基于深度学习的非定常周期性流动预测方法

惠心雨; 袁泽龙; 白俊强; 张扬; 陈刚

doi:10.7638/kqdlxxb-2019.0003

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基于深度学习的非定常周期性流动预测方法

专栏——智能空气动力学研究探索 | 更新时间：2026-05-28

- 基于深度学习的非定常周期性流动预测方法
- A method of unsteady periodic flow field prediction based on the deep learning
- 空气动力学学报 2019年37卷第3期页码：462-469
- 作者机构：
  
  1. 西北工业大学航空学院,西安,710072
  2. 西安交通大学机械结构强度与振动国家重点实验室,西安,710049
- 作者简介：
  
  惠心雨(1996-), 女, 陕西人, 硕士研究生, 研究方向:深度学习.E-mail:sakumapako@mail.nwpu.edu.cn
  白俊强(1971-), 男, 河南人, 教授, 博士生导师.E-mail:junqiang@nwpu.edu.cn
- 基金信息：
  
  国家自然科学基金（11602199）
- DOI：10.7638/kqdlxxb-2019.0003
  中图分类号： TP181;V211.3
- 纸质出版：2019
- 稿件说明：
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惠心雨, 袁泽龙, 白俊强, 张扬, 陈刚. 基于深度学习的非定常周期性流动预测方法[J]. 空气动力学学报, 2019, 37(3): 462-469.

HUI Xinyu, YUAN Zelong, BAI Junqiang, et al. A method of unsteady periodic flow field prediction based on the deep learning[J]. 2019, 37(3): 462-469.
惠心雨, 袁泽龙, 白俊强, 张扬, 陈刚. 基于深度学习的非定常周期性流动预测方法[J]. 空气动力学学报, 2019, 37(3): 462-469. DOI： 10.7638/kqdlxxb-2019.0003.

HUI Xinyu, YUAN Zelong, BAI Junqiang, et al. A method of unsteady periodic flow field prediction based on the deep learning[J]. 2019, 37(3): 462-469. DOI： 10.7638/kqdlxxb-2019.0003.

摘要

为了克服传统CFD计算需要耗费大量的计算时间与成本的缺陷，提出了一种基于深度学习的非定常周期性流场的预测框架，可以实时生成给定状态的高可信度的流场结果。将条件生成对抗网络与卷积神经网络相结合，改进条件生成对抗网络对生成样本的约束方法，建立了基于深度学习策略采用改进的回归生成对抗网络模型，并与常规的条件生成对抗网络模型的预测结果进行对比。研究表明，基于改进的回归生成对抗网络的深度学习策略能准确预测出指定时刻的流场变量，且总时长比CFD数值模拟减少至少1个量级。

Abstract

In order to overcome the shortages of the computationally expensive and time-consuming iterative process in traditional CFD simulation

a framework based on the deep learning to predict periodic unsteady flow field is proposed

which can accurately predict real-time complex vortex flow state at different moments. The conditional generative adversarial network and convolutional neural network are combined to improve the conditional constraint method from conditional generative adversarial network. The improved regression generative adversarial network based on the deep learning is proposed. The two scenarios of conditional generative adversarial network and regression generative adversarial network are tested and compared via giving different periodic moments to predict the corresponding flow field variables. The final results demonstrate that regression generative adversarial network can estimate complex flow fields

and is faster than traditional CFD simulation over one order of magnitudes.

关键词

Keywords

references

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