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Experimental investigation and ANN modeling on improved performance of an innovative method of using heave response of a non-floating object for ocean wave energy conversion

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Frontiers in Energy 2013年第3期7卷 279-287页

作者： Srinivasan CHANDRASEKARAN Arunachalam AMARKARTHIK Karuppan SIVAKUMAR dhanasekaran selvamuthukumaran Shaji SIDNEY Department of Ocean Engineering Indian Institute of TechnologyMadras Chennai 600036 India Department of Mechanical Engineering Bannari Amman Institute ofTechnology TamilNadu 638401 India

To convert wave energy into usable forms of energy by utilizing heaving body, heaving bodies （buoys） which are buoyant in nature and float on the water surface are usually used. The wave exerts excess buoyancy force on the buoy, lifting it during the approach of wave crest while the gravity pulls it down during the wave trough. A hydraulic, direct or mechanical power takeoff is used to convert this up and down motion of the buoy to produce usable forms of energy. Though using a floating buoy for harnessing wave energy is conventional, this device faces many challenges in improving the overall conversion efficiency and survivability in extreme conditions. Up to the present, no studies have been done to harness ocean waves using a non-floating object and to find out the merits and demerits of the system. In the present paper, an innovative heaving body type of wave energy converter with a non-floating object was proposed to harness waves. It was also shown that the conversion efficiency and safety of the proposed device were significantly higher than any other device proposed with floating buoy. To demonstrate the improvements, experiments were conducted with non- floating body for different dimensions and the heave response was noted. Power generation was not considered in the experiment to observe the worst case response of the heaving body. The device was modeled in artificial neural network （ANN）, the heave response for various parameters were predicted, and compared with the experimental results. It was found that the ANN model could predict the heave response with an accuracy of 99%.

关键词： ocean wave energy point absorbers heaving body non-floating object heave response ratio artificial neural network (ANN)

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