数据与计算发展前沿 ›› 2020, Vol. 2 ›› Issue (4): 121-131.doi: 10.11871/jfdc.issn.2096-742X.2020.04.010

• 技术与应用 • 上一篇    下一篇

基于AutoML的湍流建模

任荟颖1,2(),王婧1(),王彦棡1,*()   

  1. 1.中国科学院计算机网络信息中心, 北京 100190
    2.中国科学院大学, 北京 100049
  • 收稿日期:2020-03-30 出版日期:2020-08-20 发布日期:2020-09-10
  • 通讯作者: 王彦棡
  • 作者简介:任荟颖,中国科学院计算机网络信息中心,在读硕士研究生,主要研究方向为人工智能应用。
    本文主要承担工作:实验设计,论文撰写。
    Ren Huiying, is a graduate student of the at Computer Network Information Center of Chinese Academy of Sciences. Her main research direction is artificial intelligence application.
    In this paper, she is responsible for experiment design and paper writing.
    E-mail: renhuiying@cnic.cn|王婧,中国科学院计算机网络信息中心,高级工程师,主要研究方向为人工智能算法应用、多媒体信息检索及大数据分析。
    本文主要承担工作:论文撰写。
    Wang Jing, M.S., is a senior engineer of Computer Network information Center, Chinese Academy of Sciences. Her research interests include artificial intelligence algorithm application, multimedia information retrieval and big data analysis.
    In this paper, she is responsible for paper writing.
    E-mail: wangjing@cnic.cn|王彦棡,中国科学院计算机网络信息中心,博士,研究员,主要研究方向为人工智能应用,高性能计算。
    本文主要承担工作:科学问题凝结,把握论文总体结构。
    Wang Yangang, PHD, is a research fellow at Computer Network Information Center of Chinese Academy of Sciences. His main research directions are artificial intelligence application and high-performance computing.
    In this paper, he is responsible for condensing scientific problems and grasping the overall structure of the paper.
    E-mail: wangyg@sccas.cn
  • 基金资助:
    国家重点研发计划“大规模并行计算的工具库和领域相关基础软件包”(2017YFB0202202);“中国科技云”建设工程(二期)超算资源池建设(XXH13503)

Turbulence Modeling Based on AutoML

Ren Huiying1,2(),Wang Jing1(),Wang Yangang1,*()   

  1. 1. Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2020-03-30 Online:2020-08-20 Published:2020-09-10
  • Contact: Wang Yangang

摘要:

【背景】 湍流问题涉及到工程中的诸多领域,其重要性不言而喻。雷诺平均Navier-Stokes(RANS)方程提供了一种计算时间平均湍流量的有效方法,由于其计算易处理性而被广泛使用。随着深度学习技术的发展,采用数据驱动的方法建模RANS模型受到了研究者广泛的关注。【方法】本文提出了一种数据驱动建模RANS模型的方法,该方法以数值软件模拟结果为基础,利用深度学习技术构造湍流模型。由于在湍流问题中,不同的系统初始条件不同,数据的质量千差万别,难以使用统一的神经网络结构进行训练。因此本文采用AutoML(自动机器学习)的方法自动搜索神经网络的结构并进行自动调参。此外,本文发现通过混合多种初始条件下的数据进行模型训练,可以提高深度学习模型的拟合精度,增强其鲁棒性。【结果】本文选取OpenFOAM中的经典算例内壁台阶流模拟作为数据来源进行实验。实验表明,该模型在预测雷诺应力时具有较好的精度和效率,表明数据驱动方法在湍流模拟中具有良好的应用前景。【局限】为了更好的在湍流领域应用深度学习技术,下一步的研究重点在于如何将深度学习模型与湍流数值模拟软件耦合。【结论】目前,针对湍流机器学习的系统研究相对较少。在现有工作经验的基础上,机器学习在未来的湍流模型化中必将扮演着更加重要的角色。

关键词: 湍流模型, 自动机器学习, 数据驱动, 雷诺平均方程, 深度学习

Abstract:

[Background] The turbulence problem involves many fields in engineering, and its importance is self-evident. The Reynolds Average Navier-Stokes (RANS) equation provides an effective method for calculating the time-averaged turbulence, and it is widely used because of its ease in calculation. With the development of deep learning technology, data-driven modeling of RANS model has been widely concerned by researchers. [Methods] In this paper, a data-driven method for modeling RANS is proposed. Based on the results of numerical simulation software, the method uses deep learning technology to construct turbulence models. Because of the different initial conditions of different turbulence systems and various qualities of data, it is difficult to use a unified neural network structures for training. Therefore, we used the method of AutoML (automatic machine learning) in deep learning to solve the problem, which can automatically build appropriate network structures and choose proper hyper-parameters for different datasets. In addition, this paper also improves the method by mixing the data under various initial conditions to train the deep learning model, which greatly increases accuracy and the robustness of our model. [Results] In this paper, a typical example in OpenFOAM about the step flow simulation of the inner wall is selected as the data source for the experiment. The experimental results show that the model has good accuracy and efficiency in prediction of Reynolds stress, which indicates that the data-driven method has a great application prospect in turbulence simulation. [Limitations] In order to better apply deep learning technology in the field of turbulence modeling, the most important issue needs to be solved in the next step is how to couple deep learning models with turbulence numerical simulation software. [Conclusions] At present, there are few systematic researches about turbulence machine learning. Based on the current work, machine learning will play a more important role in the future turbulence modeling researches.

Key words: turbulence modeling, auto machine learning, data-driven, RANS, deep learning