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
为了探究高背压水介质条件下,固体火箭发动机垂直气体射流在浮力影响下的流场结构和发动机推力特点,建立了轴对称几何模型,在考虑有/无浮力的条件下,采用VOF(Volume of fluid)多相流模型进行气体-水两相耦合仿真计算,获取尾流气体射流...为了探究高背压水介质条件下,固体火箭发动机垂直气体射流在浮力影响下的流场结构和发动机推力特点,建立了轴对称几何模型,在考虑有/无浮力的条件下,采用VOF(Volume of fluid)多相流模型进行气体-水两相耦合仿真计算,获取尾流气体射流流场结构,以及发动机尾部壁面受力和推力振荡曲线进行分析。研究结果表明,考虑浮力的仿真结果更加符合试验结果;射流动量段气体的马赫数分布会导致喷管出口附近的气-水界面产生周期性胀鼓-颈缩,从而引起尾部空间背压振荡,在设计工况下,尾部压力变化范围为环境水深压强的0.327到2.43倍;背压振荡将引起尾壁面受力振荡和推力振荡,振荡频率为736.89Hz;气体射流喷出过程中,气-水界面由速度梯度主导的开尔文-亥姆赫兹(K-H)不稳定性逐渐转变为由重力和浮力主导的瑞利-泰勒(R-T)不稳定性。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.
文摘为了探究高背压水介质条件下,固体火箭发动机垂直气体射流在浮力影响下的流场结构和发动机推力特点,建立了轴对称几何模型,在考虑有/无浮力的条件下,采用VOF(Volume of fluid)多相流模型进行气体-水两相耦合仿真计算,获取尾流气体射流流场结构,以及发动机尾部壁面受力和推力振荡曲线进行分析。研究结果表明,考虑浮力的仿真结果更加符合试验结果;射流动量段气体的马赫数分布会导致喷管出口附近的气-水界面产生周期性胀鼓-颈缩,从而引起尾部空间背压振荡,在设计工况下,尾部压力变化范围为环境水深压强的0.327到2.43倍;背压振荡将引起尾壁面受力振荡和推力振荡,振荡频率为736.89Hz;气体射流喷出过程中,气-水界面由速度梯度主导的开尔文-亥姆赫兹(K-H)不稳定性逐渐转变为由重力和浮力主导的瑞利-泰勒(R-T)不稳定性。
