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以光谱物理为基础的神经网络用于暂时管道流量模拟

Vincent Tjuatja¹, Alireza Keramat¹, Mostafa Rahmanshahi¹

¹Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, PR China.

|March 12, 2025

概括

物理信息神经网络 (PINNs) 适用于管道中的频域波分析. 新的物理信息复杂估值神经网络 (PICVNN) 模型改善了短暂压力预测和异常检测.

科学领域:

流体动力学流体动力学
计算式水力学是指计算式水力学
在工程领域的机器学习.

背景情况:

精确的波浪传播建模对于供水管道监测和定位至关重要.
基于物理学的神经网络 (PINNs) 将物理定律与数据整合在一起,但通常仅限于时间域的短暂波分析.
频域模型为系统识别和管道异常检测提供了更高的灵敏度.

研究的目的:

开发一种新的物理信息神经网络 (PINN) 模型,用于频率领域的短暂波传播.
为了提高管道监测和评估应用的波浪预测准确度.
调查模型在处理不确定性和检测泄漏等异常方面的能力.

主要方法:

开发一个物理信息复杂估值神经网络 (PICVNN) 用于频域水建模.
整合物理原理与复杂值的神经网络来分析短暂波数据.
与具有不同观察点的经典复杂值神经网络 (CVNN) 基准进行比较分析.

主要成果:

该PICVNN模型准确地重建了短暂压力,在预测准确性方面超过了经典CVNN模型.
该模型在处理输入参数的不确定性,数学模型和识别未知的泄漏方面表现出强度.
与传统的CVNN相比,PICVNN实现了更高的准确性,但需要更长的培训时间.

结论:

开发的PICVNN是用于管道频域短暂波分析的有效工具.
PICVNN作为一种可靠的信号融合方法,通过整合各种传感器数据来提高准确性和可靠性.
这种方法促进了PINNs在管道监测和系统识别中的应用.

关键词:

频域建模频率领域建模液压过渡的过渡时间基于物理学的神经网络.水是一个水.

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