Pressure fluctuations are one of the primary concerns regarding the safety of two-phase flows in pipelines. Therefore, it is crucial to closely monitor pressure variations and predict their spatiotemporal behavior to the greatest extent possible. In this article, we present the development of a set of models based on artificial neural networks (ANN) to predict the time-dependent behavior of the pressures produced by high-viscosity gas-liquid flows at predetermined locations along a horizontal pipeline. Different volume fractions of glycerin and air constitute the mixtures of interest. The models use the measured values of the mass-flow rates of both fluids at the pipe’s inlet, together with the pressures measured at other locations further downstream. In order to determine the optimal architecture for the artificial neural network, we tested the following four transfer functions for the hidden layer: , , , and . Additionally, we utilized the linear function for the output layer. We employed the Levenberg–Marquardt algorithm to train the ANN models and the experimental data set to test their performance.

中文翻译：

---

人工神经网络的开发用于预测沿输送高粘度两相流的水平管道的压力场

压力波动是管道中两相流安全的主要问题之一。因此，密切监测压力变化并最大程度地预测其时空行为至关重要。在本文中，我们开发了一组基于人工神经网络 (ANN) 的模型，用于预测水平管道预定位置处高粘度气液流产生的压力随时间变化的行为。不同体积分数的甘油和空气构成了感兴趣的混合物。该模型使用管道入口处两种流体的质量流量测量值，以及下游其他位置测量的压力。为了确定人工神经网络的最佳架构，我们测试了隐藏层的以下四个传递函数： 、 、 和 。此外，我们在输出层使用了线性函数。我们采用 Levenberg-Marquardt 算法来训练 ANN 模型和实验数据集来测试其性能。