Research Papers: Piper and Riser Technology

Flexible Riser Configuration Design for Extremely Shallow Water With Surrogate-Model-Based Optimization

[+] Author and Article Information
Jinlong Chen

State Key Laboratory of Structural Analysis for
Industrial Equipment,
Dalian University of Technology,
Dalian 116023, China
e-mail: cjldut@163.com

Jun Yan

State Key Laboratory of Structural Analysis for
Industrial Equipment,
Dalian University of Technology,
Dalian 116023, China
e-mail: yanjun@dlut.edu.cn

Zhixun Yang

State Key Laboratory of Structural Analysis for
Industrial Equipment,
Dalian University of Technology,
Dalian 116023, China
e-mail: yangzhixun@mail.dlut.edu.cn

Qianjin Yue

State Key Laboratory of Structural Analysis for
Industrial Equipment,
Dalian University of Technology,
Dalian 116023, China
e-mail: yueqj@dlut.edu.cn

Minggang Tang

China Ship Scientific Research Center,
Wuxi 214000, China
e-mail: tangminggang999@gmail.com

1Corresponding author.

Contributed by the Ocean, Offshore, and Arctic Engineering Division of ASME for publication in the JOURNAL OF OFFSHORE MECHANICS AND ARCTIC ENGINEERING. Manuscript received August 29, 2015; final manuscript received April 15, 2016; published online June 2, 2016. Assoc. Editor: Ron Riggs.

J. Offshore Mech. Arct. Eng 138(4), 041701 (Jun 02, 2016) (7 pages) Paper No: OMAE-15-1093; doi: 10.1115/1.4033491 History: Received August 29, 2015; Revised April 15, 2016

The aim of this paper is to study the optimization design of a steep wave configuration based on a surrogate model for an extremely shallow water application of a flexible riser. As the traditional technique of riser configuration design is rather time-consuming and exhaustive due to the nonlinear time domain analysis and large quantities of load cases, it will be challenging when engineers address an extreme design, such as the configuration design in the case of extremely shallow water. To avoid expensive simulations, surrogate models are constructed in this paper with the Kriging model and radial basis function (RBF) networks by using the samples obtained by optimal Latin hypercubic sampling (LHS) and time domain analysis in a specified design space. The RBF model is found to be easier to construct and to show better accuracy compared with the Kriging model according to the numerical simulations in this work. On the basis of the RBF model, a hybrid optimization is performed to find the minimum curvature design with corresponding engineering constraints. In addition, an optimized design is found to meet all of the design criteria with high accuracy and efficiency, even though all of the samples associated with construction of the surrogate model fail to meet the curvature criterion. Thus, the technique developed in this paper provides a novel method for riser configuration design under extreme conditions.

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Grahic Jump Location
Fig. 3

Traditional RBF networks

Grahic Jump Location
Fig. 2

Design variables for the steep wave configuration

Grahic Jump Location
Fig. 1

Solution for the oil-spill incident

Grahic Jump Location
Fig. 6

Variables versus curvatures of the RBF model: (a) L1, L2 versus curvature, (b) L1, L3 versus curvature, (c) L1, P versus curvature, and (d) L3, P versus curvature

Grahic Jump Location
Fig. 5

Samples' curvature responses

Grahic Jump Location
Fig. 4

Selected samples from the optimal LHS



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