Predicting Wind Loads on Container Ships in Harbor Environments through Multi-Fidelity Modeling
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| Main Authors: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866911621798952960 |
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| author | Fiore, Matilde Bresciani, Andrea Mendez, Miguel Alfonso van Beeck, Jeroen |
| author_facet | Fiore, Matilde Bresciani, Andrea Mendez, Miguel Alfonso van Beeck, Jeroen |
| contents | Modern container ships face higher wind loads due to increased windage areas, making accurate predictions of wind loads essential for mooring design. Existing empirical models, largely developed for container ships with smaller windage areas and simpler geometrical configurations than those of modern large-scale vessels, often lack accuracy and do not account for the influence of nearby structures. This study proposes a multi-fidelity surrogate modelling framework for the prediction of wind-load coefficients, combining empirical correlations with simplified and detailed CFD models for ships in open-sea and harbor environments. The approach relies on recursive co-kriging to consistently fuse information across fidelity levels, enabling accurate predictions at a reduced computational cost. A sensitivity analysis is used to identify the most influential geometric parameters, and the resulting reduced parameter space is explored through sequential sampling to efficiently construct the training database.
The surrogate models are validated over a wide range of loading configurations and for two distinct harbor environments. The results demonstrate that the multi-fidelity approach significantly improves prediction accuracy compared to single-fidelity models, while substantially reducing the reliance on high-fidelity simulations. In particular, the proposed framework captures the dependence of wind loads on key geometric parameters and consistently outperforms traditional empirical correlations, providing a robust and efficient tool for engineering applications. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2604_22882 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Predicting Wind Loads on Container Ships in Harbor Environments through Multi-Fidelity Modeling Fiore, Matilde Bresciani, Andrea Mendez, Miguel Alfonso van Beeck, Jeroen Machine Learning Computational Physics Data Analysis, Statistics and Probability Modern container ships face higher wind loads due to increased windage areas, making accurate predictions of wind loads essential for mooring design. Existing empirical models, largely developed for container ships with smaller windage areas and simpler geometrical configurations than those of modern large-scale vessels, often lack accuracy and do not account for the influence of nearby structures. This study proposes a multi-fidelity surrogate modelling framework for the prediction of wind-load coefficients, combining empirical correlations with simplified and detailed CFD models for ships in open-sea and harbor environments. The approach relies on recursive co-kriging to consistently fuse information across fidelity levels, enabling accurate predictions at a reduced computational cost. A sensitivity analysis is used to identify the most influential geometric parameters, and the resulting reduced parameter space is explored through sequential sampling to efficiently construct the training database. The surrogate models are validated over a wide range of loading configurations and for two distinct harbor environments. The results demonstrate that the multi-fidelity approach significantly improves prediction accuracy compared to single-fidelity models, while substantially reducing the reliance on high-fidelity simulations. In particular, the proposed framework captures the dependence of wind loads on key geometric parameters and consistently outperforms traditional empirical correlations, providing a robust and efficient tool for engineering applications. |
| title | Predicting Wind Loads on Container Ships in Harbor Environments through Multi-Fidelity Modeling |
| topic | Machine Learning Computational Physics Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2604.22882 |