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 fl...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.展开更多
在周期性信号数字化测量比对中,除A/D量化误差以外还存在取决于两比对信号频率关系的量化相移步进误差。通过分析两种量化误差的产生原因及相互关系,该文提出了利用两种误差的相辅相成关系来量化误差分辨率的稳定性以及影响,在数字化处...在周期性信号数字化测量比对中,除A/D量化误差以外还存在取决于两比对信号频率关系的量化相移步进误差。通过分析两种量化误差的产生原因及相互关系,该文提出了利用两种误差的相辅相成关系来量化误差分辨率的稳定性以及影响,在数字化处理中采用边沿处理技术,能有效地抑制量化误差。实验表明,在10 bit A/D采样分辨率下,测量分辨率较原有A/D测量分辨率提高了一个数量级甚至更高。该方法可以在频率、相位等参数的精密测量、控制中发挥重要作用。展开更多
基金National Natural Science Foundation of China(1180500311947102+4 种基金12004005)Natural Science Foundation of Anhui Province(2008085MA162008085QA26)University Synergy Innovation Program of Anhui Province(GXXT-2022-039)State Key Laboratory of Advanced Electromagnetic Technology(Grant No.AET 2024KF006)。
文摘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.
文摘在周期性信号数字化测量比对中,除A/D量化误差以外还存在取决于两比对信号频率关系的量化相移步进误差。通过分析两种量化误差的产生原因及相互关系,该文提出了利用两种误差的相辅相成关系来量化误差分辨率的稳定性以及影响,在数字化处理中采用边沿处理技术,能有效地抑制量化误差。实验表明,在10 bit A/D采样分辨率下,测量分辨率较原有A/D测量分辨率提高了一个数量级甚至更高。该方法可以在频率、相位等参数的精密测量、控制中发挥重要作用。
