Salter's duck,an asymmetrical wave energy converter(WEC)device,showed high efficiency in extracting energy from 2D regular waves in the past;yet,challenges remain for fluctuating wave *** can potentially be addres...
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Salter's duck,an asymmetrical wave energy converter(WEC)device,showed high efficiency in extracting energy from 2D regular waves in the past;yet,challenges remain for fluctuating wave *** can potentially be addressed by adopting a negative stiffness mechanism(NSM)in WEC devices to enhance system efficiency,even in highly nonlinear and steep 3D waves.A weakly nonlinear model was developed which incorporated a nonlinear restoring moment and NSM into the linear formulations and was applied to an asymmetric WEC using a time domain potential flow *** model was initially validated by comparing it with published experimental and numerical computational fluid dynamics *** current results were in good agreement with the published *** was found that the energy extraction increased in the range of 6%to 17%during the evaluation of the effectiveness of the NSM in regular *** irregular wave conditions,specifically at the design wave conditions for the selected test site,the energy extraction increased by 2.4%,with annual energy production increments of approximately *** findings highlight the potential of NSM in enhancing the performance of asymmetric WEC devices,indicating more efficient energy extraction under various wave conditions.
作者:
Saad, Mohammed AyadJaafar, RosminaChellappan, Kalaivani
Faculty of Engineering and Built Environment Department of Electrical Electronics and System Engineering Selangor Bangi43600 Malaysia Al-Kitab University
Department of Medical Instrumentations Technique Engineering Kirkuk36001 Iraq Universitas Airlangga
Biomedical Engineering Study Program Faculty of Science and Technology Surabaya60115 Indonesia
Efficient data collection in wireless sensor networks (WSNs) is crucial. While traditional approaches rely on stationary data sinks, the use of mobile sinks, like unmanned aerial vehicles (UAVs), has shown promise in ...
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Phase recovery(PR)refers to calculating the phase of the light field from its intensity *** exemplified from quantitative phase imaging and coherent diffraction imaging to adaptive optics,PR is essential for reconstru...
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Phase recovery(PR)refers to calculating the phase of the light field from its intensity *** exemplified from quantitative phase imaging and coherent diffraction imaging to adaptive optics,PR is essential for reconstructing the refractive index distribution or topography of an object and correcting the aberration of an imaging *** recent years,deep learning(DL),often implemented through deep neural networks,has provided unprecedented support for computational imaging,leading to more efficient solutions for various PR *** this review,we first briefly introduce conventional methods for ***,we review how DL provides support for PR from the following three stages,namely,pre-processing,in-processing,and *** also review how DL is used in phase image ***,we summarize the work in DL for PR and provide an outlook on how to better use DL to improve the reliability and efficiency of ***,we present a live-updating resource(https://***/kqwang/phase-recovery)for readers to learn more about PR.
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