Documents published in Scipedia

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

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

  marine2023.

  
  

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

  C. Gillan, P. Schmitt

  marine2023.

  
  

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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.

  
  

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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.

  
  

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• Seakeeping optimization of bulbous bow vessels through genetic algorithms

  S. Ruiz-Capel, P. Romero-Tello, J. Gutiérrez-Romero, A. Reyes, D. Kraniotis

  marine2023.

  
  

  • 2
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• A Generalized Reinforcement Learning Based Controller for Course-keeping of Ships in Waves

  A. Bayezit, Ö. Kinaci, B. Düz, D. Rijpkema, B. Mak

  marine2023.

  
  

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• On the Use of AIS and Meteorological Data to Identify Adrift Ships and Extrapolate their Drift Characteristics

  T. Ackermann, S. Kaidi, P. Sergent, E. Lefrançois

  marine2023.

  
  

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• Parametric roll prediction based on Machine Learning strategies

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

  marine2023.

  
  

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• Development of a demonstrator for predicting the operation of unmanned vehicles on naval platforms

  J. Carmona, R. Atienza, J. Iribarren

  marine2023.

  
  

  Abstract

  The "System for Predicting the Operation of Unmanned Aerial Vehicles on Naval Platforms" (SPOVENT) project aims to determine the studies and developments necessary to obtain and characterise the data required to predict ship movements in waves in real time and, therefore, to be able to predict the optimum windows for recovering/landing UAVs. The project was developed in two phases: the objective of the first one was the identification of the available technology applicable to the prediction of the 3D vessel motions during its operation plus the determination of maximum vessel motion limits in order to carry out the capture of UAVs in safe conditions; then came the development of two algorithms to predict the movements of the vessel depending on the waves that it will encounter during its navigation. After the completion of these initial phases, the validation and evaluation of the algorithms developed is carried out, followed by the development of the SPOVENT demonstrator, implementing the algorithm with best results. To determine the algorithm which best predicts the time windows in which the UAV will be able to operate, the following evaluation and validation criteria are established: accuracy of the prediction of the operation windows, range of application, input data required for its correct operation, possibility of adaptation to different types of vessels and required calculation time. Once the algorithm to be implemented in the demonstrator has been selected, a computer application is developed that shows the user the time windows to perform these operations with adequate safety. The design of the application ensures that user intervention is minimal, considering the operating conditions on a ship, including easy-to-read indicators and easily accessible buttons. Additionally, the demonstrator will be a tool to evaluate the operational criteria (STANAG) of different missions during navigation, by means of the ship's movement variables registered by an IMU sensor

  Abstract
  The "System for Predicting the Operation of Unmanned Aerial Vehicles on Naval Platforms" (SPOVENT) project aims to determine the studies and developments necessary [...]

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• Surrogate Models of Geometrically Parameterized Flow Systems

  A. Huerta, Á. Borrás, R. Perelló-Ribas, M. Giacomini

  admos2023.

  
  

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