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首页> 外文期刊>International Journal of Heat and Mass Transfer >Machine learning classification of boiling regimes with low speed, direct and indirect visualization
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Machine learning classification of boiling regimes with low speed, direct and indirect visualization

机译：低速，直接和间接可视化的沸腾状态的机器学习分类

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摘要

Multiphase flow pattern identification is of utmost importance to the energy industry, given that thermohydraulic operating conditions are drastically affected by flow and heat transfer regimes. In industrial boilers and nuclear reactors, for instance, the heat transfer coefficient – and hence the heater temperature – is significantly affected by the boiling regime, where the onset of film boiling can be catastrophic to the equipment and cause irreparable damage. In this paper, it is shown that a machine can learn from visualization and successfully classify and separate natural convection, nucleate boiling and film boiling regimes using low speed and low resolution image frames acquired from visualization of an on-wire pool boiling experimental setup (direct visualization) even when only the departed, ascending bubbles are considered – i.e., the heater is suppressed from the image (indirect visualization). While not the main objective of this paper, principal component analysis of the frames is shown to provide information regarding bubble dynamics and hence is used for dimensionality reduction. Two types of classifiers, namely support vector machines and neural networks, are shown to be able to classify pool boiling frames with over 93% accuracy sufficiently fast, possibly enabling real-time execution and classification, even during indirect visualization and, hence, providing a non-intrusive and low-cost pool boiling regime identification.
机译：鉴于热工工况受流动和传热机制的影响很大，因此多相流模式识别对能源行业至关重要。例如，在工业锅炉和核反应堆中，传热系数（进而是加热器温度）受沸腾状态的显着影响，在这种情况下，薄膜沸腾的发生可能会对设备造成灾难性的破坏，并造成无法弥补的损害。本文表明，一台机器可以从可视化中学习，并且可以使用从在线池沸腾实验设置（直接可视化），即使只考虑了离开的上升气泡-即，从图像中抑制了加热器（间接可视化）。虽然不是本文的主要目标，但显示了框架的主成分分析可提供有关气泡动力学的信息，因此可用于降低尺寸。两种类型的分类器，即支持向量机和神经网络，显示出能够快速快速地以超过93％的精度对池沸腾框架进行分类，甚至在间接可视化期间也可以实现实时执行和分类，因此可以提供非侵入性和低成本的池沸腾状态识别。

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