| 基于流动仿真大数据应对旋涡-湍流的研究进展 |
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| 引用本文： | 王铎,刘超群,蔡小舒,徐弘一. 基于流动仿真大数据应对旋涡-湍流的研究进展[J]. 力学季刊, 2022, 43(2): 197-216. DOI: 10.15959/j.cnki.0254-0053.2022.02.001 |
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| 作者姓名： | 王铎 刘超群 蔡小舒 徐弘一 |
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| 作者单位： | 复旦大学航空航天系,上海200433;德州大学阿灵顿分校数学系,德克萨斯阿灵顿76019;上海理工大学动力工程学院,上海200093 |
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| 基金项目： | 国家自然科学基金;上海市教委联合创新计划;上海市科委科技创新行动计划 |
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| 摘 要： | 文章以流体科学进入二十一世纪后,在大规模超算、云存储、数据通信和人工智能为支撑的大数据时代背景下,结合目前在复旦大学航空航天系所构建的热流体湍流直接数值仿真数据库,以及复旦大学团队近期与美国德州大学刘超群教授、上海理工大学蔡小舒教授以及国内水动力学杂志编辑部所合作开展的第三代涡识别技术研究,初步概念性地展示旋涡和湍流,特别是针对有工程实际背景和直接应用价值的壁湍流,在这两个流体力学关键基础议题上的最新认知,和基于大数据深度学习的相关湍流工程模拟实践成果．这些成果包括：(1) 基于第三代涡识别技术的尾迹湍流中的涡运动学和动力学探索;(2) 流-热统一完整的类-1、类-2湍流边界层壁面律构建;(3) 基于第三代涡识别量对Kolmogorov 幂次律的再认知;(4) 基于DNS统计数据和神经网络深度学习构建新型湍流封闭模型及RANS计算实践．通过这些成果展示,论证解决这两个基础流体科学议题的技术路径,进而促进流体及相关学科研究在现代大数据背景下取得实质性进展和突破,并惠及现代流体、气动、水利、动力和化工等工程领域．
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| 关 键 词： | 涡识别 壁湍流 热湍流边界层 湍流壁面律 湍流幂次律 直接数值模拟 雷诺平均方法 |
Tackling Vortex/Turbulence Challenges Based on Direct Numerical Simulation Data in Fluid Science |
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| WANG Duo,LIU Chaoqun,CAI Xiaoshu,XU Hongyi. Tackling Vortex/Turbulence Challenges Based on Direct Numerical Simulation Data in Fluid Science[J]. Chinese Quarterly Mechanics, 2022, 43(2): 197-216. DOI: 10.15959/j.cnki.0254-0053.2022.02.001 |
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| Authors: | WANG Duo LIU Chaoqun CAI Xiaoshu XU Hongyi |
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| Abstract: | The current article presents the definitive lights shed onto the solution of vortex and turbulence through the in-depth analyses of the thermally-coupled fluid-mechanics big data generated at the Aeronautics and Astronautics Department of Fudan University and the applications of the state-of-the-art (third-generation) vortex identification (VI) technologies developed through closely collaborating with Prof. Liu's group at University of Texas at Arlington,Prof. Cai's group at Shanghai University of Technology and the editorial board of International Journal of Hydrodynamics. The research achievements demonstrated in the article include: (1) the vortex kinematics and dynamics in the wake transition turbulence based on the third-generation VI of Liutex; (2) the establishment of unified fluid and thermal analytical wall-law for Type-A and Type-B turbulence boundary layers (TBL); (3) re-understanding the Kolmogorov K41 -5/3 inertial-law based on the new VI method; (4) reconstructing the turbulence-closure model based on turbulence statistical data and re-practicing the modern Reynolds-averaging Navier-Stokes (RANS) computation. All these accomplishments clearly point out the technical roadmap towards the solutions of the 21st century grand challenges in fluid mechanics, namely vortex and turbulence, through establishing and utilizing the thermal wall-bounded turbulence big data. The progresses of research in this aspect are expected to not only significantly push the knowledge front of modern fluid mechanics, but also importantly benefit a broad range of other related scientific and engineering communities, such as the aerospace, hydraulics, power and chemical engineering etc.. |
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| Keywords: | vortex identification wall-bounded turbulence thermal turbulent boundary layer law-of-the-wall K41 -5/3 Kolmogorov law direct numerical simulation Reynolds-averaged Navier-Stokes method |
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