Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/43190
Title: Parametric resonance avoidance of offshore crane cable in subsea lowering operation through A* heuristic planner
Authors: Kang, Hooi-Siang
Tang, Collin Howe-Hing
Quen, Lee Kee
Steven, Adelina
Yu, Xiaochuan
Keywords: Parametric resonance;Offshore lowering;Cable dynamics;Mathieu instability;Resonance avoidance;A* heuristic
Issue Date: Dec-2017
Publisher: NISCAIR-CSIR, India
Abstract: Parametric resonance of offshore crane cable was predicted by using Mathieu equation to provide structural safety prediction during subsea lowering operation. This paper studied the predicting method and automatic resonance avoidance of offshore crane cable to complement the safety management during subsea lowering operation. The offshore crane cable was modeled as a tensioned long cylindrical structure and Mathieu instability coefficients were utilized to predict the dynamic instability of the structure. Numerical analyses were conducted to predict the parametric resonance of cable, evaluate the sensitivity of effective submerged length, dynamic tension variation, and plan for resonance avoidance mechanism automatically. Dynamic instability at sub-harmonic 2:1 unstable region of Mathieu stability diagram potentially creates high risk for lowering operation if the damping coefficient is low. Dynamic tension variation can cause instability of offshore crane cable during passing through wave splash zone and landing subsea payload. The reduction of axial tension variation can stabilize the dynamic of offshore crane cable. Parametric resonance of cable is also sensitive to the total payload. The findings of this paper can enhance structural integrity prediction of offshore crane cable and provide an automatic planner to the operator to avoid parametric resonance during subsea lowering operation.
Page(s): 2422-2433
URI: http://nopr.niscair.res.in/handle/123456789/43190
ISSN: 0975-1033 (Online); 0379-5136 (Print)
Appears in Collections:IJMS Vol.46(12) [December 2017]

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