Neural Network Modeling Of The
Nonlinear Dynamic Structural Offshore System
With Hysteresis
Author(s)
D. M. Rocha, N. F. F. Ebecken, L. P. Calôba & D. L. Kaiser
Abstract
This paper proposes an empirical modeling of the system formed by the riserplatform
connection, in deep water. This connection has the objective of
minimizing the acting bending moment, possesses high complexity and highcriticity
due to economic and environmental consequences from its fault. The
main element in the joint is made of elastomeric material, which reveals
nonlinear hysteresis. In addition, this whole connection system presents
nonlinearities due to the action of dynamic loading and large motions. TDNN
and Recurrent Neural Networks (RNN) are being investigated since they possess
the ability to model nonlinear hysteretic behaviors and also dynamic systems.
Simulation results have confirmed that RNN is the one that presents the best
representation of the system studied. Emphasis shall be given to the additional
difficulties, which arise from the utilization of real data in the modeling process
for this system.
Keywords: Recurrent Neural Network, NARX, hysteresis, Flexjoint SCR.
1 Introduction
The specific system under study is that which makes the connection between the
riser and the platform, through a connector called Flexjoint. The Flexjoint (see
Figure 1) is installed on the SCR (Steel Catenary Riser) top, and through an
elastomeric joint it allows the SCR to bend when submitted to dynamic
environmental loading of wave, wind and current. The elastomer plays an
important and complex role and any deterioration of the elastomer can affect the
Keywords
Recurrent Neural Network, NARX, hysteresis, Flexjoint SCR.
Related Book
Other papers in this volume
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