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.

References

References
1.
DNV
,
2010
, “
Dynamic Risers
,” Det Norske Veritas, Oslo, Norway, DNV Offshore Standard No. DNV-OS-F201.
2.
API
,
2014
, “
Recommended Practice for Flexible Pipe
,” American Petroleum Institute, Washington, DC, Standard No. API RP 17B.
3.
Roveri
,
F. E.
,
de Arruda Martins
,
C.
, and
Balena
,
R.
,
2005
, “
Parametric Analysis of a Lazy-Wave Steel Riser
,”
ASME
Paper No. OMAE2005-67128.
4.
Xia
,
J.
,
Das
,
P. K.
, and
Karunakaran
,
D.
,
2008
, “
A Parametric Design Study for a Semi/SCR System in Northern North Sea
,”
Ocean Eng.
,
35
(
17
), pp.
1686
1699
.
5.
Sun
,
H.
, and
Wang
,
D.
,
2013
, “
Sensitivity Analysis of Buoyancy Modules Parameters of Lazy-Wave Flexible Riser
,”
ASME
Paper No. OMAE2013-10498.
6.
Sun
,
L.
,
Zhou
,
J.
, and
Wang
,
J.
,
2010
, “
Lazy Wave Configuration and Parameter Sensitivity Analysis of Deepwater Flexible Riser
,”
China Offshore Platform
,
26
(
3
), pp.
37
42
.
7.
Hanonge
,
D.
, and
Luppi
,
A.
,
2010
, “
Challenges of Flexible Riser Systems in Shallow Waters
,” Offshore Technology Conference, Houston, TX, Paper No. OTC 20578.
8.
De Leeneer
,
Y.
, and
Eik
,
G.
,
2004
, “
Multiple Riser System for Shallow Water
,” Offshore Technology Conference, Houston, TX, Paper No. OTC 20578.
9.
Tan
,
Z.
,
Zhang
,
J.
,
Sheldrake
,
T. H.
,
Hou
,
Y.
, and
Hill
,
T.
,
2013
, “
Weight Added Wave (WAW) System in Production—A Brazil Offshore Application
,”
Offshore Technology Conference
, Houston, TX, Paper No. OTC 23937.
10.
Larsen
,
C. M.
, and
Hanson
,
T.
,
1999
, “
Optimization of Catenary Risers
,”
ASME J. Offshore Mech. Arct. Eng.
,
121
(
2
), pp.
90
94
.
11.
Vieira
,
L. T.
,
de Lima
,
B. D. S.
,
Evsukoff
,
A. G.
, and
Jacob
,
B. P.
,
2003
, “
Application of Genetic Algorithms to the Synthesis of Riser Configurations
,”
ASME
Paper No. OMAE2003-37231.
12.
de Lima
,
L. P.
,
de Souza
,
B.
,
Pinheiro Jacob
,
B.
, and
Francisco Favilla Ebecken
,
N.
,
2005
, “
A Hybrid Fuzzy/Genetic Algorithm for the Design of Offshore Oil Production Risers
,”
Int. J. Numer. Methods Eng.
,
64
(
11
), pp.
1459
1482
.
13.
Tanaka
,
R. L.
, and
de Arruda Martins
,
C.
,
2006
, “
A Genetic Algorithm Approach to Steel Riser Optimization
,”
ASME
Paper No. OMAE2006-92257.
14.
Yang
,
H.
,
Jiang
,
R.
, and
Li
,
H.
,
2009
, “
Optimization Design of Deepwater Steel Catenary Risers Using Genetic Algorithm
,”
Computational Structural Engineering
,
Springer
,
Dordrecht, The Netherlands
, pp.
901
908
.
15.
Vieira
,
I. N.
,
de Lima
,
B. S.
, and
Jacob
,
B. P.
,
2008
, “
Optimization of Steel Catenary Risers for Offshore Oil Production Using Artificial Immune System
,”
Artificial Immune Systems
,
Springer-Verlag
,
Berlin
, pp.
254
265
.
16.
de Pina
,
A. A.
,
Albrecht
,
C. H.
,
de Lima
,
B. S. L. P.
, and
Jacob
,
B. P.
,
2011
, “
Tailoring the Particle Swarm Optimization Algorithm for the Design of Offshore Oil Production Risers
,”
Optim. Eng.
,
12
(
1–2
), pp.
215
235
.
17.
Vieira
,
I. N.
,
Lima
,
B. S. L. P.
, and
Jacob
,
B. P.
,
2012
, “
Bio‐Inspired Algorithms for the Optimization of Offshore Oil Production Systems
,”
Int. J. Numer. Methods Eng.
,
91
(
10
), pp.
1023
1044
.
18.
Tanaka
,
R. L.
, and
de Arruda Martins
,
C.
,
2007
, “
Dynamic Optimization of Steel Risers
,”
17th International Offshore and Polar Engineering Conference
, Lisbon, Portugal, pp.
859
863
.
19.
Tanaka
,
R. L.
, and
de Arruda Martins
,
C.
,
2011
, “
Parallel Dynamic Optimization of Steel Risers
,”
ASME J. Offshore Mech. Arct. Eng.
,
133
(
1
), p.
011302
.
20.
Martins
,
M. A. L.
,
Lages
,
E. N.
, and
Silveira
,
E. S. S.
,
2013
, “
Compliant Vertical Access Riser Assessment: DOE Analysis and Dynamic Response Optimization
,”
Appl. Ocean Res.
,
41
, pp.
28
40
.
21.
Wang
,
G. G.
, and
Shan
,
S.
,
2007
, “
Review of Metamodeling Techniques in Support of Engineering Design Optimization
,”
ASME J. Mech. Des.
,
129
(
4
), pp.
370
380
.
22.
