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Optimal design of FPSO topside module for in-place, lift and weighing conditions utilizing meta-heuristic optimization algorithms

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Abstract

The repetitive modelling required for traditional structural design work is a laborious and time-consuming task. Yet, the cost significantly increases due to the wide variety of profiles. It is suggested to use grouping technique along with algorithms to optimize structural elements in order to address these issues. In this paper, two meta-heuristic algorithms viz; Cuckoo Search algorithm and Thermal Exchange Optimization algorithm are employed for weight optimization of FPSO supporting structure. These algorithms identify the structure’s least weight for the offshore floating platform design. The current work suggests a method for FPSO structure design that is reliability-based and optimal. The current study discusses the optimized design of the topside FPSO structural assembly for various load conditions, such as production tank pressure loads, equipment loads, piping loads, electrical and instrumentation loads, girder deformation loads, wind load, and so on. The effects of hull flexibility, as well as wind and motion-induced accelerations, are considered for both Normal Operating and Extreme Operating conditions. The analysis and design optimization are carried out in the STAAD Pro and analytical tool by selecting the least weight as an objective function while adhering to the API design constraints on displacement and stress limits. Module primary structure design can be iteratively “optimized” using the algorithm and stress ratio as described above. By implication, the algorithms, FPSO supporting structure translates into optimal design, resulting in cost savings.

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Data availability

The dataset generated and analyzed during the current study are available from the corresponding author on reasonable request.

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Correspondence to Adinath Yadhav.

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Yadhav, A., Kulkarni, M. Optimal design of FPSO topside module for in-place, lift and weighing conditions utilizing meta-heuristic optimization algorithms. Asian J Civ Eng 24, 3055–3070 (2023). https://doi.org/10.1007/s42107-023-00694-2

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