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2 Polish Maritime Research

2 2024

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Numerical study on the effect of weight variables on the roll damping coefficients for a fishing vessel

Seo Janghoon, Park Dong-Woo

Polish Maritime Research

2024

nr 3

4--14

The roll damping coefficient is essential when considering the viscous effect in the potential-based hydrodynamic analysis of fishing vessels; it is an important factor in the roll motion response. The present study performs free roll decay simulations, altering weight variables using Computational Fluid Dynamics (CFD) to investigate the correlation between the roll damping coefficient and the weight variation of a fishing vessel. The time series of roll amplitude and roll damping coefficient are compared, for varying vertical and longitudinal centres of gravity and radii of gyration in roll motion. As the vertical centre of gravity increases, both the roll decay period and the roll damping coefficient also increase. The roll decay period tends to increase with the increase in the radius of gyration during roll motion, while the roll damping coefficient exhibits a decrease. A longitudinal centre of gravity has a limited effect on free roll decay characteristics. The roll damping coefficients between the maximum and minimum combinations of weight variables show significant differences. The findings of the present study could enhance the understanding of the safety of fishing vessels based on their loading conditions. Consequently, future research could further improve the results obtained in the present study by considering various hull shapes and speeds.

Evaluation of prediction performances of deep learning models for the aerodynamic characteristics of flettner rotors

Seo Janghoon, Park Jung Yoon, Ma Juhwan, Kim Young Bu, Park Dong-Woo

Polish Maritime Research

2024

nr 4

4--20

This study investigates the prediction of the aerodynamic characteristics of Flettner rotors through three deep learning models. Various numbers of Flettner rotors, arrangements, and spin ratios are employed to consider these effects in the dataset. For the training of deep learning models, a dataset of aerodynamic force coefficients and flow fields is generated using Computational Fluid Dynamics (CFD). Three deep learning architectures (U-net, Encoder-Decoder, and Decoder models) are employed and trained to predict the aerodynamic characteristics of Flettner rotors. Three deep learning models are established through a training stage with a hyperparameter study and by altering the number of layers. The aerodynamic force coefficients and flow fields are predicted by established deep learning models and show small absolute errors compared to those from the CFD analysis. Moreover, predicted flow fields reflect the flow characteristics according to the difference of spin ratio and arrangement of Flettner rotors. In conclusion, the established deep learning models demonstrate rapid and robust predictions of aerodynamic force coefficients and flow fields for Flettner rotors under varying arrangements and spin ratios. Furthermore, a significant reduction in computational time is measured when comparing the analysis time of CFD simulations to the training and testing time of the deep learning models.