Song
,
C. Y.
,
Lee
,
J.
, and
Choung
,
J. M.
,
2011
, “
Reliability-Based Design Optimization of an FPSO Riser Support Using Moving Least Squares Response Surface Meta-Models
,”
Ocean Eng.
,
38
(
2
), pp.
304
318
.
23.
Sultan
,
I. A.
,
Reda
,
A. M.
, and
Forbes
,
G. L.
,
2013
, “
Evaluation of Slug Flow-Induced Flexural Loading in Pipelines Using a Surrogate Model
,”
ASME J. Offshore Mech. Arct. Eng.
,
135
(
3
), p.
031703
.
24.
Tang
,
M.
,
Yan
,
J.
,
Chen
,
J.
,
Yang
,
Z.
, and
Yue
,
Q.
,
2015
, “
Nonlinear Analysis and Multi-Objective Optimization for Bend Stiffeners of Flexible Riser
,”
J. Mar. Sci. Technol.
,
20
(
4
), pp.
591
603
.
25.
Rhim
,
W.
,
Ohsaka
,
K.
,
Paradis
,
P.
, and
Spjut
,
R. E.
,
1999
, “
Noncontact Technique for Measuring Surface Tension and Viscosity of Molten Materials Using High Temperature Electrostatic Levitation
,” Review of Scientific Instruments, Vol. 70, pp.
2796
2801
.
26.
American Society of Mechanical Engineers
,
2009
, “
Surface Texture: Surface Roughness, Waviness, and Lay
,” ASME B46.1-2009 (Revision of ASME B46.1-2002).
27.
Mills
,
K. C.
,
2002
,
Recommended Values of Thermophysical Properties for Selected Commercial Alloys
, Woodhead Publishing Limited, Cambridge, UK.
28.
Corcione
,
M.
,
2011
, “
Empirical Correlating Equations for Predicting the Effective Thermal Conductivity and Dynamic Viscosity of Nanofluids
,” Energy Conversion and Management,
52
(
1
), pp.
789
793
.
29.
de Pina
,
A. C.
,
Monteiro
,
B. D. F.
,
Albrecht
,
C. H.
,
de Lima
,
B. S. L. P.
, and
Jacob
,
B. P.
,
2014
, “
ANN and Wavelet Network Meta-Models for the Coupled Analysis of Floating Production Systems
,”
Appl. Ocean Res.
,
48
, pp.
21
32
.
30.
Guarize
,
R.
,
Matos
,
N.
,
Sagrilo
,
L.
, and
Lima
,
E.
,
2007
, “
Neural Networks in the Dynamic Response Analysis of Slender Marine Structures
,”
Appl. Ocean Res.
,
29
(
4
), pp.
191
198
.
31.
Yang
,
H.
, and
Zheng
,
W.
,
2011
, “
Metamodel Approach for Reliability-Based Design Optimization of a Steel Catenary Riser
,”
J. Mar. Sci. Technol.
,
16
(
2
), pp.
202
213
.
32.
Tanese
,
R.
,
1989
, “
Distributed Genetic Algorithms
,”
Third International Conference on Genetic Algorithms
, Morgan Kaufmann Publishers, San Mateo, CA, pp.
434
439
.
33.
Schittkowski
,
K.
,
1986
, “
NLPQL: A fortran Subroutine Solving Constrained Nonlinear Programming Problems
,”
Ann. Oper. Res.
,
5
(
2
), pp.
485
500
.
34.
Cressie
,
N.
,
1990
, “
The Origins of Kriging
,”
Math. Geol.
,
22
(
3
), pp.
239
252
.
35.
Queipo
,
N. V.
,
Haftka
,
R. T.
,
Shyy
,
W.
,
Goel
,
T.
,
Vaidyanathan
,
R.
, and
Tucker
,
P. K.
,
2005
, “
Surrogate-Based Analysis and Optimization
,”
Prog. Aerosp. Sci.
,
41
(
1
), pp.
1
28
.
36.
Lee
,
K. H.
,
Park
,
G. J.
, and
Joo
,
W. S.
,
2006
, “
A Global Robust Optimization Using Kriging Based Approximation Model
,”
JSME Int. J., Ser. C
,
49
(
3
), pp.
779
803
.
37.
Park
,
J.
, and
Sandberg
,
I. W.
,
1991
, “
Universal Approximation Using Radial-Basis-Function Networks
,”
Neural Comput.
,
3
(
2
), pp.
246
257
.
38.
Park
,
J. S.
,
1994
, “
Optimal Latin-Hypercube Designs for Computer Experiments
,”
J. Stat. Plann. Inference
,
39
(
1
), pp.
95
111
.
39.
Wang
,
G. G.
,
2003
, “
Adaptive Response Surface Method Using Inherited Latin Hypercube Design Points
,”
ASME J. Mech. Des.
,
125
(
2
), pp.
210
220
.
40.
Orcina
,
2015
, “
Orcaflex User Manual (version 9.8d)
,” Orcina Ltd, Cumbria, UK.
41.
Dassault Systèmes Simulia Corp.
,
2011
, “
Isight 5.0 User's Guide
,”
Dassault Systèmes Simulia Corp.
,
Providence, RI
.
42.
Kohavi
,
R.
,
1995
, “
A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection
,”
International Joint Conference on Artificial Intelligence (IJCAI)
, Montreal, Canada, pp.
1137
1145
.
43.
Herrera
,
L. J.
,
Pomares
,
H.
,
Rojas
,
I.
,
Guillén
,
A.
,
Rubio
,
G.
, and
Urquiza
,
J.
,
2011
, “
Global and Local Modeling in RBF Networks
,”
Neurocomputing
,
74
(
16
), pp.
2594
2602
.
You do not currently have access to this content.