Abstract

The prediction accuracies of the two-phase heat transfer coefficient for the flow in a small channel, which are usually based on the mean absolute error (MAE) between the correlation and experimental data, have remained unsatisfactory. Conventionally, the regression method has been used to determine the correlation that best represents the experimental data. In this paper, an improved heat transfer correlation for the evaporation of propane is developed by applying the genetic algorithm method. A total of 789 data points from 4 sources with circular diameters ranging from 1.0 to 6.0 mm are used to minimise the MAE while searching for the optimum conditions for the suppression factor, S, and convective factor, F, in a selected superposition correlation for two different vapour quality ranges. The optimisation can minimise the MAE at 33% and 25% for Case I and Case II, respectively. The proposed method assists in attaining a precise empirical prediction that fits well with the experimental data.

中文翻译：

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遗传算法改进丙烷两相传热相关叠加型

摘要

小通道中流动的两相传热系数的预测精度（通常基于相关性和实验数据之间的平均绝对误差（MAE））仍然无法令人满意。按照惯例，已经使用回归方法来确定最能代表实验数据的相关性。本文采用遗传算法方法，提出了一种改进的丙烷蒸发传热相关系数。在寻找抑制因子S和对流因子F的最佳条件时，使用了来自4个直径在1.0到6.0 mm范围内的圆形源的789个数据点，以最小化MAE在两个不同蒸气质量范围的选定叠加关系中。优化可以将案例I和案例II的MAE分别降至33％和25％。所提出的方法有助于获得与实验数据非常吻合的精确的经验预测。