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首页> 外文期刊>International Journal of Heat and Mass Transfer >Water pool boiling in metal foams: From experimental results to a generalized model based on artificial neural network
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Water pool boiling in metal foams: From experimental results to a generalized model based on artificial neural network

机译：水池在金属泡沫中沸腾：从实验结果到基于人工神经网络的广义模型

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摘要

This paper shows the promising capabilities that the aluminum metal foams offer in enhancing the water pool boiling on flat surfaces. Different metal foam samples, with pore density ranging between 5 and 40 Pore Per Inch (PPI), with similar porosity values, around 0.92, and two different thickness values, 5 and 10 mm, were tested to investigate their pertinent pool boiling performance. The saturated boiling curves at atmospheric pressure were obtained. Furthermore, using a high speed camera, heat and fluid flow inside and above the porous layer were investigated. The results showed that boiling heat transfer can be greatly enhanced by the use of metal foams partly because of an earlier onset of the nucleate boiling. Moreover, over a wide range of operating conditions foams lead to remarkably higher heat transfer coefficients, compared with a smooth aluminum reference surface. For a more comprehensive understanding of the problem, we tried to correlate the existing data in the literature for different foam geometries and base materials. However, the existing correlations and independent test data in the literature do not agree well; not even within the same order of magnitude. Hence, in order to offer a generic solution, predictions based on the results of an artificial neural network (ANN) are sought. A large database, comprising 758 experimental data points available in the open literature, was collected and then used to develop, train and validate a model based on ANN to estimate the heat transfer performance of metal foams during water pool boiling. Our data show that the predicted Nusselt number is within 10% of the measured experimental data. Moreover, we demonstrate that the developed ANN tool can be successfully implemented to predict the intrinsic complexity of water pool boiling inside metal foams that in most cases cannot be articulated by merely relying on conventional semi-empirical methods.
机译：本文展示了铝金属泡沫在增强水池沸腾在平坦表面上的有前途的能力。测试不同的金属泡沫样品，孔密度在每英寸（PPI）之间的孔密度范围为5至40孔（PPI），孔隙率值约为0.92和两个不同的厚度值，5和10mm，以研究其相关的池沸腾性能。获得了大气压下的饱和沸腾曲线。此外，研究了使用高速相机，在多孔层内部和上方的热量和流体流动进行了研究。结果表明，通过使用金属泡沫，可以大大提高沸腾的热传递，部分原因是核心沸腾的早期发作。此外，与光滑的铝基参考表面相比，在多种操作条件下泡沫导致较高的传热系数。为了更全面地了解问题，我们试图将文献中的现有数据与不同的泡沫几何形状和基础材料相关联。但是，文献中现有的相关性和独立的测试数据并不吻合;甚至不在同一级别内。因此，为了提供通用解决方案，寻求基于人工网络（ANN）的结果的预测。包括在开放文献中提供的758个实验数据点的大型数据库，然后用于开发，列车和验证基于ANN的模型，以估计水池沸腾过程中金属泡沫的传热性能。我们的数据表明，预测的讨论数量在测量的实验数据的10％以内。此外，我们证明了开发的ANN工具可以成功地实施以预测水池沸腾内部金属泡沫的内在复杂性，在大多数情况下，通过仅仅依赖于传统的半经验方法，不能阐明。

著录项

  • 来源
    《International Journal of Heat and Mass Transfer》 |2021年第9期|121451.1-121451.15|共15页
  • 作者

    M. Calati; G. Righetti; L. Doretti; C. Zilio; G.A. Longo; K. Hooman; S. Mancin;

  • 作者单位

    Department of Management and Engineering University of Padova Str.lla S. Nicola 3 36100 Vicenza-IT Italy;

    Department of Management and Engineering University of Padova Str.lla S. Nicola 3 36100 Vicenza-IT Italy;

    Department of Civil Architectural and Environmental Engineering University of Padova Via Venezia 1 35131 Padova-IT Italy;

    Department of Management and Engineering University of Padova Str.lla S. Nicola 3 36100 Vicenza-IT Italy;

    Department of Management and Engineering University of Padova Str.lla S. Nicola 3 36100 Vicenza-IT Italy;

    Queensland Geothermal Energy Centre of Excellence School of Mechanical and Mining Engineering University of Queensland Brisbane Queensland 4072 Australia;

    Department of Management and Engineering University of Padova Str.lla S. Nicola 3 36100 Vicenza-IT Italy;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Pool boiling; Aluminum foam; Artificial Neural Network; Model; Heat transfer;

    机译：池沸腾;铝泡沫;人工神经网络;模型;传播热量;

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