Frontiers of Data and Computing ›› 2020, Vol. 2 ›› Issue (4): 121-131.doi: 10.11871/jfdc.issn.2096-742X.2020.04.010

• Technology and Applicaton • Previous Articles     Next Articles

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 E-mail:renhuiying@cnic.cn;wangjing@cnic.cn;wangyg@sccas.cn

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