基于无量纲人工智能的水平管道多相流型识别模型

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Ala AL-Dogail, R. Gajbhiye, Abdullatif Alnajim, Mustafa Alnaser
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引用次数: 1

摘要

多相流分析引起了各学科研究者的广泛关注。为了研究多相流,进行了包括实验、理论建模和数值分析在内的多项研究。然而,由于多相流极其复杂的性质,多相流的许多方面仍未解决。不同相的复杂相互作用导致了不同的流态,这些流态很难预测,但对开发计算模型至关重要。识别流动模式仍然是一项具有挑战性的任务。其中一个不断发展的领域是机器学习方法,它可以解决如此复杂的问题。本研究旨在使用机器学习开发能够识别多相流中流型的模型。为了实现这一目标,收集了大量的实验数据。通过改变流体性质,介绍了密度、粘度和表面张力等流体性质对流型的影响。通过应用机器学习技术来预测流态,对广泛的数据进行了处理。使用无量纲参数建立模型,以扩展其在各种设计和操作条件下的有效性。这种方法能够捕获水平管道中的主流模式以及流模式的子类别。对不同的机器学习工具进行了比较和分析,以研究多相流模式的分类。结果表明,不同的人工智能方法可以高精度地预测水平管道中的流型。使用液体雷诺数(ReL)和气体雷诺数(ReG)作为输入来预测流型的结果在支持向量机(SVM)和判别分析(DA)的准确性方面不足。然而,通过引入液体(WeL)和气体(WeG)的韦伯数以及雷诺数(ReL和ReG),提高了模型的预测能力。由于流体性质的变化,捕捉到了流体动力学现象(惯性和表面张力),因此推测由于引入韦伯数而导致的流型预测的改进。它推断,捕捉影响流型及其转变的流体动力学现象对于预测多相流中的流型至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dimensionless Artificial Intelligence-Based Model for Multiphase Flow Pattern Recognition in Horizontal Pipe
Multiphase flow analysis attracts a lot of attention from researchers from diverse disciplines. There are several studies including experimental, theoretical modeling, and numerical analysis that were carried out to investigate the multiphase flow. However, many facets of multiphase flow are still unresolved owing to the extremely complex nature of the multiphase flow. The complex interactions of the different phases are leading to different flow regimes that are difficult to predict but essential for developing the computational model. The identification of the flow pattern is still a challenging task. One of the growing fields is the machine learning approach, which can address such complex problems. This study aims to use machine learning to develop models that can identify the flow patterns in multiphase flow. To achieve the objective, a large set of experimental data was collected. The effect of fluid properties, such as density, viscosity, and surface tension, on the flow pattern was introduced by changing the fluid properties. The wide range of data was processed by applying a machine learning technique for predicting the flow regimes. The models were built using dimensionless parameters to extend their validity for various design and operational conditions. This approach enables to capture the main flow pattern as well as subcategories of flow patterns in the horizontal pipe. Comparison and analyses of the different machine learning tools were carried out to investigate classification of multiphase flow patterns. The results showed that different artificial intelligence (AI) methods can predict the flow pattern in horizontal pipes with high accuracy. The results of using Reynold’s number for liquid (ReL) and gas (ReG) as an input to predict the flow patterns are deficient in accuracy for the support vector machine (SVM) and discriminant analysis (DA). However, the prediction capability of the model was improved by introducing Weber’s number for liquid (WeL) and gas (WeG) along with the Reynolds numbers (ReL and ReG). The improvement in the flow pattern prediction owing to the introduction of Weber’s number is speculated because of the capturing hydrodynamic phenomenon (inertia and surface tension) owing to change in fluid properties. It infers that capturing hydrodynamic phenomena affecting the flow pattern and their transition is essential for the prediction of flow patterns in multiphase flow.
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来源期刊
Accounts of Chemical Research
Accounts of Chemical Research 化学-化学综合
CiteScore
31.40
自引率
1.10%
发文量
312
审稿时长
2 months
期刊介绍: Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
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