Vibration Analysis of a Pipeline System Based on Model Truncation

Designing a complex pipeline for a floating production storage and offloading (FPSO), particularly over long distances, presents significant challenges in vibration analysis. Traditional modeling methods, which often assume finite lengths and simple configurations, fall short as they do not account...

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Bibliographic Details
Main Authors: Huawei Han, Qiang Fu, Changhui Wang, Yuhai Sun, Changfei Li
Format: Article
Language:English
Published: Wiley 2025-01-01
Series:Shock and Vibration
Online Access:http://dx.doi.org/10.1155/vib/2806828
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Summary:Designing a complex pipeline for a floating production storage and offloading (FPSO), particularly over long distances, presents significant challenges in vibration analysis. Traditional modeling methods, which often assume finite lengths and simple configurations, fall short as they do not account for the integrality and locality of long spans and the presence of multiple elastic supports. In response to these limitations, this paper introduces a novel vibration analysis method based on reduced-order modeling, specifically tailored for complex pipelines with multielastic supports. This innovative approach incorporates the constraint interface stiffness at the elastic supports of the pipeline substructure. By employing modal truncation and assembling a reduced element matrix, the method significantly reduces the complexity of the dynamic model, thereby overcoming the inefficiencies associated with traditional modeling techniques. To demonstrate the effectiveness of this proposed method, two typical FPSO pipelines are analyzed. The study compares the natural frequencies, mode shapes, and calculation times between the full FEM and the reduced-order model. Results show that the proposed method maintains high accuracy, with natural frequency errors under 4% and minimum MAC values of 0.883. Thus, this study proposes a reduced-order modeling approach for the vibration analysis of complex pipeline systems on FPSOs.
ISSN:1875-9203