三维高分辨率流场重构的深度学习方法

doi:10.3901/JME.2025.16.338

机械工程学报 ›› 2025, Vol. 61 ›› Issue (16): 338-346.doi: 10.3901/JME.2025.16.338

• 交叉与前沿 • 上一篇

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三维高分辨率流场重构的深度学习方法

战庆亮^1,2, 刘鑫¹, 白春锦², 葛耀君³

1. 大连海事大学交通运输工程学院大连 116026；
2. 辽宁省交通规划设计院有限责任公司沈阳 110111；
3. 同济大学桥梁结构抗风技术交通行业重点实验室上海 200092

接受日期:2024-09-22 出版日期:2025-03-06 发布日期:2025-03-06
作者简介:战庆亮(通信作者)，男，1987年出生，博士。主要研究方向为人工智能、流体力学。E-mail：zhanqingliang@163.com
基金资助:
辽宁教育厅研究计划(LJ212410151014)、大连海事大学博联科研基金(3132023619)、国家自然科学基金(51778495)和交通行业重点实验室开放课题(KLWRTBMC21-02)资助项目

3D High-resolution Flow Reconstruction Based on Deep Learning

ZHAN Qingliang^1,2, LIU Xin¹, BAI Chunjin², GE Yaojun³

1. College of Transportation and Engineering, Dalian Maritime University, Dalian 116026;
2. Liaoning Provincial Transportation Planning and Design Institute Co., Ltd., Shenyang 110111;
3. Key Laboratory of Transport Industry of Wind Resistant Technology for Bridge Structures, Tongji University, Shanghai 200092

Accepted:2024-09-22 Online:2025-03-06 Published:2025-03-06

摘要/Abstract

摘要： 获得机械结构中的三维高分辨率流场对机械设计与优化尤为关键。然而受机械结构及传感器的尺寸等因素制约，难以直接获取三维高分辨率的流场数据，而基于二维卷积的深度学习方法无法直接处理三维时变流场数据。针对上述问题，提出基于有限测点流场时程样本的三维高分辨率流场的深度学习重构方法。对高雷诺数(Re=2.2×10⁴)下方柱绕流样本进行了建模与训练，得到三维湍流场的低维表征模型，进而构建高精度的坐标-表征编码的映射关系，实现流场的三维高分辨率重构，与目标值吻合良好。证明了方法能够实现三维湍流场的高分辨率重构，可为机械流动问题中基于测点传感器的时程数据分析提供新的方法。

关键词: 三维流场重构, 时程深度学习, 高分辨率流场, 流场表征模型

Abstract: Obtaining three-dimensional high-resolution flow field in mechanical structures is especially critical for mechanical design and optimization. However, due to the constraints on the size of mechanical structures and sensors, it is difficult to obtain the 3D high-resolution flow field data directly, and the deep learning method based on 2D convolution cannot handle the 3D time-varying flow field data directly. To address the above problems, this paper proposes a deep learning reconstruction method for 3D high-resolution flow field based on time-varying flow samples at finite measurement points. The high Reynolds number (Re=2.2×10⁴) flow around square cylinder were modeled and trained. A low-dimensional representation model of the 3D turbulent field is obtained, and then a high-precision coordinate-representation coding mapping is constructed to achieve a 3D high-resolution reconstruction model of the flow data, which is in good agreement with the target value. It is demonstrated that the proposed method can realize the high-resolution reconstruction of the 3D turbulent field, which can provide a new method for the analysis of time-history data based on the measurement sensors in mechanical flow problems.

Key words: 3D flow reconstruction, flow time-history deep learning, high resolution flow, flow representation model

中图分类号:

O375

战庆亮, 刘鑫, 白春锦, 葛耀君. 三维高分辨率流场重构的深度学习方法[J]. 机械工程学报, 2025, 61(16): 338-346.

ZHAN Qingliang, LIU Xin, BAI Chunjin, GE Yaojun. 3D High-resolution Flow Reconstruction Based on Deep Learning[J]. Journal of Mechanical Engineering, 2025, 61(16): 338-346.

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三维高分辨率流场重构的深度学习方法

3D High-resolution Flow Reconstruction Based on Deep Learning

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