Perumal, Veeradasan
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A Novel Steel Lazy Wave Riser Configuration for Ultra-Deepwater Haoran, Chen; Karuppanan, Saravanan; Perumal, Veeradasan; Ovinis, Mark; Lim, Frank; Kumar, Suria Devi Vijaya
Civil Engineering Journal Vol 11, No 1 (2025): January
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2025-011-01-03

Abstract

A steel lazy wave riser (SLWR) configuration combines buoyancy modules with a traditional steel catenary riser (SCR). The buoyancy section at the riser separates the floater's motion and acts as a damper toward the critical area in the touchdown point, improving the strength and fatigue performance. In ultra-deepwater environments, the substantial payload of risers due to extreme riser length imposes considerable tension and stress, challenging the limits of traditional configurations such as SLWR and SCR. The effective tension, maximum stress, and minimum bend radius at ultra-deep depths of these conventional risers would exceed the allowable limits, leading to potential structural failure. To address these limitations, this study proposes a novel riser configuration, the shaped steel lazy wave riser (SSLWR), specifically for ultra-deepwater conditions. By introducing an additional buoy section, SSLWR effectively reduces the effective tension while ensuring allowable stress distribution across the riser length, enhancing structural reliability and operational feasibility over traditional risers. OrcaFlex, a fully 3D non-linear finite element software widely used in maritime structure analysis, was used to simulate the effective tension, maximum stress, and minimum bend radius of the SCR, SLWR, and SSLWR configurations at 3000 m depth. The SSLWR shows a maximum effective tension that is less than half of that observed in the SCR, and it remains consistently lower than SCR and SLWR, suggesting that SSLWR holds promise as a robust alternative for ultra-deepwater applications. This study offers new insights into how modifying riser shape and buoy placement can effectively balance tension reduction with stress distribution, providing an alternative to traditional riser designs. The SSLWR's specific responses to buoy placements and varying currents expand an understanding of riser performance under varying conditions, guiding future advancements in offshore riser engineering. Doi: 10.28991/CEJ-2025-011-01-03 Full Text: PDF
Stress Concentration Factors in Tubular T-Joint Braces Under Compressive Loads Using Artificial Neural Networks Rasul, Adnan; Karuppanan, Saravanan; Perumal, Veeradasan; Ovinis, Mark; Iqbal, Muhammad; Badshah, Saeed; Alam, Khurshid
Civil Engineering Journal Vol. 11 No. 6 (2025): June
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2025-011-06-013

Abstract

Stress concentration factors (SCFs) are often calculated using formulas based on experimental testing and finite element analysis (FEA). While maximum SCF could occur at any location along the brace axis of the tubular T-joint’s brace, only the SCFs at the crown and saddle points can be determined from the available formulae, which can result in imprecise fatigue life determination. The current study presents a methodology to determine the SCFs in T-joints using FEA and ANN. ANNs are more effective than conventional data-fitting techniques at modelling intricate phenomena. In this work, parametric equations to estimate the SCFs of the T-joint’s brace under compressive loading were developed. Utilizing parametric equations allows for rapid estimates of SCFs, in contrast to time-consuming FEA and expensive testing. The equations are based on an artificial neural network’s training weights and biases (ANN). 625 finite element simulations were performed on tubular T-joints with various dimensions under compressive loads to determine the SCFs at the brace of the T-joint. These SCFs were then used to train an ANN. The weights and biases of the ANN were subsequently used to derive equations for calculating SCFs based on dimensionless parameters. The equations can estimate the SCF of a T-joint brace with less than 7% error and a root mean square error (RMSE) of less than 0.19.