基于卷积神经网络的撞击流反应器浓度场混合特性  

Mixing characteristics of concentration field in impingement flow reactor based on convolutional neural network

作　　者：张建伟[1] 许蕊[1] 张忠闯 董鑫[1] 冯颖[1] ZHANG Jianwei;XU Rui;ZHANG Zhongchuang;DONG Xin;FENG Ying(School of Mechanical and Power Engineering,Shenyang University of Chemical Technology,Shenyang 110142,Liaoning,China)

机构地区：[1]沈阳化工大学机械与动力工程学院,辽宁沈阳110142

出　　处：《化工进展》2023年第2期658-668,共11页Chemical Industry and Engineering Progress

基　　金：国家自然科学基金(21476141);辽宁省“兴辽英才计划”高水平创新创业团队项目(XLYC1808025)。

摘　　要：基于PLIF测试技术结合卷积神经网络技术提出混合性能预测方法,分析水平对置撞击流反应器浓度场混合特性,能准确预测其内部浓度场的混合均匀度及混合时间。基于卷积神经网络构建了混合性能预测模型,利用水平对置撞击流反应器浓度场实验数据对构建的模型进行有监督地训练并进行预测,预测结果显示对混合均匀度的预测准确率达95%,计算效率提高了99.99%。为更好地理解混合性能预测模型对混合均匀度的预测机理,本文对其卷积层输出进行可视化处理,通过功率谱分析卷积核的响应给出了撞击流反应器浓度场特征提取的物理解释。最后利用预测模型搭建混合均匀度快速获取系统并应用于撞击流混合特性研究。所提出的基于卷积神经网络的预测模型可以有效分析水平对置撞击流反应器的混合特性,预测模型可靠、适用范围广,为深度学习算法应用于撞击流领域提供了方案经验。In this paper, a mixing performance prediction method based on PLIF test technology and convolutional neural network technology was proposed to analyze the mixing characteristics of the flow field in the impingement flow reactor, which could accurately predict the mixing uniformity and mixing time of the concentration field in the impingement flow reactor. Mixed performance prediction model was constructed based on CNN, using the impinging stream reactor concentration field experimental data for the construction of the model of supervised training and forecasting. The prediction results showed that the mixing uniformity of prediction accuracy was 95%, and the calculation efficiency increased by 99.99%. In order to better understand the prediction mechanism of the mixing performance prediction model on the mixing uniformity, the output of the convolution layer was visualized, and the physical interpretation of the concentration field feature extraction of impingent flow reactor was given by analyzing the response of the convolution kernel through power spectrum analysis. Finally, the prediction model was used to build a fast acquisition system of mixing uniformity and apply it to study the mixing characteristics of impingement flow. The proposed prediction model based on CNN could effectively analyze the mixing characteristics of impingement flow reactor, and the prediction model was reliable and applicable to a wide range of applications, providing scheme experience for the application of deep learning algorithm in the field of impingement flow.

关 键 词：撞击流反应器 卷积神经网络 混合 浓度场 预测 

分 类 号：TP389.1[自动化与计算机技术—计算机系统结构]