Consolidated Modeling and Prediction of Heat Transfer Coefficients for Saturated Flow Boiling in Mini/Micro-channels using Machine Learning Methods
Consolidated Modeling and Prediction of Heat Transfer Coefficients for Saturated Flow Boiling in Mini/Micro-channels using Machine Learning Methods
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DOI：
10.1016/j.applthermaleng.2022.118305
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发表时间：
2022-03
影响因子：
6.4
通讯作者：
Ari Bard;Yue Qiu;Chirag R. Kharangate;R. French
中科院分区：
文献类型：
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作者：
Ari Bard;Yue Qiu;Chirag R. Kharangate;R. French
流动沸腾已成为补偿更大功率密度和更强大设备功能的可靠模式，因为它能够利用指定冷却剂中包含的潜热和显热。目前，在预测微型/微通道中流动沸腾期间的传热系数时，很少有可用的工具被证明是可靠的。最流行的方法依赖于从实验数据得出的半经验相关性。这些相关性只能应用于测试条件的非常狭窄的子集。本研究使用多种数据科学方法和技术，在数据库中准确预测迷你/微通道流动沸腾期间的传热系数，该数据库包含使用 12 种工作流体进行 50 次实验收集的 16,953 个观测值。探索性数据科学用于在使用机器学习算法之前获得数据的置信度并研究特征变量之间的关系。使用随机森林非参数插补来插补缺失数据。采用各种特征分析技术来组合和选择不同的最佳特征变量作为输入值，例如主成分分析以减少数据集和Boruta包的整体维数、递归特征消除、最小绝对收缩和选择算子（LASSO）回归以及逐步选择以减少建模时使用的原始变量数量，同时保留尽可能多的信息。使用线性建模、广义加性建模、随机森林、支持向量机和神经网络等多种模型来预测传热系数，并将结果与现有的通用相关性进行比较。支持向量机模型表现最好，平均绝对百分比误差 (MAPE) 为 11.3%。事实证明，在 110 多个不同模型中的 90% 中，热通量、仅蒸汽弗劳德数和质量是特别重要的贡献变量。事实证明，机器学习在预测各种不同流体的传热系数时是一种非常有用的工具，但在预测以水为工作流体的极高异常值数据时却遇到了困难。
Flow boiling has become a reliable mode of compensating with larger power densities and greater functions of devices because it is able to utilize both the latent and sensible heat contained within a specified coolant. There are currently very few available tools proven reliable when predicting heat transfer coefficients during flow boiling in mini/micro-channels. The most popular methods rely on semi-empirical correlations derived from experimental data. These correlations can only be applied to a very narrow subset of testing conditions. This study uses a number of data science methods and techniques to accurately predict the heat transfer coefficient during flow boiling in mini/micro-channels on a database consisting of 16,953 observations collected across 50 experiments using 12 working fluids. Exploratory data science is used to obtain confidence in the data and investigate relationships between feature variables before employing machine learning algorithms. Missing data is imputed using random forest nonparametric imputation. A variety of feature analysis techniques are employed to combine and select different optimal feature variables as input values such as principal component analysis to reduce the overall dimensionality of the dataset and the Boruta package, recursive feature elimination, Least Absolute Shrinkage and Selection Operator (LASSO) regression, and stepwise selection to reduce the number of original variables used when modeling while preserving as much information as possible. A variety of models including linear modeling, generalized additive modeling, random forests, support vector machines, and neural networks are used to predict the heat transfer coefficient and compare the results with existing universal correlations. The support vector machine model performed best, with a Mean Absolute Percentage Error (MAPE) of 11.3%. The heat flux, vapor-only Froude number, and quality proved to be especially significant contributing variables across 90% of over 110 different models. Machine learning proved to be an extremely useful tool when predicting the heat transfer coefficient across a variety of different fluids but did struggle to predict extremely high outlier data where water was the working fluid.