Learning new physical descriptors from reduced-order analysis of bubble dynamics in boiling heat transfer
Learning new physical descriptors from reduced-order analysis of bubble dynamics in boiling heat transfer
复制标题
从沸腾传热中气泡动力学的降阶分析中学习新的物理描述符
DOI：
10.1016/j.ijheatmasstransfer.2021.122501
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发表时间：
2022
影响因子：
5.2
通讯作者：
Sun, Ying
中科院分区：
文献类型：
--
作者：
Rokoni, Arif;Zhang, Lige;Soori, Tejaswi;Hu, Han;Wu, Teresa;Sun, Ying
理解沸腾过程中的气泡动力学是具有挑战性的，由于系统参数，如成核，气泡形态，温度和压力的急剧变化。在这项研究中，主成分分析（PCA），一个无监督的降维算法，被用来提取新的沸腾传热的物理描述符池沸腾实验图像没有标记和训练。时间序列的主成分（PC）的主频率和振幅进行了分析，其中前几个占主导地位的PC被用来近似的瞬时气泡形态，大大减少了数据的维数。结果表明，主频率和主振幅可以作为区分不同沸腾区的新的物理参数。第一个PC的主频率被发现增加与热通量的离散气泡制度，直到它达到一个峰值，然后减少与热通量的气泡干扰和聚结制度，其中前者被认为是与气泡成核网站的增加，后者是与池沸腾过程中的气泡聚结。从本无监督学习中提取的主频率和幅度与从监督深度学习算法中提取的气泡计数和大小进行了定性比较，并且该方法在多个数据集和加热器表面上表现出高度鲁棒性。为了预测未来的沸腾状态，以减轻沸腾危机，双向长短期记忆（BiLSTM）神经网络被用来估计未来的变化的PC，因此气泡动力学，从时间序列的PC。PCA-BiLSTM模型很好地预测了降阶气泡图像，并且与卷积LSTM相比，显示出更高的预测精度。
Understanding bubble dynamics during boiling is challenging due to the drastic changes in system parameters, such as nucleation, bubble morphology, temperature, and pressure. In this study, principal component analysis (PCA), an unsupervised dimensionality reduction algorithm, is used to extract new physical descriptors of boiling heat transfer from pool boiling experimental images without labeling and training. The dominant frequency and amplitude of the time-series principal components (PCs) are analyzed, where the first few dominant PCs are used to approximate the instantaneous bubble morphologies, drastically reducing the data dimensions. The results show that the dominant frequency and amplitude can be used as new physical descriptors to distinguish different boiling regimes. The dominant frequency of the first PC is found to increase with heat flux in the discrete bubble regime until it reaches a peak and then decreases with heat flux in the bubble interference and coalescence regime, where the former is believed to be associated with the increase in bubble nucleation sites and the latter is associated with the bubble coalescence during pool boiling. The dominant frequency and amplitude extracted from the present unsupervised learning are qualitatively compared to the bubble count and size extracted from a supervised deep-learning algorithm, and the approach appears highly robust over multiple datasets and heater surfaces. To predict future boiling states for mitigating boiling crises, bidirectional long short-term memory (BiLSTM) neural network is used to estimate the future variations of PCs and hence the bubble dynamics, from time-series PCs. The PCA-BiLSTM models predict reduced-order bubble images well and show significantly higher prediction accuracy compared to the Convolutional-LSTM.