强激光与粒子束2024，Vol.36Issue(12)：42-49,8.DOI:10.11884/HPLPB202436.240015
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
High-resolution reconstruction of the ablative RT instability flow field via convolutional neural networks
摘要
Abstract
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 specific analysis of flow field evolution.With the development of deep learning technology,convolutional neural networks are used to achieve high-resolution reconstruction of the flow field.In this paper,an ordinary convolutional neural network and a multi-time-path convolutional neural network are established for the ablative Rayleigh-Taylor instability.These two methods can reconstruct the high-resolution flow field in just a few seconds,and further greatly enrich the application of high-resolution reconstruction technology in fluid instability.Compared with the ordinary convolutional neural network,the multi-time-path convolutional neural network model has smaller error and can restore more details of the flow field.The influence of low-resolution flow field data obtained by the two pooling methods on the convolutional neural networks model is also discussed.关键词
卷积神经网络/烧蚀瑞利-泰勒不稳定性/高分辨率重建/多重时间路径/池化Key words
convolutional neural networks/the ablative Rayleigh-Taylor instability/high-resolution reconstruction/multi-time-path/pooling分类
数理科学引用本文复制引用
夏治洋,旷圆圆,卢艳,杨名..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[J].强激光与粒子束,2024,36(12):42-49,8.基金项目
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) （GXXT-2022-039）
State Key Laboratory of Advanced Electromagnetic Technology(Grant No.AET 2024KF006) （Grant No.AET 2024KF006）
