A TensorFlow-based new high-performance computational framework for CFD
| Autor: | Xizeng Zhao, Tian-yu Xu, Wei-jie Liu, Zhouteng Ye |
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| Rok vydání: | 2020 |
| Předmět: |
Computer science
business.industry Mechanical Engineering Computation Graphics processing unit 020101 civil engineering 02 engineering and technology Solver Computational fluid dynamics Condensed Matter Physics 01 natural sciences 010305 fluids & plasmas 0201 civil engineering Computational science Mechanics of Materials Modeling and Simulation Conjugate gradient method 0103 physical sciences Vectorization (mathematics) Benchmark (computing) business Interpolation |
| Zdroj: | Journal of Hydrodynamics. 32:735-746 |
| ISSN: | 1878-0342 1001-6058 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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