A super resolution flow field reconstruction method using PINN
Kaicheng YANG, Xia LIU, Wenhui FENG, Feng LIAN, Yinan KONG · 2024
The super-resolution reconstruction based on sparse measurement information is an important issue in the field of experimental fluid dynamics, and using a small amount of information for high-precision turbulent flow field reconstruction has high research value. Traditionally, interpolation and reconstruction methods are used to reconstruct the flow field. In recent years, artificial intelligence technologies represented by deep neural networks have been increasingly applied to flow field reconstruction problems due to their advantages in handling high-dimensional and nonlinear function fitting problems. However, these methods do not fully consider the physical mechanisms followed by flow field evolution and require a large amount of high-precision data, which may result in high experimental costs. Physical Information Neural Network (PINN) is an unsupervised learning method that utilizes neural networks to fit solutions to partial differential equations of flow field evolution, thereby overcoming the strict requirements of traditional methods on data attributes. However, due to the complexity of the Navier-Stokes equations, the accuracy of traditional PINN methods cannot meet the modeling requirements. This article proposes a PINN based method that effectively combines test data and physical information, and applies this method to the problem of super-resolution turbulent flow field reconstruction. Using high-precision measurement data of turbulence in a square channel as the experimental dataset, the method was used to reconstruct a high-resolution velocity field based on downsampling information of turbulent flow field, and compared with traditional bicubic interpolation methods and flow field reconstruction methods based on Convolutional Neural Networks(CNN). The experimental results show that the PINN method proposed in this paper can quickly complete super-resolution flow field reconstruction using sparse flow field measurement information, and is insensitive to noise in the data. It has significant advantages in accuracy and robustness when the sampling ratio is large.