Extreme riser experimental loads caused by sea currents in the Gulf of Eilat. (April 2022)

• Record Type: Journal Article
• Authors: Sun, Jiayao; Gaidai, Oleg; Wang, Fang; Naess, Arvid; Wu, Yu; Xing, Yihan; van Loon, Eric-Jan; Medina, Ausberto Rivera; Wang, Junlei
• Is Part Of: Probabilistic engineering mechanics. Volume 68(2022)
• Publication Date: 2022-04
• Subjects: Riser -- VIV -- Probability distribution -- Offshore operations -- Statistics -- Sea current, TLP, GEV; Engineering -- Statistical methods -- Periodicals; Mechanics, Applied -- Statistical methods -- Periodicals; Probabilities -- Periodicals; Ingénierie -- Méthodes statistiques -- Périodiques; Mécanique appliquée -- Méthodes statistiques -- Périodiques; Probabilités -- Périodiques; 620.100727
• DOI: 10.1016/j.probengmech.2022.103243
• Languages: English
• ISSNs: 0266-8920

Abstract

Abstract: Risers are widely used in offshore industry and are subjected to dynamic environmental loads. Robust prediction of extreme riser external loading under the action of sea currents and subsequently VIV (vortex-induced vibrations) is an important safety concern for various offshore installations, for example TLP (Tension Leg Platform) and FPSO (Floating Production Storage and Offloading Unit). Excessive loading acting on riser may occur during offshore operations, posing an operational risk. In this paper, experimental results were used to study hydrodynamic current loads, acting on a riser under actual sea conditions. This paper presents methodology for estimating extreme loads, based on laboratory measurements. The ACER (averaged conditional exceedance rate) method has been briefly presented. Suggested methodology provides accurate extreme value predictions, utilizing available measured data efficiently. In this study estimated extreme value prediction, corresponding to a relatively large return period, was obtained by ACER method, based on the in situ sea current environmental statistics input. Based on the overall performance of the suggested method, it was concluded that ACER method can incorporate environmental input and provide robust and accurate prediction based on experimental measurements. Data de-clustering issue has been briefly addressed. Paper highlights ACER method ability to have environmental statistical distribution as an input, as required in long term statistical analysis. Described approach may be well used at the riser design stage, while defining optimal riser parameters that would minimize potential riser damage.