测量了声能和神经网络模型,用于钢球的低速水流入
Yihan Yang1,2,3, Qi Li1,2,3, Dajing Shang1,2,3
1National Key Laboratory of Underwater Acoustic Technology, Harbin Engineering University, Harbin 150001, China.
The Journal of the Acoustical Society of America
|March 4, 2026
概括
这项研究使用总声能量化了水进入的声辐射. 一种新的测量和计算方法与神经网络相结合,可以预测浅水中的水下物体声学.
科学领域:
- 声学 声学 在声学方面
- 流体动力学 流体动力学
- 水下声学 水下声学
背景情况:
- 有限的研究存在于水进入的声波辐射,大多数研究集中在流体动力学上.
- 现有的声学测量通常受到实验条件的影响,例如距离和水深.
- 这项研究解决了对水下物体可靠声学表征的需求.
研究的目的:
- 为了研究和描述由固体钢球进入水所产生的声波辐射.
- 开发一种最优化的方法来测量和计算来自入水事件的总声能.
- 使用测量-计算-建模方法建立水进入声能的预测模型.
主要方法:
- 利用近距离和远距离测量用于水进入的声音.
- 提出了一种优化的细分计算方法,以减轻回声和反响效应.
- 综合计算结果使用基于神经网络的数据拟合方法进行预测建模.
主要成果:
- 开发了一种可靠的方法来测量入水声音的总声能,减少实验条件的影响.
- 创建了一个基于神经网络数据的总声能小规模预测模型.
- 演示了用于声学分析的测量-计算-建模方法.
结论:
- 拟议的方法提供了一种可靠的方式,通过水进入水下物体的声学特性.
- 这项研究为在浅海环境中的工程应用提供了理论基础.
- 应用包括提取水下目标的声学特征和定位入水事件.
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