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Research Papers: Ocean Renewable Energy

Efficient Dynamic Analysis of a Nonlinear Wave Energy Harvester Model

[+] Author and Article Information
Pol D. Spanos

Honorary Mem. ASME
G. R. Brown School of Engineering,
Rice University,
Houston, TX 77005;
Tongji University,
Shanghai 200092, China
e-mail: spanos@rice.edu

Felice Arena

DICEAM Department,
“Mediterranea” University of Reggio Calabria,
Loc. Feo di Vito,
Reggio Calabria 89122, Italy
e-mail: arena@unirc.it

Alessandro Richichi

DICEAM Department,
“Mediterranea” University of Reggio Calabria,
Loc. Feo di Vito,
Reggio Calabria 89122, Italy
e-mail: alessandro.richichi@unirc.it

Giovanni Malara

DICEAM Department,
“Mediterranea” University of Reggio Calabria,
Loc. Feo di Vito,
Reggio Calabria 89122, Italy
e-mail: giovanni.malara@unirc.it

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 October 9, 2015; final manuscript received February 10, 2016; published online April 7, 2016. Assoc. Editor: António Falcão.

J. Offshore Mech. Arct. Eng 138(4), 041901 (Apr 07, 2016) (8 pages) Paper No: OMAE-15-1104; doi: 10.1115/1.4032898 History: Received October 09, 2015; Revised February 10, 2016

In recent years, wave energy harvesting systems have received considerable attention as an alternative energy source. Within this class of systems, single-point harvesters are popular at least for preliminary studies and proof-of-concept analyses in particular locations. Unfortunately, the large displacements of a single-point wave energy harvester are described by a set of nonlinear equations. Further, the excitation is often characterized statistically and in terms of a relevant power spectral density (PSD) function. In the context of this complex problem, the development of efficient techniques for the calculation of reliable harvester response statistics is quite desirable, since traditional Monte Carlo techniques involve nontrivial computational cost. The paper proposes a statistical linearization technique for conducting expeditiously random vibration analyses of single-point harvesters. The technique is developed by relying on the determination of a surrogate linear system identified by minimizing the mean square error between the linear system and the nonlinear one. It is shown that the technique can be implemented via an iterative procedure, which allows calculating statistics, PSDs, and probability density functions (PDFs) of the response components. The reliability of the statistical linearization solution is assessed vis-à-vis data from relevant Monte Carlo simulations. This novel approach can be a basis for constructing computationally expeditious assessments of various design alternatives.

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References

Figures

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Fig. 1

Scheme of a single-point absorber with PTO and tight mooring line

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Fig. 2

Short time history of excitation and response components of the single-point wave energy harvester

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Fig. 3

Comparison between the PDFs of the surge (upper panel) and heave (lower panel) motions calculated via Monte Carlo data (dotted line) and a theoretical Gaussian distribution (continuous line)

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Fig. 4

Comparison between the PSDs of the surge (upper panel) and heave (lower panel) motions calculated by statistical linearization (continuous line) and Monte Carlo data (dotted line)

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