针对高阶DG数值格式的非定常流场预测建模

丁子元; 安慰; 刘学军; 吕宏强

doi:10.7638/kqdlxxb-2021.0174

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针对高阶DG数值格式的非定常流场预测建模

研究论文 | 更新时间：2026-05-28

- 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
- 空气动力学学报 2022年40卷第6期页码：51-63
- 作者机构：
  
  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
- 作者简介：
  
  丁子元（1997-），男，江苏人，硕士研究生，研究方向：深度学习. E-mail：dylan_dzy@nuaa.edu.cn
  刘学军*（1976-），女，教授，研究方向：机器学习. E-mail：xuejun.liu@nuaa.edu.cn
- 基金信息：
  
  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）
- DOI：10.7638/kqdlxxb-2021.0174
  中图分类号： TP181;V211.3
- 纸质出版：2022
- 稿件说明：
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丁子元, 安慰, 刘学军, 等. 针对高阶DG数值格式的非定常流场预测建模[J]. 空气动力学学报, 2022, 40(6): 51−63.

DING Ziyuan, AN Wei, LIU Xuejun, et al. Prediction modeling of unsteady flow field aimed at high-order DG numerical scheme[J]. 2022, 40(6): 51-63.
丁子元, 安慰, 刘学军, 等. 针对高阶DG数值格式的非定常流场预测建模[J]. 空气动力学学报, 2022, 40(6): 51−63. DOI： 10.7638/kqdlxxb-2021.0174

DING Ziyuan, AN Wei, LIU Xuejun, et al. Prediction modeling of unsteady flow field aimed at high-order DG numerical scheme[J]. 2022, 40(6): 51-63. DOI： 10.7638/kqdlxxb-2021.0174.

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。

Abstract

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 two-dimensional 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.

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Keywords

references

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