regression predictions, which do not presume any knowledge of the system other than the training data provided with methods such as semi-empirical models, which incorporate physical insights along with data-fitting. PBLM provides a framework wherein intermediate models, which capture (some) physical aspects of the problem, are incorporated into modern generic learning tools to substantially improve the predictions of the latter, minimizing the reliance on costly experimental measurements or high-resolution highfidelity numerical solutions. To illustrate the versatility and efficacy of PBLM, we present three wave-ship interaction problems: 1) at speed waterline profiles; 2) ship motions in head seas; and 3) three-dimensional breaking bow waves. PBLM is shown to be robust and produce error rates at or below the uncertainty in the generated data at a small fraction of the expense of high-resolution numerical predictions

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Published date: March 2013
Keywords: computers in design, hull form hydrodynamics, seakeeping, machine learning, CFD
Organisations: Fluid Structure Interactions Group

Identifiers

Local EPrints ID: 349803
URI: http://eprints.soton.ac.uk/id/eprint/349803
ISSN: 0022-4502
PURE UUID: da128d2b-df13-4985-bbb3-9d55c0f97f4a
ORCID for G.D. Weymouth: ORCID iD orcid.org/0000-0001-5080-5016

Catalogue record

Date deposited: 11 Mar 2013 14:06
Last modified: 09 Jan 2022 03:44

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Contributors

Author: G.D. Weymouth ORCID iD
Author: D.K.P. Yue

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