针对高阶DG数值格式的非定常流场预测建模  

Prediction modeling of unsteady flow field aimed at high-order DG numerical scheme

作　　者：丁子元 安慰 刘学军[1,4] 吕宏强 DING Ziyuan;AN Wei;LIU Xuejun;LYU Hongqiang(MUT Key Laboratory of Pattern Analysis and Machine Intelligence,College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China;State Key Laboratory of Aerodynamics,Mianyang 621000,China;Key Laboratory of Aerodynamics Noise Control,Mianyang 621000,China;Collaborative Innovation Center of Noval Software Technology and Industrialization,Nanjing 210023,China;College of Aerospace Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China)

机构地区：[1]南京航空航天大学计算机科学与技术学院,模式分析与机器智能工业和信息化部重点实验室,南京211106 [2]空气动力学国家重点实验室,绵阳621000 [3]气动噪声控制重点实验室,绵阳621000 [4]软件新技术与产业化协同创新中心,南京210023 [5]南京航空航天大学航空学院,南京210016

出　　处：《空气动力学学报》2022年第6期51-63,共13页Acta Aerodynamica Sinica

基　　金：航空科学基金(2018ZA52002,2019ZA052011);空气动力学国家重点实验室基金(SKLA20180102);气动噪声控制重点实验室基金(ANCL20190103)。

摘　　要：高阶间断伽辽金方法作为一种数值求解方法,具备精度高和适用于复杂外形等特点,同时由于其良好的色散以及耗散特性,非常适用于隐式大涡模拟。然而在求解非定常流场时,通常需要计算很长的时长,如何降低计算代价仍然是一个挑战。针对这一问题,提出了一种由三维卷积、二维残差网络和注意力机制组成的深度神经网络,该网络能够从数据中捕捉隐含的流场时空特征。对不同雷诺数下的圆柱绕流进行数值模拟得到用于训练的数据集,将训练完成后的网络用于预测未来时间段的流场原始数据,实验结果显示深度神经网络对圆柱绕流实验数据具备良好的建模能力,用该深度神经网络预测的流场与直接用CFD求解器计算出的结果高度一致。As a numerical method,the high-order discontinuous Galerkin(DG) method has the characteristics of high precision and is suitable for complex geometries.Meanwhile,due to its good dispersion and dissipation properties,the high-order DG method is well suited for implicit large eddy simulations.However,it usually takes a long time for solving unsteady flow fields,and how to reduce the computation cost is still a challenge.To tackle this issue,a deep neural network consisted with the three-dimensional convolution,the twodimensional residual network and the attention mechanism has been proposed,which can extract the implied spatio-temporal characteristics of the flow field from the data.The numerical simulation for flow around a cylinder at different Reynolds numbers is carried out to obtain the data set for training,which is then used to predict the flow field for the future period.The results show that the deep neural network has a satisfactory ability of modeling the flow around a cylinder.The flow fields predicted by the deep neural network is in good agreement with those directly calculated by the CFD solver.

关 键 词：深度学习 三维卷积 残差网络 注意力机制 高阶间断伽辽金方法 非定常流场预测 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] V211.3[自动化与计算机技术—控制科学与工程]