Abstract The importance of reducing the noise impact of ships is being recognised worldwide. Consequently, the inclusion of this principle among the objectives and constraints of new designs is becoming a standard. For this reason, considerable attention is given to the propeller being often the dominant source of underwater radiated noise, especially when cavitation occurs, as it happens in most cases when a ship sails at design speed. The designers of quieter propulsion systems require the availability of predictive tools able to verify the compliance with noise requirements and to compare the effectiveness of different design solutions. In this context, tools able to provide a reliable estimate of propeller noise spectra based just on the information available during propeller design represent a fundamental tool to speed up the design process avoiding model scale tests. This work focuses on developing a tool able to predict the cavitating marine propeller generated noise spectra at design stage exploiting the most recent advances in Deep Learning, able to take advantage of both structured and unstructured data, and in hybrid modelling, able to exploit both data and physical knowledge about the problem. For this purpose authors will make use of a dataset collected by means of dedicated model scale measurements in a cavitation tunnel combined with the detailed flow characterisation obtainable by calculations carried out with a Boundary Element Method. The performance of the proposed approaches are analysed considering different scenarios and different definitions of the input and output variable used during the modelisation.

中文翻译：

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在设计阶段预测空化船用螺旋桨噪声：一种基于深度学习的方法

摘要 降低船舶噪声影响的重要性正在世界范围内得到认可。因此，将这一原则纳入新设计的目标和约束正在成为一种标准。由于这个原因，螺旋桨通常是水下辐射噪声的主要来源，尤其是在发生空化时，因为在大多数情况下，当船舶以设计速度航行时，就会发生这种情况。更安静的推进系统的设计人员需要能够验证是否符合噪声要求并比较不同设计解决方案有效性的预测工具的可用性。在这种情况下，能够仅根据螺旋桨设计期间可用的信息提供螺旋桨噪声谱的可靠估计的工具代表了加快设计过程避免模型规模测试的基本工具。这项工作的重点是开发一种能够在设计阶段预测空化船用螺旋桨产生的噪声频谱的工具，利用深度学习的最新进展，能够利用结构化和非结构化数据，以及在混合建模中，能够利用这两种数据和有关问题的物理知识。为此，作者将利用通过空化隧道中的专用模型比例测量收集的数据集，并结合通过边界元方法进行的计算获得的详细流动特征。