In this study, a computational framework in the field of artificial intelligence was applied in computational fluid dynamics (CFD) field. This Framework, which was initially proposed by Google AI department, is called “TensorFlow”. An improved CFD model based on this framework was developed with a high-order difference method, which is a constrained interpolation profile (CIP) scheme for the base flow solver of the advection term in the Navier-Stokes equations, and preconditioned conjugate gradient (PCG) method was implemented in the model to solve the Poisson equation. Some new features including the convolution, vectorization, and graphics processing unit (GPU) acceleration were implemented to raise the computational efficiency. The model was tested with several benchmark cases and shows good performance. Compared with our former CIP-based model, the present TensorFlow-based model also shows significantly higher computational efficiency in large-scale computation. The results indicate TensorFlow could be a promising framework for CFD models due to its ability in the computational acceleration and convenience for programming.

中文翻译：

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基于TensorFlow的CFD新型高性能计算框架

在这项研究中，人工智能领域的计算框架被应用于计算流体动力学（CFD）领域。该框架最初由Google AI部门提出，称为“ TensorFlow”。使用高阶差分方法开发了基于此框架的改进CFD模型，该方法是针对Navier-Stokes方程中对流项的基本流求解器和预处理共轭梯度（PCG）的约束插值剖面（CIP）方案在模型中实施）方法来求解泊松方程。实现了一些新功能，包括卷积，矢量化和图形处理单元（GPU）加速，以提高计算效率。该模型已在多个基准案例下进行了测试，并显示出良好的性能。与我们以前的基于CIP的模型相比，当前基于TensorFlow的模型在大规模计算中也显示出明显更高的计算效率。结果表明，TensorFlow具有计算加速能力和编程便利性，因此可能成为CFD模型的有前途的框架。