Abstract

An annular water-film lubricates an oil-core by functioning as a barrier between the core and the pipe wall in the water-lubricated flow of non-conventional viscous oil. A dependable model for appraising the frictional energy losses in such a core annular flow system is necessary to ensure its widespread implementation in the industry. In the current study, the modeling was conducted using an artificial neural network (ANN) based on 223 data sets. Seven input variables applied in the current ANN model are pipe diameter, average velocity, fluid properties, and water fraction. The optimum architecture was identified as a feed-forward neural network with backpropagation technique involving two hidden layers, each of which was consisted of 20 neurons or nodes. Comparative statistical analysis demonstrated promising accuracy of the current model, the coefficient of determination was 0.992, and the root mean square error was 0.111. In addition to validating the model, the relative significance of the input parameters was evaluated with a sensitivity analysis.

摘要

环形水膜通过在非常规粘性油的水润滑流中充当芯和管壁之间的屏障来润滑油芯。有必要建立一个可靠的模型来评估这种核心环形流动系统中的摩擦能量损失，以确保其在行业中的广泛实施。在当前的研究中，使用基于 223 个数据集的人工神经网络 (ANN) 进行建模。当前 ANN 模型中应用的七个输入变量是管道直径、平均速度、流体特性和水分数。最佳架构被确定为具有反向传播技术的前馈神经网络，涉及两个隐藏层，每个隐藏层由 20 个神经元或节点组成。比较统计分析证明了当前模型的有希望的准确性，决定系数为0.992，均方根误差为0.111。除了验证模型外，还通过敏感性分析评估了输入参数的相对重要性。