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  5. Numerical Modeling and Testing of Ship Maneuvering and Hydrodynamics for Inland Unconventional Ship

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Preprint
English
2023

Numerical Modeling and Testing of Ship Maneuvering and Hydrodynamics for Inland Unconventional Ship

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0 Files

English
2023
DOI: 10.2139/ssrn.4673977

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Mao Zheng
Mao Zheng

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Shigan Ding
Mao Zheng
Mingyang Zhang
+3 more

Abstract

Hydrodynamic modeling and the calculation of hydrodynamic derivatives for unconventional ships have consistently presented challenges. This paper introduces a methodology for developing ship maneuvering models utilizing Computational Fluid Dynamics and the Maneuvering Motion Group model. Determining the forces and moments acting on the hull from fluid dynamics is crucial for calculating the hydrodynamic derivatives of unconventional ship hulls. Static Oblique Towing Tests and Dynamic Circular Motion Tests are used to gather pertinent data for the 3 Degrees of Freedom of the MMG model. To improve the fitting accuracy of unconventional ship models, this study suggests using an interpolation fitting method instead of the traditional least squares fitting method. Considering the unique characteristics of the unconventional ship, the reliability of its maneuvering model is validated through the creation of a scaled ship model corresponding to the CFD computational model. Ship model maneuvering tests, including the 15/15 Zigzag test, are conducted to refine certain propeller and rudder parameters. The proposed method is applied to the inland unconventional ship. The results show that the mathematical model, established using interpolation fitting to determine hydrodynamic derivatives, accurately forecasts the ship maneuvering features, which is corroborated by the experimental data from ship model tests.

How to cite this publication

Shigan Ding, Mao Zheng, Mingyang Zhang, Jiafen Lan, Bowen Lin, Tianyue Zou (2023). Numerical Modeling and Testing of Ship Maneuvering and Hydrodynamics for Inland Unconventional Ship. , DOI: 10.2139/ssrn.4673977.

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Publication Details

Type

Preprint

Year

2023

Authors

6

Datasets

0

Total Files

0

Language

English

DOI

10.2139/ssrn.4673977

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