Session: 01-02-01 Station Keeping - I

Submission Number: 156359

Automation and Optimization of Mooring Line Designs 

This paper studies mathematical optimization applied to the design and analysis of mooring lines for offshore structures.

Mooring line design and analysis for offshore structures is carried out in either time-domain or frequency-domain. Global Maritime has developed GMoor, a commercially available frequency-domain software commonly used in industry. While GMoor streamlines the analysis, the process still requires a manual approach to iterative design; an approach that often results in designs that satisfy requirements but may not be optimal. The automation and optimisation of this design problem allows for optimal solutions while saving on computational time and resources.

The optimisation of mooring lines is a multi-objective, multi-variable, non-linear problem. Utilising the in-built API interface in GMoor, this study uses the ‘Constrained Optimization BY Linear Approximation’ (COBYLA) model within the NLOPT library to address this optimisation. COBYLA is a gradient free optimisation algorithm that iteratively approximates constraints and objective functions using linear models, making it well-suited for problems where derivatives are difficult to compute.

This study sets the objective function as a minimisation of total mooring line length of a semi-submersible drilling platform; the constraints are applied inline with the requirements of codes and standards for mooring line design under environmental loading, including single line failure. The optimisation is first tested on a 3-mooring line design, with the optimal result validated against an exhaustive search of the design space. The algorithm is then applied to an 8-mooring line design.

Presenting Author: Hamid Shayanfar Global Maritime

Presenting Author Biography: Hamid Shayanfar is an Intermediate Machine Learning Engineer at Global Maritime. He has over five years of experience as a project engineer in the field of offshore engineering at C-CORE. Hamid earned an M.Eng. in Mechanical Engineering from Memorial University and recently completed a second master’s degree in Data Science at the same university, aiming to integrate ML-AI methods into offshore and ocean engineering.

At Global Maritime, he worked as a graduate intern over the past year, focusing on developing optimization methods for mooring line design. Hamid has multidisciplinary experience in mechanical and offshore engineering, primarily focusing on Pre-FEED and FEED studies for clients in the offshore oil and gas industry. His responsibilities include assessing environmental conditions, evaluating ice loads, and conducting risk assessments for offshore structures and subsea infrastructure. Through these projects, Hamid has gained extensive expertise in data analysis, engineering programming, dynamic modeling, probabilistic modeling, structural analysis, mechanics of materials, fieldwork, and experimental testing.