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Stochastic Analysis of a Nonlinear Energy Harvester Model

[+] Author Affiliations
P. D. Spanos

Rice University, Houston, TX

A. Richichi

Rice University, Houston, TX‘Mediterranea’ University of Reggio Calabria, Reggio Calabria, Italy

F. Arena

‘Mediterranea’ University of Reggio Calabria, Reggio Calabria, Italy

Paper No. OMAE2014-24489, pp. V04BT02A036; 7 pages
doi:10.1115/OMAE2014-24489
From:
  • ASME 2014 33rd International Conference on Ocean, Offshore and Arctic Engineering
  • Volume 4B: Structures, Safety and Reliability
  • San Francisco, California, USA, June 8–13, 2014
  • Conference Sponsors: Ocean, Offshore and Arctic Engineering Division
  • ISBN: 978-0-7918-4543-1
  • Copyright © 2014 by ASME

abstract

Floating oscillating-bodies are a kind of wave energy converter developed for harvesting the great amount of energy related to water waves (see Falcão [1] for a review).

Although the assumptions of small-wave and linear behavior of oscillating system are reasonable for most of the time during which a floating point harvester is in operation, nonlinear effects may be significant in extreme sea states situations.

In this paper a nonlinear dynamic analysis of a point harvester wave energy converter is conducted. The model involves a tightly moored single-body floating device; it captures motion in the horizontal and vertical directions. The stiffness and damping forces, being functions of the displacement and velocity components, make the system nonlinear and coupled.

For the input forces, the erratic nature of the waves is modeled by a stochastic process. Specifically, wind-generated waves are modeled by means of the JONSWAP spectrum.

The method of statistical linearization [2] is used to determine iteratively the effective linear stiffness and damping matrices and response statistics of the system and to proceed to conducting a dynamic analysis of the harvester model.

The reliability of the linearization based approach is demonstrated by comparison with time domain integration, Monte Carlo simulation, data. This approach offers the appealing feature of conducting efficiently a variety of parameter studies which can expedite preliminary evaluations, inter alia, of competing design scenarios for the energy converter in a stochastic environmental setting.

Copyright © 2014 by ASME

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