A viscosity equivalent reduced-order method and its application to ship roll prediction. (1st April 2023)

• Record Type: Journal Article
• Authors: Jiang, Yichen; Dong, Yu; Li, Peng; Zong, Hongyun; Li, Jiawen
• Is Part Of: Ocean engineering. Volume 273(2023)
• Publication Date: 2023-04-01
• Subjects: Ship roll motion -- Roll damping -- Reduced-order method -- Seakeeping problem; Ocean engineering -- Periodicals; Ocean engineering; Periodicals; 620.4162
• DOI: 10.1016/j.oceaneng.2023.113878
• Languages: English
• ISSNs: 0029-8018

Abstract

Abstract: The prediction of ship roll response in waves is still a major issue. The widely used potential flow theory can provide the results in a short time. However, its accuracy is highly dependent on the correction of fluid-viscosity. Viscous-flow theory can simulate the fluid viscosity directly, but it is accompanied by high computational costs. This study proposes a viscosity equivalent reduced-order method (VEROM) for predicting ship roll motion to achieve a balance between computational efficiency and prediction accuracy. To shorten the simulation time, the method neglects the fluid viscosity, solves the Euler equation, and uses large-size compatible meshes. To improve the prediction accuracy, the viscous effect is corrected in the time domain based on the principle of viscosity equivalence. A new type of forced roll motion with gradually increasing roll amplitude was introduced to obtain the nonlinear roll damping property for different KC numbers. The effectiveness of the VEROM model was examined by comparing the predicted roll responses with the experimental measurements. It was found that the maximum prediction error was less than 11%. Concerning the simulation time, the VEROM model is 20 times faster than the traditional viscous-flow model. The reduce-order method is effective for the seakeeping performance assessment. Highlights: A viscosity equivalent reduced-order method is proposed to balance the prediction accuracy and efficiency for seakeeping problem. A forced roll motion with gradually increasing amplitude was introduced to get the nonlinear damping for different KC numbers. The effectiveness of VEROM model was examined, and its maximum prediction error was found to be less than 11%. The VEROM model is 20 times faster than the traditional viscous-flow model while maintaining the same level of accuracy.