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Predicted power performance of a submerged membrane pressure-differential wave energy converter

Algie, C, Ryan, S and Fleming, A ORCID: 0000-0002-2738-4521 2017 , 'Predicted power performance of a submerged membrane pressure-differential wave energy converter' , International Journal of Marine Energy, vol. 20 , pp. 125-134 , doi:

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The compromise between Wave Energy Converter (WEC) performance, cost and survival is both a delicate and critical one. A successful WEC design must effectively address the exploitable wave energy, but survive the climate extremes. Bombora Wave Power has focussed on designing a WEC that performs well in less extreme nearshore climates and is able to decouple its working surfaces from extreme waves. Numerical modelling of the performance of their submerged, pneumatic, flexible membrane WEC, the mWave, is presented. The mWave power matrix is found to provide good performance over a broad range of wave periods, with a broad peak in performance at wave periods of 9s for the assumed design parameters. This broad peak corresponds favourably to the sea-state probabilities in a typical near-shore shallow water wave climate, yielding a predicted mean annual electrical power production of 240kW in such conditions. Small scale physical modelling of the relationship between the initial level of inflation of the mWave cell membranes and the system’s power capture has confirmed the possibility of an mWave survival strategy that can potentially allow safe, de-rated performance in extreme conditions. Future work is planned to further improve predicted mWave performance by refinement of power take-off damping and to physically validate these performance modelling results at full scale.

Item Type: Article
Authors/Creators:Algie, C and Ryan, S and Fleming, A
Keywords: wave energy converter, mWave, numerical modelling, power matrix, membrane, submerged
Journal or Publication Title: International Journal of Marine Energy
Publisher: Elsevier B.V.
ISSN: 2214-1669
DOI / ID Number:
Copyright Information:

Copyright 2017 Elsevier

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