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首页> 外文期刊>International communications in heat and mass transfer >A generalized-numerical correlation study for the determination of pressure drop during condensation and boiling of R134a inside smooth and corrugated tubes
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A generalized-numerical correlation study for the determination of pressure drop during condensation and boiling of R134a inside smooth and corrugated tubes

机译:确定光滑和波纹管内R134a冷凝和沸腾过程中压降的广义数值相关研究

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摘要

The measured pressure drop of R134a, flowing downward and horizontally inside smooth and corrugated copper tubes, is estimated by the closed form of artificial neural network method to have a reliable empirical correlation using some dimensionless numbers. The working fluids are R134a and water flowing in the test tube and annular tube, respectively. This paper is a continuation of the authors' previous work and includes all their previous works about condensation and boiling in tubes. All data used in the present paper are obtained from the authors' previous studies. The training sets have the experimental data of convective condensation and boiling experiments including various mass fluxes and saturation temperatures of R134a. Froude number, Weber number, Bond number, Lockhart and Martinelli number, void fraction, the ratio of density to dynamic viscosity, liquid, vapor and equivalent Reynolds numbers, surface tension parameter and liquid Prandtl number are the inputs of the formula as the dimensionless numbers obtained from measured values of test section, while the output of the formula is the measured pressure drops in the analysis. A closed form of multi-layer perceptron(MLP)method of artificial neural network(ANN)is used to estimate the experimental pressure drop of R134a numerically. 1177 data points are used in the analyses of the ANN method to be able to have a single generalized empirical correlation for both condensation and boiling flows. The evaluation of the closed form of multi-layer perceptron(MLP)with two or three inputs and one hidden neuron architecture was successful predicting the measured pressure drops with their error bands being within the range of ±30% for all used data. The proposition of empirical correlations are performed for both condensation and boiling flows separately. A single empirical correlation is able to calculate the measured pressure drop of both condensation and boiling flows together. Moreover, the dependency of output of the proposed formula from input values is examined in the study. By means of the dependency analyses, liquid Prandtl number, Butterworth's void fraction and Lockhart and Martinelli parameter are found to be the most dominant parameters among other dimensionless numbers.
机译:R134a的测量压降在光滑和波纹铜管内向下和水平流动,是通过人工神经网络方法的闭合形式估计的,使用一些无量纲数可以得出可靠的经验相关性。工作流体是R134a,水分别在试管和环形管中流动。本文是作者先前工作的延续,包括他们先前有关管中冷凝和沸腾的所有工作。本文使用的所有数据均来自作者先前的研究。训练集具有对流冷凝和沸腾实验的实验数据,包括各种质量通量和R134a的饱和温度。弗劳德数,韦伯数,邦德数,洛克哈特和马蒂内利数,空隙率,密度与动态粘度之比,液体,蒸气和等效雷诺数,表面张力参数和液体普朗特数是该公式的输入,作为无量纲数从测试部分的测量值获得,而公式的输出是分析中测得的压降。人工神经网络(ANN)的多层感知器(MLP)方法的闭合形式用于数值估计R134a的实验压降。在ANN方法的分析中使用了1177个数据点,以便能够对冷凝流和沸腾流具有单一的广义经验相关性。对具有两个或三个输入和一个隐藏的神经元架构的多层感知器(MLP)的封闭形式的评估成功地预测了所测得的压降,其误差带在所有使用的数据中均在±30%的范围内。对冷凝流和沸腾流分别进行经验相关性的命题。单一的经验相关性能够一起计算出冷凝水和沸腾水的压力降。此外,在研究中检查了建议公式的输出与输入值之间的依赖性。通过相关性分析,发现液体Prandtl数,Butterworth的空隙率以及Lockhart和Martinelli参数是其他无量纲数中最主要的参数。

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  • 作者单位

    Computer Engineering Department, Yildiz Technical University, Davutpasa, Istanbul 34349, Turkey;

    fluid Mechanics, Thermal Engineering and Multiphase flow Research Lab.(FUTURE), Department of Mechanical Engineering, Faculty of Engineering, King Mongkut's University of Technology Thonburi, Bangmod, Bangkok 10140, Thailand;

    Heat and Thermodynamics Division, Department of Mechanical Engineering Faculty of Mechanical Engineering, Yildiz Technical University, Yildiz, Besikms, Istanbul 34349, Turkey;

    fluid Mechanics, Thermal Engineering and Multiphase flow Research Lab.(FUTURE), Department of Mechanical Engineering, Faculty of Engineering, King Mongkut's University of Technology Thonburi, Bangmod, Bangkok 10140, Thailand;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Condensation; Boling; Pressure drop; Modeling; Neural network; Corrugated tubes;

    机译:缩合;波灵压力下降;造型;神经网络;波纹管;

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