High-resolution reconstruction of the ablative RT instability flowfield via convolutional neural networks  

基于卷积神经网络的烧蚀瑞利-泰勒不稳定性流场高分辨率重建

作　　者：Xia Zhiyang Kuang Yuanyuan Lu Yan Yang Ming 夏治洋;旷圆圆;卢艳;杨名(安徽大学物理与光电工程学院,合肥230601;安徽大学电子信息工程学院,合肥230601;合肥综合性国家科学中心人工智能研究院,合肥230088)

机构地区：[1]School of Physics and Optoelectronic Engineering,Anhui University,Hefei 230601,China [2]School of Electronic and Information Engineering,Anhui University,Hefei 230601,China [3]Institute of Artificial Intelligence,Hefei Comprehensive National Science Center,Hefei 230088,China

出　　处：《强激光与粒子束》2024年第12期42-49,共8页High Power Laser and Particle Beams

基　　金：National Natural Science Foundation of China(11805003;11947102;12004005);Natural Science Foundation of Anhui Province(2008085MA16;2008085QA26);University Synergy Innovation Program of Anhui Province(GXXT-2022-039);State Key Laboratory of Advanced Electromagnetic Technology(Grant No.AET 2024KF006)。

摘　　要：High-resolution flow field data has important applications in meteorology,aerospace engineering,high-energy physics and other fields.Experiments and numerical simulations are two main ways to obtain high-resolution flow field data,while the high experiment cost and computing resources for simulation hinder the specificanalysis of flow field evolution.With the development of deep learning technology,convolutional neural networks areused to achieve high-resolution reconstruction of the flow field.In this paper,an ordinary convolutional neuralnetwork and a multi-time-path convolutional neural network are established for the ablative Rayleigh-Taylorinstability.These two methods can reconstruct the high-resolution flow field in just a few seconds,and further greatlyenrich the application of high-resolution reconstruction technology in fluid instability.Compared with the ordinaryconvolutional neural network,the multi-time-path convolutional neural network model has smaller error and canrestore more details of the flow field.The influence of low-resolution flow field data obtained by the two poolingmethods on the convolutional neural networks model is also discussed.高分辨率流场数据在气象学、航空航天工程、高能物理等领域有着重要的应用价值。实验和数值模拟是两种获取高分辨率流场数据的主要途径。但是高昂的实验成本和仿真计算资源阻碍了研究者对流场演化的具体分析。随着深度学习技术的发展,卷积神经网络被用来实现流场的高分辨率重建。针对烧蚀瑞利-泰勒不稳定性流场重建提出了普通卷积神经网络模型和多重时间路径卷积神经网络模型。这两个模型可以在很短的时间内对流场进行高分辨率重建,极大地丰富了高分辨率重建技术在流体不稳定性研究中的应用。与普通卷积神经网络相比,多重时间路径卷积神经网络模型的误差较小,可以还原流场的更多细节。此外,还讨论了用于获取低分辨率流场的不同池化方法对卷积神经网络模型性能的影响。

关 键 词：convolutional neural networks ablative Rayleigh-Taylor instability high-resolutionreconstruction multi-time-path pooling 

分 类 号：O357[理学—流体力学]