A Fast Algorithm for the Prediction of Ship-Bank Interaction in Shallow Water
Abstract
:1. Introduction
2. Problem Statement and Method of Solution
2.1. Coordinate Systems Definition and Transformation
2.2. Underlying Theory
2.3. Determination of Sinkage and Trim
- (1)
- Initialize the parameters, including advance , transfer , velocity U, iteration number n, flotation, etc.
- (2)
- Obtain the vertical force Z and pitch moment M based on the numerical solution mentioned in Section 2.2.
- (3)
- Calculate the variation of draft and trim , and then update the flotation.
- (4)
- If both and hold, then go to Step 5; if not, n = n + 1, and turn to Step 2.
- (5)
- The sinkage and trim are finally determined.
3. Ship Model, Canals and Test Conditions
3.1. Ship Model
3.2. The Canals
3.3. Test Conditions
4. Comparison and Analysis
4.1. Sinkage and Trim in Canal A
4.2. Hydrodynamic Forces and Moments in Canal A
4.3. Sinkage and Trim in Canal B
4.4. Hydrodynamic Forces and Moments in Canal B
5. Conclusions
- In the case of a vertical bank, the sinkage and trim due to the ship-bank hydrodynamic interaction can be estimated by the present method with satisfactory accuracy. For the sloped bank, the present method is less accurate, but still fairly acceptable for online simulations.
- In general, the accuracy of the present method is sufficient for estimating the hydrodynamic interaction forces on ships in moderate shallow water cases. Improvement in accuracy by accounting for the sinkage and the trim can be seen in some cases, in general, and are not substantial which agrees with the estimates obtained by Lima et al. [29] on the basis of an empiric model. For extreme shallow water cases, i.e., h/T = 1.1, the prediction for the sway force and the yaw moment is poor, but such situations should be avoided in good seamanship practice.
- In general, the accuracy provided by the present panel method is comparable to that reached by much more complex and slow RANSE-based CFD code and by the free-surface Rankine source method. A possible explanation is that this is due to the lucky error cancellation phenomenon.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Parameters | Full Scale | Model Scale |
---|---|---|
Length () (m) | 320.0 | 4.2667 |
Breath () (m) | 58.0 | 0.7733 |
Draft at midship () (m) | 20.8 | 0.2776 |
Longitudinal CoG () (m) | 10.87 | 0.1449 |
Vertical CoG () (m) | 20.8 | 0.2776 |
Longitudinal CoF () (m) | −2.67 | −0.0356 |
Waterplane area () (m2) | 16,706.25 | 2.9700 |
Displacement () (m3) | 312,622 | 0.7410 |
Longitudinal metacentric height () (m) | 398.55 | 5.3140 |
Block coefficient () | 0.8098 | 0.8098 |
h (h/T) | Frh | ys | |||
---|---|---|---|---|---|
0.5175 m | 0.5866 m | 0.9731 m | 1.3594 m | ||
0.4160 m (1.50) | 0.1763 | Case 7 | Case 4 | ||
0.3744 m (1.35) | 0.1859 | Case 1 | Case 2 | Case 3 | Case 6 |
0.3051 m (1.10) | 0.2059 | Case 8 | Case 5 |
h (h/T) | Frh | ys | |||
---|---|---|---|---|---|
0.3170 m | 0.3872 m | 0.7735 m | 1.1613 m | ||
0.3744 m (1.35) | 0.1859 | Case 9 | Case 10 | Case 11 | Case 12 |
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Huang, J.; Xu, C.; Xin, P.; Zhou, X.; Sutulo, S.; Guedes Soares, C. A Fast Algorithm for the Prediction of Ship-Bank Interaction in Shallow Water. J. Mar. Sci. Eng. 2020, 8, 927. https://doi.org/10.3390/jmse8110927
Huang J, Xu C, Xin P, Zhou X, Sutulo S, Guedes Soares C. A Fast Algorithm for the Prediction of Ship-Bank Interaction in Shallow Water. Journal of Marine Science and Engineering. 2020; 8(11):927. https://doi.org/10.3390/jmse8110927
Chicago/Turabian StyleHuang, Jin, Chen Xu, Ping Xin, Xueqian Zhou, Serge Sutulo, and Carlos Guedes Soares. 2020. "A Fast Algorithm for the Prediction of Ship-Bank Interaction in Shallow Water" Journal of Marine Science and Engineering 8, no. 11: 927. https://doi.org/10.3390/jmse8110927
APA StyleHuang, J., Xu, C., Xin, P., Zhou, X., Sutulo, S., & Guedes Soares, C. (2020). A Fast Algorithm for the Prediction of Ship-Bank Interaction in Shallow Water. Journal of Marine Science and Engineering, 8(11), 927. https://doi.org/10.3390/jmse8110927