Relative Heave Measurement During Ship-to-Ship (STS) Transfer of Cargo

Master thesis (2023)

Authors

P. Eduardo Correia Mechanical Engineering

Contributors

V. Reppa Transport Engineering and Logistics - Mechanical, Maritime and Materials Engineering (supervisor 1)
Y. Pang Transport Engineering and Logistics - Mechanical, Maritime and Materials Engineering (supervisor 1)
A. Grammatikopoulos Ship and Offshore Structures - Mechanical, Maritime and Materials Engineering (supervisor 2)
Holger Caesar Intelligent Vehicles - Mechanical, Maritime and Materials Engineering (supervisor 2)
Jeroen Dijkstra Huisman Equipment BV (supervisor 1)
Faculty
Mechanical Engineering
Copyright: © 2023 Pedro Correia
Data Analysis Sensing Measurement Point Cloud STS Ship-to-Ship Relative Heave Data Processing
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Published Date

15-12-2023

Language

English

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Faculty

Mechanical Engineering

Abstract

Ship-to-Ship (STS) cargo transfers can significantly improve the efficiency of offshore installations while reducing their associated costs. However, the added complexity of cargo transfers involving ship-mounted cranes at sea poses significant challenges. To address this, crane manufacturers have introduced Relative Heave Compensation (RHC) systems to assist crane operators. Nevertheless, concerns have emerged among installation vessel companies regarding the relative heave measurement systems, which rely on Motion Reference Unit (MRU) sensors placed on both ships. Given that many supplier vessels lack the required sensors and the communication protocols pose bureaucratic hurdles, this research focuses on proposing novel, feeder-independent relative heave measuring solutions. This work introduces two unexplored sensor units, namely the LiDAR-MRU and RADAR-MRU. In the absence of actual sensor data, a simulation workflow using Simulink and Unreal Engine (UE) is presented to generate synthetic data. After, four distinct measurement solutions are developed, implemented, and evaluated using the simulated data in MATLAB. The implemented solutions estimate relative heave distance and speed with a Mean Absolute Error (MAE) ranging from 0.3-5.2% and 0.8-4.3% of the reference's maximum amplitude, and processing times from 1.8-91.4 [ms]. The results underscore their potential for field implementation. However, future validation is needed with actual sensor data.