Documents published in Scipedia

• Numerical Study of Cavitation on a Ship Propeller in Regular Waves of Different Headings

  K. Shin, H. Mikkelsen, Y. Shao, J. Walther, J. Nielsen

  marine2023.

  
  

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• Numerical Study of Ultrasound-Driven Bubble Collapse and Solid Deformation

  G. Son, J. Park

  marine2023.

  
  

  Abstract

  As ships operate under sea wave conditions most of time, it is desirable to consider the wave effect on propeller performance and cavitation safety in the propeller design process. In this work, unsteady cavitation simulations are carried out on a five-bladed propeller of KRISO container ship in calm water and regular waves of five different headings. Bare-hull simulations are made for estimating nominal hull wake fields by URANS solver. Cavitation simulations are made on the propeller and rudder by DES with a cavitation model and an Eulerian multiphase flow model. Nominal hull wake is numerically modelled in cavitation simulations as a propeller inflow instead of including a hull model. The maximum cavity area on the suction side of the blade is increased by 19 – 32% for beam, stern-quartering and following sea waves compared to calm water mostly due to the stronger axial hull wake. As the sheet cavity is more extended, tip vortex cavitation is intensified especially for stern-quartering and following waves. The maximum cavity area is on a similar level with less than 3% differences for head and bow waves as for calm water. The CFD investigation shows that hull wake differs depending on the wave direction and it can lead to significant changes in cavitation safety. 

  Abstract
  As ships operate under sea wave conditions most of time, it is desirable to consider the wave effect on propeller performance and cavitation safety in the propeller design [...]

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• A Multi-scale Methodology to Assess the Cavitation Erosion Risk on a Twisted Hydrofoil

  Z. Wang, H. Cheng, R. Bensow, B. Ji

  marine2023.

  
  

  Abstract

  The aim of the current paper is to evaluate the cavitation erosion on a Delft twisted hydrofoil using a coupled Euler-Lagrange methodology. The transport equation modelling approach is introduced to handle the macroscopic liquid-vapor mixture, which is regarded as a homogeneous continuum. The Keller-Herring equation and bubble motion equation are used to track the bubble's dynamics and trajectory. A two-way coupling method is employed to describe the interaction between the mixture and bubbles. A newly developed Lagrangian erosion model is used to assess the cavitation erosion on the hydrofoil. The numerical results are in good agreement with the experimental test data. The statistical results reveal the evolution characteristics of cavitation erosion. The relationship between macroscopic cavitation structure and potential erosion sensitive zone indicates the cavitation erosion intensity at different stages of cloud cavitation. This study contributes to a deeper understanding of the mechanism of cavitation damage from a multi-scale perspective.

  Abstract
  The aim of the current paper is to evaluate the cavitation erosion on a Delft twisted hydrofoil using a coupled Euler-Lagrange methodology. The transport equation modelling [...]

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• Model to Full Scale Numerical Considerations and Analysis

  Q. Khraisat, M. Persson, M. Vikström, R. Bensow

  marine2023.

  
  

  • 1
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• Accurate Evaluation for Low-Carbon Shipping Using Wave Hindcast Database

  Y. Sato, M. Hata, T. Suzuki, S. Takeda

  marine2023.

  
  

  • 1
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• Hydrodynamic study of a bioinspired Underwater vehicle by CFD

  R. Bardera Mora, Á. Rodriguez-Sevillano, E. Barroso Barderas, J. Matias García

  marine2023.

  
  

  • 2
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• Optimisation of ship form based on seakeeping behaviour using Machine Learning

  P. Romero-Tello, J. Gutiérrez-Romero, B. Serván-Camas, A. Lorente-López

  marine2023.

  
  

  • 4
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• On the Use of Artificial Intelligence to Define Tank Transfer Functions

  C. Gillan, P. Schmitt

  marine2023.

  
  

  • 1
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• Prediction of Aerodynamic Performance for Multiple Flettner Rotors on the Oil Tanker using Deep Learning Methodology

  J. Seo, D. Park

  marine2023.

  
  

  • 0
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• Combining deep reinforcement learning and computational fluid dynamics for efficient navigation in turbulent flows

  A. Lidtke, D. Rijpkema, B. Düz

  marine2023.

  
  

  • 7
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