A tabular optimisation technique for steel lazy wave riser

Ogbeifun, A. M. and Oterkus, S. and Race, J. and Naik, H. and Moorthy, D. and Bhowmik, S. and Ingram, J. (2021) A tabular optimisation technique for steel lazy wave riser. IOP Conference Series: Materials Science and Engineering, 1052 (1). 012022. ISSN 1757-899X (https://doi.org/10.1088/1757-899x/1052/1/012022)

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Abstract: Steel lazy wave riser (SLWR) is derived from the simple catenary riser (SCR) by the installation of buoyancy modules on its section. Infinite SLWR configurations are possible, and this poses difficulties in determining the best configuration. However, it is possible to capture some suitable configurations which satisfy some given design criteria specific to a project. We referred to these as the optimum configurations for the problem. Several advanced optimization tools and techniques for engineering optimization are available. In this paper, we present a 2D tabular optimization method for SLWR, which is an index-based optimization technique. The approach reduces a multidimensional problem to a 2D type providing a significant reduction in the required computational resources. It combines the design variables in pairs and assigns indices to the resulting design points (configurations) for each combination. The optimum design points are then tracked through index matching using techniques such as data sorting and intersection operations. In the application of the technique to SLWR design, we set the number of design variable for the problem to three. This results in three pair of combinations of the design variables. The design variables are the apparent mass ratio, the sag bend elevation, and the arc height. The output variables of interest to be optimized include the SLWR hanging length, the smeared buoyancy section length, the smeared buoyancy thickness, the riser hang-off tension, the stress utilization and fatigue damage around the bends. Selected optimum SLWR configurations from the optimization process are subjected to an irregular wave simulation to demonstrate the suitability of the approach for such optimisation problems.


Ogbeifun, A. M., Oterkus, S. ORCID logoORCID: https://orcid.org/0000-0003-0474-0279, Race, J. ORCID logoORCID: https://orcid.org/0000-0002-1567-3617, Naik, H., Moorthy, D., Bhowmik, S. and Ingram, J.;