海洋可再生能源装置的水声影响

Pedro Pregitzer, F. Lau, G. Vaz, E. Cruz
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引用次数: 0

摘要

可再生能源设备产生的噪音可能会影响当地的野生动物,并干扰军事导航技术。鉴于水下声音传播带来的特殊挑战,需要对这一现象进行仔细分析。为了进行这样的分析,需要一个能够将详细的环境描述和低频计算结合起来的模型。出于这些目的,已经发现正常模式模型最适合手头的任务,特别是经典的KRAKEN算法。在本文中,将介绍正常模式模型相对于光线追踪模型(如BELLHOP)的优势。通过网格细化研究评估算法的数值不确定性收敛特性,对算法的性能进行了数值验证。最后,通过在葡萄牙佩尼切海岸附近安装的一个发电装置附近测量的实验数据,验证了正常模式方法的能力。在1000 Hz以上的频率下,发现正模态解与实验数据中观察到的趋势密切相关。在500和1000赫兹之间,在整个域上组装现场解决方案出现了困难。在这些频率以下,观察到正常模式假设的明显破坏。正模态方法已被认为是预测海洋结构声传播的有力候选方法,但需要做更多的工作来改进近场效应的计算,可能会使用格林函数算法。
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Underwater Acoustic Impact of a Marine Renewable Energy Device
Noise generated by renewable energy devices may impact local wildlife and interfere with military navigation techniques. Given the particular challenges posed by underwater sound propagation, careful analysis of this phenomenon is required. To conduct such an analysis, a model that is able to integrate detailed environmental descriptions and low frequency computations is sought. For these purposes, it has been found that normal mode models are the most suited to the task at hand, in particular the classical KRAKEN algorithm. In this article, the advantages of normal mode models over ray-tracing ones such as BELLHOP will be presented. Furthermore, the algorithm’s performance is numerically verified through the employment of mesh refinement studies to evaluate its numerical uncertainty convergence characteristics. Finally, the normal mode method’s capabilities are validated against experimental data measured near an energy-generating device installed off the coast of Peniche, Portugal. At frequencies above 1000 Hz, it was found that the normal mode solution closely followed the trends observable in the experimental data. Between 500 and 1000 Hz, difficulties in assembling a field solution over the domain arose. Below these frequencies, a clear break-down of the normal mode assumptions was observed. The normal mode method has been concluded to be a strong candidate to predict sound propagation from marine structures, but more work is needed to improve the calculation of near-field effects, potentially by use of Green’s function algorithms.
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